evaluation of adhesively bonded steel sheets using

Evaluation of Adhesively Bonded Steel Sheets Using Ultrasonic Techniques

by

Chrysostomos Kyriacou Tavrou

Thesis submitted to Swinburne University of Technology for the Degree of

Doctor of Philosophy

2005

Faculty of Engineering and Industrial Sciences Swinburne University of Technology

Melbourne, Australia

Abstract Adhesives have presently reached a stage where they have become part of everyday

life both in a professional sense as well as for household applications. They offer

advantages that in many respects surpass other joining processes such as bonding

of large areas, joining a wide range and dissimilar materials; and without the need for

special tooling or operator training, that is often required by many other joining

processes. They are of course not a panacea to all fastening applications, but they

can easily be described as the most versatile and most widely used joining method at

present.

Engineering applications have also benefited from the advantages offered by

adhesives, but they are not as liberally used due to the severe consequences that

may result from bond failure. Although adhesives can demonstrate their ability to

fulfil the joining strength requirements under laboratory conditions, their application in

industry proved to be not as reliable as expected. A number of parameters that can

easily be controlled under laboratory conditions such as temperature, humidity,

surface preparation and uniform adhesive application are not as easily observed in

industry. Quality assurance during manufacturing can achieve excellent results;

however even in these cases the probability of having adhesive bond defects is still

present. Therefore, there is a need for post process inspection of adhesive bonds

where risk levels require higher reliability than what is offered though process quality

control.

Adhesive bond inspection is a well researched area with respectable outcomes. Non

destructive inspection techniques such as x-ray, thermal, and ultrasonic are well

utilised in the inspection of adhesive bonds. However, despite all the effort in this

area for more than forty years, there is still no singular technique that can achieve the

confidence level required in some engineering applications. Therefore, the need for

continuing research in the area of non-destructive evaluation of adhesive bonds is as

necessary today as it’s ever been. The research presented in this thesis, continues

in the same endeavour as many other researchers; that of achieving the ultimate

technique in adhesive bond inspection, capable of reaching the confidence level

required for all engineering applications.

Abstract

The research in the thesis commenced with coverage of adhesives used for

engineering applications and a study of the adhesion science that was considered

necessary to enable an informed approach to the problem. Adhesive bond failure is

also analysed through a literature survey as well as experimental tests on standard

specimens. At the completion of the literature survey and preliminary tests, a

decision was taken to follow the ultrasonic path of non-destructive testing of adhesive

bonds. The reasons for this, are clearly outlined in the main body of this thesis but in

summary, the literature has shown that ultrasonic evaluation is the most widely used

technique by industry. Therefore, improvements on data analysis using existing

techniques that exploit ultrasonic inspection have the potential to reach the widest

spectrum of industrial applications.

Ultrasonic inspection equipment was sourced that was capable of achieving

experimental results to the accuracy level required in this research. A precision test

rig was designed and constructed that was subsequently calibrated using computer

based statistical techniques to ensure the validity of all results. Other ancillary

equipment, such as a portable tensile testing device were also designed and

constructed during the research as it became necessary.

Research concentrated on techniques found to be inadequately researched in this

domain. The first technique evaluated was to measure bond quality through the

stress distribution in adherent and adhesive. Computer based Finite Element

Analysis showed that the ability to detect variation in stress distribution at the

adhesion interface is capable of revealing the local bond strength. Having found that

there is no technique available at present that can measure the stress distribution at

the interface, a different direction was taken that showed potential in achieving

excellent quantitative results in the analysis of ultrasonic signals from adhesive

bonds. This technique was rigorously evaluated and the results are systematically

reported in this work.

Statement of original authorship

This thesis contains no material which has been accepted for the award to the

candidate of any other degree or diploma, except where due reference is made in the

text of the thesis.

This thesis, to the best of my knowledge, contains no material previously published

or written by another person except where due reference has been made in the text

of the thesis.

Signed: . . . . . . . . . . . . . . . . . . . . . . .

Acknowledgements Firstly I would like to sincerely thank my supervisor Dr Cameron Jones, for his advice

on scientific and project management issues as well as patience and encouragement

throughout this thesis. My thanks also extend to Professor Guoxing Lu who as my

second supervisor often offered his help and support where needed. Dr Igor Sparsky

and Professor Elias Siores, the proposer of this project, also played an instrumental

role in this research and I would like to sincerely thank them.

I would also like to extend my sincere thanks to Mr Andrew McCulloch of Hawker de

Havilland, Melbourne and Mr David Humphreys of HenkelOrbseal, Australia, who

have provided me with the industrial realisation of this thesis. Special thanks to Ms

Meredith Jewson, Swinburne University, for her assistance in building the

experimental test rig and other devices needed in this research.

Lastly, I would like to thank my wife Anna for her infinite support and encouragement

throughout this thesis as well as my children, Evis, Lyvia, Tricia, Kerry, Leonie and

Hannah for their patience and understanding over the last few years that I have been

working on my thesis.

i

Table of contents

_

Chapter 1 Introduction and project outline

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

1.2 Adhesives in Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.3 Non destructive testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.4 Ultrasonic testing of adhesive bonds . . . . . . . . . . . . . . . . . . . . . . . . 2

1.5 Research aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.6 Outline of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.6.1 Adhesion and adhesives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.6.2 Design of experimental set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.6.3 Ultrasonic tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.6.4 Ultrasonic signal analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.6.5 The Wavelet transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.6.6 Industrial application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Chapter 2

Adhesion and adhesives 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Adhesion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.1 Bondline Attraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.2 Surface Wetness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.3 Mechanisms of Adhesion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2.3.1 Mechanical Interlocking . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2.3.2 Diffusion Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2.3.3 Electronic Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.3.4 Absorption Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2.4 Surface Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3 Structural Adhesives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3.1 Acrylic Adhesives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3.2 Anaerobic Adhesives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

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2.3.3 Cyanoacrylate Adhesives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3.4 Epoxy Adhesives . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . 16

2.3.5 Imide-Based Adhesives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3.6 Phenolic Adhesives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3.7 Polyurethane Adhesives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.3.8 Silicon Adhesives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.4 Adhesive Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.5 Adhesive Joint Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.5.1 Shear Lap Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.6 Stress Distribution in Bonded Areas . . . . . . . . . . . . . . . . . . . . . . . . 23

2.6.1 Boundary Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.6.2 FEA Mesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.6.3 FEA Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.7 Stress Measurement Using Ultrasound . . . . . . . . . . . . . . . . . . . . . 26

2.7.1 Tension Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.8 FEA Simulation of Stress in Adherent . . . . . . . . . . . . . . . . . . . . . . . 28

2.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Chapter 3 Experimental set-up

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.2 Ultrasonic Test Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.3 Utex UT340 Pulser / Receiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.4 Ultrasonic Test Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.4.1 XYZ Scanning Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.4.1.1 Structural Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.4.1.2 Motion System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.4.2 Test Rig Set Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.5 Motion Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.6 Ultrasonic Probe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.7 Ultrasonic Signal Analysis Software . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.7.1 Digitizer Card . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.7.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.7.3 Motion Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.8 Instrument Commissioning and Calibration . . . . . . . . . . . . . . . . . . . 44

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3.8.1 Signal Speed Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.8.2 C-scan Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.8.3 Scanning Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.8.4 Transducer Height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.9 Instrument Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.9.1 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.9.2 C-Scan Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.9.3 Mathematical Modelling and Optimisation . . . . . . . . . . . . . . . . . . . . 52

3.9.3.1 Mathematical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.9.3.2 Refinement of Mathematical Models . . . . . . . . . . . . . . . . . . . 54

3.9.3.3 Parameter Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3.9.4 Graphical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.9.5 Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Chapter 4 Ultrasonic test experiments

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.2 Signal attenuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.2.1 Normal incidence transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.3 Ultrasonic testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.3.1 Amplitude variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.3.2 Signal decay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.3.3 Signal frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.4 Ultrasonic C-scans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.4.1 C-Scan experimental tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.4.1.1 Detection of delaminated areas . . . . . . . . . . . . . . . . . . . . . . . 72

4.4.1.2 Prediction of failure location . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.4.1.3 Effect of load on adhesive bond c-scans . . . . . . . . . . . . . . . . 77

4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

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Chapter 5 Ultrasonic signal analysis

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

5.3.1 Visual Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.3.2 Statistical Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.3.3 Three-Dimensional Frequency Analysis . . . . . . . . . . . . . . . . . . . . . . 86

5.3.4 Visual Recurrence Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.3.5 State Space Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

5.3.6 Wavelet Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Chapter 6 Wavelet Analysis

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

6.2 Wavelet Signal Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

6.3 Wavelet theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

6.4 Wavelet Energy Conservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

6.4.1 Wavelet energy of Ultrasonic signals . . . . . . . . . . . . . . . . . . . . . . . . 100

6.5 Comparison with Traditional Techniques . . . . . . . . . . . . . . . . . . . . . 102

6.6 Noise reduction using Wavelets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

6.7 Data compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

6.8 Data de-noising and compression . . . . . . . . . . . . . . . . . . . . . . . . . . 118

6.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Chapter 7 Industrial applications

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

7.2 Orbseal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

7.2.1 Test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

7.3 Hawker De Havilland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

7.4 Hand held Ultrasonic tester . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

7.4.1 MAUS IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

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7.4.2 BaNDIcootFAQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

7.4.3 Discrete Ultrasonic tester . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

7.5 Ford Motor Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

Chapter 8 Conclusions

8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

8.2 Non-Destructive testing techniques . . . . . . . . . . . . . . . . . . . . . . . . . 131

8.3 Adhesion and adhesives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

8.3.1 Preliminary tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

8.3.2 FEA of adhesive bonds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

8.3.3 Ultrasonic measurement of stress distribution . . . . . . . . . . . . . . . . . . 134

8.3.4 Further FEA tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

8.4 Design of experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

8.4.1 Ultrasonic scanning setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

8.4.2 Instrument calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

8.4.3 Instrument optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

8.4.4 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

8.5 Ultrasonic Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

8.5.1 Signal Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

8.5.2 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

8.6 Wavelet Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

8.6.1 Noise Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

8.6.2 Data Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

8.6.3 Suitability of Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

8.7 Industrial Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

8.7.1 Automotive Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

8.7.2 Aircraft Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

8.7.3 Blind tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

8.8 Further Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

Appendix A Experimental Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

Appendix B Central Composite Second-Order Rotatable Design B.1 Mathematical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

B.2 Refinement of Mathematical models . . . . . . . . . . . . . . . . . . . . . . . . . . 163

B.3 Parameter Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

B.4 Optimum instrument settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

B.5 Graphical representation of results . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

Appendix C Industrial application results

C.1 Specimens provided by Orbseal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

C.2 Maximum Energy approximation results . . . . . . . . . . . . . . . . . . . . . . . 180

C.3 Maximum Amplitude results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184

vii

List of figures

Figure 2.1 Advancing angle of liquid drop on solid surface.

Figure 2.2 Schematic representation of adhesion by mechanical interlocking.

Figure 2.3 Schematic representation of adhesion by diffusion.

Figure 2.4 Schematic representation of adhesion by electronic forces.

Figure 2.5 Schematic representation of adhesion by absorption.

Figure 2.6 Shear lap test layout with adhesive shown sandwiched between two steel plates. All variables are also shown in this figure.

Figure 2.7 Shear lap test specimen with introduced rectangular defect at the centre of the adhesive patch.

Figure 2.8 Load-Strain characteristics from shear lap test. Specimens 16-20 were designed to have varying degree of defect area.

Figure 2.9 This graph shows the relationship between the adhesive area of the specimen in mm2, against of maximum load of each specimen. The maximum adhesive area refers to a specimen without defect.

Figure 2.10 Shear lap test configuration before and after tensile force (F) is applied.

Figure 2.11 Thick adhesive simulation plate with grey area indicating the adhered area.

Figure 2.12 I-DEAS simulation model depicting the anchoring of adhered area and the shear force applied to the opposite surface.

Figure 2.13 Finite element mesh of adhesive test plate with solid tetrahedral elements that were sized at half the thickness of the adhesive.

Figure 2.14 Stress distribution in adhesive at the bond interface.

Figure 2.15 Tension devise with test piece and ultrasonic pulser and receiver in place for testing ultrasonic signal velocity under different stress conditions.

Figure 2.16 Simulation plate with grey area indicating the adhered area.

Figure 2.17 Stress distribution at bottom of adhered test plate.

Figure 2.18 Stress distribution at top of adhered test plate.

Figure 2.19 Stress distribution at 1 mm distance above the adhesive level.

List of figures

viii

Figure 2.20 Stress distribution at 2.5 mm distance above the adhesive level.


Figure 2.22 Stress distribution at 7.5 mm distance above the adhesive level


Figure 3.1 Experimental set up for ultrasonic scanning and analysis.

Figure 3.2 XYZ scanning device made from aluminium structure with ball sliders on cylindrical steel rails.

Figure 3.3 Base plate and rails for linear movement.

Figure 3.4 Cross bridge member that provides traverse movement on the scanning probe.

Figure 3.5 XYZ test rig, constructed for Ultrasonic c-scans of flat plates.

Figure 3.6 Stepper motors used in scanning device to provide accurate movement of scanning probe.

Figure 3.7 Stepper motor control box that includes power supply, control cards and limit switches. Details of all parts are given in Appendix A.

Figure 3.8 Instruments set up and control window.

Figure 3.9 A-scan viewer with two interdependent gates.

Figure 3.10 FFT analysis result of an ultrasonic signal shown here as an example of the FFT capability of the software.

Figure 3.11 C-scan results with colour coded thresholds to demonstrate the ability of the software to differentiate the levels of output on a colour scale.

Figure 3.12 Software control pallet that defines the macro scale as well as the micro scale within definable threshold values.

Figure 3.13 Motion control window for X Y Z test rig.

Figure 3.14 Stepped specimen constructed specifically for calibrating the XYZ ultrasonic scanning rig.

Figure 3.15 Grey scale “ultrasonic scan” revealing the time of flight difference between the different heights of the specimen. Darker shade, which were at the lower end of the grey scale, revealed higher steps.

Figure 3.16 Test specimen for signal of observation from adherent / adhesive interface.

Figure 3.17 Ultrasonic signal responses from part without adhesive (a) and part with adhesive (b).

List of figures

ix

Figure 3.18 C-scan results of ultrasonic tests with varying speed of scanning.

Figure 3.19 Test specimen with delamination areas between steel plate and adhesive indicated by A1, A2, A3, A4 and A5.

Figure 3.20 Placement of defects at interface between adherent and adhesive.

Figure 3.21 Example of c-scan results on a test piece impregnated with defects at pre determined size and location.

Figure 3.22 (a) Graph of fraction error in Area 1 against Gain and Voltage variation.

Figure 3.22 (b) Graph of fraction error in Area 2 against Gain and Voltage variation.

Figure 3.22 (c) Graph of fraction error in Area 3 against Gain and Voltage variation.

Figure 3.22 (d) Graph of fraction error in Area 4 against Gain and Voltage variation.

Figure 3.22 (e) Graph of fraction error in Area 5 against Gain and Voltage variation.

Figure 4.1 Schematic description of reflection and through transmission pressure wave and velocity of an ultrasonic signal.

Figure 4.2 Ultrasonic signals, from area with defect (a) and without (b). The signal shown in (a) has higher amplitude and slower signal decay when compared to signal in (b).

Figure 4.3 FFT of ultrasonic signals, from area with defect (a) and without (b).

Figure 4.4 Results from adhesive bond evaluation [Roye, 2001]. Conclusion on bond quality is based on the frequency signature for bonded areas. Figure (a) comes from a “good” bond whereas (b) comes from a “bad” bond.

Figure 4.5 Grey scale and colour display of a c-scan with variation in grey scale or colour indicating variation in amplitude of the ultrasonic signal.

Figure 4.6 Test specimen with defects included between the adhesive and metal plate.

Figure 4.7 C-scan results from a specimen with introduced defects. Lighter areas indicate the location of the defects.

Figure 4.8 Superimposition of introduced defect location and defect location as detected by the ultrasonic c-scan. Lighter areas indicate defect location.

Figure 4.9 Single shear lap specimen prepared to test the prediction of failure location using an ultrasonic c-scan.

Figure 4.10 C-scan results of test specimen before destructive test. Darker (blue) areas show better adhesion.

Figure 4.11 Rainbow scale showing the frequency distribution of the c-scan.

Figure 4.12 Ultrasonic c-scan results after destructive test.

List of figures

x

Figure 4.13 Portable device for testing specimens under tensile load.

Figure 4.14 Single shear lap test piece made of two steel plates bonded with structural adhesive.

Figure 4.15 Direction of force applied to the test piece by tightening the nut at one end.

Figure 4.16 Ultrasonic c-scan of test piece before load was applied. Light grey and white areas indicating least contact with adhesive.

Figure 4.17 C-scan of test piece under load. Image shows redistribution of adhesive contact.

Figure 4.18 C-scan results of test piece after the load was removed.

Figure 5.1 Typical results from the ultrasonic A-scan. Gates were used to retrieve data from the areas of interest.

Figure 5.2 C-scan results from an adhesive bond with varying degrees of adhesion. Lighter areas reveal lower adhesion quality.

Figure 5.3 Results from ultrasonic A-scan tests on adhesive bonds with varying degree of adhesion.

Figure 5.4 Graph of statistical results indicating trends between “Good” and “Bad” bonds. Data used for these graphs comes from signals in Figures 5.3 (a-d).

Figure 5.5 Three dimensional analysis of signals from “Good”, “Bad” and in between bond quality corresponding to figure 5.3 (a-d).

Figure 5.6 Three Dimensional Analysis of signals from “Good”, “Bad” and in between bond quality corresponding to figure 3 (a-d).

Figure 5.7 State Space plots of the ultrasonic signals shown in Figure 5.3 (a-d).

Figure 5.8 Wavelet Analysis of signals from “Good” to “Bad” bond quality corresponding to figure 3 (a-d). The Haar, Level 5 wavelet was used.

Figure 6.1 Wavelet decomposition of signal S using Matlab software. Graphs show the original signal and 16 coefficients from a 5 level Haar wavelet transform. Graphs exclude the five levels of detail coefficients.

Figure 6.2 Wavelet decomposition coefficients from a 5 level Haar wavelet transform. Graphs exclude the five levels of detail coefficients.

Figure 6.3 Normalized graph, -1 to +1 Volts, from ultrasonic tests on the four specimens under investigation.

Figure 6.4 Original signal and 3 levels of decomposition of ultrasonic signal from adhesive bond quality tests. Total energy is recorded for “Good” and “Bad” bonds for comparison.

List of figures

xi

Figure 6.5 Full length of ultrasonic signal from adhesive bond test.

Figure 6.6 Ultrasonic C-scan results from a specimen with varying degree of adhesion quality.

Figure 6.7 Full length of ultrasonic signal from all six locations with varying degree of adhesion quality.

Figure 6.8 Graph from one cycle of the ultrasonic signal from all six locations with varying degree of adhesion quality.

Figure 6.9 Ultrasonic signal result from a “Good” and a “Bad” adhesive bond.

Figure 6.10 Maximum amplitude values of first 10 cycles from Ultrasonic test of adhesive bond with varying degree of adhesion quality, 1-best, 6-worst.

Figure 6.11 Total Energy values of the first 10 individual cycles from Ultrasonic test of adhesive bond with varying degree of adhesion quality, 1-best, 6-worst.

Figure 6.12 Total Energy values of progressively longer signal from 1 to 10 cycles. The signal comes from an Ultrasonic test of adhesive bond with varying degree of adhesion quality, 1-best, 6-worst.

Figure 6.13 Ultrasonic signal from tests on adhesively bonded specimen that shows extensive noise interference.

Figure 6.14 Ultrasonic signal shown in Figure 13 with noise removed using Wavelet transform technique.

Figure 6.15 Step type signal used in demonstrating the effectiveness of using Wavelet transform to remove noise.

Figure 6.16 Manufactured noise signal between values -0.05 and 0.05.

Figure 6.17 Original signal with added noise.

Figure 6.18 Original signal with noise removed using Haar 8 transform in Matlab software.

Figure 6.19 Original, noisy and de-noised signals shown on the same scale for comparison. Wavelet transform Haar 5 was used for de-noising.

Figure 6.20 De-noised signal using Haar 5 and Coif 5 Wavelet transforms.

Figure 6.21 Signal including noise and de-noised signal using Haar 5 Wavelet transform.

Figure 6.22 Signal including noise and de-noised signal using Coif 5 Wavelet transform.

Figure 6.23 Typical ultrasonic signal form adhesive bond used to investigate the capability of Wavelet transform to compress data files from tests in this area.

List of figures

xii

Figure 6.24 Result of using the Wavelet transform to compress the ultrasonic

signal shown in Figure 6.23.

Figure 6.25 Signal energy retained after removal of zero coefficients during the data compression process using the Wavelet transform. These results refer to the signal shown in figures 6.23 and 6.24.

Figure 6.26 De-noised and compressed signal of Figure 6.23, using the Haar 5 Wavelet transform.

Figure 7.1 Results from c-scan test on one specimen with blind defects in the adhesive bond interface; (a) showing c-scan results using the Maximum Amplitude method while (b) shows the results from the RMS processing technique. The latter is used as an approximation of the Maximum Energy technique developed in this research.

Figure 7.2 Ultrasonic equipment facilities used for testing adhesively bonded aircraft components.

Figure 7.3 Portable Ultrasonic equipment [MAUS IV] used for testing small areas on adhesively bonded aircraft components.

Figure 7.4 Portable Ultrasonic equipment [BaNDIcoot FAQ] developed for c-scan ultrasonic capabilities by CSIRO, Australia.

Figure 7.5 Conceptual design of portable ultrasonic tester [Lee, 2004].

Figure 7.6 Electrical / Electronic assembly of portable ultrasonic tester [Lee, 2004].

Figure 7.7 Internal automobile panel showing the adhesive applied before assembly and curing.

Figure A.1 UTEXTM UT340 pulser / receiver.

Figure A.2 Zwick / Z010 Tensile testing machine used for hear lap test of adhesively bonded specimens.

Figure B.1 Graphical results showing the influence of Gain and Voltage on defect area detection. The Z-axis of the graph labelled Area ‘n’, shows the fraction error of measured versus actual area of defect. The third variable, Scan Resolution, was kept constant at 1.5 mm in all cases.

Figure B.2 Graphical results showing the influence of Scan Resolution and Gain on defect area detection. The Z-axis of the graph labelled Area ‘n’, shows the fraction error of measured versus actual area of defect. The third variable, Voltage, was kept constant at 140 V in all cases.

Figure B.3 Graphical results showing the influence of Scan Resolution and Voltage on defect area detection. The Z-axis of the graph labelled Area ‘n’ shows the fraction error of measured versus actual area of defect. The third variable, Gain, was kept constant at 20 dB in all cases.

List of figures

xiii

Figure C.1 Adhesive bond specimens prepared in the OrbsealTM laboratory.

Figure C.2 Sample_B_RMS c-scan results.

Figure C.3 Sample_B__Rev. RMS c-scan results.

Figure C.4 Sample_M_RMS c-scan results.

Figure C.5 Sample_M__Rev. RMS c-scan results.

Figure C.6 Small_Sample_RMS c-scan results

Figure C.7 Small_Sample_Rev_RMS c-scan results

Figure C.8 Sample_T_RMS c-scan results.

Figure C.9 Sample_T_Rev. RMS c-scan results.

Figure C.10 Sample_B_MaxAmp c-scan results.

Figure C.11 Sample_B_Rev_MaxAmp c-scan results.

Figure C.12 Sample_M_MaxAmp c-scan results.

Figure C.13 Sample_M_Rev_MaxAmp c-scan results.

Figure C.14 Sample_T_MaxAmp c-scan results.

Figure C.15 Sample_T_Rev_MaxAmp c-scan results.

Figure C.16 Small_Sample_MaxAmp c-scan results.

Figure C.17 Small_Sample_Rev_MaxAmp c-scan results.

xiv

List of Tables

Table 2.1 Types of attraction forces in adhesive bonding process.

Table 2.2 Surface roughness factor for surface treated aluminium.

Table 2.3 Bond types and typical bond energies.

Table 2.4 Results from shear lap test on specimens with varying size of defect area.

Table 3.1

Dimensions of delaminated areas.

Table 3.2 Maximum and minimum values of selected variables.

Table A.1 Technical specifications of UT340 Pulser / Receiver.

Table A.2 Technical specifications of all moving parts of the Ultrasonic scan test rig.

Table A.3 Technical specifications of stepper motors used in the Ultrasonic scan test rig.

Table A.4 Technical specifications of stepper motor control cards used in the Ultrasonic scan test rig.

Table B.1 Matrix of the second order rotatable design (Code values).

Table B.2 Assignment of Code values to the Natural values of experimental variables X1, X2, X3.

Table B.3 Second order rotatable design matrix with natural values of assigned variables.

Table B.4 Extended matrix of the second order rotatable design (Code values).

Table B.5 Experimental values of response functions (criteria for optimisation). Each column of this table is the list of elements of vector Y.

Table B.6 Matrix (X`X)-1.

Table B.7 Matrix X`Y.

Table B.8 Values of calculated model coefficients (B) (Code factors).

Table B.9 Fisher criterion test results on the significance of the second order model coefficients.

Table B.10 Mathematical model accuracy test results.

Table B.11 Optimum values of parameters using coded values.

xv

Nomenclature α Attenuation coefficient

A Wave amplitude

a Wavelet running average

d Wavelet detail

Dd Constant for macromolecule mobility

E Tensile modulus of substrate

E Signal energy

ε Wavelet energy

F Force

f Discrete signal

g Analogue signal

Ga Shear modulus of adhesive

I Signal intensity

k4 Constant for molecular characteristics

λ Wavelength

M Molecular weight of polymer

n Signal Noise

N Avogadro’s number

P Pressure wave

p Pressure waves

ρ Polymer density

rf Roughness factor

s Original Signal

τ Shear stress

T Period

Tn Noise threshold

Ts Signal threshold

U Velocity of incident, reflected and transmitted waves

ω Frequency

1

Chapter 1

Introduction and project outline

1.1 Introduction

This project is about improving the testing of adhesive bonds non-destructively.

There are currently many and varied techniques that are capable of defect detection

in adhesive bonds, however cautious application of adhesives in engineering reflects

the low confidence of designers to use adhesives in cases where high reliability is

required.

This chapter will introduce the intention of this research in relation to non-destructive

testing of adhesives such as adhesives and the adhesion process, non destructive

testing techniques, experimental setup and analysis of results.

1.2 Adhesives in Engineering

Adhesives have been widely used in engineering applications mainly due to their

ease of use, large area coverage and low cost compared with other joining

processes. They are capable of joining dissimilar materials; require no fusion and no

specialized skills by the operator. In short, adhesives should be the preferred option

for joining parts in most engineering applications. However this is not the case due

to the inability to determine their strength either when they are applied or while in

service for a while. Statistical data from laboratory tests offers nominal strength for

specific adhesives under certain conditions of manufacture and application.

Conditions such as surface preparation, application temperature pressure and in

some cases humidity [Gilmore, 1994], are variables that have to be carefully

monitored for successful bonding. The probability of these variables not replicated

on field applications, coupled with other sources of ineffective adhesion due to

porosity or inclusions, restricts liberal use of adhesives to applications that have no

detrimental effects in case of failure. Engineering applications fall within the “too

dangerous” category which means limited use of adhesives. Efforts to increase

confidence in the use of adhesives for engineering applications, has lead to utilizing

non destructive testing.


2

1.3 Non destructive testing

A variety of Non Destructive Testing (NDT) techniques are available for general

engineering applications [Halmshaw, 1997]. Some of these techniques, in particular

X-Ray and Ultrasonic waves, are widely used for testing adhesive bonds. These

techniques can detect a variety of defects in adhesive bonds such as delaminations

voids, porosity and cracks [Adams, 1988]. Other defects such as zero volume

disbonds and poor cure of adhesive are more difficult to detect by either of these

techniques which further highlights the complexities in testing adhesive bonds.

Extensive research has been carried out in this field in an endeavour to improve the

reliability of these techniques. However limited success has been achieved leaving

room for extensive research to be done toward that goal.

1.4 Ultrasonic testing of adhesive bonds

The propagation of Ultrasonic signals through solid, liquid and viscous medium has

been the most widely used method of evaluating adhesive bonds. A number of

techniques, using ultrasonic signals, currently in use, Leaky Lamp waves [Mal, et. al,

1990, Bar-Cohen et al, 2001], Lamp Waves [Heller, et al, 2000, Mustafa, et al, 1997],

Pulse Echo and through signals [Blitz and Simpson 1996] have achieved some

degree of success in evaluating adhesive bonds but in general the problem of reliably

evaluating the quality of adhesive bonds remains largely unsolved. Nevertheless it is

also clear, by the extensive utilization of Ultrasonic signals, that this technique

deserves further research to achieve what is considered the “Holy Grail” [Scala,

1997] in this area which is the evaluation of adhesive bond strength non

destructively. Different hardware configurations have lead to improvements in

detecting adhesive bond defects though ultrasonic signals however signal analysis

can also be considered to have just as good potential for improvement. Signals from

the area under test would certainly be influenced by variations in material properties

hence providing a rich source of information for further analysis.

1.5 Research aims

The aim of this project is to explore existing and new approaches to enable

improvements in the reliability of adhesive bond evaluation. Hardware and software

solutions will be considered at the initial stages of the project from where directions

for deeper research will be decided.


3

It is aimed that a new, novel technique will be developed that will be capable of

higher resolution in the detection of adhesive bond defects. Increased sensitivity of

detection is expected to lead to improvements in the reliability of adhesive bond

evaluation both in detecting and analysis of the defect.

The research will have a focus on automotive and aircraft applications and where

possible it is aimed that collaboration with professionals from these areas will be

sought. Outcomes from this research will be tested on real case data either from

field tests or real applications.

1.6 Outline of thesis

1.6.1 Adhesion and adhesives

Thorough understanding of the adhesive process is considered essential prior to any

experimentation taking place. Chapter 2 is devoted to fundamentals of adhesion,

adhesives and their behaviour under test conditions. Aspects such as surface and

adhesive and preparation are necessary in preparing all specimens while the

mechanisms of adhesion need to be understood thoroughly in analyzing failure

modes. A study of existing adhesives and their application was also considered

which lead to the selection of structural adhesive to be used in this research.

A series of destructive tests is carried out at this early stage of the project to enable

understanding of adhesive bond failure. Further tests are conducted to study the

effect of defect inclusions in the adhesive bondline, by preparing suitable test-pieces

to enable correlation between defect area and load behaviour and failure. Computer

modeling is also utilised for adhesive bond behaviour under loading conditions.

Stress patterns from this study led to conclusions about the possibility of using stress

patterns to evaluate the adhesive bond quality.

1.6.2 Design of experimental set-up

Following the literature survey and preliminary experimentation, decisions on the

direction of this research influenced the design of experimental equipment. Special

test rigs, manually or software driven, were designed and constructed to

accommodate suitable test pieces for planned experimentation. The purchase of


4

other, suitable ultrasonic test equipment is also reported in Chapter 3. Hardware and

software commissioning and calibration clearly reported, shows the diligence shown

in optimizing instrument setup to achieve accurate measurements. Specially

designed test pieces with known defects are tested under the experimental set-up

and the accuracy tested by using statistical techniques.

1.6.3 Ultrasonic tests

There are various Ultrasonic techniques used in non destructive testing of adhesive

bonds. All these different techniques produce a similar signal output that is analysed

to achieve interpretation of results. In Chapter 4, ultrasonic signals from test-pieces

prepared for this research were studied to enable understanding of their specific

nature. Analysing ultrasonic signals is an important task in the ultrasonic test

process that requires knowledge and expertise to avoid misinterpretation. Therefore

the study conducted in this chapter is perhaps one of the most important in this

research as it concentrates on the nature of ultrasonic signals from adhesive bonds.

The validity of adhesive bond integrity detection is tested using an experimental rig

capable of ‘a’, ‘b’ and ‘c’ scans. A suitable water bath was used to enable efficient

coupling for the ultrasonic signal. Adhesively bonded specimens with known defects

are prepared according to manufacturers specifications and are used in this part of

the project. Signals from a Pulse Echo probe are recorded and analysed to establish

the characteristics of the signal that best reflect changes in the bond quality. Three,

well established radio wave characteristics, frequency, amplitude and decay, are

considered and their applicability to this research established.

This chapter also includes reports on a study for predicting failure patterns based on

results from ultrasonic tests. Specimens are specifically prepared for this study and

the results are revealing on the correlation of defect location and subsequent failure

under loading.

Results from tests on adhesive bonds under varying load conditions are also

reported in this chapter. A portable tensile test rig, suitable for underwater tests, was

designed for this purpose.


5

1.6.4 Ultrasonic signal analysis

All traditional ultrasonic signal indicators, frequency, amplitude and signal decay

have been used for adhesive bond evaluation. Tests in this research confirm these

findings except frequency analysis. Taking signal amplitude and decay as the

suitable characteristics, work in Chapter 5 concentrates on the use of statistical

techniques that have the potential to improve the resolution of results. Several well

established statistical/graphical techniques are explored where their ability to detect

small variations in bond characteristics is tested by observing variations in their

graphical output. The generic approach to signal analysis allows consideration of

suitable techniques and selection of one with the highest potential for ultrasonic

signal analysis.

1.6.5 The Wavelet transform

Experimentation with different statistical techniques, in the analysis of ultrasonic

signals from adhesive bonds, proved that the Wavelet Transform offers indicators

capable of highly improved resolution. In Chapter 6, the foundations of Wavelet

Transform and its capability to detect bond quality variation is further explored.

Ultrasonic signals from laboratory tests on specimens with known defects are used to

confirm and establish the advantage offered by this technique.

A brief mathematical consideration of the Wavelet Transform foundations showed

that the wavelet coefficients are affected by bond quality variation and are therefore

selected for further investigation. Further work on the use of wavelet coefficients

leads to indicators that clearly reflect variations in the quality of adhesive bonds. A

comparison of the wavelet transform indicators with existing techniques shows the

advantage of the proposed technique.

Other capabilities of the Wavelet Transform are further explored in this chapter to

establish other characteristics of this technique that offer more refinement to the

proposed technique. Indeed, signal noise reduction and data compression are also

of interest to the evaluation of adhesively bonded areas due to the nature of

ultrasonic signals and the large areas that are usually tested in the aircraft

construction industry.


6

1.6.6 Industrial application

The validity of the proposed technique for evaluating adhesive bond quality, can only

be tested by proposing it for industrial applications that are currently using other

techniques so that feedback from industry experts can be sought and conclusions

drawn. Chapter 7 covers the collaborative efforts with industry partners, to confirm

that the proposed technique offers the suggested advantages. The industrial

applications considered in this chapter are primarily from the automotive and aircraft

manufacturing industries. Adhesive materials from a local supplier that also provides

manufacturers were used. Test pieces prepared by the adhesive supplier are also

tested and the results reported in this chapter.

Collaborative work with industry revealed the need for a handheld ultrasonic device

to test adhesive bonds. This study and product design considerations lead to the

design of such a device that although not prototyped, includes all the elements of

required for its purpose of use. Details of this device are included in Chapter 7.

1.7 Conclusion

The work of this research is summarized and concluded in the final chapter of this

report, Chapter 8. The results and benefits of the proposed technique are evaluated

against the initial objectives of this research. Future directions are also discussed

with recommendations on areas that can further improve the evaluation of adhesive

bonds.

7

Chapter 2

Adhesion and adhesives

2.1 Introduction

The study of developing effective means to test the quality of adhesive bonds can only

begin after comprehensive understanding of aspects that relate to the adhesive

materials used, chemical and mechanical properties, their use in engineering

applications and their characteristics and behaviour under varying environmental

conditions. The field that covers all aspects of adhesives, their application and

performance is very wide due to the very large variations possible in chemical

composition of the adhesive, materials and types of the parts to be adhered and the

environmental conditions under which the bond will be operating etc. There are epoxy

adhesives, cyanoacrylate adhesives, polychloroprene adhesives, liquid adhesives, two

part adhesives, film adhesives, metal to metal, paper, wood adhesion, solvent based

adhesive, cold hardening/curing adhesive, hot melt adhesive to name a few of the

variables involved. After a general introduction to the adhesion process and adhesive

materials, the discussion will lean toward structural adhesives and conclude with the

focus turned further toward structural adhesives that are used in the automotive and

aerospace industries.

This chapter will also be used to explore adhesive bond behaviour under stress. Both,

destructive and simulations tests will be conducted to determine a path to be followed

in this research.

2.2 Adhesion

2.2.1 Bondline Attraction

The success of adhesion depends on a number of factors however the first prerequisite

to be fulfilled in this process is the establishment of intimate molecular contact between

the adherent and adhesive. This requires the ability by the adhesive to spread over the

surface of the adherent displacing air or other gas or liquid in its dispersion path. For

an adhesive process to succeed in the level of contact required, certain conditions

[Kinloch, 1994] must be fulfilled:

i. Liquid must exhibit zero or near zero contact angle;

Chapter 2 Adhesion and adhesives

8

ii. At some time during the bonding operation have a viscosity that should be

relatively low;

iii. Be brought together with the substrate in a manner that should assist in the

displacement of any trapped air.

Having those requirements met, the next mechanism to take effect is the attraction

force that holds the adherent and adhesive together. The most common type of

physical attractive forces are the van der Waals forces. These forces may be attributed

to the:

i. Dispersion (or London) forces arising from the internal electron motions which

are independent of dipole moments and

ii. Polar (or Keesom) forces arising from the orientation of permanent electric

dipoles and the induction effect of permanent dipoles on polarizable molecules.

A further type of attraction force that may affect the bonding process is the hydrogen

bond, which is the result of the attraction between a hydrogen atom and an

electronegative atom such as a fluorine, oxygen or nitrogen atom. Table 2.1, [Pauling,

1960, Good, 1967], lists various types of attractive forces with estimates of their bond

energy range.

Type Bond energy (kJ/mol)

Permanent dipole-dipole interactions:

o Hydrogen bonds involving fluorine

o Hydrogen bonds excluding fluorine

o Other dipole-dipole (excluding hydrogen) bonds

Dipole-induced dipole

Dispersion (London) forces

Up to 40

10-25

4-20

Less than 2

0.08-40

Table 2.1 Types of attraction forces in adhesive bonding process.

2.2.2 Surface wetness

In the case of liquid adhesives, the ease of spreading of the adhesive on the adherent

surface is one of the critical factors in the success of adhesion. The ease with which


9

the liquid adhesive wets a surface may be measured by advancing angle θ° of a liquid

drop on a solid surface as shown in Figure 2.1.

Figure 2.1 Advancing angle of liquid drop on solid surface.

The quality of adhesion is also dependant on physical properties not only of the

adhesive but also the surface preparation of the adherent. [Wenzel, 1936] has shown

that surface roughness may change the wetness factor of a liquid and has also derived

an equation (Eqn 2.1) to relate the advancing contact angle on smooth and rough

surfaces.

cos θf = rf cos θs (2.1)

Where rf is the roughness factor or the ratio of the actual area to the projection area of

the solid, θf and θs being the advancing contact angle on rough and smooth surfaces

respectively.

More recent research, [Carre and Schultz, 1983] has used Wenzel’s equation to

determine the value of roughness factor rf for different material taking metallised glass

as the smooth reference plane in determining θs and then repeating the test on the

rough surface to determine θf. An example of these values on surface treated

aluminium is given in Table 2.2.

Solid surface rf

Smooth glass plate (reference)

Anodized aluminium

Sealed anodised aluminium

Phosphated aluminium

1.0

1.47

1.08

1.01

Table 2.2 Surface roughness factor for surface treated aluminium.


10

Research by Sharpe and Schonhorn,1964, has proposed that one of the most

important factors influencing adhesive joint strength is the ability of the adhesive to

spread spontaneously on the substrate when the joint is initially formed. It goes as far

as suggesting that adhesives at their hot-melt state would result in stronger bonds

compared to being used in the cold state. The liquid adhesives with small or zero

contact angle is of prime importance and as such will spread readily and flow into

crevices to achieve true interfacial contact with little weakness. In subsequent work

Cherry and Muddaris, 1970, it was shown that a ‘wetting constant’ could be established

for an adhesive that would enable the bond strength determination assuming the

required conditions were met.

Other factors have also been proven to have an effect on the wettability of an adhesive.

Work done by Pauling, 1960, on the effects of relative humidity on the wettability of

mild steel surfaces and consequently the implications on joint strength has resulted in

the relationship that shows the strength of the bond to be lowered as the relative

humidity is increasing. However, it was also pointed out that such relationships are not

universally applicable. For example epoxy adhesive with a high viscosity and poor

wetting characteristics would be unlikely to have similar characteristics.

2.2.3 Mechanisms of Adhesion

As explained earlier in this chapter, the degree of intimate contact between adherent

and adhesive is initially a requirement for contact at molecular level to facilitate the

subsequent formation of a strong bond. There are however a number of theories that

specifically refer to the principles of adherent adhesive interface strength. Namely,

these are Mechanical Interlocking, Diffusion Theory, Electronic Theory and Adsorption

Theory. An overview of each of these theories and their contribution to the adhesion

process will be included in this section as basic understanding will certainly prove to be

beneficial in the context of this research.

2.2.3.1 Mechanical Interlocking This theory refers to having the adhesive material “wedged” on a surface irregularity of

negative angle, Figure 2.2. This can be compared to a tooth cavity created by the

dentist in order to keep the filling in place. Although a reasonable claim, mechanical


11

interlocking alone cannot claim the sole adhesion mechanism. It may in some cases

contribute to the strength of the bond if the surface is rough however it has not been

proven that it is because of mechanical interlocking and not due to the increased area

in contact with the adhesive as well as the “cleaner” substrate achieved by surface

blasting. It has, nonetheless, been observed that some surface roughening increases

the measured bond strength, [Jennings, 1972]. If indeed true interlocking occurs,

mechanical “holding” can claim increase of the force requirement to dislodge the

adhesive from the adherent. It is therefore accepted that roughening of the surface

prior to adhesion is applied to improve the quality of the bond. Surface preparation is

carried out either by physical means such as grid blasting, or chemical such as

anodization as in the case of aluminium. Due to the nature of mechanical abrasion for

surface preparation, it is difficult to achieve suitable cavity form for mechanical

interlocking. The cavities would probably resemble a moon crater with the upper

opening wider than the lower part. This confirms that any improvement after

mechanical blasting it’s more likely to occur due to other reasons than mechanical

interlocking. In the case of chemically roughened substrates however, it is possible to

achieve the desired shape cavity. Surface preparation by oxidization was

experimented, [Hine etal, 1984], in bonding zinc using epoxy adhesives, which proved

to achieve higher joint strength.

Figure 2.2 Schematic representation of adhesion by mechanical interlocking.

2.2.3.2 Diffusion Theory Another theory refers to adhesion of polymers to themselves through mutual diffusion

of polymer molecules across the interface, Figure 2.3. In this case, it is supported,

Voyutsdii, et al, 1965, that polymer chains across polymer/polymer interface occur

when adhesive and substrate are mutually soluble and that their chain segments have

sufficient mobility. Through experimental work in this area, Voyutsdii has developed

equations that correlate adhesive forces due to interfusion in compatible polymers to


12

contact time, temperature, polymer type, molecular weight and viscosity. One such

mathematical relationship correlates peeling energy, P, in an adhesive bond to the

contact time, tc, through Eqn 2.2:

4/12/13/2

42

ctdDMNkP

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛=

ρ (2.2)

Where:

k4 : constant for molecular characteristics

N : Avogadro’s number

ρ : polymer density

M : Molecular weight of polymer

Dd : constant for macromolecule mobility

However this theory is not without its critics, [Anand 1973], that support at least part of

the adhesion achieved in this case, can be attributed to other reasons such as

improved wetting by varying time of contact and polymer molecular weight, and that if

any interfusion occurs it would be minimal and difficult to determine its respective

contribution to the adhesive forces. The afore mentioned work was reported as

suitable to the adhesion of elastomers and amorphous plastics, however work done on

polymer/metal interface, [Curie, etal 1984], extends this theory to support that adhesion

due to diffusion between copper (metal) and polyimide (adhesive) occurs where copper

atoms interdiffuse into the polyimide surface.

Figure 2.3 Schematic representation of adhesion by diffusion.


13

2.2.3.3 Electronic Theory

A third theory on adhesive bonding is based on electrostatic forces created by the

contact of the two dissimilar materials, Figure 2.4. These forces are essentially

attributed to the electron transfer between the two materials in contact, which can be

compared to capacitance effect between two plates. Work done to prove this theory,

[Deryaguin and Smlga, 1969], was later disputed, [Wake 1982] as the energy required

to separate the joint during a peel test was attributed to viscous and viscoelastic

responses of the material rather than solely due to electrical energy.

Figure 2.4 Schematic representation of adhesion by electronic forces.

2.2.3.4 Adsorption Theory

This theory is based on how closely the adherent and adhesive are in contact to allow

intermolecular forces to apply, Figure 2.5. Research in this area has been able to

attribute this type of bond to a number of related theories as shown in Table 2.3,

[Cherry, 1970, Good 1967 and Fowkes 1983]. Although not intended to go any deeper

into this field, the values of bond energy and a brief explanation of each will enable an

understanding on which of these are the major contributors to the adhesion process by

absorption.

Figure 2.5 Schematic representation of adhesion by absorption.


14

Primary bonds are established across the interface due to ionic, covalent or metallic

interaction between the adherent and adhesive materials. Secondary bonds are

attributed to the interaction between atoms and molecules from each part to effect

strong bonding forces, Van der Waals forces. There is also an in-between type of bond,

the Donnor-acceptor bond, which again is ranked due to the resulting strength of the

bond.

Type

Bond energy kJ/ mol

Primary bonds Ionic Covalent Metallic

600-1100

60-700

110-350

Donor-acceptor bonds Bronsted acid-base interactions (i.e. up to a primary ionic bond) Lewis acid-base interactions

Up to 1000

Up to 80

Secondary bonds Hydrogen bonds Hydrogen bonds involving fluorine Hydrogen bonds excluding fluorine Van der Waals bonds Permanent dipole-dipole interactions Dipole-induced interactions Dispersion (London) forces

Up to 40

10-25

4-20

Less than 2

0.08-40

Table 2.3 Bond types and typical bond energies.

2.2.4 Surface Preparation

In many cases where adhesives are used, it is almost assumed that no surface

preparation is required or applied. However true this may be, it is only so because the

surface is already at a stage that can be adhered without further treatment. In all cases

there are surface requirements for adhesion to occur effectively but in some cases

either due to the previous operation e.g. heating or washing process, the surface may


15

already be ready hence no further action is required. In other cases where it may

seem that preparation is not required, it could be because the adhesive was designed

to accommodate the condition of the surface e.g. presence of light protective oil. It is

certainly advisable to make sure that the adherent surface has met the requirements in

order to achieve “best” adhesion while it is also true, in many cases, that some kind of

specific surface treatment is required. In some cases it may even be required that

suitable primers are applied prior to adhesion which may serve a dual purpose in some

cases; prepare the surface for adhesion while also protecting the surface from

contamination in the time between preparation and adhesion. Manufacturers normally

issue surface preparation requirements along with each particular adhesive which in

most cases is a result of extensive research.

2.3 Structural Adhesives

Due to the nature of this research, which is related to the use of adhesives in the

automotive industry, a brief introduction will be given to the main types of adhesives

used in this area. These are characteristically known as structural adhesives and are

expected to have the high bonding strength that enables them to be used instead of

other fastening means e.g. welding, riveting or threaded fasteners. Their origins,

characteristics and applications, as reported in the Structural Adhesives Directory and

Handbook [Hussey and Wilson, 1996], shown below to enable a clear understanding of

their nature.

2.3.1 Acrylic Adhesives

First introduced in Germany in 1901, these are polymer adhesives that have good

adhesion to a variety of materials, they are water resistant, durable, flexible (under

correct formulations), with good low temperature properties, excellent optical properties

and with low toxicity. Engineering acrylic adhesives are two part adhesives that cure

when mixed to form an impact resistant plastic layer. Toughened, this adhesive has

been successfully used for vehicle construction, wood to metal bonding, aerospace

applications, panels and computer equipment.

2.3.2 Anaerobic Adhesives

First produced in 1940, they are able to cure without the presence of oxygen. Their

main features are: cure at room temperature, chemical resistance to oils and solvents,


16

low shrinkage, contain no solvents, excess adhesive can easily be removed. One of

the most important applications of anaerobic adhesives is thread locking of mechanical

fasteners. Varying strength may allow unfastening when required. Other applications

include securing gears on shafts, keys in keyways, filling imperfections on metals and

sealing and for gasket jointing. Curing can be accelerated by heat, UV radiation,

primers and metal ion catalysts.

2.3.3 Cyanoacrylate Adhesives

First appeared as adhesives in 1958 and after overcoming some initial shortcomings

due to brittleness, odour, surface and moisture effects they are now effective

adhesives. Their main features are: one part adhesive, ‘instant’ curing occurs under

ambient conditions and it can be applied on a wide variety of adherents. Their main

use is in joining small parts in many industries however they are primarily used for

domestic applications.

2.3.4 Epoxy Adhesives

First introduced in the 1950’s, due to their high strength and endurance are now

extensively used in the aerospace industry, vehicle and boat building industries. Their

main features are: good adhesion to many different substrates, range of mechanical

properties, have a wide range of cure characteristics, low shrinkage. They can be of

one or two parts adhesive and have varying cure characteristics ranging from room

temperature to 175 0C.

2.3.5 Imide-Based Adhesives

These have evolved from the development of resins for fibre-reinforced composites.

Two Imide based adhesives are available, Bismaleimide and Polyimide. Some of the

features of the former are: high temperature capability (200°C to 230°C), have

excellent electrical properties release no volatiles during cure but have generally poor

peel resistance. Some of the main features of the latter are: high temperature

capability (up to 300°C), excellent electrical properties but require extraction or high

processing pressure as volatiles (often water molecules) are evolved during cure.


17

2.3.6 Phenolic Adhesives

These adhesives appeared as early as the 1900’s known as Bakelite. Their main

features are: high mechanical strength, good solvent and water resistance, high

thermal stability and flame retardant properties. Their major applications are in

bonding wood, plywood, chip, block board manufacture, bonding abrasives (grinding

wheels), binders for moulding sands, binders for friction composites (brakes, clutches

and automatic transmissions), fibre-reinforced composite manufacture. Phenolics are

also use as additives in synthetic adhesives, eg epoxies and ‘contact’ neoprenes.

2.3.7 Polyurethane Adhesives

These can be accredited to the research of German scientists during World War II.

One main characteristic is that they have to be melted in order to be applied but need

to subsequently cool down to room temperature for curing. Their main features are:

good adhesion to a variety of substrates, good chemical resistance to oils, solvents etc.

provide tough and flexible bonds. They have many and varied industrial uses but their

major applications are in the manufacture of furniture and in the construction, footwear

and vehicle industries.

2.3.8 Silicone Adhesives

Depending on their formulation, these can be one part adhesive that cures by exposure

to atmospheric moisture or two-part adhesive that cures by addition of cross linking

agents. Their main features are: capable of varying viscosities, the have good

flexibility, very good temperature tolerance (-115°C to 265°C), good resistance to UV

and IR radiation and good resistance to oxidation. Due to good chemical resistance,

sealant and adhesive properties, its main uses are in areas of construction, marine and

vehicle assembly industries.

2.4 Adhesive Selection

The selection of the most suitable adhesive deserves individual attention in each and

every case, however there are a number of common factors that need to be

considered.

Firstly, the material to be bonded needs to be considered and usually the adhesive

manufacturer gives the best information on its suitability. However general rules


18

include chemical compatibility, mechanical performance requirements among

secondary but just as important issues include compatibility of expansion coefficient

between adherent and adhesive. Design considerations such as load and durability

requirements, preparation, testing and inspection requirements are essential.

Adhesive characteristics eg one or two part, viscosity, temperature, moisture sensitivity

etc. need to be considered. Cost, application method, health and safety requirements,

cure factors, appearance and service environment would also play a major role in

selecting an adhesive. For the purpose of this research, the recommended [Hussey

and Wilson, 1996] adhesives for metal to metal joining are, Acrylic, Anaerobic,

Cyanoacrylate, Epoxy, Polyurethane, Phenolic and Silicone.

After careful consideration and consultation with a major, local vehicle manufacturer,

Ford Australia [Ford], a one-part Epoxy adhesive, Orbseal Australia [Obseal], was

selected for experimentation in this research. Selection of this particular adhesive was

based on its wide use in the automotive industry for metal to metal adhesion and its

suitability for non-destructive testing.

2.5 Adhesive joint characteristics

Extensive research has been done in the area of adhesive strength. This area is of

particular interest to this research, as the effectiveness of non-destructive testing of

adhesive bonds will in some cases be measured as a function of bond strength. Many

and varied configurations have been developed to test various characteristics of an

adhesive bond which are comprehensively described in National and International

Standards such as the American Society for Testing and Materials Standard [ASTM]

for single lap joint test (ASTM D 1002-72) and the British Standards Institution series

(BS 5350). Some results from analytical and experimental work in this area will be

included briefly in this section, which will serve as a vehicle to better understanding of

the theoretical background in adhesive bond analysis.

As early as 1938 [Voldersen, 1938], work was done using an analytical approach to

determine the load and stress distributions in shear lap test specimens. Although very

straightforward in its linear approach, the shear stress in the adhesive (τ) can be

calculated using the equation Eqn 2.3,


19

( ) 2/12/10 )2/coth(2/ ΔΔ=ττ (2.3)

where:

F

SubstrateAdhesive

Substrateds

ba

ha

F

la

Figure 2.6 Shear lap test layout with adhesive shown sandwiched between two steel

plates. All variables are also shown in this figure.

aa lbF

=0τ (2.4)

and

ass

aa

hdElG 2

=Δ (2.5)

Ga : Shear Modulus of adhesive

E : Tensile Modulus of substrate

This early work had taken a simplistic approach by assuming that all of the parameters

involved were totally linear. For example it was assumed that the shear and transverse

tensile stresses were uniform across the thickness of the adhesive, the shear strains in

the substrates were ignored and only stresses in two directions were considered. More

recent work [Renton and Vinso, 1975], utilized advancements in computer technology

that enabled taking into consideration all the above variables through analytical,

experimental and computer (FEA) techniques. Work done in developing equation 2.3,


20

was used to extend it to three dimensions by using FEA as well as experimental and

analytical work [Adams and Peppiatt,1973]. The relationship between transverse shear

stress and longitudinal stress was established, the effect of diverse substrates on

adhesive stresses and even stress concentration areas were investigated. This work

lead to important conclusions on the effect of fillets at the ends of the adhesive [Adams

and Peppiatt, 1974]. Elastic / plastic behaviour of adhesives, adhesive geometry

effects, other adhesive properties were all analysed extensively and are well reported

in texts [Kinloch, 1994].

The use of FEA in analyzing the behaviour of adhesives became of special interest to

research in this area, especially the work done in adhesive fracture characterization

[Luxmoore and Owen, 1984], adhesive fracture initiation [Pluralis and Dan 1987] and

shape optimization of adhesive bonds [Nordlund, 1991].

The research cited, confirmed that stress analysis of adhesive bonds while in service

could be an effective technique to determine the quality and life expectation of

adhesive bonds. Based on this principle, it was considered worthwhile to explore the

possibility of measuring stress distribution in an adhesive bond under varying load. A

series of tests were planned to determine the effect of defects on load variation and

bond fracture. Finite Element Analysis (FEA) was also planned in order to determine

the behaviour of load distribution through the bulk of the material. Finally

experimentation on utilizing Ultrasonic signals to measure stresses in a material was

explored by constructing suitable testing jigs.

2.5.1 Shear lap test

After careful consideration of the different standard tests for adhesive bond and the

equipment available at our research facility, it was decided that the “metal-to-metal

single-lap joint test” would be used as it best reflects the operational conditions of

adhesives in the automotive area.

A variety of preliminary experiments were carried out to determine the direction of this

research of which one of them was referring to the strength of adhesive bonding.

These tests were designed to determine the effect of partial delamination in an

adhesive patch.

Five shear lap test specimens with the same area of adhesive, 25 x 25 mm were

prepared with an artificial, rectangular defect (delamination) at varying sizes as


21

recorded in Table 4. The defect was placed at the centre of the adhesive patch.

Figure 7 shows a typical specimen. Each of the test specimens were in subsequently

stretched on a tensile testing machine (Figure A.2, Appendix A) to destruction and the

maximum load recorded for each case. Table 2.4 shows the results deduced from the

shear lap test results shown in Figure 2.8.

Figure 2.7 Shear lap test specimen with introduced rectangular defect at the centre of the

adhesive patch.

Figure 2.8 Load-Strain characteristics from shear lap test. Specimens 16-20 were

designed to have varying degrees of defect area.

Adhesive Patch

Defect


22

Test Piece

Patch Area

Defect Area

Effective Area

Max Load

Effective Area

Percent Load

Nr mm2 mm2 Mm2 KN % % 16 702 0 702 7.22 100 100 17 702 65 637 7.13 91 99 18 676 144 532 5.57 79 77 19 676 203 473 6.69 70 93 20 702 272 430 6.66 61 92

Table 2.4 Results from shear lap test on specimens with varying size of defect area.

Figure 2.9 This graph shows the relationship between the adhesive area of the specimen

in mm2, against of maximum load of each specimen. The maximum adhesive area refers to a specimen without defect.

The results of the experiment were subsequently collated and plotted as shown in

Figure 2.9. This graph showed that there was limited correlation between the size of

the adhered area and the maximum load carried by the bond before failure. However,

substansive reduction in the area (39%) resulted in a moderate reduction (8%) of

maximum load carried at failure, which is in agreement with previous experimental

work [Jennings 1972]. However our results have shown that there is a clear trend that

correlates the effective bonded area to the maximum load. Although marginal,

nonetheless it is present and can be utilised in detecting cases of potential failure at

lower loads than specified by the manufacturer’s specifications. The shape, size and

location of the defect area in relation to intensity, direction and rate of application of the

applied force are all factors that would affect the nature of the failure and should be

considered in all cases. Therefore, non destructive evaluation, in this case ultrasonic,


23

of adhesive bonds at predetermined chronological periods can reveal the behaviour of

the adhesive bond and even have the potential to predict when the failure may occur.

Fracture mechanics of adhesive bonds [Kinloch 1994] under shear lap test conditions,

revealed that failure and load bearing of the adhesive bond is highly dependant on the

bonding strength of the edges. This explains the non-proportional load / area

relationship as the area of defect introduced was at the centre of the adhesive patch.

Single shear lap tests are prone to misaligned load application due to metal

deformation, Figure 2.10(b), hence introducing a combination of shear and peal failure

taking place at the edges of the adhesive.

(a)

(b)

Figure 2.10 Shear lap test configuration before and after tensile force (F) is applied.

This is followed by catastrophic failure once the edges fail hence the brittle failure type

of stress/strain curve. Referring to Figure 2.9, the results from specimen 18 did not

follow the trend of the other specimens however the result also supported the finding

that reduced size of effective area by delamination would result in reduced strength.

Work on the failure modes of adhesive bonds should include the effect of defect size

and location in relation to the direction of loading in order.

2.6 Stress distribution in bonded areas

The experimental work in this section prompted the question of stress distribution

dependency on adhesive bond effectiveness. Similar work done for rivet joints [Liu and

Sawa, 2000] revealed that the stress distribution of on the joint plates was correlated to

the position of the rivets suggesting that an analogous assumption should reveal the


24

location of strong bonding. A Finite Element Analysis of this case was carried out to

confirm the results of this test. The Simulation module of the I-DEAS software [I-

DEAS], was used for this purpose where a plate representing the thick layer of epoxy

adhesive [Orbseal], was assigned uniform shear modulus across its thickness [Post,

1988]. The shaded area at the bottom of the plate, shown in Figure 2.11, was

assigned to represent the effective area of the adhesive while the center white area

represented the delaminated area.

Figure 2.11 Thick adhesive simulation plate with grey area indicating the adhered area.

2.6.1 Boundary conditions

The effective bond area was anchored against the six degrees of freedom, to simulate

adhesion to the bottom plate of the test specimen. A uniform load was applied along

the top surface to simulate the shear force that would have been transmitted by the top

plate of the specimen during the shear lap test. Figure 2.12, reflects the restrain and

applied shear forces.

(a) (b)

Figure 2.12 I-DEAS simulation model depicting the anchoring of adhered area and the

shear force applied to the opposite surface.

2.6.2 FEA Mesh

A suitable Finite Mesh was then applied to enable calculations of the stress distribution

within the bulk of the plate as shown in Figure 2.13.


25

Figure 2.13 Finite element mesh of adhesive test plate with solid tetrahedral elements that

were sized at half the thickness of the adhesive.

Figure 2.14 Stress distribution in adhesive at the bond interface.

2.6.3 FEA results

The results from the FEA test are shown in Figure 2.14. The significant observation to

be made from these results, is the load distribution rather than the values of resulted

loads. Taking the load bar colour scheme, blue (bottom of bar) as minimum force and

red (top of bar) as maximum, it is clear that the edges of the delaminated area attracted

the highest stress while the well adhered areas showed lower levels of stress indicating

better load bearing ability. The question of course is whether the FEA results confirm

or otherwise the results of the single shear lap tests done earlier. As shown by the

FEA results, the edges of delaminated areas attract high stress forces that would

obviously break down when the stress reaches the adhesion breaking point.


26

Therefore, it would be reasonable to conclude that as these edges become larger i.e.

the delaminated area becomes larger, so does the possibility of adhesion breakdown.

On the contrary, as the edges around delamination areas become smaller, the stress

concentration is reduced as the load is distributed amongst the much larger area of

well adhered bonding. The result would be that smaller delamination within the

adhesive patch should result to better load bearing hence higher breaking load

required to break the bond. This is in agreement with the experimental results, which

leads to the conclusion that if the stress distribution in the adhesive can be measured

accurately, it may be possible to identify areas of likely failure and even the level of

force required to break the bond. Extensive work has been done in the area of stress

distribution in adhesive bonds [Adams 1978, Renton 1977], however all this work was

done on regular shapes such as cylindrical and rectangular specimens under controlled

conditions that were use to prove analytical derivations of formulae. It was felt that the

ability to determine stress distribution in test pieces of irregular shapes would certainly

lead to useful conclusions about adhesive bond behaviour. Additional work done in

this area using photo-elastic technology [Kawai, et al 1993, Vishay], holography

[Bischof and Juptner, 1992] and neutron diffraction [Li and Perrin, 1992, Lodini et al,

1992] to evaluate stress distribution in adhesive bonds confirmed that pursuing this

avenue of bond quality evaluation by utilising the ultrasonic signals to measure stress

distribution was a worthwhile direction to follow at this stage of this research.

2.7 Stress measurement using ultrasound

As mentioned in the previous section, the intention here was to map the stress

distribution in the adhesive at the adhesive bond interface. In the absence of

experimental equipment to measure the stress levels in the adhesive directly, it was

decided to measure the stress in the adherent with the assumption that at the

adherent/adhesive interface, the stress distribution should be the same. This

assumption would enable the use of ultrasonic testing to achieve this objective. The

concept of using stress distribution to map non effective adhesion may be compared to

riveted parts [Jiemin and Toshiyuki, 2001] where areas adjacent to the rivets have

different stress distribution under load compared to those further away. An

extrapolation of this concept would be that load sharing across a bonded area would

depend on the location and even the strength of the bond, i.e. less well bonded areas

would carry less load and vice versa.


27

Literature survey in this area revealed that stress could be determined using ultrasonic

signals [Prassianakis, 1993, 1995, 2000, Bray, 2000]. Such stress measurements are

based on the principle that ultrasound velocity is affected by the level of stress/strain in

the material at the molecular level. Therefore if two ultrasonic transducers, emitter and

receiver, were placed at a known distance apart, the time taken for the signal to travel

from one to the other should vary according to the stress levels in the material.

Following a calibration exercise it should be possible to correlate the stress level to the

speed of ultrasound signal for a particular material. This theory was proven

experimentally [Landa, 2000], and is in use for testing stress levels in turbine blades

[Bray, 1997].

2.7.1 Tension Device

A tension device was designed and built, Figure 2.5, to test this hypothesis on steel

specimens of the same material and approximate size to those used for adhesion

testing.

Figure 2.15 Tension devise with test piece and ultrasonic pulser and receiver in place for

testing ultrasonic signal velocity under different stress conditions

A series of tests were conducted where varying stress levels were achieved by

stretching the test piece, while measuring the velocity of the signal from the pulser to

the receiver. Despite several attempts, no visible differentiation could be made on the

variation of ultrasonic signal speed. Failure to detect variation in signal speed was

attributed to inadequate sensitivity of our equipment. Time variation in signal speed for


28

steel is 0.002 ns/MPa/mm. Therefore, over the 20 mm distance tested, there should be

0.04 ns/Mpa well beyond the resolution of our equipment. However it has to be noted

that our application required testing between distances that were too small for this

technique, and it has to be noted that such an approach may well be applicable in

cases with greater distances between the probes.

2.8 FEA Simulation of stress in adherent

Having failed to detect stress levels on the surface of a steel plate by using Ultrasonic

Signals prompted an FEA analysis in an effort to establish the reasons for not being

able to detect stress at the top surface. The Simulation module of the I-DEAS software

was used for this purpose where a plate that was assigned steel material properties

was anchored in the same manner as the adhesive analysis case in section 2.6. The

reason for the similarity between the two models is because adherent and adhesive

share the same physical adhesion characteristics at the interface. Shown in Figure

2.16, with the grey area.

Figure 2.16 Simulation plate with grey area indicating the adhered area.

The plate was assigned mild steel properties and was subjected to shear forces at the

top surface simulating those of the shear lap adhesive test. Several runs were

implemented to determine a suitable FEM element size. The results, shown in Figure

2.17, had revealed distinctive variation in stress that confirmed the hypothesis of

adhesive quality determination by use of stress distribution at the adherent adhesive

interface. This location was selected as the stress distribution on the adherent was the

same as that of the adhesive as the two are in contact at the interface and the forces

are transmitted through the bonded areas.


29

Figure 2.17 Stress distribution at bottom of adhered test plate.

However when the stresses at the top of the plate were examined, the stress level and

distribution was totally different to the adherent / adhesive interface as shown clearly in

Figure 2.18.

Figure 2.18 Stress distribution at top of adhered test plate.

Further analysis was conducted to observe the stress distribution at different heights

away from the bottom of the test plate. These tests showed quite clearly that the

further away from the adhesive area the less stress levels were visible. Figures 2.19 to

2.23 show the stress levels in the steel plate at various distances away from the bottom

of the plate where maximum stress levels occur.


30





31



The findings from this experimental and FEA analyses discouraged any further

exploration of this hypothesis either by using ultrasound to measure surface stress

distribution or even using the more common technique of using photoelastic coating in

the Polariscope technique, [Vishay]. A method that would be able to measure the

stress distribution either in the adhesive (preferably) or the adherent at the interface will

need to be found for effective experimentation based on this hypothesis.


32

2.9 Conclusion

This Chapter has covered some fundamental aspects on adhesives and adhesion that

were considered necessary in pursuing our endeavors in finding effective ways of

establishing the quality of adhesive bonds non-destructively. Different adhesive

materials were examined and the most suitable one was selected for our application

which was a structural adhesive that is used in the manufacture of vehicles by a local

manufacturer. Some attention was also given to analytical information available in the

literature, about adhesive bond stress levels and distribution as after all this research is

directed toward testing bond quality which in turn depends on the ability of the

adhesive to withstand the applied stresses. Finally experimentation assisted by FEA

analysis was carried out in an effort to propose a technique of measuring stress in the

adherent as a measure of the adhesive bond quality. A method using ultrasonic

signals was used to determine stress distribution but was unable to do so primarily due

to the small distances between the ultrasonic pulser and receiver probes. It is

suggested that this technique may be suitable for cases where much larger areas need

to be inspected.

33

Chapter 3

Design of experimental set-up

3.1 Introduction

Work done to this stage of the project, as well as scientific literature in the area of

adhesive bond evaluation, has lead to a clear understanding of the problem at hand

and the alternative methods currently used to solve it. Many and varied techniques

have been researched and developed in order to evaluate adhesive bonds reliably.

These techniques have taken advantage of scientific advances in the areas of

Thermography, Radiography, Holography and others [Bar-Cohen, 2000], to non-

destructively evaluate adhesive bonds. However, the most commonly used method

in industrial applications was found to be based on Ultrasonic signal techniques. It

allows a flexible equipment setup, it is suitable for on-site testing and is one of the

main techniques used in the Automotive and Aircraft industries to test adhesively

bonded structures. Due to the above reasons, as well as special interest in the afore

mentioned industries, ultrasonic testing was the chosen method for this research in

evaluating adhesive bonds.

Ultrasonic testing equipment is widely available and selection was based on its

applicability to adhesive bond testing. The major elements of equipment required

were, an ultrasonic pulser/receiver, ultrasonic probes, recording, analysis and display

software. Aspects such as ultrasonic frequency range and data recording speed

were the most important factors in selecting equipment for this research.

Another essential component to this research equipment was the design of a three-

dimensional moving mechanism to automate the scanning process. Precise

positioning of the ultrasonic probe and an accurate record of its location is required to

enable c-scans of an area to be constructed.

A water bath was needed for specimen immersion during experiments. A plastic

container 30 cm wide x 50 cm long x 30 cm deep was constructed from 10 mm

Perspex walls.

The experimental apparatus was either purchased or designed and constructed and

eventually commissioned for the experimental tests on adhesively bonded joints.

One fairly important issue in this endeavour was the fine tuning of experimental

parameters to enable reliable data collection. For this purpose, a detailed study was

Chapter 3 Design of experimental set-up

34

carried out in optimising experimental parameter settings using an appropriate

mathematical technique.

3.2 Ultrasonic Test Equipment

A number of companies that manufacture ultrasonic testing equipment were

considered prior to purchasing equipment for this research, [Modsonic, Staveley,

Krautkramer, Sierra Matrix, UTEX]. A selection was based on the ability of the

equipment to provide accurate frequency generation and variation, accurate control

of amplification, pulse repetition, voltage control, fast data logging and generally a

“clean” signal. The chosen equipment was purchased from UTEX Scientific

Instruments Inc [Utex]. The main reasons for purchasing this equipment was its

capability to pulse and amplify ultrasonic transducers with center frequencies from 1

to 150 MHz. Its transducer excitation was achieved with an ultra-fast square wave

pulser with adjustable pulse voltage. The amplifier in the pulser / receiver was

directly gain controllable which eliminated the need for attenuators that usually

generates receiver noise. High speed, 100MHz, data logging was achieved via the

SonixTM digitizer STR8100 [STR8100] interface card. The hardware came with

accompanying software, WispectTM [Winspect] that was capable of complex signal

analysis A-scans, B-scans, c-scans, FFT and motion control devices to enable c-

scanning.

Other equipment necessary for this research was a test rig to enable c-scans and

ultrasonic probes. Figure 3.1 shows a conceptual diagram of the experimental setup

that would enable Ultrasonic scanning of adhesive bonds.

Figure 3.1 Experimental set up for ultrasonic scanning and analysis.


35

3.3 UTEX UT340 Pulser / receiver

The pulser receiver instrument was one of the most important instruments in this

research. Accurate control of the various experiment parameters was, as in all

research activities, of paramount importance. Voltage control, pulse width, pulse

repetition rate, internal or external triggering and pulse/echo or pitch/cutch modes

were possible. As mentioned earlier however, the most important feature of this

instrument was its ability to work within 1MHz to 150MHz frequency transducers.

Specifications of this instrument are given in Appendix A.

3.4 Ultrasonic Test Rig

3.4.1 XYZ scanning mechanism

A suitable system was designed and constructed to enable experimental procedures

planned for this research (Figure 3.2). The supporting structure, motion and control

parts of the test rig were designed with special consideration given to the size of

specimens, resolution of measurement, speed of measurement and accuracy

requirements.

Figure 3.2 XYZ scanning device made from aluminium structure with ball sliders on

cylindrical steel rails.


36

3.4.1.1 Structural Elements

A rigid base to support all moving parts, drive systems and ultrasonic probe was

constructed of cast aluminium. Other than the base plate (Figure 3.3), the rest of the

structure was made up of the legs to accommodate space for the water bath, a

bridge to provide traverse movement support and a tower to provide vertical

movement support. Special dampener inserts were placed in the legs to minimise

vibrations during scanning.

Figure 3.3 Base plate and rails for linear movement.

3.4.1.2 Motion System

The primary requirements of the motion mechanism were to provide linear movement

of the ultrasonic probe at an acceptable level of accuracy and to provide accurate

correlation between the probe location and specimen. Aspects such as straightness

of movement, parallax between slides and backlash in screws when reversing

direction were major considerations in selecting or manufacturing the suitable parts.

Linear movement was achieved by using high accuracy cylindrical rails with linear

ball bearings. Specifications of all moving parts are given in Table A.2 of Appendix

A.

Figure 3.4 Cross bridge member that provides traverse movement on the scanning probe.


37

To minimise errors due to backlash, ball screw/nut arrangement was selected and

fitted to all axes of the test rig, parallel to the slights as shown in Figure 3.4.

Accuracy tests were conducted to ensure errors in linear movement were within

acceptable limits. The error was contained between ±0.01 mm, which was well

within the limits of this research.

3.4.2 Test Rig Set-Up

All parts were constructed and assembled to produced the Ultrasonic Test Rig as

shown in Figure 3.5. Extensive adjustment in locating each part accurately was

required to achieve the intended function of the device.

Figure 3.5 XYZ test rig, constructed for Ultrasonic c-scans of flat plates.

3.5 Motion Control System

In order to achieve predetermined locations of the ultrasonic probe for c-scan

measurements, suitable stepper motors were selected and connected to the driving

screw via a flexible coupling as shown in Figure 3.6. Details of stepper motors are

given in Table A.3 of Appendix A.


38

Figure 3.6 Stepper motors used in scanning device to provide accurate movement of

scanning probe.

A control box (Figure 3.7) including the power supply, control cards and limit switch

sensors was designed to provide motion control of the stepper motors based on

commands given by the scanning software [Winspect].

Figure 3.7 Stepper motor control box that includes power supply, control cards and limit

switches. Details of all parts are given in Appendix A.


39

3.6 Ultrasonic Probe

A 10 MHz non focused, immersion probe was used for this research. The non

focused nature of the probe was preferred as the response signal was expected from

the interface of the adhesive / adherent interface that varied according to the

adherent thickness. Research done in this area also showed that variation in through

transmission of the ultrasonic signal was dependant on its frequency [Chambers and

Tucker, 1999]. Due to the nature of Pulse / Echo tests done in this research and due

to the need for minimum reflection of signal from the adhesive interface when a

“Good” bond is present, a higher frequency transducer was selected to maximise

signal energy absorption by the adhesive.

3.7 Ultrasonic signal analysis software

The WinspectTM [Winspect] software was an integral part of the hardware purchased

from UTEXTM Scientific Instruments [Utex]. This software controls all ultrasonic

equipment and drives the scanning mechanism. Data collected is analysed and

displayed within WispectTM. The software operates on a Personal Computer within

the MicrosoftTM Windows Operating System.

3.7.1 Digitizer Card

A number of variable parameters needed to be setup for successful operation of the

experimental equipment. The most important of which were the selection and data

synchronisation of the digitizing card, communication parameters such as board

number, memory locations and baud rates. A sample screen is shown in Figure 3.8.


40

Figure 3.8 Instruments set up and control window.

3.7.2 Data collection

Control of data collection was achieved by defining the gate parameters that

bounded the signal under investigation. Gates were either independent or linked to

another gate that controlled the position and triggering of the dependent gate. The

type of data to be collected was another controlled variable. Maximum or minimum

values of gated signal, time between two parts of the signal and waveform data

within the gate were some of the variables collected. The data was automatically

saved in a predefined file that was made available for further analysis. Figure 3.9

shows a typical screen of the ultrasonic signal, with two interdependent gates. Gate

2 has a fixed distance from Gate 1 and will trigger data collection only when Gate 1 is

in operation otherwise it switches off.


41

Figure 3.9 A-scan viewer with two interdependent gates.

The data collected from the ultrasonic tests were analysed by the WinspectTM

software either in real time as they were collected or data files were saved for further

analysis at a later stage. Maximum and minimum values, Waveform and FFT (Figure

3.10) analysis were the most useful analysis tools in this research. C-scans were

also extremely useful due to the macro view of the area under test. Colour coded

thresholds were used to focus on specific Regions of Interest (ROI), exemplified in

Figure 3.11. Each colour or shade of grey, depending on the scale used (Figure

3.12), represents the level of measured variable in a standardized scale –1 to +1

Volts. Further subdivisions can be achieved by defining thresholding the region of

interest on the colour or grey scale. These and other controllable functions of the

software enable detailed analysis of ultrasonic signals, however the most important

function is its ability to differentiate between the levels of measured output for

identification of defective areas in adhesives.


42

Figure 3.10 FFT analysis result of an ultrasonic signal shown here as an example of the FFT capability of the software.

Figure 3.11 C-scan results with colour coded thresholds to demonstrate the ability of the software to differentiate the levels of output on a colour scale.


43

Figure 3.12 Software control pallet that defines the macro scale as well as the micro scale within definable threshold values.

3.7.3 Motion Controller

All stepper motors of the XYZ scanning rig were controlled from this part of the

WinspectTM software. For example the limits of the scan area could be defined e.g.

xy values of start and xy values at end, the speed of transducer movement, the

interval of ultrasonic measurements, file to save collected data and interval for data

collection. A sample of the Motion Controller window is shown if Figure 3.13.

Figure 3.13 Motion control window for X Y Z test rig.


44

3.8 Instrument Commissioning and Calibration

The final step in the design and construction of the test rig was to commission,

calibrate and test the device. Axes and stepper motor calibration was done through

the Motion Controller function of WinspectTM software where the direction, and step

increments of the motors were aligned with data collection to represent the true

shape and size of specimens. Function and accuracy were evaluated by running

various scans on specimens with known dimensions.

3.8.1 Signal Speed Calibration

A special test piece was prepared, Figure 3.14, where the dimensions of the scan

results were compared to the actual results form where adjustments were made.

Vertical heights of the steps were used to calibrate “Time of Flight” measurements

from the Ultrasonic Transducer using the known frequency of the signal (10 MHz)

and the speed of sound through water (1500 ms-1). The c-scan that resulted from the

test is shown if Figure 3.15, where the areas closest to the transducer appear darker

as a grey scale was used that ranges from black to white. The measurements

confirmed accurate correlation existed between calibrated parameters and actual

dimensions of the test piece.

Figure 3.14 Stepped specimen constructed specifically for calibrating the XYZ ultrasonic

scanning rig.


45

Figure 3.15 Grey scale “ultrasonic scan” revealing the time of flight difference between the different heights of the specimen. Darker shade, which were at the lower end of the grey scale, revealed higher steps.

3.8.2 C-scan Calibration

The first step in the process of taking measurements was to determine the type of

ultrasonic signals that reflected from the adhesive / steel interface. A test piece was

prepared from structural adhesive, Orbseal 2000 [Orbseal], adhered to a mild steel

plate with dimensions as shown in Figure 3.16.

1.2 mm

30 mm 50 mm

Adhesive

Mild Steel

25 mm

0.8 mm

Figure 3.16 Test specimen for signal of observation from adherent / adhesive interface.


46

The specimen was placed in a water bath with its steel side towards the ultrasonic

probe. A non-focused, 10 MHz ultrasonic transducer, was used as results at

different depths were required to reveal information about both sides of the adhesive.

However some adjustment was still required to enable best results. This was

achieved by varying the distance of the transducer from the specimen while

observing the signal. Although it was an experiential result it was quite clear that one

position was giving the best signal response.

Readings from parts without adhesive were taken and compared with those from

parts of the specimen with adhesive, Figures 3.17 (a) and (b).

(a)

(b)

Figure 3.17 Ultrasonic signal responses from part without adhesive (a) and part with

adhesive (b).


47

The results from this test showed variation mainly in the amplitude or the signal. This

is in agreement with theory introduced in the preceding chapter that supports higher

amplitude signal returning from delaminated parts of adhesive joints.

3.8.3 Scanning Speed

The speed of transducer over the specimen during c-scan readings was another

independent variable that was tested to ensure accurate collection of data. A series

of c-scans were carried out and while keeping all other variables constant, the speed

of the transducer was set at 10, 20 and 30 mm/sec. The results recorded are as

shown in Figures 3.18 (a), (b) and (c) respectively. As it can be seen there was no

significant variation in the accuracy of size definition although the surface quality of

the c-scan shows to have deteriorated at higher speeds.

(a)

(b)


48

(c)

Figure 3.18 C-scan results of ultrasonic tests with varying speed of scanning.

3.8.4 Transducer Height

The height of transducer and speed of scanning were found to be independent from

other variable settings hence were tested individually. The results revealed that,

within the limits tested, the speed of the transducer during scanning had little effect

on the ultrasonic readings although it is understandable that excessive speeds would

have some effect on the accuracy. As excessive speeds of scanning were not

necessary or intended, this variable was considered to be not significant and was

therefore not considered for further testing. The transducer height, distance from the

specimen, was easily varied by vertical adjustment of the scanning device which

enabled optimal setting of the height by observation of output signal. However there

were a number of other variable instrument settings that were considered

interdependent, i.e. Voltage, Pulse width, Repetition rate and Pulse / Echo Gain.

Empirical testing would have resulted in lengthy experimentation and inconclusive

results due to the high number of permutations required to achieve optimal

instrument settings. It was therefore decided to use a computer based statistical

analysis routine for this purpose.

3.9 Experimental Set-Up Optimisation

The primary instrumentation used in all the Ultrasonic tests were the UT340 Pulser

Receiver System (Appendix A), and XYZ scanning machine (Figure 3.1) in a setup

detailed in Figure 3.5. The former handled all of the ultrasonic signals while the latter

guided the probe for taking measurements. In this instance, due to the requirement


49

of producing c-scans, the XYZ machine would traverse the probe in such a manner

as to allow covering the areas under test (ROI) while allowing readings to be taken at

predetermined intervals. These intervals would normally be adjusted in terms of

distance between readings and are hereafter referred to in this work as “Scanning

Resolution”. The UT340 ultrasonic pulser receiver required a number of settings for

ultrasonic measurement of which the ones perceived to be relevant to this work were

the Voltage, Pulse width, Repetition rate and Pulse / Echo Gain. However further

experimentation showed that Repetition rate and Pulse width did not show any

significant effects on A-scan response with reference to the amplitude of the

returning signal. The same preliminary tests also revealed that the scanning

resolution appeared to have an influence on the accuracy of c-scans results.

As stated above, three instrument variables were potentially capable of affecting the

accuracy of experimental results. Each of these could of course affect the accuracy

of the results individually but there was also the possibility that they would also have

interdependent influence. A decision was made at this stage to determine the level

of influence by each of the variables on the accuracy of ultrasonic results, the

interdependence and search for the optimal setting of each and as a combination.

Even with only three variables, such a task may prove to be very time consuming and

highly prone to errors. A statistical method, Analysis of Variance, was used to firstly

identify the significance of each of these variables in affecting measurement

accuracy and secondly to optimise the instrument settings.

3.9.1 Experimental Design

Specimen: A mild steel sheet 50x40x1.6 mm with one side bonded to a 42x25x1 mm

structural adhesive patch. A series of defects were placed between the adhesive

patch and steel sheet. These were rectangular pieces of paper placed in-between

the adherent and adhesive prior to curing. The specimen was subsequently baked

according to the manufacturer’s specifications [Orbseal]. Figure 3.19 shows the

specimen including the sheet steel, the adhesive and all the defect placements at the

interface between the adhesive and adherent.


50

Figure 3.19 Test specimen with delamination areas between steel plate and adhesive

indicated by A1, A2, A3, A4 and A5.

Area A1 A2 A3 A4 A5

Width x

height (mm)

14.8 x 14.8 10.7 x 10.2 6.3 x 6.5 4.2 x 4.3 2.3 x 2.5

mm2 219 109.1 41 18 5.8

Table 3.1 Dimensions of delaminated areas.

Instrument set-up:

1. The specimen was placed in a water bath, steel side up

2. A 10 MHz, immersion, ultrasonic probe was placed at a distance of

65 mm above the surface of the specimen

3. The UT340 Pulser Receiver system was set at 5 ns pulse width,

200 Hz repetition rate. Other settings such as Voltage and Pulse

Echo Gain were varied during the experiment

4. The scanning resolution values were varied through the

WinspectTM software [Winspect]

Procedure: The specimen was prepared with the defects placed at the interface of

adherent and adhesive (Figure 3.20).


51

Figure 3.20 Placement of defects at interface between adherent and adhesive.

It was then baked at a temperature of 180 °C for 30 minutes according

to the specifications given by the manufacturer (Orbseal). Once the

specimen was baked and cooled in air, it was placed in the water bath

for examination. The steel side was nearest to the ultrasonic probe

such that the bond line is examined through the steel rather than the

adhesive. The instrument settings were prepared and c-scans were

taken of the specimen at different instrument settings. Instrument

settings were kept constant while each of the variables was tested

individually. Maximum and minimum values for each of the variables

were decided again through experiential testing.

Table 3.2 Maximum and minimum values of selected variables.

Variable Minimum Maximum

Voltage (V) 140 300

Pulse / Echo Gain

(dB) 6 34

Scan resolution

(mm) 0.5 2.5


52

3.9.2 C-scan Test

A series of c-scan tests were carried out with varying parameter settings. An

example from results of one such test is shown in Figure 3.21. The instrument

variables for this test were: resolution, 0.5x0.5 mm, voltage setting at 200V and P/E

Gain at 20 dB.

Figure 3.21 Example of c-scan results on a test piece impregnated with defects at pre

determined size and location.

Grey scale was used to differentiate the results from the Gated area. Referring to

Figure 3.21, and based on a Grey scale that depicts low signals darker, one may see

clear identification of defects by the lighter areas. Although not very sharp, each and

every one of the areas is reproduced with a certain degree of dimensional accuracy.

However the boundaries of each defect is not well defined.

3.9.3 Modelling and optimisation

The accuracy of adhesive defect location and identification is at the centre of this

research. Inability to determine the location and size of the defect with the highest

possible accuracy that the instruments can afford would jeopardise the success of all


53

subsequent work. It was therefore decided that a mathematical method was utilised

to determine the optimum settings of instruments. The results from this analysis are

reported briefly in this section while detailed analysis and results are reported in

Appendix B.

The Central Composite Second-Order Rotatable Design [Peng 1967] method was

chosen as a method for optimising instrument settings for this research. This is a

design of experiment technique that enables the derivation of a mathematical model

that can be used for optimisation. Coefficients b0, bi, bii etc., are determined by this

method which results in a second order mathematical model as shown in Eqn 3.1.

Y = bo + ∑ biXi + ∑ biiXi2 + ∑ bijXiXj (3.1)

This technique is also capable of determining the accuracy of the model as well as

the optimal values of the variables under investigation.

The experimental variables selected as having the most dominant influence on the

accuracy of results were:

X1: Voltage setting

X2: Pulse/Echo Gain

X3: Scan resolution

The selected target variable ‘Y’ for optimisation was the accuracy of defect area

detection.


54

3.9.3.1 Optimisation models

The mathematical models derived for each of the five areas under investigation are

given below.

Y1 = 0.23 + 0.0014 X1 – 0.009 X2 + 0.0352 X3 – 0.0113 X12 + 0.0277 X2

2 (3.2)

-0.0097 X32 + 0.0032 X1X2 - 0.0196 X1 X3 + 0.0081 X2 X3

Y2 = 0.3729 – 0.0148 X1 + 0.023 X2 + 0.0633 X3 - 0.0301 X12 + 0.0746 X22 (3.3)

- 0.0646 X32 - 0.0017 X1 X2 - 0.0298 X1 X3 + 0.0494 X2 X3

Y3 = 0.272 + 0.0085 X1 - 0.0024 X2 + 0.0675 X3 - 0.0003 X12 + 0.0745 X22 (3.4)

+ 0.0404 X32 - 0.0212 X1 X2 + 0.0103 X1 X3 + 0.0789 X2 X3

Y4 = 0.4508 - 0.0078 X1 - 0.0024 X2 + 0.0611 X3 + 0.0336 X12 + 0.0765 X22 (3.5)

+ 0.0043 X32 + 0.0098 X1 X2 - 0.0136 X1 X3 + 0.0280 X2 X3

Y5 = 0.6293 + 0.0197 X1 - 0.0001 X2 - 0.0525 X3 - 0.0512 X12 - 0.0565 X22 (3.6)

- 0.0579 X32 - 0.0223 X1 X2 + 0.0034 X1 X3 - 0.0016 X2 X3

3.9.3.2 Refinement of optimisation models

The Error of experiments was determined from:

Error = Σ(Y - Yav)2 (3.7)

where Yav is the mean of Y values.


55

The adequacy of the model was indicated by the value of the Fisher criterion Fd.f, d.f,

0.05 (Peng 1967) from where parts of the model were rejected if the conditions were

not fulfilled. Based on these findings, all insignificant coefficients were ignored to

give equations for target variable ‘Y’ as shown below:

Y1 = 0.23 + 0.0352 X3 + 0.0277 X2

2 (3.8)

Y2 = 0.3729 + 0.0633 X3 + 0.0746 X22 (3.9)

Y3 = 0.272 - 0.0675 X3 + 0.0745 X2

2 + 0.0789 X2 X3 (3.10)

Y4 = 0.4508 + 0.0611 X3 + 0.0765 X22 (3.11)

Y5 = 0.6293 - 0.0525 X3 - 0.0512 X12 - 0.0565 X2

2 - 0.0579 X32 (3.12)

3.9.3.3 Parameter optimisation

These mathematical models can now be used to determine the optimum instrument

settings of variables Voltage, Gain and Scan resolution, (X1, X2 and X3). This is

possible by using “Maxima” and “Minima” calculation through a series of differential

equations (eqn 3.13), or graphical plotting.


56

⎪⎪⎪⎪

⎩

⎪⎪⎪⎪

⎨

⎧

=

=

=

03

02

01

XY

XY

XY

∂∂

∂∂

∂∂

(3.13)

Using the “Maxima”, “Minima” calculations revealed that Voltage (X2), variation did

not contribute in a consistent manner and that its influence depended on the size of

the defect area. When solving for the optimum value for Gain (X2), it was found that

a setting of 20 dB would give the best results. In the case of Scan Resolution (X3),

the mathematical model revealed a linear relationship between this value and

accuracy. This linear relationship lead to the conclusion that the lower the value of

scan resolution, the lower the reading error.

3.9.4 Graphical Evaluation

The mathematical models were also used to examine the influence of the three

variables using a graphical approach. Figure 3.22 (a – e) shows the results from 3D

graphs plotted for the accuracy of detecting the different size areas (A1 – A5) and the

influence of Gain and Voltage settings. A complete set of all graphs is given in

Section B.5 of Appendix B.

The graphical results were generally in agreement with the results of the

mathematical approach for optimisation. The Voltage setting is inconsistent, and

dependant on the size of the area. Considering the results for each area, Area 1 to

Area 5, Voltage setting seems to be resulting in minimum error at the lowest, 140 V,

and highest, 240 V, settings. In the case of Area 3, the minimum setting also gives

the minimum error while in the case of Area 4, the minimum error of measurement

occurs when Voltage is set at 190 V. The smallest area tested, Area 5, gave

completely opposite results to those found for Area 4, a voltage setting of 190 V,

resulted in the highest error.


57

With reference to the Gain setting, the graphs were consistent in four out of the five

cases tested with 20 dB gain setting resulting in minimum error in the measurement

of defect area. The smallest area, Area 5, was again inconsistent with the other

results.

Having considered all the graphical results, it was concluded that a Voltage setting of

190 V, a Gain setting of 20 dB and a scan resolution setting of 1.0 mm would give

optimum results.

Figure 3.22 (a) Graph of fraction error in Area 1 against Gain and Voltage variation.


58

Figure 3.22 (b) Graph of fraction error in Area 2 against Gain and Voltage variation.

Figure 3.22 (c) Graph of fraction error in Area 3 against Gain and Voltage variation.


59

Figure 3.22 (d) Graph of fraction error in Area 4 against Gain and Voltage variation.

Figure 3.22 (e) Graph of fraction error in Area 5 against Gain and Voltage variation.


60

3.9.5 Discussion of results

Where research is based on experimental results, instrumentation settings are very

important that affect the outcomes of the research. Although instrument settings can

be decided upon past experience or trial and error, operators may not have adequate

expertise hence leading to the possibility of inferior quality results that may lead to

the wrong conclusions. Even in cases where instrument setup is done based on past

experience, it does not necessarily mean that it is done correctly. For this very

reason, and even when certain aspects of experimentation seem to be obvious, it is

best to approach in a scientific manner to reduce or even eliminate possible errors

due to incorrect instrument settings. This case is no exemption where a

mathematical approach was used to optimise instrument settings prior to

experimentation. Due to the complexity of calculations, a Design of Experiment

computer software [Design Expert DX6] was used to enable all the permutations of

instrument settings and the determination of each for minimum error.

The results gave some interesting outcomes that were not expected at the beginning

of the study. The three variable settings used were the ultrasonic Pulser / Receiver Voltage setting (X1), the pulse / echo Gain (X2) and the scan resolution (X3). The

Target variable was the accuracy with which the area of a defect was detected (Y).

Table B.9 (Appendix B) indicates, through the Fisher criterion, which coefficients are

significant in the mathematical models. The starred items in the table refer to those

coefficients that have significant influence on the optimum settings. By observation it

is revealed that two of the three variables are consistently significant in all five cases

studied. These are the Pulse/Echo Gain (dB) (X1) and Scan Resolution (X3). The

latter indicates the spacing of taking reading during the c-scan process (mm).

Although it was originally thought that the Voltage setting has some influence on the

readings, it was revealed by this mathematical modelling approach that it either has

no influence at all or that it is of no influential significance. The only case in which

voltage setting is shown by the values of the Fisher Criterion to have influential

significance, is in the case where the smallest area inspected was involved. This is

not surprising as the area inspected was in some cases smaller than the scan

resolution setting. It was intentionally used to confirm the instability of the process in

cases outside its resolution capabilities.


61

As mentioned above, the first variable parameter, Voltage setting (X1), was declared

as non significant by the method used for optimisation. The Fisher criterion in Table

B.9 resulted in value greater than 0.05 for all coefficients referring to variable X1, i.e.

B1, B11, B12, B13. This is also confirmed by the graphical results of Figures 3.22 (a-e).

Voltage setting is shown to not be able to influence the accuracy with any

consistency.

The second variable, Pulse / Echo Gain (X2), was found to have an optimum setting

of 20 dB (Table B.11, Appendix B). This was one of the variables that followed a

quadratic relationship with the error variable (eqns 3.8 – 3.12). However in one case

this parameter was found to have a different value, 26.84 dB. Although not at 20 dB,

it still supports that the dominant value is at or near 20 dB. This value will therefore

be used in this research.

Referring to the third variable, Scan Resolution, a different picture emerges. The

differential equations used for finding the minimum values of area detection error (Y),

showed that their second derivative had a positive value indicating that the minimums

were achieved except in the case of the smallest area with Target variable Y5. The

negative value indicates that the equation was referring to maximums. Considering

that the upper and lower limits of Scan Resolution were 2.1 mm and 0.5 mm

respectively, it was not surprising that this result was contrary to the others. Having

in mind that the Scan Resolution was set at 1.5 mm during the test, the scanner

would hit this defect once or at best twice as its dimensions were 2.3 x 2.5 mm.

When differentiated, the equation indicated a linear relationship between this variable

and reading error (Y5). Since the Target variable was required to be at minimum, it

was correctly recorded in Table B.11 as “Lowest” meaning that the lowest value

would give the best results. There are of course limitations that restrict an infinitely

small setting of scan resolution such as data file size, slower data processing and

extended data collection times due to scan speed restrictions. It would therefore be

suggested here that the Scan Resolution depends on the size of the area to be

detected. Where large areas are tested a scan resolution of 5 mm would be

reasonable whereas in this case 1.0 mm would be more suitable as the test area is

25x25 mm. Suggesting a Scan resolution setting of 1.0 mm for this research was

confirmed by the results in the case of Target variables Y3 and Y5. As shown in Table

B.11, these were found to have coded values of –1.616 and 0.453 respectively.

When converted to “Natural” values, using Eqn B.22, the optimum settings for Scan

resolution are recommended between 0.53 mm and 1.77 mm. A setting of 1.0 mm is


62

therefore a reasonable suggestion for general use as it is approximately the average

of the two.

3.10 Conclusion

This chapter covered one of the most important parts of this research. Selection of a

testing technique for adhesive bond evaluation, design of experimental test rigs,

selection of hardware and software for Ultrasonic testing and commissioning of the

experimental set up was carried out. The Ultrasonic method for testing adhesive

bond quality was selected for this research due to the special interest in applications

in the automotive and aerospace industries. In addition, literature survey findings

indicated that this is the most commonly used method in industry at present due to its

versatility, availability, affordable and portability of ultrasonic testing equipment.

Having decided on the non-destructive testing method to be used, hardware and

software were selected that were capable of detecting adhesive bond characteristics

with the required accuracy. The equipment was tested for its ability to reveal

adhesive bond quality by running a series of experiments on known defects. The

results were positive and the equipment was adopted. In order to have a

comprehensive set of data for bond evaluation, an area needed to be scanned and

analysed. A purpose designed test rig was built that enabled c-scans to be carried

out on adhesively bonded specimens. The equipment was then commissioned and

tuned by using the Central Composite Second-Order Rotatable Design mathematical

approach for optimum setting of the experimental parameters. Having followed the

proper selection of hardware and software, constructed a reliable test rig and fine

tuned the experimental parameters via a mathematical technique, it was possible to

continue with the next step of this research, experimentation in search of an

improved method for reliably evaluating adhesive bonds.

63

Chapter 4

Ultrasonic test experiments

4.1 Introduction

The principle of Ultrasonic testing is based on the propagation of sound through a

medium and its partial reflection in cases where density changes occur in the bulk of

the material. These density changes are normally due to voids, variation in material

density or cracks that either cause total reflection of the sound wave, allow some of

the sound to be transmitted through, or cause it to disperse into the material bulk.

This is known to depend on the nature of the obstructing feature and the angle of

incidence of the sound wave. The velocity of the sound wave through the material is

another important parameter of this technique. Speed of sound within a material,

and time of travel are used in determining various parameters either directly or

indirectly, but it is most valuable in calculating distances between reflections of the

signal. Using time calculations, the position of a defect or location of an event may

be determined by pulse and echo signal from a probe placed in-line with signal or

from two probes placed at an angle opposite each other. Ultrasonic testing is used

extensively in adhesive bond testing, where the location of the bond can be

accurately detected, due to material density change. However in this case, it is also

required to determine the bond quality at the interface between adhesive and

adherent as well as defects within the adhesive. The ability to have conclusive

results from such tests has the potential of leading to quality evaluation of adhesive

bonds. The most common ultrasonic signal characteristics that are used to

determine bond quality are variations in signal amplitude, frequency and signal

decay. This chapter explores these signal characteristics for adhesive bond

evaluation through experimental testing where possible, or through literature survey

in cases where laboratory facilities were not available.

4.2 Signal attenuation

As mentioned in the introduction, signal characteristics from an ultrasonic test can

reveal information about the material under test. Signal attenuation is one of the

characteristics that is often used and its variation is often measured by the amplitude

of the reflected signal. This is represented by the “Attenuation Coefficient” (α), in


64

units of neper (m-1), which was used in an equation [Blitz and Simpson, 1996] to

correlate signal amplitude to attenuation.

As the signal travels though the material it looses some of its intensity due to

dissipation in adjacent matter. For the purpose of this derivation, this energy will be

considered as lost from the signal and is assumed to have uniform fractional loss of

energy (E), 2α, per unit path-length.

Considering path-length dx:

dE/E = -2α dx (4.1)

Since the intensity, I, is proportional to E:

dI/I = -2α dx (4.2)

Putting I = I0 when x = 0 and integrating with respect to x yields:

I = I0 exp(-2αx) (4.3)

And, because intensity I is proportional to the square of the amplitude, A,

A = A0 exp(-αx) (4.4)

This leads to the ability to determine the “Attenuation Coefficient” by using the

reducing amplitude (y1/y2) of the attenuating sinusoidal wave at frequency 1/T (Hz)

through relationship,

α = 1/T loge (y1/y2) (4.5)

It is common practice to express attenuation in units of decibels where 1 neper is

equivalent to 10 log10 e = 8.686 dB.

Equation (4.4) indicates that the amplitude of the reflected signal (A) is inversely

proportional to the attenuation coefficient as well as signal frequency, which is shown

by equation (4.5).


65

4.2.1 Normal incidence transmission

When an ultrasonic signal encounters a different density medium than the one it

currently travels, it is partially or totally reflected and/or dispersed depending on the

nature of the second medium. In the case of normal incidence, this effect can be

shown schematically as in Figure 4.1, where Pi is the incident wave, Pr is the

reflected wave and Pt is the transmitted pressure wave and Ui, Ur, Ut, the velocity of

incident, reflected and transmitted waves. This layout refers to two different but

homogeneous materials, 1 and 2, which have different characteristics that

consequently lead to different impedances R1 and R2.

Figure 4.1 Schematic description of reflection and through transmission pressure wave

and velocity of an ultrasonic signal.

A quantitative estimation of the sound energy transmitted, reflected or dispersed can

be determined using the following derivation [Blitz and Simpson, 1996].

Assuming that the signal is traveling through a non-absorbing material, the pressure

waves pi, pr and pt can be expressed in terms of wave amplitude A1, B1 and A2,

frequency ω and wave numbers k1, and k2 (2π/λ) as:

pi = A1 sin (ωt – k1x) (4.6)

pr = B1 sin (ωt + k1x) (4.7)


66

pt = A2 sin (ωt – k2x) (4.8)

At the boundaries where x = 0,

pr / pi = B1 / A1 (4.9)

Indicating that the amplitude of the reflected signal, B1, from an ultrasonic test is a

measure of reflected energy assuming that:

pt = pr + pi (4.10)

The principle of energy balance and signal attenuation dependency on material and

defect characteristics will be further pursued in this research in qualitatively

evaluating adhesive bonds. It is evident from the above that better bonding would

provide a better pathway to the signal hence resulting in greater energy being

transmitted though the interface. This in turn can be interpreted as less intimate

contact exists between adherent and adhesive when higher levels of energy are

reflected from the adhesive bond reveals. Maximum reflection of energy would be

expected from non-contact between adherent and adhesive while minimum will occur

from “good” adhesive bonding. As the objective of this research is identification of

bond quality, close attention will be given to the signal reflected from an adhesive

bond as accurate utilization of signal characteristics will enable conclusions on bond

quality for cases between “best” and “worst” case scenarios. As shown above, both

the amplitude and attenuation of the returning signal can be used as a measure of

the bond quality; therefore these will be the signal parameters that will be considered

for signal analysis either individually or preferably devise a method that would reflect

both these characteristics in one result.


67

4.3 Ultrasonic testing

A series of preliminary tests were carried out to confirm that the energy balance

hypothesis is applicable in this research and that signal amplitude and decay were

able to reveal adhesive bond characteristics. A number of specimens were prepared

using a one part structural epoxy adhesive [Orbseal] with known areas of non-contact

between adherent and adhesive. These specimens were subsequently tested in a

water bath using a 10 MHz ultrasonic probe and UTEX pulser receiver [Utex].

4.3.1 Amplitude variation

Results from the preliminary tests are shown in Figure 4.2. The signal shown in

Figure 4.2(a) comes from an area with an introduced defect, whereas Figure 3(b)

shows a signal that comes from an area without defect. This result is in agreement

with the hypothesis of having more of the signal energy reflected in cases of less

intimate contact between adherent and adhesive.

4.3.2 Signal decay

Further observations from the experimental results, shown in Figure 4.2, also showed

variations in the signal decay. These results can be related to the presence of

defects in the bonded area. Figure 4.2(a) shows a slower decay, and these come

from a poor adhesive bond whereas figure 4.2 (b) shows a very rapid signal decay.

The latter comes from an area on the adhesive bond that did not have any defects.

This result confirms the energy hypothesis introduced in 4.2 but also agrees with

earlier research [Goglio and Rossetto, 1999] that used the degree of signal decay to

derive an index that would represent the quality of an adhesive bond.


68

(a) (b)

Figure 4.2 Ultrasonic signals, from area with defect (a) and without defect (b). The

signal shown in (a) has higher amplitude and slower signal decay when

compared to signal in (b).

4.3.3 Signal frequency

A frequency analysis was also carried out to observe changes in the natural

frequency of the returning signal. FFT results did not reveal significant frequency

variations between areas with defects and those without. Although some difference

in magnitude, signals from a bond with defect, Figure 4.2 (a), showed mainly the

same dominant frequencies as that from the bond without defect, Figure 4.2 (b).


69

(a) (b)

Figure 4.3 FFT of ultrasonic signals, from area with defect (a) and without (b).

It must however be noted that research [Bagnhammar, et. al] in this area revealed

that frequency analysis of ultrasonic signals from adhesive bonds has been used to

evaluate their quality. This particular area is referred to as “Ultrasonic Resonance

Spectroscopy” and it uses the principle of mechanical resonance in a multilayer

structure. It is based on the hypothesis that if subjected to an external input, each of

the layers in a multilayer structure would normally produce a different resonance

signature that depends on its physical characteristics. This hypothesis was tested on

structures bonded with adhesive where the frequency spectral characteristics were

correlated to the presence of defects in the bond line. It is claimed that the method

had achieved 100% successful detection of delaminated bond lines. Coupled with

Artificial Neural Networks (ANN), the researchers [Stepinski, et. al, 1998] achieved

automatic recognition of defects at the bond line. Named “The Neural Spectrum

Classifier” the system uses a preprocessor to examine the data and reject any

irrelevant information, otherwise referred to as feature extractor, hence making the

training of ANN achievable with less data input. The system has been effectively

used in civil engineering applications [Andrews, et. al, 1995] as well as in adhesively

joined multi-layer aerospace structures [Bagnhammar, et. al, 1997].

Work done using this technique to test adhesive bonds in the automotive industry

[Roye, 2001], made some recommendations with regard to the frequencies used for

testing. Low ultrasonic frequencies, 2 MHz, are recommended in determining the

quality of the adhesive bulk as higher frequencies, 20 MHz, that are valuable in


70

detecting interfacial problems are unable to give a returning signal due to high

attenuation in the adhesive. Results from the same research are shown in Figures

4.4 (a) and (b). The lower window in these figures shows the A-Scans of the

returning signals from the plate and the adhesive, which are clearly inconclusive in

the time domain as they interfere with each other. The two upper windows show

copy of the gated signal (left) and frequency spectrum (right) where the signals differ

considerably. Since these signals come from adhesive bonds with known quality,

they can be used as a signature to evaluate adhesive bonds.

(a) (b)

Figure 4.4 Results from adhesive bond evaluation [Roye, 2001]. Conclusion on bond

quality is based on the frequency signature for bonded areas. Figure (a)

comes from a “good” bond whereas (b) comes from a “bad” bond.

Samples with known “Good” and a “Bad” adhesion areas were used and as it can be

clearly seen, the frequency spectrum analysis shows marked differences in dominant

resonant frequencies. These dominant frequencies were correlated with the quality

of the bond.

As shown in these preliminary experiments, ultrasonic signal amplitude and decay

characteristics were able to detect bond defects quite clearly. However FFT did not

result in as clear differentiation between “good” and “bad” adhesive bonds although

research has shown that this is possible [Roye, 2001]. Based on these findings, it is

evident that the equipment available for this research is not capable of utilising

ultrasonic frequency variation to discriminate between “good” and “bad” bonds.


71

4.4 Ultrasonic C-scans

Adhesive bond testing is almost in all cases found over a large area in comparison to

other means of fastening parts together e.g. arc welding, spot welding or other forms

of welding. In many cases the bonded area could be of considerable size especially

in the area of laminated structures. Aircraft body parts and wings are one such

example of large bonded areas. It is therefore necessary to inspect enough

proportion of the area in order to draw conclusions about the quality of the adhesive

bonding. In contrast, welded areas are usually either at a spot or along a line where

testing can be achieved with one-dimensional ultrasonic testing. To test larger areas

such as those with adhesive bonds, two directional testing is required. The area is

scanned in the X and Y directions and readings from the ultrasonic signal are taken.

The results are plotted in these two directions as scanned, while a third dimension is

displayed either on a grey scale or colour variation. The results are able to highlight

differences that can be related to the quality of bonding. This is an ultrasonic c-scan

and typical results are shown in Figure 4.5.

(a) (b)

Figure 4.5 Grey scale and colour display of a c-scan with variation in grey scale or colour

indicating variation in amplitude of the ultrasonic signal.


72

4.4.1 C-Scan experimental tests

A series of experimental tests were carried out using the principles outlined above to

explore the capabilities of using c-scans in the area of Adhesive Bond testing. These

tests included:

• Detection of delaminated areas

• Prediction of failure location

• Effect of load on adhesive bond c-scans

• Load/adhesive area relationship

4.4.1.1 Detection of delaminated areas

The first test was to establish the ability to detect delaminations between adherent

and adhesive. A test specimen was prepared with a number of intentional defects

placed at the interface between the adherent and adhesive. The delamination

defects were simulated by placing rectangular pieces of paper between metal and

adhesive and before curing. The location and size of the defects was noted for

comparison with the planned c-scan tests. Figure 4.6, shows the layout of the paper

pieces within an irregular shaped rectangle that represents the approximate size

shape and location of the adhesive. The outer rectangle represents the 1.5 mm steel

plate.

AdhesiveMetal

Figure 4.6 Test specimen with defects included between the adhesive and metal plate.


73

An ultrasonic c-scan was carried out in a water bath with a 10 MHz probe. Figure 4.7

shows the results of the c-scan as tested by a pulse / echo ultrasonic signal from the

steel side. The grey scale image ranges from dark, indicating lower levels of energy

returning to the probe to light indicating higher levels of energy. The grey scale is

proportional to the amplitude of the reflected waves which in turn is proportional to

wave energy received. Lighter grey areas represent higher amplitudes which

correspond to the defective areas. This result confirms the theoretical correlation

between levels of ultrasound energy and amplitude of signal. Another conclusion

that can be drawn from here is that the better the contact at the bond line, the more

ultrasound energy was transmitted through the material hence the lower the

amplitude of returning signal. Therefore the lighter areas indicating the defective or

less contacting bonds.

Figure 4.7 C-scan results from a specimen with introduced defects. Lighter areas

indicate the location of the defects.

The location and shape of defects was superimposed on the c-scan results as shown

in Figure 4.8. By visual inspection, it was established that there is a definite

correlation between the introduced defects and the defective areas detected by the

ultrasonic c-scan. However, as it can also be clearly seen, that the accuracy and

clarity of defect detection is questionable. The defects on the c-scan are shown to be

smaller and in some cases not clearly visible. This may be due to not having

optimum settings on the measuring instruments used e.g. Voltage setting, scan


74

resolution, frequency etc. The inefficiency to clearly show the defects may also be

attributed to the technique used i.e. Maximum Amplitude measurement. These are

therefore two areas that need further attention, which will be further researched in

this thesis.

Figure 4.8 Superimposition of introduced defect location and defect location as detected

by the ultrasonic c-scan. Lighter areas indicate defect location.

4.4.1.2 Prediction of failure location

Detection of defects in an adhesive bond is normally done in order to predict the

failure of the bond. It was therefore decided that before proceeding any further with

this research, it would be necessary to establish if failure prediction could be

achieved with the researched technique used in this case. A specimen was prepared

for a single shear lap test to establish the correlation between predicted failure

location by an ultrasonic c-scan of the adhesive bond and the location of adhesive

bond failure during a destructive test. Figure 4.9 shows the plan and elevation of the

single shear lap test piece with dashed lines showing the adhesive area.


75

Figure 4.9 Single shear lap specimen prepared to test the prediction of failure location

using an ultrasonic c-scan.

The defect introduced was a straight strip of 10 mm wide that went though the whole

length of the adhesive patch. The defective area was achieved by placing a

waterproof, polymer based material between adherent and adhesive prior to curing.

Since the tests were to be carried out in a water bath, water absorbent material

would have become a good conductor to the ultrasound signal that would have led to

the wrong conclusion of good bonding.

Figures 4.10 (a) and (b) show the results of the ultrasonic c-scan on both sides of the

test piece. The defective area is clearly visible in figure 4.10(a) while Figure 4.10(b)

reveals variations in the Ultrasonic testing results even though no artificial defects

were placed in the reverse side of the specimen.

(a) (b)

Figure 4.10 C-scan results of test specimen before destructive test. Darker (blue) areas

show better adhesion.


76

The rainbow scale colours shown in Figure 4.11, vary approximately between 0.1 –

0.45 Volts with blue representing the lowest signal amplitude and red the highest. As

shown earlier low amplitude indicates lower energy returning from the adhesive

bond, which in turn reveals good bonding. Higher amplitude would reveal the

opposite indicating reduced bonding. However in this case the red area is beyond

the boundaries of the test piece as shown in Figure 4.10. The rectangular box

indicates the area of the adhesive whilst the rest of the figure shows the metal plates.

Figure 4.10(a) shows quite clearly the area where the defect exists as a green strip at

the centre of the adhesive. Other than the edge effect due to the resolution of the c-

scan, 1x1 mm, the image represents the defective area quite clearly. The error in

width measurement is approximately equal to 10% as in some instances it shows a

width of the defect strip to be 11 mm instead of 10 mm.

Figure 4.11 Rainbow scale showing the frequency distribution of the c-scan.

Since the aim of this experiment was to establish whether there is correlation

between the c-scan results and the fracture behaviour of the bond, the specimen was

pulled from either end on a tensile testing machine until failure occurred. Figures

4.12 (a) and (b) show the ultrasonic c-scan results after destructive tests.


77

(a) (b)

Figure 4.12 Ultrasonic c-scan results after destructive test.

The images in figures 4.10 and 4.12 show a definite relationship between the initial

ultrasonic c-scans and the failure characteristics at the adhesive bond-line. The

dark blue (lower amplitude) areas within the rectangular boundary of Figure 4.12 (a)

and (b) show areas that the adhesive did not break away from the steel plate during

the destructive test. This simple test indicated quite clearly that by using the

ultrasonic c-scans technique on the adhesive bond under test, it was possible to

predict which parts of the bond would fail under destructive loads.

4.4.1.3 Effect of load on adhesive bond c-scans

The application of c-scan technique was also tested for detecting transient conditions

of adhesive bonding. A special device was designed and built to enable load

changes between c-scans. The device is shown in Figure 4.13, which was operated

manually to vary the load between scans. The results were quite revealing not only

in the ability of this technique to detect change at the interface between the adhesive

and adherent but also due to the findings.


78

Figure 4.13 Portable device for testing specimens under tensile load.

A single shear lap test specimen was made of 50x40 mm steel plates bonded to

structural adhesive as shown in Figure 4.14.

Figure 4.14 Single shear lap test piece made of two steel plates bonded with structural

adhesive.

The force applied to the test piece as shown in Figure 4.15.

Figure 4.15 Direction of force applied to the test piece by tightening the nut at one end.


79

Figure 4.16 shows the c-scan results from the test piece before any stress was

applied. The amplitude of the ultrasonic signal is indicated by changes in colour

according to rainbow scale shown in Figure 4.11. According to this scale, which

ranges from minimum blue to maximum of white through green, red and grey, areas

that are closely bonded are blue while totally disponded areas are indicated by white.

Large red and black areas are out of range values that are not significant in our

experiment.

Figure 4.16 Ultrasonic c-scan of test piece before load was applied. Light grey and white

areas indicating least contact with adhesive.

A force was applied, as shown in Figure 4.15, and a c-scan was performed while the

specimen was held in a stressed state. C-scan results shown in Figure 4.17 show

that there is a redistribution of adhesive bonding characteristics when the specimen

is put under load. The lighter grey area indicates poor contact or delamination while

the blue area indicates improved contact according to rainbow the colour scale in

Figure 4.11. Some areas appear to have improved contact rather than delamination

although the bond is under stress. This may be attributed to the compression and

twisting forces due to misalignment of tensile forces (Figure 4.15) which in turn

brought the adherent and adhesive closer rather than pulled further apart.


80

Figure 4.17 C-scan of test piece under load. Image shows redistribution of adhesive

contact.

The force was removed and the same c-scan taken again. This was done to

examine if it was possible to detect residual changes due to the stresses introduced

earlier. The results are shown in Figure 4.18 where a much larger area is shown as

having more delaminations with the worst areas shown in lighter grey to white.

Comparing this scan (Figure 4.18) to the original, unloaded test piece (Figure 4.16),

the conclusion leads to a reasonable outcome with the white areas showing the

weakest contact which correspond well with the original weak areas. However it can

also be seen that the grey area has expanded considerably indicating that the force

applied had damaged the bond in varying degrees.

Figure 4.18 C-scan results of test piece after the load was removed.


81

4.5 Conclusions

This chapter explored the ability of Ultrasonic signals to detect adhesive bond quality.

Three main features of the ultrasonic signal were considered, amplitude, frequency

and signal decay. These parameters were selected as they are most commonly

used in industry as well as the availability of equipment to measure them. A brief

mathematical investigation was made to establish the correlation between these

variables and bond quality. As shown by Equations 4.1 – 4.10, all three variables

were theoretically capable of bond quality measurement. Each of these techniques

was subsequently tested using experimental procedures, which proved to be positive

in the case of Amplitude and Signal Decay. However frequency variation did not

reveal clear distinction between “good” and “bad” bonds even though this method is

used in industry.

The application of ultrasonic testing using amplitude variation was further

investigated by carrying out a number of experiments. Two experimental tests were

carried out, one to confirm the “yes”/“no” ability to detect a defect in the adhesive,

and a second test to detect if ultrasonic testing could reflect the varying degrees of

defect severity. The former was clearly demonstrated by testing a specimen with

intentional defects however the clarity of the results were in some doubt as smaller

defects were not detected and the size of the defects was accurately represented.

This finding led to the conclusion that further work was required to improve this

drawback. After careful consideration, it was decided that further research was

needed to derive a new technique that would enable higher accuracy and resolution

than the existing methods. It was also decided that as a first step, an in-depth

analytical approach should be taken to ensure that the instrument settings were at

optimum before the new signal evaluation technique is sought.

A second series of experiments was carried out to establish the ability of the

ultrasonic testing technique to detect variations in defect severity. A special device

was constructed for this purpose and the specimens were tested while under varying

degrees of stress. The results revealed that it was certainly possible to detect

variations in the degree of bond quality. It was further established that ultrasonic

testing of an adhesive bond could detect failure locations based on c-scan results.

The next step in this research will be the construction of a suitable test rig and

implementation of an analytical approach to optimize instrument parameter settings.

82

Chapter 5

Ultrasonic Signal Analysis

5.1 Introduction

As shown in preceding chapters, Ultrasonic techniques have been extensively used

in quality evaluation of adhesive bonds. Experimentation in this project has shown

that signals from ultrasonic tests on adhesive bonds, provide a rich source of

information for further investigation. Since the intention of this research was to

determine a reliable method for evaluating adhesive bonds, graphical evaluation

seemed a reasonable direction to follow. It was therefore decided to experiment with

a number of well established, suitable graphical techniques. Data from adhesively

bonded tests conducted earlier, was analysed with the aim at finding a suitable

parameter to discriminate between varying degrees of adhesive bond quality. Each

of the techniques used, showed ability in revealing variations between tested cases,

although some showed more promise than others. However interpretation was not

always straightforward and in some cases only visual, qualitative information was

presented, which was interpretable only to the experienced eye. Nevertheless,

experimentation with these techniques proved that both qualitative and quantitative

data interpretation shows significant promise for adhesive bond evaluation.

5.2 Method

Ultrasonic c-scan recordings were performed on adhesive bonds purposely

constructed from structural adhesive [Orbseal] on mild steel plate of 0.8 mm

thickness. Delaminations were introduced at predetermined locations to enable

signals from “good” and “bad” bonds to be collected for further analysis. The signal

information was collected in the form of Ultrasonic A-scans that indicated the level of

response intensity in Volts and the time of travel in micro seconds (Figure 5.1). As

expected, signals from locations with varying degree of adhesion revealed variations

that were clearly visible by observing the c-scan of the whole area under test (Figure

5.2).

Chapter 5 Ultrasonic Signal Analysis

83

Figure 5.1 Typical results from the ultrasonic A-scan. Gates were used to retrieve data from

the areas of interest.

Figure 5.2 C-scan results from an adhesive bond with varying degrees of adhesion. Lighter

areas reveal lower adhesion quality.

Data collected from predetermined locations of the adhered specimen was

processed by using existing data analysis techniques. Although the analyses

methods used were considered suitable for this application, they were selected from

existing software available to the author. It is certain that other techniques could

have been used however the objective is to investigate the possibility of analysing

signals from this research using graphical and statistical methods. It is considered

that conclusions can be drawn from using a selected few, rather than exhaustively

searching through all available graphical techniques. The initial investigation in any

case was followed by deeper examination of techniques found to have potential in

providing quantitative solutions in this research.


84

5.3 Results

Signals from four locations were selected that included the best and worst case

scenarios: “good” and “bad” bonds, as well as two intermediate results to enable

more comprehensive evaluation, (Figure 5.3 a-d). Two gates (filters) were used to

collect data from selected locations of the signal. One gate was used to measure the

maximum amplitude of the second impulse response, while the second gate

collected waveform data over a wider range of the signal. The same instrument

settings and gate locations were used for all tested cases.

(a) Signal from “Good” bonding (b) Signal from intermediate quality bond

(c) Signal from intermediate quality bond (d) Signal from “Bad” bonding

Figure 5.3 Results from ultrasonic A-scan tests on adhesive bonds with varying degree

of adhesion.


85

5.3.1 Visual Inspection

Visual observation revealed quite clearly that the signals differ considerably from

cases of “Good” and “Bad” bonding. Although the general form of the signal remains

the same, the magnitude and detail varied considerably. The results from

intermediate locations, between the two extremes, were also recorded to examine

the possibility of detecting gradual change of the signal, which was clearly confirmed.

Based on this observation, the ability to detect variations in bond quality may be

possible by visually inspecting the shape of the signal. However it is also clear that

there is detail in the signal that cannot be analysed or compared visually.

5.3.2 Statistical Indicators

Firstly, simple statistical indicators were calculated for each data set. This was done

to observe any obvious discriminatory characteristics that may clearly differentiate

between the varying degree of adhesion quality. Data taken from ultrasonic signal

data shown in Figures 5 (a-d) were used to construct the graph shown in Figure 5.4.

Observation of these results reveals that the signals contain information that can

clearly differentiate between each case. Other than the “Mean”, each of the other

statistical results indicate either positive or negative trend from “Good” to “Bad”

bonding. Considering these variables as a means to discriminate between the

varying bond quality levels, the “Variance” value may be the most suitable as it

shows the widest variation between “Good” and “Bad” adhesive bonds.


86

-1

0

1

2

3

4

Min. -0.3828 -0.5156 -0.7031 -0.7266

Max. 0.4453 0.5469 0.5938 0.6953

Mean -0.31 -0.22 -0.56 -0.54

Var. 0.71 1.23 2.56 3.38

SD 0.845 1.108 1.6 1.837

Skew. 0.6374 0.5413 0.0517 -0.0534

Kurt. 0.5368 0.51199 0.2175 0.14458

Good Intermediate 1 Intermediate 2 Bad

Figure 5.4 Graph of statistical results indicating trends between “Good” and “Bad”

bonds. Data used for these graphs comes from signals in Figures 5.3 (a-d).

5.3.3 Three Dimensional Frequency Analysis

The second technique used was the “Three dimensional frequency analysis” that

appears to have confirmed earlier conclusions [Section 4.3.3] that signal frequency

variation did not reveal any significant differences between the varying degrees of

bond quality. Figure 5.5(a) shows the results from a “Good” bond while Figures 5.5

(b) and (c) come from progressively worsening bond condition with Figure 5.5 (d)

showing the results from a “Bad” bond. By observing each of these figures, it can be

seen that other than a small variation on the horizontal scale no other variations

occur. Also by observation, the variation appears in each of the figures except figure

(b). The fact that all variations are similar in three out of four cases is inconclusive

itself but it is also confirmed by the fact that figure (b) has not variation along its

horizontal scale without identified reasoning.


87

(a) (b)

(c) (d)

Figure 5.5 Three dimensional analysis of signals from “Good”, “Bad and in between

bond quality corresponding to figure 5.3 (a-d)

5.3.4 Visual Recurrence Analysis

Visual Recurrence Analysis is a signal analysis technique first introduced [Eckman,

et.al, 1987] to enable a multidimensional graphical interpretation of one dimensional

data. Additional dimensions such as time scale and variation of the data, are plotted

to produce a multivariable graphical output that can reveal similarities and differences

between signals.

Due to the nature of the calculations and graphical display requirements, the Visual

Recurrence Analysis software [Kononov, 2004] was used. This method was selected

as it has the potential to graphically detect hidden patterns and structural changes in

data or see similarities in patterns across the time series under study. The nature of

the recurrence plot allows highlighting of differences between the signals under study

as it generates a two-dimensional map from the one-dimensional data of the signal.

Although the results appear as two dimensional due to the nature of display devices,

influences from more than two variables are also revealed. Interpretation of results

from this method is not straightforward, however its usefulness stems from the fact

that differences can be observed at a glance.


88

The results from the four cases are shown in Figure 5.6 (a-d).

(a) (b)

(c) (d)

Figure 5.6 Three Dimensional Analysis of signals from “Good”, “Bad and in between

bond quality corresponding to figure 3 (a-d)

This analytical format demonstrates the obvious differences between the ultrasonic

signals from fully bonded, Figure 5.6(a) to totally delaminated, Figure 5.6(d). The in

between cases, Figures 5.6(b) and (c), show a gradual shift in the region of stability

from one extreme to the other. Although the patterns have clearly changed with the

degree of delamination, it is not possible to classify each data set quantitatively that

differentiates between “Good” and “Bad” bonds.


89

5.3.5 State Space Chart

Visual recurrence analysis unfortunately does not give a unique numerical index to

represent the spatial distribution of the pattern and colour of such maps, which is also

demonstrated by the results of the State Space Chart shown in Figure 5.7 (a-d).

Here again, the variation is clearly visible between the cases of “Good” and “Bad”

bonds, Figures 5.7(a) and 5.7(d) respectively and although not quantitatively

differentiated, the potential of signal analysis to detect influencing parameters is just

as clear.

(a) (b)

(c) (d)

Figure 5.7 State Space plots of the ultrasonic signals shown in Figure 5.3 (a-d).

5.3.6 Wavelet Analysis

Another characteristic of a signal that is often used for analysis is its frequency

variation. Fast Fourier Transform (FFT) is the most commonly used technique in

analysing radio frequency signals. Although proven successful with other research,

as seen in section 4.33, it did not appear to be very successful for this research.

However since frequency is so important to the signal analysis, another method,


90

which is often compared to FFT, the Wavelet technique was considered. One

dimensional Wavelet analysis techniques have been particularly useful in analysing

sound wave signals and since ultrasonic signals share similar wave characteristics

with plain sound signals, the method is included in this exploratory exercise. The

MatlabTM Wavelet Toolbox was used in this investigation, where the same signals,

ranging from “Good” to “Bad” as in the previous cases, were analysed. The results

are shown in Figure 5.8.

(a)

(b)

(c)

(d)

Figure 5.8 Wavelet Analysis of signals from “Good” to “Bad bond quality corresponding

to figure 3 (a-d). The Haar, Level 5 wavelet was used.


91

The S-graph part of Figure 5.8 represents the ultrasonic signal as received from field

tests corresponding to the signals shown in Figure 5.3 (a-d). The ‘a5’ graph of Figure

5.8 shows the coefficients of the compressed signal using the Haar Wavelet

Transform, Level 5. Visual examination of the ‘a5’ part of Figure 5.8 a-d, shows

significant variation between the Wavelet coefficients graphs even though the original

signals show little difference in general shape and pattern. The magnitude of the

coefficients varies considerably as shown by the numerical values of the vertical

scale of the ‘a5’ graph. This scale varies between a range of 0.03 (0.001 to -0.002) in

the case of a “Good” bond to a range of 0.1 (0.04 to -0.06) in the case of “Bad” bond

which is at least 300% greater. Comparing the range variation shown by using

Wavelets to the variation of the original signal, which varies between 0.5 to -0.50 in

all cases, it is evident that there is a definite improvement in the detection of

variability between the “Good” and “Bad” adhesive bonds.

Since the Wavelet coefficients inherently carry the signature of the initial signal, it is

reasonable to assume that a method utilising these coefficients may lead to an

effective way to analyse the signal. Research in vibration analysis [Goumas et.al.,

2001] utilised the Wavelet coefficients as the selected feature to represent the

“feature Space” [Looney, 1997; Tate, 1996]. This research supported that these

coefficients retained a significant amount of features from the original signal to

enable reliable analysis.

The Wavelet Transform analysis was used in other research [Yang-Lijian et.al., 2001]

due to additional characteristics that offer useful tools in this area. Signal noise

reduction is one of these tools that was effectively used in this research to enable

detection of small variation in the signal. The ability to reduce noise interference

before analysing a signal, was also used to test the effectiveness of adhesio between

laminated parts [Weibin, 2004]. The researchers in this case, used the Wavelet

Transform to detect variation in the frequency of ultrasonic signal to determine the

quality of adhesion.

Further literature survey revealed that the Wavelet transform has also been used in

other NDT application using radio frequency signals. Flaw echo location [Liu, 2000,

Cavaccini, 2000] and processing of acoustic waves [Ma et.al., 2000]. All these

applications coupled with the experimental experiences from this research lead to the


92

conclusion that there is potential in using Wavelet analysis to improve adhesive bond

evaluation.

5.4 Conclusions

The objective of this chapter was to find a quantitative, statistical technique that was

best adapted to enable the analysis of Ultrasonic signals form adhesive bonds. A

number of techniques were evaluated and although most were able to detect

variations in signal characteristics it was difficult if not impossible to give a

quantitative classification that represented the quality of adhesive bonds. These

techniques were only able to give visual differentiation that is considered inadequate

for analysing signals in this application. However, the Wavelet Transform method

was capable of providing an output that showed excellent promise in quantitative

signal analysis. In this technique, a relatively small number of coefficients

representing the original signal with adequate accuracy were used for evaluation.

The small number of wavelet coefficients, inherently reduce the complexity in

analysing a radio frequency signal such as the Ultrasonic signal from adhesive bond

examination. It is therefore concluded that this technique deserves further

investigation in the evaluation of adhesive bond quality.

93

Chapter 6

Wavelet Analysis

6.1 Introduction

Ultrasonic techniques are extensively used in Non Destructive Testing. Due to their safe

and portable application, ultrasonic instruments have become very common in the

engineering industry. Utilization of adhesives has also become one of the emerging

technologies in engineering design. These two areas converge to a common

denominator when it comes to evaluating the quality of adhesive bonds as it has to be

done non destructively. Ultrasonic techniques have been utilized quite effectively in the

evaluation of adhesive bonds but are not yet considered adequate in this endeavour.

This chapter covers work on the utilization of Wavelet Analysis, theory and practice, in

developing techniques that vastly improve the ability of ultrasonic signals to evaluate

adhesive bond quality.

Wavelet methodology is by comparison a more recent technique in the area of signal

analysis that has been implemented in a number of areas but has been most successful

in data compression and denoising. Successful work using wavelets has led to image

compression [Usevitch, B. E., 2001, Zhang, et al, 1993], signal de-noising [Neville, 1998]

and image enhancement [Mao-Yu, et al, 2002]. All this is made possible by

decomposing the signal using wavelets and extracting the targeted information during

the process. The application of wavelets in this research is not very different as it aims

to extract information from ultrasonic signals to discriminate effectively between varying

levels of adhesive bond quality. Signals from adhesive bond tests will be decomposed

using wavelets while looking for special characteristics to quantitatively identify those

that do not meet the expected standards. The wavelet theory will be explored from its

basic principles to enable the discovery of such signal characteristics. These signal

characteristics will be tested on signals from ultrasonic tests to establish their

effectiveness on classifying adhesive bond quality.


94

6.2 Wavelet Signal Analysis

The previous chapter explored a number of graphical, data analysis techniques to

identify potential methods in discriminating between the “Good” and “Bad” quality of

adhesive bonds. Almost all techniques introduced were able to differentiate between

different levels of adhesion quality; however the difficulty in applying them emerged from

the fact that that they were not able to produce a quantitative output to represent the

classification result. Graphically, it was quite clear that signal characteristics were able

to show adhesive bond quality variation but the measure of that variation was not clearly

quantitatively evaluated. However these preliminary tests also revealed that one of the

techniques tested, the Wavelet Signal Analysis, displayed excellent promise in

discriminating between different levels of adhesion quality quantitatively. In this

technique, the signal was reduced down to a series of coefficients that reflected a

distinct and measurable variation by their minimum to maximum graphical output. Figure

6.1 shows the results of preliminary wavelet analysis on four classes of bond quality from

the previous chapter while Figure 6.2 shows the same wavelet coefficients on a common

scale to highlight the discriminatory nature of wavelet analysis.

(a)

(b)


95

(c)

(d)

Figure 6.1 Wavelet decomposition of signal S using Matlab software [Matlab]. Graphs show

the original signal and 16 coefficients from a 5 level Haar wavelet transform.

Graphs exclude the five levels of detail coefficients.

Wavelet coefficients

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 5 10 15 20

Good Bad Med4 Med7

Figure 6.2 Wavelet decomposition coefficients from a level 5 Haar wavelet transform.

Graphs exclude the five levels of detail coefficients.

Observation of Figure 6.2 confirms that the wavelet coefficients from the different types

of adhesive bond quality vary considerably from a “Good” to a “Bad” bond.


96

Raw Data

-1

-0.5

0

0.5

1

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

Good Bad Med4 Med7

Figure 6.3 Normalized graph, -1 to +1 Volts, from ultrasonic tests on the four specimens

under investigation.

The advantage of Wavelet decomposition of the ultrasonic signal can be confirmed more

positively if Figure 6.2 is compared to Figure 6.3 where the latter shows the signal

graphed from the raw data. Discrimination between the signals which in turn reveal the

different levels of adhesion quality is very is poor as there is only mild discrimination

between them.

Having established that Wavelet decomposition of the ultrasonic signal improves the

discrimination factor between different levels of adhesion quality, it is now necessary to

find a quantitative index that can faithfully represent adhesion quality.

6.3 Wavelet theory

The basic notion of wavelet analysis will be explored here in an effort to identify the

characteristic feature(s) of this technique that may lead to clear discrimination between

different levels of adhesion quality. The Wavelet theory will be developed using the

Haar wavelet transform [Walker, 1999] as applied to discrete data.

Consider a signal, ‘f’, represented by its coefficients:


97

f = (f1 , f2 , f3 ,. . ., fn) (6.1)

For clarification purposes, this discrete signal can be compared to a continuous, time

dependant analogue ‘g’ signal that is measured at time values of t = t1 , t2 , .., tn and

f1 = g(t1 ) , f2 = g(t2 ), . . . , fn = g(tn ) (6.2)

with uniform time intervals of same length.

The Haar transform is similar to other wavelet transforms in dividing a signal into two

subsignals, halving signal length at each level of decomposition. One subsignal is a

running average or trend, and the other half is the running difference or fluctuation.

The running average values are calculated by averaging running parts of discrete values

e.g. (f1 + f2 )/2, (f3 + f4 )/2 etc. and then multiplying the by 2 . A general equation

may be written as:

22)1(2 mm

m

ffa

+= −

(6.3)

The other sub-signal, the first fluctuation detail ‘d’ is calculated by the running difference

of the successive values e.g. (f1 - f2 )/2, (f3 - f4 )/2 etc. and then multiplying the by

2 . The general equation in this case may be written as:

22)1(2 mm

m

ffd

−= − (6.4)

Where m = 1, 2, 3, . . , N/2


98

For example a discrete signal S, with values:

f = ( 8, 10, 6, 4, 6, 3, 7, 5 ) (6.5)

The first level signal decomposition coefficients can be calculated using Eqn (6.3) and

will have a running average, also known as “trend”, as shown below:

a1 = (9 2 , 5 2 , 4.5 2 , 6 2 ) (6.6)

This can be shown diagrammatically:

8 10 6 4 6 3 7 5

9 5 4.5 6

9 2 5 2 4.5 2 6 2

The running difference, also known as “detail” can also be calculated using eqn 6.4,

which will result in values:

d1 = ( - 2 , 2 , 1.5 2 , 2 )

This process is repeated on the running average values to produce several stages or

levels of the Haar transform. Mapping each of the levels can be defined by:

( )nnHn daf |⎯→⎯ (6.7)

where ‘a’ is the “trend” and ‘d’ is the “detail”

Using the values in the example, the decomposed signal can be written as:


99

1 Level: (a1 | d1)

f = ( 8, 10, 6, 4, 6, 3, 7, 5 ) ⎯→⎯ 1H (9 2 , 5 2 , 4.5 2 , 6 2 | - 2 , 2 ,

1.5 2 , 2 ) (6.8)

2 Level: (a2 | d2 | d1)

(14, 10.5 | 4, -1.5 | - 2 , 2 , 1.5 2 , 2 )

3 Level: (a3 | d3 | d2 | d1 )

(12.25 2 | 1.25 2 | 4, -1.5 | - 2 , 2 , 1.5 2 , 2 )

Therefore a signal may be “decomposed” and represented with its coefficients by using

the Haar transform principle otherwise known as the Haar Wavelet transform. Other

transforms e.g. Daubechies, [Daubechies, 1990], Coiflet [Walker, 1999] wavelet

transforms, have also been devised with the sole aim of representing signals with less

data information which is generally termed as “Data Compression” [Brislawn, et al,

1996]. All these methods are also capable of reconstructing the original signal from its

coefficients by using an inversion algorithm that resembles the reverse activity of the

signal deconstruction method shown by Equations (6.3) and (6.4).

6.4 Wavelet Energy Conservation

The energy of a wavelet decomposed signal is defined as the sum of the squares of its

coefficients and is identified as “Total Energy”:

222

21 .... Nf fff +++=ε (6.9)


100

This energy is reserved through each of the wavelet transform levels although the

coefficients may be less in each case. Even more importantly, when compared with the

energy of the original signal, the energy is accurately represented by the wavelet

coefficients. For example the energy of the original signal, S, Eqn (6.5) is:

3355.....108 222 =+++=Sε (6.10)

Using the values of the 1st level Haar wavelet transform coefficients shown in Eqn (6.8),

the Total Energy of the compressed signal will also be found to be 335. Another

interesting phenomenon in relation to the signal energy, the energy of the trend values

includes 96.87% of the original energy whereas the detail coefficients contains only

3.14%. Although not known of what significance is this information it is useful to be

aware it exists. Experimentation with higher level of decomposition proved that the Total

Energy of the signal was reserved at all levels assuming all Trend and Detail coefficients

were used in the calculation. However the ratio of energy content between the two

varied but in any case reserved more energy in the Trend coefficients than the Detail.

6.4.1 Wavelet energy of Ultrasonic signals

Using data from ultrasonic signals taken during experiments on varying degrees of

adhesion quality, also shown in Figure 1, a test will be carried out to examine the

applicability of “Total Energy” measurement for adhesive quality discrimination. Each of

the signals will be decomposed using the Matlab Wavelet Toolbox and its total energy

measured at different levels of decomposition. Daubechies 1 type of wavelet transform

was used in this analysis. A range of other Wavelet filters were empirically trialed and a

small group was selected for use in this research [Tewfik, et al, 1992, Reza, 1999].


101

Original Ultrasonic signalTotal Energy: Good=1.88, Bad=6.35

-1

-0.5

0

0.5

1

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64

Bad Good

(a)

Daub1, Level 1 coeff icientsTotal energy: Good=1.77, Bad=5.93

-1.5-1

-0.50

0.51

1.5

1 6 11 16 21 26 31

Bad Good

(b)


-1

-0.5

0

0.5

1

1.5

2

1 3 5 7 9 11 13 15

Bad Good

(c)


102


-1

-0.5

0

0.5

1

1 2 3 4 5 6 7 8

Bad Good

(d)

Figure 6.4 Original signal and 3 levels of decomposition of ultrasonic signal from adhesive

bond quality tests. Total energy is recorded for “Good” and “Bad” bonds for

comparison.

Figure 6.4 shows the results of wavelet decomposition of the ultrasonic signal from two

levels of adhesive quality, “Good” and “Bad” the latter meaning total delamination while

the former refers to full contact. The energy of the initial signal is found to be 6.35 for the

bad bond while the good bond shows a value of 1.88. Level 1, wavelet decomposition

has 5.93 and 1.77 respective energy level for “bad” and “good” adhesive bonds. If

compared to the initial signal energy level there is little loss of energy and most

importantly it maintains the ratio between the two levels. However when referring to

subsequent levels of wavelet decomposition, the Total Energy value drops considerably

in relation to the initial signal while the maximum to minimum ratio also suffers

extensively in Level 3 decomposition. Therefore it may be concluded that the Wavelet

energy technique is capable of clear, quantitative discrimination between “Good” and

“Bad” bond quality.

6.5 Comparison with Traditional Techniques

The two most commonly used characteristics of the ultrasonic signal for detecting

variation are; Maximum Amplitude and Signal Decay rate [Goglio and Rosetto, 1999]. A

third feature that is also commonly used is the frequency signature of the signal through

an FFT evaluation [Bagnhammar, 1997]. The wavelet energy technique proposed in this

research depends primarily on the magnitude of the signal rather than its frequency

characteristic. This is based on work done earlier (Section 4.2) during the investigation


103

of energy conservation in the ultrasonic signal with reference to adhesive bond quality.

It was proven that as the bond quality increases, so does the transmission of the signal

through it, which in turn leads to the conclusion that lower energy is reflected to the

ultrasonic receiver probe in a pulse / echo test. The magnitude of the returning signal is

also dependant on the reflected energy from the adhesive bond hence the justification of

the Maximum Amplitude. The Signal Decay technique is valid due to the fact that as the

ultrasonic signal reverberates within the adherent, more of its energy is transmitted

through the bond each time it returns to it resulting in signal decay that depends on bond

quality. The Wavelet energy technique introduced in this research also depends on

signal magnitude, but differs from the other two techniques as it utilizes data from the

“whole” signal rather than one or very few discrete measurements of the signal.

A set of ultrasonic test results from adhesive bond tests [Tavrou and Jones, 2003] will be

utilized to demonstrate the advantage of using wavelet analysis instead of Maximum

Amplitude or Signal Decay measurements.

The full length of one set of reverberating signal from an ultrasonic test on adhesive

bonding will be considered. The signal is shown in Figure 6.5.

-1.5

-1

-0.5

0

0.5

1

1.5

Figure 6.5 Full length of ultrasonic signal from adhesive bond test.

Signals from six locations with varying degree of adhesion quality were collected from a

purposely prepared specimen as shown if Figure 6. Locations 1,2 and 3 were classified

as “Good” quality bonds and 4,5 and 6 as “Bad” quality bonds.


104

Figure 6.6 Ultrasonic C-scan results from a specimen with varying degrees of adhesion

quality.

The graphical results from these locations are displayed in Figure 6.7. All signals were

superimposed on the same scale which highlights clearly the difficulty of discriminating

between the different signals.

-1.5

-1

-0.5

0

0.5

1

1.5

Figure 6.7 Full length of ultrasonic signal from all six locations with varying degrees of

adhesion quality.

An encouraging outcome however, is the fact that all signals have the same pattern as it

means that one feature can represent the whole signal. Focusing on one cycle will also

confirm that this is the case as shown in Figure 6.8. Here again, as it can be seen from

this graph, the discrimination between the different categories of adhesion is poor.


105

-1.2-1

-0.8-0.6-0.4-0.2

00.20.40.60.8

1

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

6

5

4

3

2

1

Figure 6.8 Graph from one cycle of the ultrasonic signal from all six locations with varying

degrees of adhesion quality.

However, if the two extremes, signals from “Good” and “Bad” adhesion locations, are

considered, shown in Figure 6.9, there is clear discrimination between them. The latter

observation confirms that the “Maximum Amplitude” and “Signal Decay” methods are

valid and capable of discriminating between the two extremes but not as effective in

discriminating signals from in-between cases.

-1.2-1

-0.8-0.6-0.4-0.2

00.20.40.60.8

1

Figure 6.9 Ultrasonic signal result from a “Good” and a “Bad” adhesive bond.

Maximum values from each cycle were selected to determine how ultrasonic signals,

from varying degrees of adhesion, behave in relation to each other. Figure 6.10 shows

the six cases relating to the locations given in Figure 6.6. Results from locations 4,5 and

6 that come from varying degrees of delamination, “Bad” bonds, reveal clear

differentiation between each case. Results from “Good” bonds on the other hand show


106

some degree of differentiation within the first 3 cycles but subsequently overlap showing

little or no difference.

Figure 6.10 Maximum amplitude values of first 10 cycles from Ultrasonic test of adhesive

bond with varying degrees of adhesion quality, 1-best, 6-worst.

The same data was used with the Wavelet method to demonstrate its ability to

differentiate between the different levels of adhesion quality. Figure 6.11 shows

graphical results comparable to those of Figure 6.10. The difference of course is that in

the first case, the Maximum Amplitude method is used whereas in the second the Total

Energy using Wavelet analysis was used. The graphs reveal a distinct similarity

between them which confirms that the data is not disturbed, however the difference to be

noticed is the scale of the two graphs. From a 0-1 units scale it has been expanded to a

0-7 unit scale. Although the curves have also moved accordingly, the move is

disproportional to the scale magnification especially when comparing increases between

the two extremes of bond quality. Considering the difference between the worst case

scenario, the ratio has changed from 1.86 when using the Maximum Amplitude value to

3.21 when using the Total Energy value which is a 72.6% improvement on the existing

method under these conditions. There are even better results toward the end of the

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

6 0.82 0.71 0.67 0.6 0.57 0.54 0.50 0.46 0.42 0.38 5 0.71 0.63 0.57 0.53 0.48 0.46 0.41 0.39 0.35 0.34 4 0.55 0.48 0.46 0.39 0.36 0.33 0.28 0.28 0.23 0.24 3 0.52 0.37 0.31 0.22 0.20 0.15 0.10 0.10 0.07 0.07 2 0.47 0.35 0.28 0.22 0.18 0.16 0.12 0.10 0.07 0.06 1 0.43 0.34 0.29 0.22 0.20 0.16 0.11 0.10 0.07 0.07

1 2 3 4 5 6 7 8 9 10


107

scale, in cycle 10, where the signal from a “Bad” bond is 30 times larger than a “Good”

bond compared to a factor of 5 with the existing method. However the latter does not

give as good differentiation between the different grades of “Bad” bonds.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

6 6.10 5.22 4.34 3.71 3.16 2.81 2.42 2.12 1.78 1.54

5 4.70 3.94 3.22 2.71 2.27 1.99 1.69 1.46 1.22 1.02

4 3.06 2.50 1.98 1.63 1.33 1.14 0.95 0.81 0.68 0.57

3 2.73 1.72 1.04 0.63 0.40 0.26 0.17 0.11 0.07 0.05

2 2.11 1.32 0.80 0.52 0.34 0.24 0.16 0.11 0.07 0.05

1 1.90 1.30 0.84 0.56 0.37 0.26 0.18 0.12 0.09 0.07

1 2 3 4 5 6 7 8 9 10

Figure 6.11 Total Energy values of the first 10 individual cycles from Ultrasonic test of

adhesive bond with varying degree of adhesion quality, 1-best, 6-worst.

As shown by both graphs, Figures 6.10 and 6.11, discrimination between varying

degrees of bond quality varies with each cycle. It is therefore pertinent that the correct

cycle is selected for maximum effect. In this case, both methods show that the first

reverberation cycle of ultrasonic signal within the adherent gives the highest level of

discrimination.

In addition to selecting an individual cycle however, the Total Energy method allows

selection of more than one cycle or even the whole signal for its calculation. Figure 6.12

shows the discrimination capabilities of selecting more than one reverberation cycle. As

it can be seen from here, the range between the two extremes has increased by 500%.

It is therefore concluded that if more of the signal is analyzed by the Wavelet method,

the better the result will be.


108

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

6 6.10 11.32 15.65 19.36 22.52 25.33 27.75 29.87 31.65 33.19

5 4.70 8.64 11.86 14.57 16.84 18.83 20.52 21.99 23.21 24.23

4 3.06 5.56 7.54 9.17 10.50 11.65 12.59 13.40 14.08 14.65

3 2.73 4.45 5.49 6.12 6.53 6.79 6.96 7.07 7.14 7.18

2 2.11 3.43 4.23 4.75 5.09 5.34 5.50 5.60 5.67 5.72

1 1.90 3.20 4.04 4.60 4.97 5.23 5.41 5.54 5.62 5.69

1 2 3 4 5 6 7 8 9 10

Figure 6.12 Total Energy values of progressively longer signal from 1 to 10 cycles. The

signal comes from an Ultrasonic test of adhesive bond with varying degree of

adhesion quality, 1-best, 6-worst.

6.6 Noise reduction using Wavelets

More often than not, signals from ultrasonic tests are not as clearly defined as those

used in the analysis above, they would normally contain some noise that may interfere

with calculations. Of particular interest to this research is that the Total Energy method

introduced above may suffer extensively from the presence of noise in the signals under

investigation. By its nature, this technique includes a measure of each element of the

signal which in itself is desirable; however features that come from sources other than

the adhesive, bond interfere with the accuracy of the result. It is therefore necessary to

identify and remove interfering contents of the signal that come from unrelated sources.

One such signal is shown in Figure 6.13 where extensive noise is present in the

ultrasonic signal from an adhesive bond test.


109

Signal filtering techniques mainly use the Fourier transform to eliminate undesirable

noise from a signal. The Wavelet transform is also capable of noise filtering that is

based on time dependant, signal variation rather than its frequency. The Wavelet

technique is preferred in this case as it is already utilized in calculating the Total Energy

bond quality index. It is therefore possible to have a two step Wavelet transform process

that can eliminate noise from the signal followed by Wavelet coefficient extraction and

Total Energy calculation. This would certainly make it more efficient as software and

data format compatibility can be accommodated in the same package.

-1.5

-1

-0.5

0

0.5

1

1.5

Figure 6.13 Ultrasonic signal from tests on adhesively bonded specimen that shows

extensive noise interference.

As shown in Figure 6.13, the type of noise in this case is additive to the signal that

results in distorted outcome. This can be represented by simple relationship:

(contaminated signal) = (original signal) + (noise) (6.11)

The equation can be re written using ‘f’ for contaminated signal, ‘s’ for original signal

and ‘n’ for additive noise as:

f = s + n (6.12)


110

The basic premise used in Wavelet transform for the removal of noise makes two

assumptions [Walker, 1999]:

1. The energy of the original signal ‘s’ is effectively captured, to a high percentage,

by transform values whose magnitudes are all greater than a threshold Ts > 0.

2. The noise signal’s transform values all have magnitudes which lie below a noise

threshold Tn satisfying Tn < Ts.

Then the noise in ‘f’ can subsequently be removed by placing a threshold in its

transform where all values of its transform with magnitudes below the noise threshold,

‘Tn’ are set equal to 0. Application of an inverse Wavelet transform can restore the

original signal without the noise to provide a ‘clean’ signal for analysis.

Considering the signal shown in Figure 6.13 as an example, a Wavelet based noise

elimination process using Matlab, Haar 8, transform has resulted in the signal shown in

Figure 6.14.

Figure 6.14 Ultrasonic signal shown in Figure 6.13 with noise removed using Wavelet

transform technique.


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As it can be seen that removal of noise from the signal was very effective. However

there is doubt about the loss of useful signal that may affect the Total Energy signal

calculation that is used for classifying the quality of the adhesive bond. A simple test will

be carried out to establish the validity of noise reduction without affecting the effective

signal. The test will:

• Use a signal with known, controlled values

• Introduce noise with known dimensions to the original signal

• Remove the noise using the Wavelet transform

• Compare original signal with noise removed signal

The original signal was made up of step values for clear visualization of noise

introduced.

Figure 6.15 Step type signal used in demonstrating the effectiveness of using Wavelet

transform to remove noise.

The noise introduced was produced by random numbers between -0.05 to 0.05.


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Figure 6.16 Manufactured noise signal between values -0.05 and 0.05.

The noise was added to the original signal which resulted in the signal shown in Figure

6.17.

Figure 6.17 Original signal with added noise.

The Haar 8 transform was used within Matlab software to remove the noise from the

original signal. The results are as shown in Figure 6.17.


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Figure 6.18 Original signal with noise removed using Haar 8 transform in Matlab software.

Noise has been removed from the original signal effectively although it shows slightly a

deformed signal in comparison to the original, step type signal. Figure 6.19 shows the

original, noisy and de-noised signal on the same graph for comparison. As it can be

seen the de-noised signal follows the trend of the original signal as well as having similar

magnitude. There is a difference which will be tested by calculating the Total Energy of

the signal before and after noise removal. The Total Energy of the original signal was

higher than that of the de-noised signal by 13%. The difference is of significance if view

in an absolute term however in this case since the objective is to have a relative variation

assessment of the signal from bonds with varying degree of quality, it is not as

detrimental. In any case the effect of using different Wavelet transforms will be

examined to assess their influence on the accuracy of the result.


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Figure 6.19 Original, noisy and de-noised signals shown on the same scale for comparison.

Wavelet transform Haar 5 was used for de-noising.

Figure 6.20 shows the results from two different Wavelet transforms, Haar 5 and Coif 5,

of a typical ultrasonic signal collected from a field test on adhesive bond quality. The

graphical results show clearly that there are similarities between the signals but there are

also distinct differences. For the purpose of this research, the ability of the de-noising

technique to retain the Total Energy of the original signal is of primary importance and in

this case it was calculated to be 6.07 from the Haar 5 transform and 6.00 from the Coif 5

transform. Compared to the Total Energy of the original signal which was 6.11, the Haar

5 transform resulted in 0.6% difference and the Coif 5 transform 1.8% difference

indicating that the Haar 5 transform should be used for better accuracy. This

comparison is also shown graphically in Figures 6.21 and 6.22.

Figure 6.20 De-noised signal using Haar 5 and Coif 5 Wavelet transforms


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Figure 6.21 Signal including noise and de-noised signal using Haar 5 Wavelet transform

Figure 6.22 Signal including noise and de-noised signal using Coif 5 Wavelet transform

What has been shown here is that Wavelet transform de-noising of a signal is possible

and within acceptable accuracy limits.

6.7 Data compression

Ultrasonic signals inherently contain large digital data sets that in turn require substantial

computer memory if the whole signal from each test location is to be stored. Because of

this reason, data from a small part of the signal is stored, and in some cases only one

value, the maximum amplitude. Although adequate in evaluating minimum aspects of


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the signal, if further analysis or investigation of a signal is required at a later stage there

is not enough information. Wavelet transform analysis has been known to compress

data to smaller computer file sizes while preserving adequate detail of the signal. The

capability of Wavelet data compression will be tested here to evaluate its applicability to

ultrasonic signals from adhesive bonds.

Figure 6.23 Typical ultrasonic signal form adhesive bond used to investigate the capability of

Wavelet transform to compress data files from tests in this area.

Using the same signal as used to test the ability of Wavelet transform to de-noise an

ultrasonic signal, shown in Figure 6.23, data compression will also be investigated. The

test signal is highly affected by noise resulting in extensive data content that not only

interferes with the evaluation of the adhesive bond but also presents another problem

due to the increased data size. The signal was compressed using the Haar 5 transform

within the Matlab software and the result is shown in Figure 6.24.


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Figure 6.24 Result of using the Wavelet transform to compress the ultrasonic signal shown in

Figure 6.23.

As shown earlier in this chapter, compression of a signal by using the Wavelet transform,

results in breaking down, decomposing, a signal to a series of coefficients. The process

of decomposition results in having an increasing number of coefficients with zero or near

zero values within each level. Equating all these small values, below a user defined

threshold, and removing them from the data, reduces the size of the file with limited

effect on the original signal. The effect of compressing signals with the Wavelet

transform can be measured in terms of loss of signal energy due to the elimination of the

zero values. A histogram based on zero and non-zero coefficients gives a clear picture

of the zero removal on the signal energy, shown in Figure 6.25. Higher zero coefficient

removal results in greater compression but not without first affecting the quality of the

signal representation. The cross over point is regarded as the optimal, threshold level of

decomposition.


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Figure 6.25 Signal energy retained after removal of zero coefficients during the data

compression process using the Wavelet transform. These results refer to the

signal shown in figures 6.23 and 6.24.

6.8 Data de-noising and compression

One of the advantages of using the Wavelet transform for ultrasonic signal analysis is

because it is capable of various operations on signals that may otherwise require

unrelated and independent analyses. For example de-noising is normally done using

the Fourier Transform separate the different signal frequencies and eliminate those

above or below as predefined threshold. However the Fourier Transform cannot

compress the signal, something that is possible in the case of Wavelet transform. The

latter is capable of de-noising as signal first then followed by signal compression or other

operations such as in this case calculation of the Total Energy of the signal. The signal

shown in Figure 6.23 was used to demonstrate the ability of the Wavelet transform in de-

noising and compressing an ultrasonic signal. The results are presented in Figure 6.26.


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Figure 6.26 De-noised and compressed signal of Figure 6.23, using the Haar 5 Wavelet

transform.

The de-noised and compressed signal has clearly kept its main elements while

eliminating the excess information (noise) that interferes with data analysis. The amount

of data has also reduced through this process as it can be seen by the large regions of

zero or near zero values. Wavelet analysis is well suited to ultrasonic testing as it allows

the user to decide the level of data compression and noise reduction for optimized

results.

6.9 Conclusion

Based on the experiments done in this chapter it was evident that the application of

Wavelet Transform in the evaluation of adhesive bond quality, offers a number

advantages over existing techniques used for the same purpose. Maximum amplitude

measurement, signal decay and frequency analysis are all techniques currently in use

for ultrasonic signal analysis. These techniques have also been applied in the

evaluation of adhesive bonds. Research has shown that each of these techniques has

been successful to a certain degree but more is needed in this area of research as

adhesive bond evaluation is far from satisfactory as it is at present.

The Wavelet Transform analysis was selected after an explorational exercise on

statistical and graphical tools for analyzing ultrasonic signals. It was selected as it


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showed promise in representing a complex ultrasonic signal with a much reduced

number of coefficients. These coefficients were able to conserve the integrity of the

original signal accurately as proven by reverse transformation to its initial state.

The research for an index to reflect adhesive bond quality was successful when the

“Total Energy” of the Wavelet Coefficients correlated accurately with the quality of

adhesion. This index was proved to be more successful than the existing techniques as

it represented the whole signal rather than one characteristic such as the Maximum

Amplitude or a short part of the signal to determine the signal decay. In addition, this

index gave a wider spectrum between the two extreme conditions of adhesion, bonded

and delaminated, that can be translated to a refinement capable of detecting conditions

between these two extremes more accurately.

The Wavelet Transform offered additional advantages that further confirmed their

suitability in the application of adhesive bond evaluation using ultrasonic, non destructive

testing. This technique is capable of removing unwanted noise from the signal to enable

a more accurate result. It is also capable of compressing the original signal to a lower

computer memory size that subsequently enables archiving ultrasonic signals in a

compressed form that can later be recovered from further analysis.

This chapter has shown quite clearly that the use of Wavelet Transform analysis offers

an improved tool in the analysis of ultrasonic signals, for the purpose of adhesive bond

quality evaluation, in comparison with the existing methods of Maximum Amplitude,

Frequency Analysis and Signal Decay.

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Chapter 7

Industrial applications

7.1 Introduction

Work in this research was focused at increasing the reliability in testing adhesive

bonds non-destructively. Experiments using a number of established techniques,

based on Ultrasonic signals, were explored as a means of establishing a base from

where new approaches could be explored. The selection of a range of techniques

was primarily justified by their extensive use in industry. New techniques were also

trialled with the aim of either introducing new technology or enhancing existing

methods. One of the techniques researched was based on stress distribution in the

adhesive and adherent due to adhesive forces and the use of ultrasonic signal to

detect the variation of these stresses. Another line of research followed, was the

analysis of ultrasonic signals using graphical techniques. Several conventional and

non-traditional graphical techniques were used that showed some promise, however

the technique that had shown the best results was based on the Wavelet Transform.

Experimentation using this technique showed that higher resolution could be

achieved in detecting defects in adhesive bond. The Wavelet Transform is capable

of decomposing the ultrasonic signal down to a smaller number of coefficients that

are subsequently used to determine bond quality. A number of specimens with

known defects were prepared and tested using traditional, analytical techniques in

contrast to the proposed Wavelet Transform based technique. Similar tests were

repeated with specimens prepared elsewhere and tested in our laboratory without

knowledge of the type and size of the defects. Contact with three relevant industries

was kept throughout this project, Orbseal Adhesives, Ford Motor Company and

Hawker De Havilland Aerospace from where feedback was sought to confirm

applicability of this research to each of these areas.

Work with industry partners revealed that although automated equipment is generally

utilised for large areas, when it comes to specific area or point detection, smaller

devices are used. Two devices [MAUS IV, BaNDIcoot FAQ] used for this purpose

which are also typical for this purpose were considered to determine whether the

Wavelet Transform technique developed in this research was applicable. A new

device is also proposed that determines the quality of the bond at a particular

location without the need to have constant location tracking.


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7.2 Orbseal

Collaboration with laboratory staff from Orbseal Adhesives [Humphreys, 2003], lead

to having a number of adhesively bonded specimens with unknown defects prepared,

and provided for testing at our university laboratory [Swinburne]. All specimens were

processed through Ultrasonic c-scans and the data kept in a neutral format for further

investigation. The data collected was analysed by both the Maximum Amplitude

method as well as by an approximation method of the Wavelet Transform technique.

The approximation technique was necessary for the latter as the equipment used did

not have data processors based on the Wavelet Transform.

7.2.1 Test results

The result from the ultrasonic c-scan on one of the samples provided is shown in

Figure 7.1(a) from Maximum Amplitude technique and (b) from the Wavelet

Transform approximation technique. The results from the complete set of specimens

are included in Appendix C. Comparison between Figures 7(a) and (b) shows

several characteristics that demonstrate the advantage of the Wavelet Transform

approximation compared to the existing Maximum Amplitude technique. Considering

that the c-scan pattern follows the rainbow colour scale below it, Figure 7(a) shows a

much flatter result structure with reduced capability to identify the defect area. Figure

7(b) on the other hand reveals a much more definite discrimination of the defect. The

top right hand corner of the specimen is clearly shown to include a defective area in

the adhesive bond. This corresponds with conclusions drawn in Chapter 6 of this

thesis where it is stated that the discrimination capabilities of the proposed technique

are much higher than existing techniques.

Considering the data histogram shown below the main display of the c-scan it is

evident that the proposed technique has a more definite discrimination between the

different states of the adhesive / adherent bond interface. Figure 7(a), which is

based on the existing technique of Maximum Amplitude, shows a very wide range in

the data dispersion the signal histogram while Figure 7(b) that represents the results

from the Wavelet transform approximation technique, shows a much higher data

concentration in the rainbow histogram. As a result, a highly discrete determination

of the defect is achieved which in this case is in the top right hand corner of the

specimen.


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(a)

(b)

Figure 7.1 Results from c-scan test on one specimen with blind defects in the adhesive bond interface; (a) showing c-scan results using the Maximum Amplitude method while (b) shows the results from the RMS processing technique. The latter is used as an approximation of the Maximum Energy technique developed in this research.

7.3 Hawker De Havilland

This company (Melbourne, Australia) manufactures laminated aircraft components

that undergo extensive Non Destructive Testing (NDT) due to the high reliability

requirements. The testing techniques used currently are based on Ultrasonic signals


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and X-Ray radiation. The Ultrasonic tests are done using a variety of approaches,

manual, semiautomatic, submerged and water jet coupling. Figure 7.2 shows the

testing facilities at this company. Due to the nature of the product, components

under investigation can be up to five meters in length and two meters wide and are

tested either using pulse echo or through signals techniques depending on the type

and size of the part to be tested. Because of the large size of these components, it is

often not practical or too expensive to use X-Ray radiation which would in most

cases be satisfactory. On the other hand, ultrasonic testing may be effective in most

cases but in some it may not be sensitive enough to give the detail of a defect or

detect small defects. In particular, porous defects in the adhesive may often be

missed altogether or if detected, they are difficult to analyse. Therefore, parts are

initially tested using Ultrasonic signals to determine the existence and location of

defects, followed by manual or semiautomatic Ultrasonic testing or in some cases re-

tested using X-Ray radiation, for further analysis and classification of the defects.

The latter is particularly necessary in the case of porous defects.

Figure 7.2 Ultrasonic equipment facilities used for testing adhesively bonded aircraft components.

Ultrasonic testing is therefore utilised for initial examination, as its ability to reveal

defect details is not adequate in this application due to the large areas under test.

The enhanced discriminatory power of the technique proposed in this research, can

provide added advantages over what is currently used for evaluating the adhesive

bond quality. As proven in Chapter 6 of this thesis, the “Total Energy” index is

capable of discriminating more clearly the difference between varying bond quality in

comparison to the “Maximum Amplitude” index which is the technique used at

present. As shown in the same chapter, the proposed technique is capable of


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expanding the range between the extremes of totally bonded vs totally delaminated

bonds, by tenfold. Such an improvement offers two advantages in this application,

firstly, defects not detectable with the existing technique will be brought to light and

secondly, defects that needed further investigation may be fully evaluated at this

stage. These advantages offer significant benefits both in time and cost but more

significantly by reducing risks associated with the use of X-Ray radiation.

The proposed, Wavelet Transform, technique offers further advantages in the area of

Ultrasonic testing. Signal analysis in this area is often impeded by the irregularity of

the signals due to noise that interferes both with the energy of the signal as well as it

frequency composition. Existing methods of noise filtering are based on frequency

analysis which essentially ignores frequencies considered to be outside the bounds

of expected results. The Wavelet Transform on the other hand is capable not only of

filtering signals with unexpected frequencies but also removing the energy

associated with these frequencies. This aspect was of specific importance to the

proposed technique as its validity is based on accurate measurement of signal

energy reflected from the defect rather than adjacent secondary effects.

Due to the large areas under test, small steps in c-scan tests result in large data files

that are difficult to archive due to computer memory requirements. Archiving of

complete signals rather than the “Maximum Amplitude” value, offers great

advantages in subsequent analysis of areas of parts involved in failure. The

proposed Wavelet Transform technique enables data compression that has the

potential to overcome this problem. Signals can be compressed by wavelet

transforms that result in a reduced number of signal coefficients that can reproduce

the signal at a later stage if required.

7.4 Hand held Ultrasonic tester

Hand held Ultrasonic testing devices have been the subject of research for some

years owing to the necessity of couplant for effective use. Duplication of scanning

rigs to produce c-scans on a portable scale has been difficult not only due to the

necessity of couplant but also due to the need to have locating sensors that can

record coordinates from a reference point. Often such devices are used in addition

to large scale Ultrasonic testing rigs to enable closer examination of areas that are

considered to have spurious results. As mentioned earlier, in Section 7.3, tests on


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large aircraft components are initially tested on large rigs with subsequent testing of

smaller areas if required.

7.4.1 MAUS IV

The hand held equipment used, shown in Figure 7.3. This device is capable of

recording the location of the transducer thus enabling a c-scan of the area.

Figure 7.3 Portable Ultrasonic equipment [MAUS IV] used for testing small areas on

adhesively bonded aircraft components.

7.4.2 BaNDIcoot FAQ

Similar equipment researched [BaNDIcoot FAQ] in an effort to determine the

suitability of the developed technique. Although not trialled it is believed that it will be

easily applicable as the data collected by these devices are in the form of A-scans

which are similar to those utilised in developing the “Maximum Energy” analyser.

Figure 7.4 Portable Ultrasonic equipment [BaNDIcoot FAQ] developed for c-scan ultrasonic capabilities by CSIRO, Australia.


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7.4.3 Discrete Ultrasonic tester

An ultrasonic tester was also conceptually designed [Lee, 2004] as a result of this

research using a completely different approach thus not requiring coordinate

tracking. It is based on the principle that discrete points on the surface under

investigation would be examined and that the instrument would be capable of giving

an output to indicate whether the bonding falls within the specified limits of adhesion

quality. The device is shown in Figure 7.5.

Figure 7.5 Conceptual design of portable ultrasonic tester [Lee, 2004].

Special features of this design are it’s capability to locate the transducer

perpendicular to the surface under test. Three sensors at the bottom of the device

light up a blue light on the device when perpendicularity is achieved. At that

instance, a button can be depressed to take a measurement. A green and a red light

on the device would subsequently be lit to indicate whether bonding is within

acceptable limits. Manual mapping of all the results can reveal the state of a larger

area without the need of displacement tracking devices. The electrical and electronic

circuitry is self sufficient with only the ultrasonic probe requiring to be connected to

an external device, Figure 7.6. The device is adaptable to enable use of existing

ultrasonic testing equipment however software needs to be developed to enable

calculations using the Wavelet Transform. Results based on the “Total Energy”

calculations are fed back to the instrument light indicators for quality assessment.


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Figure 7.6 Electrical / Electronic assembly of portable ultrasonic tester [Lee, 2004].

7.5 Ford Motor Company

The application of this research was also discussed at Ford Motor Company,

Melbourne, Australia where possibilities of implementing the developed technique

were evaluated. Like other automotive manufacturers this company is no exemption

from making extensive use of adhesives in the manufacture of vehicles. However,

the use of adhesives in the manufacture of cars does not appear to be as extensive

as in the case of aircraft manufacture. In this case, adhesives are primarily used

where no major load bearing members are concerned or used in combination with

spot welding either for dampening vibration or in places hard to reach by the spot

welding robots. Their use would be substantially greater if reliable, non destructive

testing techniques were available. In this research, ultrasonic testing techniques

were considered only however there is extensive research using other thechniques in

this endeavour [Zweschper et al, 2003, Turler, et al, 1999].

Discussions lead to the conclusion that although work in this research was relevant

and useful, several major practical issues were presented that would make its

application very difficult. Firstly the base of the Ultrasonic tester designed was too

large to be applied to the non flat surfaces of automotive components which in

contrast to the large, flat nature of aerospace parts, they are almost in all cases at a

curvature. Figure 7.7 shows a typical, internal panel of an automobile with the

adhesive applied at the bottom end of the part.


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Figure 7.7 Internal automobile panel showing the adhesive applied before assembly and curing.

Other issues such as accessibility to adhered areas after curing were in most cases

impossible as they were internal with air gaps that prevented ultrasonic signal

propagation. Coupling techniques such as water bath and water jet that are

extensively used for aircraft parts could not be applied in this case, as most parts

were made of steel rather than aluminium and fibre structures used for aircraft.

Testing in this area requires further work in designing suitable fixtures to enable

ultrasonic probes to be positioned appropriately. The pulse echo technique used in

this research makes it easier to achieve such measurements. Such problems were

also highlighted by other researchers where techniques used for adhesive bond

inspection in the aerospace industry are not suitable for automotive applications

[Allin, 2002].

7.6 Conclusion

Work done in this research was tested against the needs of industrial applications. In

particular, the aircraft and automobile industries were considered that were included

in the main aims of this research. Constant contact with engineers from these

industries influenced the direction of the research towards relevant needs. Most of

the experimental work was done using adhesives and adherents that are common to

the automotive industry. Contact with suppliers of structural adhesives to the Ford

motor company provided all the adhesives used in this research. Results were

shared with this supplier and confirmation of the developed testing technique

occurred in the latter stages of the research. The developed technique, “Total

Energy” measurement, was found to be most beneficial to the aircraft industry. The


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developed technique increased the sensitivity of ultrasonic c-scans that has the

potential to increase the resolution of results from large aircraft components. In

addition to the increased resolution of measurement, the developed technique also

offers data compression. Due to the large sizes of components, results from

ultrasonic tests in the aircraft industry are mainly restricted to one value per discrete

location tested, eg Maximum Amplitude of the A-scan. The developed technique,

due to its ability to compress the signal using the Wavelet Transform, will be able to

save the entire A-scan signal from each location tested. This has tremendous

advantages over existing techniques as aircraft go through regular testing and

maintenance procedures to fulfil extremely high reliability requirements. Using the

entire signature of the signals can be used for comparison with previous tests that

can lead to more reliable conclusions. This last part of the project was a very brief

report of industry involvement in this research that merely highlights the applications

of the developed technique but in reality a much higher collaboration took place

through visits to industry and other forms of regular communication.

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Chapter 8

Conclusion

8.1 Introduction

The topic of adhesive bond evaluation has been extensively research as shown by the

literature survey done in this project. Various principal, non destructive techniques such

as X ray, thermography, laser and ultrasonic have been used in the endeavour of

reliably identifying the quality of an adhesively bonded area. Each of these techniques

showed some degree of success, although none has yet claimed total bond quality

evaluation with reliable outcomes on the strength of a bond, fatigue characteristics,

dynamic behaviour and failure modes. Nevertheless, these techniques proved they

were capable of outcomes that can reliably identify defects such as inclusions and voids

within the adhesive and at the interface.

The research in this project has concentrated on improving the reliability of adhesive

bond evaluation by trialling a new technique based on stress distribution analysis, as

well as the application of Wavelet Transform analysis of Ultrasonic signals.

8.2 Non-Destructive testing techniques

The research commenced by a comprehensive study of existing, non-destructive

evaluation techniques, in the area of adhesive bond evaluation. X-ray tests on adhesive

bonds can identify medium density variations that in the case of adhesive bond

evaluation reveal disbonds, inclusions and voids in the adhesive. Density variation in

the latter can also be detected by this technique that reveals variations in the curing

process of the adhesive. This is considered to be a good technique, as all the variables

tested provide a quality index that, to a certain degree can discriminate “Good” from

“Bad” adhesive bonds. However, this method is not without its drawbacks. X-ray

radiation is a dangerous medium to work with, requiring highly specialised equipment

and recording media that make it most suitable for small to medium size work that

enables access on both sides of the tested area. In many cases, such as large

Chapter 8 Conclusion

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laminated areas, aircraft components, or in situ testing of vehicle components, render

this technique unsuitable. Thermography was another technique found in the literature

that offered promise in evaluating the quality of adhesive bonds [Zweschper et al, 2003,

Turler, et al, 1999].

However, the most commonly used techniques in evaluating the quality of adhesive

bonds used ultrasonic signals. These were emitted by a probe capable of ultrasonic

frequencies and were received by a similar probe capable of receiving the signal after it

has travelled through the material or at a transverse direction. The characteristics of the

received signal were used to evaluate the quality of the adhesive bond.

This technique proved to be the most commonly used technique as it offered a

comparatively reliable method of detecting variation in the properties of the material

tested. This was done primarily by detecting variation in the attenuation of the signal, its

frequency composition and time of travel. However, this technique also has its

drawbacks of which the most prevalent is its inability ot test products without the use of a

couplant between the surface to be tested and the ultrasonic probe. Research to

overcome this difficulty has led to the design of liquid filled wheels housing the ultrasonic

probe or using lower frequency signal that can travel in free air. Other research utilise

laser and microwave signals to carry the ultrasonic signals through air. However using

couplant, in most cases water, is currently the most reliable and most common

Ultrasonic technique in use.

8.3 Adhesion and adhesives

A comprehensive study of the adhesion process was carred out during this project to

enable understanding of the mechanisms of adhesion. Such understanding was

essential in searching for a reliable technique to evaluate adhesive bonds. As shown,

there are a number of mechanism at play in the adhesion process, i.e. interlocking,

diffusion, electronic and absorption. The prime element for effective adhesion was

intimate contact between adherent and adhesive. Therefore, surface preparation and

adhesive dispersion are important elements in achieving adhesive bonding.


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As the research in this project was aimed at automotive and aerospace applications, it

was of primary importance to refer to structural adhesives. A literature survey has

revealed an extensive selection of structural adhesives that offered a variety of qualities

in temperature tolerance, ultra-violet and infra-red radiation tolerance, flexibility,

resistance to oxidation, and others. Knowledge in this area enabled the selection of a

suitable adhesive for this project, one part Epoxy adhesive that is currently used

extensively in the manufacture of vehicles.

8.3.1 Preliminary tests

All work to this stage of the project was aimed at building knowledge about the adhesion

process and the techniques currently used for non-destructive testing of adhesive bonds.

However destructive tests were also essential in our endeavour to improve the reliability

of non destructively testing adhesive bonds. Literature survey in testing procedures for

adhesive bonds had shown that a commonly used test in adhesive bonds is the “Shear

Lap” test as prescribed by the American Society of Testing and Materials Standard

(ASTM). Following this standard, a number of specimens were prepared using structural

adhesive to test the effect of defective area on maximum stress

A mathematical model representing the relationship between stress levels and adhered

area showed that the stress in the adhesive was inversely proportional to each other.

However research in the area [Jennings, 1972] disputed this by supporting that bond

strength was related to the peripheral length of the bond rather than its area. Although

not the main objective of this research, a series of destructive tests were carried out

using shear lap specimen to test the effect of defective area on the overall strength of

the bond. The relevance of such tests comes from the fact that non-destructive testing

can reveal the size and location of delaminated areas. Specimens with varying size

defects were tested to breaking point and maximum loads recorded. The results

revealed that although the peripheral dimension of the adhesive was the dominant factor

in load bearing, it was also evident that there was a load dependency on the adhered

area albeit to a much lesser extent.


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8.3.2 FEA of adhesive bonds

The results of the preliminary tests led to the enquiry of stress distribution in the

adhesive under load and whether they could reveal the condition of the adhesive bond if

they were measured. A Finite Element Analysis (FEA) was carried out on a computer

model similar to the specimen tested in the preliminary tests. The computer simulation

results showed that there certainly was a relationship between the location and size of

the defect and the stress distribution in the adherent and adhesive. The highest stress

concentration was shown to be at the defect boundaries and minimum at the centre of

the defect. Stress distribution in the well bonded area showed to be uniform throughout.

The results for the FEA test were encouraging as the correlation between stress

distribution and bonded interface was very clear. Such a result meant that if the stress

distribution in an adhesive bond system could be measured, the quality of the bond

could be determined.

8.3.3 Ultrasonic measurement of stress distribution

As described in section 8.3.2, the stress distribution within the adhesive system could

lead to an effective means of non destructive bond quality evaluation. A method for

stress measurement using ultrasonic signals was sourced through the literature on the

subject and a technique applied for stress measurement in turbine blades was used. A

portable tension device was designed and constructed to carry out laboratory tests in

measuring stress by ultrasonic means. Extensive variation in stress in the specimen

revealed no variation in the ultrasonic signal to reflect the stress changes. As the test

piece was 20 mm long, it was concluded that changes in the ultrasonic signal would be

difficult to detect of such small distances. Further experimentation to confirm if this

technique could detect stress over larger distances was not carried out as its application

in adhesive testing would require small distance testing for stress mapping over a large

area.


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8.3.4 Further FEA tests

As the results of the ultrasonic stress measurement were not successful, further FEA

was carried out to confirm that this technique was not suitable for testing adhesive bond

quality. The results of the additional FEA tests showed that the stressed distribution due

to the adhesion forces varied as the distance away from the bond interface was

increased. At some distance from the adhesive bond interface, no stress was

detectable. It was therefore concluded that this theory can only be true if stress

measurements were taken at the interface. Further more it also proved that experiments

carried out earlier, using ultrasonic signals to measure stress, could not have succeeded

as the measurements were done away from the adhesive interface. Further testing to

detect stress distribution was therefore abandoned due to the results of the FEA

conclusions. However this line of research could prove to be successful if stress

distribution at the bond interface was measurable. For example, load sensitive polymer

materials may be included in the adhesive assuming suitable means are made available

to measure stress in this material.

8.4 Design of experiment

Literature survey had shown that ultrasonic testing techniques are well suited to

application in the automotive and aerospace industries. Since these are the areas of

interest in this research, it was decided to use Ultrasonic means to evaluate adhesive

bond quality in metal to metal applications.

The experimental setup requirements were fairly standard with an ultrasonic pulser /

receiver unit, ultrasonic probes, data collection and analysis software and a scanning rig

to enable c-scans on adhesively bonded areas.

8.4.1 Ultrasonic scanning setup

The X-Y-Z scanning rig was design and constructed specifically for this research to suit

the specimen size, measurement resolution and accuracy. A robust structure was

designed specifically for this research with minimum deflection and vibration to reduce


136

experimental errors. The rails for each axis were selected to enable high accuracy

movement and the drive mechanism consisted of ball screws for minimal backlash

inaccuracies. The scanning rig was tested for repetitiveness prior to experimental tests

on adhesively bonded specimens.

The driving mechanism was made up of stepper motors with adequate resolution, driver

control cards and power supply. Limit switches were also fitted to avoid accidental

damage to the test rig. The ultrasonic probe was securely held on the Z-axis of the

machine and was manually adjusted to the required height for testing. A water tank was

constructed of polymeric material and was placed directly below the probe.

The test rig movement was controlled by a software that synchronised a number of

variable parameters, i.e. probe speed in the X and Y directions and generating a pulse at

predetermined distances. The software was also capable of collecting the results from

gated areas of the signal and subsequently analysing the ultrasonic signal data.

The test rig design and construction was considered to be a very important part of this

research as all results depended on the ability to setup the experiment according to the

requirements, carry out the tests, and to be able to collect accurate and representative

measurements.

8.4.2 Instrument calibration

Before any tests were conducted, calibration of all instrumentation was carried out.

Aspects such as stepper motor movement, signal velocity through the different media

were tested and calibrated to reflect correct measurements. Variables such as location

of probe relative to specimen, ultrasonic pulser voltage setting, gain and repetition rates

were also calibrated to enable accurate and representative measurements. Calibration

is necessary for obvious reasons and this case is no exception as the success of the

research depends highly on the accuracy of measurement.


137

8.4.3 Instrument optimization

Finally, the experimental setup was completed by designing a test piece with

predetermined location and size of defects to test the ability of the setup to detect these

defects. Furthermore, instrument variable settings for optimum results were also

determined using the same test pieces. The accuracy of locating the defects and the

resolution with which they were detected was placed under scrutiny. Independent

instrument variable settings such as Voltage, Pulse Echo Gain and scanning resolution

were calibrated using the Central Composite Second-Order Rotatable Design method.

This is an optimisation technique that uses settings of the different variables and results

in accuracy to determine optimum settings for maximum accuracy results. Computer

software was necessary for this analysis and the results were conclusive both

analytically and graphically. These settings were used in further experiments.

8.4.4 Experimental set-up

The experimental set-up was finally concluded by running numerous tests to confirm its

accuracy and repeatability. The results showed the desirable outcomes and tests in for

research testing was ready to commence.

8.5 Ultrasonic Experiments

Having completed the experimental set-up, the research concentrated on the type of

experiments to be conducted. Theory on ultrasonic signal utilization for non-destructive

testing, lead to the conclusion that the ultrasonic signal energy variation as it travels

through either liquid or solid material, is capable of detecting changes in the bulk of the

material. Based on this principle, preliminary tests were carried out to determine

whether this principle could be applied in the evaluation of adhesive bond quality.

8.5.1 Signal Characteristics

Traditionally used signal characteristics were tested to confirm that the experimental

equipment used in this research was capable of detecting adhesive bond defects. The


138

results were quite conclusive in revealing that signal amplitude and signal decay showed

clear variations between signals from defective and non-defective locations. Frequency

analysis was also tested but did not show adequate variation for conclusive results in

detecting bond defects. It must be noted that the latter approach was successfully used

by other researchers.

These preliminary tests revealed that each technique was capable of detecting adhesive

bond defects, however their sensitivity to variation between adhesion extremes was

poor. The output range between total delamination and good adhesion was too narrow

to allow detection of intermediate results. It was therefore decided to explore other

signal / data analysis techniques in an effort to widen this range.

8.5.2 Data Analysis

Data collected and utilised in earlier tests was further analysed using other statistical and

graphical techniques in an effort to expand the resolution in evaluating adhesive bond

quality.

A number of existing techniques were explored, starting with conventional, statistical

indicators such as “Max”, “Min”, “Mean” etc. Even at this early stage some encouraging

results were emerging. “Variance” for example, had shown higher discrimination

capabilities than any of the methods discussed earlier. However all of the other indices

examined showed little if any improvement compared to existing techniques.

A series of other, more advanced graphical analysis techniques were also explored,

which showed there were possibilities in improving signal analysis in adhesive bond

testing. Unfortunately most of these were unable to give an “index” to represent bond

quality. Their output, although conclusive, was of a graphical nature that required expert

translation for meaningful interpretation. However, one of these techniques, the Wavelet

Transform, showed potential in achieving our objective of improving the measurement

resolution from adhesive bonds. The output from this method reduced the signal data to

a series of coefficients that retained substantial characteristics of the original signal to

enable easier manipulation of conclusive characterisation and evaluation.


139

8.6 Wavelet Analysis

Explorational tests on analytical tools had shown that the Wavelet Transform offered

statistically significant improvements in the analysis of signals from adhesive bonds. It

was therefore decided to further investigate this technique with regards to it application

within the domain of this research.

The suitability of Wavelet Transform analysis was confirmed early in Chapter 6 when

fairly long and complex ultrasonic signals were reduced to a small number of coefficients

that could clearly indicate the differences between varying adhesive quality. These

coefficients were directly deduced from the original signal in such a way, through the

Wavelet Transform, that they preserved their identity. Inverse Wavelet Transform

operation on the coefficients resulted to the original signal confirming their suitability.

The Wavelet Transform is a mathematical technique that is normally used for

applications in the area of data compression. It was considered for this application, as

the objective was to represent as much of an ultrasonic signal with as little coefficients

as possible to enable effective comparison between signals. If taken to its extreme, this

method can represent the entire spectrum with one “detail” coefficient.

Extensive experimentation with this technique led to the conclusion that the “Total

Energy” of the Wavelet coefficients was very sensitive to changes in the ultrasonic

signal. Furthermore, a small number of coefficients that represented the entire signal

preserved a great proportion if not all the “Energy” of the entire signal under

investigation.

Continued experimentation showed that the “Total Energy” index represented varying

degrees of adhesive bond quality. Although detection of variation was also possible with

the other techniques explored, the Wavelet Transform method widened the range

between “Good” and “Bad” adhesive bonds. Depending on which part of the signal was

analysed, up to thirty times magnification was achieved by the “Total Energy” index.


140

8.6.1 Noise Reduction

The Wavelet Transform offered other benefits to the analysis of ultrasonic signals. Part

of this analytical technique allows filtering unwanted noise from the original signal. This

capability reinforced the application of “Total Energy” to measure bond quality through

ultrasonic testing, as noise could be removed from the signal prior to energy

calculations. Inclusions of noise would have had negative effects on the final result.

8.6.2 Data Compression

One other factor that was considered in selecting the Wavelet Transform technique for

this application was its ability to compress data. Existing techniques such as “Maximum

Amplitude”, “Signal Decay” and “Frequency Analysis”, represent the signal with one

index value whilst all other characteristics of the signal are dismissed due to the

requirement of large memory banks of computer space. The ability of Wavelet

Transform technique to reproduce the whole signal at a later stage by utilising a small

number of coefficients enables storage of the whole signal in much smaller files. In the

case of adhesive bond analysis, where large areas may be under investigation, such a

tool offers tremendous possibilities in the investigation of subsequent recount of results

from earlier tests.

8.6.3 Suitability of Wavelet Transform

This research has proven that the Wavelet transform offers considerable improvements

on existing techniques in the investigation of ultrasonic signals from adhesive bonds.

Not only is it capable of providing an improved measurement index, the “Total Energy”,

but is also capable of increasing measurement accuracy by removing signal noise.

Lastly this technique is also capable of preserving the original signal through a small

number of coefficients that can be recalled at a later stage for further investigation. The

latter enables archiving signal data that would have been otherwise impossible due to

the extensive computer memory requirements.


141

8.7 Industrial Application

The suitability of work carried out in this thesis was tested for its applicability in industrial

applications though contact with industries that are most relevant to this type of non

destructive testing, the automotive and aircraft industries. Although contact was kept

with these industries from the beginning of the project in an effort to keep the research

topic relevant, at the end of the research the results were discussed with industry

specialists and conclusions drawn.

8.7.1 Automotive industry

Discussions with personnel from Ford motor company, Australia, lead to conclusions that

the work done with the use of Wavelet transform would certainly be useful as it offers the

advantage of improved resolution of ultrasonic results. However a number of difficulties

relating to the implementation of adhesive bond testing using the ultrasonic technique

were highlighted. The inaccessibility to the majority of bonded areas on the car metal

structure, the irregularity of the surfaces and the need for couplant confirmed that there

is much research needed in this area before effective use of ultrasonic testing can be

applied.

8.7.2 Aircraft industry

All contacts in this area were with a local manufacturer of aircraft components, Hawker

De Havilant. The reception from this industry to the work done in this research was

entirely different to that from the automotive industry. Data compression, de noising and

increased resolution of ultrasonic signals are all problems that if overcome, would

improve non-destructive testing of adhesive bonds considerably. Firstly the issue of data

compression was very welcome due to the very large size of components normally

tested. Minimal data from each location is stored presently even though a complete set

of data would serve favourably in cases were maintenance is required or where

component failure occurs. The wavelet transform has had extensive success in other

cases were excessive data storage was prohibitive eg. fingerprint image storage, which

in this case would mean that complete sets of ultrasonic data can be kept in its


142

compressed form. The efficiency of reconstructing the initial signal through the Wavelet

transform has made it the standard in image data compression, which confirms its

suitability in this case.

Noise reduction in ultrasonic signals is always welcome due to the high sensitivity of

ultrasonic transducers as well as the plethora of signals generated due to unrelated

phenomena within the component under test. Having the ability to reduce or even

eliminate the effects of such noise was viewed as a positive outcome of this research as

it has the potential to increase the accuracy of the measurements.

Finally, the most appreciated feature of this research was the improved resolution of the

ultrasonic results. The normal process followed in testing large aircraft components at

this facility, is to run an ultrasonic test on a large, water coupled device followed by a

handheld device on areas that had suspicious results. This means that any defects

undetected in the first test would not be found by a more refined test process that

follows. It was therefore considered that the outcomes of this research were extremely

useful as initial defect detection would be considerably improved.

Overall, the proposed technique in this research was valued as extremely useful in the

ultrasonic testing of adhesive bonds in aircraft components.

8.7.3 Blind tests

A series of test were carried out on test pieces prepared by a local Structural Adhesive

supplier. These test pieces included defects that were not known prior to testing them

using the proposed technique. The tests were originally carried out using the Maximum

Amplitude method followed by analysis of the same results using an approximation

technique to the Maximum Energy technique developed in this research. The proposed

Wavelet Transform based technique could not be used in live tests as the equipment

used was not capable of implementing the Wavelet Transform to the collected data. The

closest data processor available in Winspect [Winspect], was the RMS processor. The

results showed a marked improvement even with the approximated processor. It can

therefore be concluded that the application of the “Total Energy” technique, that uses the

Wavelet Transform, offers an improved alternative to existing Ultrasonic signal analysis

from adhesive bond tests.


143

8.8 Further Work

Based on the results presented in this thesis, several areas have been identified for

further study. The following approaches are considered to complement the existing

results already presented.

1. Stress distribution of adherent and adhesive at the bond interface was proven to

have great potential in evaluating the quality of an adhesive bond non-destructively. The

use of Finite Element Analysis proved that such a technique is capable of identifying not

only disbonded areas but possibly areas with reduced bonding strength. A Lack of

equipment or available technique to measure stress distribution at the bond interface

limited this work to computer simulation. It is therefore suggested that a technique be

developed that can measure stress levels in the adherent or adhesive at the bond

interface. Such a technique may be based on Ultrasonic principles [Bray, 2000,

Prassianakis, 2000] or in the case of stress measurement in the adhesive, smart

material additives may be included in the adhesive that may emit a measurable output

that is mechanical stress dependant.

2. A second recommendation is software development based on work done in this

research that used the Wavelet Transform to analyse Ultrasonic signals from adhesive

bonds. Suitable algorithms need to be developed into a comprehensive software suite

that enables selection of the most suitable Wavelet type and decomposition level that is

best matched to the materials under investigation. Such software should be able to also

perform signal compression and decompression of ultrasonic signals in different file

formats. In addition to the above, Wavelets are also capable of noise filtering and this

feature should be incorporated in software developed for adhesive bond evaluation

using the Wavelet Transform.

3. One final area of development that may further the work presented here, is to

fully develop the handheld, portable Ultrasonic testing device designed during this work.

The advantages offered by this portable device, such as perpendicular to the surface

under inspection and ability to give an output that can differentiate between “good” and

“bad” bonds will prove to be very acceptable by industry.

144

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150

Appendix A Experimental Equipment

This appendix gives the technical specifications of the experimental equipment used in

this research.

Figure A.1 UTEXTM UT340 pulser / receiver.

Table A.1 Technical specifications of UT340 Pulser / Receiver.

Company: UTEX Scientific Instruments Inc. Canada

Pulser Specifications Standard Option - 001 Pulse Specification Rise time

Fall time Load Impedance Minimum Load Impedance

<2.0 ns <2.0 ns 50 ohms capacitive to 10 Amps

<1.0 ns <1.0 ns 50 ohms capacitive to 5.2 Amps

Pulse Voltage Control Minimum Output Voltage Maximum Output Voltage Maximum Voltage Increment

100 V 500 V 2 V

100 V 250 V 2 V

Pulse Width Control Pulse Width Minimum Pulse Width Maximum Pulse Width Resolution Pulse Width Accuracy

5.0 ns 80 ns 0.20-0.50 ns 10%

2.0 ns 80 ns 0.20-0.50 ns 10%

PRF Control PRF Minimum PRF Maximum

200 Hz 20 kHz

200 Hz 20 kHz

Receiver Specifications Gain Specification Bandwidth (-3 dB)

Input Impedance Equivalent Input Noise Maximum Output Level Output Headroom

1 MHz to 150 MHz 50 ohms 100 µV p-p max 0 dBm 6 dB

Gain Control Voltage Gain Minimum Voltage Gain Maximum Gain Increment

0 dB 63 dB 1 dB

Interfaces Serial Communications RS-232 (connection to host computer)

RS-485 (connection to additional pulser receivers) User Input/Output Four programmable digital inputs and outputs

Two sets of programmable relay contacts


151

Table A.2 Technical specifications of all moving parts of the Ultrasonic scan test rig.

Company Description Part Number Ball Screw 0.375 x 0.125 x 22” Ball nut NUT 7824973 Bearings 77R4 Carriage 7826440 Accu Glide Carriage CG20 AAAN

ACTION Bearing Distributors, Australia

Accu Glide Rail RG20 L0300 Table A.3 Technical specifications of stepper motors used in the Ultrasonic scan test rig.

Company Description Part Number 2 Amp Stepper Modules 24 VDC Input

OEM 230

Frame 23 Single Stack Z axis

S57-51-MO

3 Amp Stepper Modules 24VDC Input

OEM330

Motion Solutions Australia Pty Ltd

Frame 23 Three Stack X-Y Axis

S57-102-MO

Table A.4 Technical specifications of stepper motor control cards used in the Ultrasonic

scan test rig.

Company Description Part Number

PCI 3 AXIS for Stepper and Servo Motors

DMC1832

High Density 100 pin cable CABLE-100-1M Interconnect Board ICM2900

Motion Solutions Australia Pty Ltd

Communications Drivers and Terminal

COM DISK


152

Figure A.2 Zwick / Z010 Tensile testing machine used for hear lap test of adhesively bonded

specimens.

153

Appendix B Central Composite Second-Order Rotatable Design

B.1 Mathematical models

The Central Composite Second-Order Rotatable Design [Peng 1967, et. al] method was

chosen as a method for optimising instrument settings for this research. This is a design

of experiment technique that enables the derivation of equations for optimisation. It is

capable of calculating the coefficients b0, bi, bii etc., that relate the target variable (Y) to

the assigned variables (X) in a second order equation as shown in Eqn B.1.

Y = bo + ∑ biXi + ∑ biiXi2 + ∑ bijXiXj (B.1)

The experimental variables selected as having the most dominant influence on the

accuracy of results were:

X1: Voltage setting

X2: Pulse/Echo Gain

X3: Scan resolution

The selected target variable ‘Y’ for optimisation was the magnitude of fraction error

between introduced defect area and measured defect area.

Y = (((Aint - Ameas) / Aint)2)1/2 (B.2)

where:

Ameas = Area of defects as measured by the ultrasonic c-scan

Aint = Area of the defect introduced at adhesive interface

Appendix B Instrument setup optimisation

154

This method utilises a second order rotatable design matrix D, (Table B.1). The natural

values of variables X1, X2, and X3 replace the coded values of the rotatable matrix

according to Table B.2, to produce a modified matrix D, with the natural values of the

variables under investigation (Table B.3). The matrix is further extended to include

column vectors according to the second order model (Eqn B.1). The extended matrix is

shown in Table B.4.

According to Cohran & Cox (1957), if Y is the column vector with experimental

observations, (Table B.5), X is the instrument setting matrix (Table B.3), B, the column

vector of coefficients, can be determined from equation:

B = (X`X)-1X`Y (B.3)

The results from Eqn (B.3) are shown in Table B.8. Using these results, the equations

for target variable can now be written as:

Y1 = 0.23 + 0.0014 X1 – 0.009 X2 + 0.0352 X3 – 0.0113 X12 + 0.0277 X2

2 (B.4)

-0.0097 X32 + 0.0032 X1X2 - 0.0196 X1 X3 + 0.0081 X2 X3

Y2 = 0.3729 – 0.0148 X1 + 0.023 X2 + 0.0633 X3 - 0.0301 X12 + 0.0746 X22 (B.5)

- 0.0646 X32 - 0.0017 X1 X2 - 0.0298 X1 X3 + 0.0494 X2 X3

Y3 = 0.272 + 0.0085 X1 - 0.0024 X2 + 0.0675 X3 - 0.0003 X12 + 0.0745 X22 (B.6)

+ 0.0404 X32 - 0.0212 X1 X2 + 0.0103 X1 X3 + 0.0789 X2 X3


155

Y4 = 0.4508 - 0.0078 X1 - 0.0024 X2 + 0.0611 X3 + 0.0336 X12 + 0.0765 X22 (B.7)

+ 0.0043 X32 + 0.0098 X1 X2 - 0.0136 X1 X3 + 0.0280 X2 X3

Y5 = 0.6293 + 0.0197 X1 - 0.0001 X2 - 0.0525 X3 - 0.0512 X12 - 0.0565 X22 (B.8)

- 0.0579 X32 - 0.0223 X1 X2 + 0.0034 X1 X3 - 0.0016 X2 X3


156

Table B.1 Matrix of the second order rotatable design (Code values).

X1 X2 X3

-1 -1 -1

+1 -1 -1

-1 +1 -1

+1 +1 -1

-1 -1 +1

+1 -1 +1

-1 +1 +1

+1 +1 +1

-1.682 0 0

+1.682 0 0

0 -1.682 0

0 +1.682 0

0 0 -1.682

0 0 +1.682

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0


157

Table B.2 Assignment of Code values to the Natural values of experimental variables X1,

X2, X3.

Code values -1.682 -1 0 +1 +1.682

Voltage (V)

X1

100 140 200 240 300

P/E Gain (dB)

X2

6 12 20 28 34

Scan Resolution (mm)

X3

0.5 0.9 1.5 2.1 2.5


158

Table B.3 Second order rotatable design matrix with natural values of assigned variables.

Test No.

N

Voltage (V)

X1

P/E Gain (dB)

X2

Scan Resolution (mm)

X3

1 140 12 0.9

2 240 12 0.9

3 140 28 0.9

4 240 28 0.9

5 140 12 2.1

6 240 12 2.1

7 140 28 2.1

8 240 28 2.1

9 100 20 1.5

10 300 20 1.5

11 200 6 1.5

12 200 34 1.5

13 200 20 0.5

14 200 20 2.5

15 200 20 1.5

16 200 20 1.5

17 200 20 1.5

18 200 20 1.5

19 200 20 1.5

20 200 20 1.5


159

Table B.4 Extended matrix of the second order rotatable design (Code values).

X0 X1 X2 X3 X12 X2

2 X32 X1X2 X1X3 X2X3

+1 -1 -1 -1 +1 +1 +1 +1 +1 +1

+1 +1 -1 -1 +1 +1 +1 -1 -1 +1

+1 -1 +1 -1 +1 +1 +1 -1 +1 -1

+1 +1 +1 -1 +1 +1 +1 +1 -1 -1

+1 -1 -1 +1 +1 +1 +1 +1 -1 -1

+1 +1 -1 +1 +1 +1 +1 -1 +1 -1

+1 -1 +1 +1 +1 +1 +1 -1 -1 +1

+1 +1 +1 +1 +1 +1 +1 +1 +1 +1

+1 -1.68 0 0 2.28 0 0 0 0 0

+1 +1.68 0 0 2.28 0 0 0 0 0

+1 0 -1.68 0 0 2.28 0 0 0 0

+1 0 +1.68 0 0 2.28 0 0 0 0

+1 0 0 -1.68 0 0 2.28 0 0 0

+1 0 0 +1.68 0 0 2.28 0 0 0

+1 0 0 0 0 0 0 0 0 0

+1 0 0 0 0 0 0 0 0 0

+1 0 0 0 0 0 0 0 0 0

+1 0 0 0 0 0 0 0 0 0

+1 0 0 0 0 0 0 0 0 0

+1 0 0 0 0 0 0 0 0 0


160

Table B.5 Experimental values of response functions (criteria for optimisation). Each column

of this table is the list of elements of vector Y.

Test No.

N

Response Function

Y1 (Area 1)

Response Function

Y2 (Area 2)

Response Function

Y3 (Area 3)

Response Function

Y4 (Area 4)

Response Function

Y5 (Area 5)

1 0.15 0.29 0.37 0.51 0.48

2 0.18 0.28 0.41 0.50 0.58

3 0.17 0.30 0.33 0.54 0.58

4 0.22 0.32 0.26 0.44 0.46

5 0.26 0.33 0.30 0.69 0.39

6 0.22 0.19 0.30 0.50 0.35

7 0.31 0.54 0.52 0.69 0.36

8 0.30 0.43 0.52 0.69 0.35

9 0.22 0.29 0.24 0.50 0.39

10 0.18 0.23 0.32 0.64 0.48

11 0.35 0.67 0.60 0.73 0.48

12 0.32 0.57 0.43 0.63 0.49

13 0.20 0.06 0.21 0.39 0.51

14 0.25 0.37 0.60 0.52 0.49

15 0.19 0.27 0.23 0.36 0.58

16 0.25 0.39 0.32 0.41 0.65

17 0.22 0.34 0.32 0.50 0.58

18 0.26 0.29 0.28 0.44 0.69

19 0.25 0.47 0.20 0.54 0.69

20 0.22 0.47 0.31 0.48 0.63


161

Table B.6 Matrix (X`X)-1.

0.166 0 0 0 -0.057 -0.057 -0.057 0 0 0

0 0.073 0 0 0 0 0 0 0 0

0 0 0.073 0 0 0 0 0 0 0

0 0 0 0.073 0 0 0 0 0 0

-0.057 0 0 0 0.069 0.069 0.069 0 0 0

-0.057 0 0 0 0.069 0.069 0.069 0 0 0

-0.057 0 0 0 0.069 0.069 0.069 0 0 0

0 0 0 0 0 0 0 0.125 0 0

0 0 0 0 0 0 0 0 0.125 0

0 0 0 0 0 0 0 0 0 0.125

Table B.7 Matrix X`Y.

ΣYu

ΣX1Yu

ΣX2Yu

ΣX3Yu

ΣX12Yu

ΣX22Yu

ΣX32Yu

ΣX1X2 Yu

ΣX1X3 Yu

ΣX2X3 Yu


162

Table B.8 Values of calculated model coefficients (B) (Code factors).

Model

Coefficients

Calculated

Values of

Coefficients

(Y1)

Calculated

Values of

Coefficients

(Y2)

Calculated

Values of

Coefficients

(Y3)

Calculated

Values of

Coefficients

(Y4)

Calculated

Values of

Coefficients

(Y5)

Bo 0.23 0.3729 0.272 0.4508 0.6293

B1 0.0014 -0.0148 0.0085 -0.0078 0.0197

B2 0.009 0.023 -0.0024 -0.0024 -0.0001

B3 0.0352 0.0633 0.0675 0.0611 -0.0525

B11 -0.0113 -0.0301 -0.0003 0.0336 -0.0512

B22 0.0277 0.0746 0.0745 0.0765 -0.0565

B33 -0.0097 -0.0646 0.0404 0.0043 -0.0579

B12 0.0032 -0.0017 -0.0212 0.0098 -0.0223

B13 -0.0196 -0.0298 0.0103 -0.0136 0.0034

B23 0.0081 0.0494 0.0789 0.028 -0.0016


163

B.2 Refinement of mathematical models

The Error of experiments can be determined from:

Error = Σ(Y - Yav)2 (B.9)

where Yav is the mean of Y values.

The Sum of Squares of Deviations (SSD) can be found by equation (Peng 1967):

SSD = Y`Y - B`(X`Y) (B.10)

with degree of freedom 10 (for 3-factor matrix of design).

Adequacy is indicated by the value of the (Lack-of-fit/D.F)/(Error/D.F) which should be

less than Fisher criterion Fd.f, d.f, 0.05. If the observed values of Fisher criterion are more

than the theoretical values the hypothesis is rejected and the coefficients are

insignificant. Values of Fisher criterion can be obtained by using (Peng 1967):

F(bi) = bi(ΣXiuYu)/(SSD/10)>F1,10,0.05 (B.11)

F(bii) = bii(ΣXiu2Yu)/(SSD/10)>F1,10,0.05 (B.12)

F(bij) = bij(ΣXiuXjuYu)/(SSD/10)>F1,10,0.05 (B.13)

The results of the Fisher hypothesis are shown in Table B.9. Based on these findings,

all insignificant coefficients were ignored to give equations for target variable Y as shown

below:


164

Y1 = 0.23 + 0.0352 X3 + 0.0277 X2

2 (B.14)

Y2 = 0.3729 + 0.0633 X3 + 0.0746 X22 (B.15)

Y3 = 0.272 - 0.0675 X3 + 0.0745 X2

2 + 0.0789 X2 X3 (B.16)

Y4 = 0.4508 + 0.0611 X3 + 0.0765 X22 (B.17)

Y5 = 0.6293 - 0.0525 X3 - 0.0512 X12 - 0.0565 X2

2 - 0.0579 X32 (B.18)

The mathematical models given by Equations B.14 -B.18 were tested against the

experimental results to confirm their validity. Table B.10, shows the results of this test

from were it can be observed that the mathematical model results follow those from

experiments quite accurately. The maximum average error is ±5.1% however the

individual error in one case is recorded as 57%. As these tests were carried out for

optimisation purposes, the extreme cases will be considered as outliers and consider

that the models are satisfactory.


165

Table B.9 Fisher criterion test results on the significance of the second order model

coefficients.

Model

Coefficients

Response

function Y1

Response

function Y2

Response

function Y3

Response

function Y4

Response

function Y5

Bo 0.0226 0.0319 0.0169 0.0436 0.0325

B1 0.8768 0.5513 0.6684 0.6955 0.3143

B2 0.3691 0.3899 0.9095 0.9088 0.9946

B3 0.0035* 0.0358* 0.0091* 0.0156* 0.0268*

B11 0.1162 0.1364 0.9848 0.0486 0.0052*

B22 0.0082* 0.0106* 0.0030* 0.0026* 0.0120*

B33 0.3131 0.0314* 0.0797 0.8405 0.0160*

B12 0.7959 0.9617 0.4491 0.7241 0.4110

B13 0.1303 0.3950 0.7098 0.6264 0.8997

B23 0.5133 0.1747 0.0156* 0.3273 0.9517

*Coefficients are considered to be significant when the observed Fisher criterion “F” is less than

theoretical value of F5,5,0.05 = 0.05 (Eqns B.11, B.12, B13)


166

Table B.10 Mathematical model accuracy test results.

Y1 Y2 Y3 Y4 Y5

Actual Predicted Actual Predicted Actual Predicted Actual Predicted Actual Predicted

0.15 0.19 0.29 0.30 0.37 0.38 0.51 0.54 0.48 0.48

0.18 0.22 0.28 0.33 0.41 0.42 0.50 0.53 0.58 0.55

0.17 0.18 0.30 0.25 0.33 0.26 0.54 0.46 0.58 0.52

0.22 0.23 0.32 0.28 0.26 0.21 0.44 0.49 0.46 0.51

0.26 0.28 0.33 0.39 0.30 0.34 0.69 0.63 0.39 0.37

0.22 0.24 0.19 0.30 0.30 0.42 0.50 0.57 0.35 0.46

0.31 0.31 0.54 0.53 0.52 0.53 0.69 0.66 0.36 0.41

0.30 0.28 0.43 0.44 0.52 0.53 0.69 0.64 0.35 0.41

0.22 0.19 0.29 0.30 0.24 0.26 0.50 0.57 0.39 0.43

0.18 0.18 0.23 0.19 0.32 0.29 0.64 0.60 0.48 0.42

0.35 0.30 0.67 0.56 0.60 0.51 0.73 0.69 0.48 0.47

0.32 0.33 0.57 0.64 0.43 0.49 0.63 0.68 0.49 0.45

0.20 0.15 0.06 0.09 0.21 0.27 0.39 0.37 0.51 0.56

0.25 0.26 0.37 0.28 0.60 0.50 0.52 0.56 0.49 0.38

0.19 0.23 0.27 0.37 0.23 0.27 0.36 0.45 0.58 0.63

0.25 0.23 0.39 0.37 0.32 0.27 0.41 0.45 0.65 0.63

0.22 0.23 0.34 0.37 0.32 0.27 0.50 0.45 0.58 0.63

0.26 0.23 0.29 0.37 0.28 0.27 0.44 0.45 0.69 0.63

0.25 0.23 0.47 0.37 0.20 0.27 0.54 0.45 0.69 0.63

0.22 0.23 0.47 0.37 0.31 0.27 0.48 0.45 0.63 0.63


167

B.3 Parameter optimisation

Having validated the mathematical models that were used for determining the optimum

settings for the experimental parameters. In order to find the optimum values for

Voltage, Gain and Scan resolution settings, (X1, X2 and X3), a series of differential

equations were utilised:

⎪⎪⎪⎪

⎩

⎪⎪⎪⎪

⎨

⎧

=

=

=

03

02

01

XY

XY

XY

∂∂

∂∂

∂∂

(B.19)

The optimisation equations are given in Table B.11. These equations utilise Coded

Values which are subsequently converted to Natural values using Equations B20 – B22.

The converted, optimal settings are give in the last column of Table B.11.

X1(nat) = 200 + (40*X1(code)) (B.20)

X2(nat) = 20 + (8*X2(code)) (B.21)

X3(nat) = 1.5 + (0.6*X3(code)) (B.22)


168

B.4 Optimum instrument settings

The results indicated that the Voltage setting (X1), was found to have Fisher criterion F >

0.05 which meant that its significance in influencing the outcome in a consistent manner

too low to be taken into consideration. The dominant value for Gain (X2), was found to

be 20 dB in most cases. In the case of Scan Resolution (X3), there is a linear

relationship between the error and this parameter. It is therefore concluded that the

lower the value of scan resolution, the lower the reading error. For this reason it is

recorded as “Lowest” in the optimisation table meaning the lowest possible setting is

recommended for best results.


169

Table B.11 Optimum values of parameters using coded values.

Optimum values Target

Variable

Differential Equations

Param. Coded Natural

Y1

0352.03

020554.02

=

==

XY

XXY

∂∂

∂∂

X1*

X2

X3

-

0

Lowest

-

20

Lowest

Y2 -

0633.03

021492.02

=

==

XY

XXY

∂∂

∂∂

X1*

X2

X3

-

0

Lowest

-

20

Lowest

Y3

020789.00675.03

030789.02149.02

=+−=

=+=

XXY

XXXY

∂∂

∂∂

X1*

X2

X3

-

0.855

-1.616

-

26.8

0.53

Y4

0611.03

02153.02

=

==

XY

XXY

∂∂

∂∂

X1*

X2

X3

-

0

Lowest

-

20

Lowest

Y5

031158.00525.03

02113.02

011024.01

=−−=

==

=−=

XXY

XXY

XXY

∂∂

∂∂

∂∂

X1

X2

X3

0

0

0.453

200

20

1.8


170

B.5 Graphical representation of results

(a)


171

(b)

(c)

(d)


172

(e)

Figure B.1 Graphical results showing the influence of Gain and Voltage on defect area detection. The Z-axis of the graph labelled Area ‘n’, shows the fraction error of measured versus actual area of defect. The third variable, Scan Resolution, was kept constant at 1.5 mm in all cases.


173

(a)

(b)


174

(c)

(d)


175

(e)

Figure B.2 Graphical results showing the influence of Scan Resolution and Gain on defect area detection. The Z-axis of the graph labelled Area ‘n’, shows the fraction error of measured versus actual area of defect. The third variable, Voltage, was kept constant at 140 V in all cases.


176

(a)

(b)


177

(c)

(d)


178

(e)

Figure B.3 Graphical results showing the influence of Scan Resolution and Voltage on defect area detection. The Z-axis of the graph labelled Area ‘n’ shows the fraction error of measured versus actual area of defect. The third variable, Gain, was kept constant at 20 dB in all cases.

179


C.1 Specimens provided by OrbsealTM

Four specimens were prepared by Mr David Humphreys, Laboratory manager at

OrbsealTM, Broadmeadows, Melbourne, Australia. These specimens were made up of

sheet steel, 0.8 mm thickness, typical of the material used in the automotive industry.

They were prepared using structural adhesive, Orbseal 20000TM, to bond the two plates.

Defects were placed at the adhesive/metal interface by Orbseal laboratory personnel

unknown to us. The test pieces were subsequently tested at our laboratory using two

techniques, the Maximum Amplitude parameter while the other measurement parameter

was an approximation of the Wavelet Transform technique developed in this research.

The approximate alternative was necessary as Wavelet Transform processor was not

available in the WinspectTM software that was used for all the c-scans in this research.

Figure C.1 Adhesive bond specimens prepared in the OrbsealTM laboratory.


180

C.2 Maximum Energy approximation results

Figures C.2 – C.9, show the results of c-scan tests using the RMS, WinspectTM

processor as an approximation of the Maximum Energy technique.

Figure C.2 Sample_B_RMS c-scan results.

Figure C.3 Sample_B_Rev. RMS c-scan results.


181

. Figure C.4 Sample_M_RMS c-scan results.

Figure C.5 Sample_M__Rev. RMS c-scan results.


182

Figure C.6 Small_Sample_RMS c-scan results

Figure C.7 Small_Sample_Rev_RMS c-scan results


183

Figure C.8 Sample_T_RMS c-scan results.

Figure C.9 Sample_T_Rev. RMS c-scan results.


184

C.3 Maximum Amplitude results

Figures C.10 – C.17, show the results from Ultrasonic c-scan tests using the Maximum

Amplitude processor from the Winspect software.

Figure C.10 Sample_B_MaxAmp c-scan results.

Figure C.11 Sample_B_Rev_MaxAmp c-scan results.


185

Figure C.12 Sample_M_MaxAmp c-scan results.

Figure C.13 Sample_M_Rev_MaxAmp c-scan results.


186

Figure C.14 Sample_T_MaxAmp c-scan results.

Figure C.15 Sample_T_Rev_MaxAmp c-scan results.


187

Figure C.16 Small_Sample_MaxAmp c-scan results.

Figure C.17 Small_Sample_Rev_MaxAmp c-scan results.

evaluation of adhesively bonded steel sheets using

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