characterising the inherent variability of textile...

GORDON KANYIKE - 0705970

Characterising the Inherent Variability of

Textile Composites

Gordon Kanyike

0705970

Department of Mechanical Engineering

(BEng) Final Year Project

Supervisor: Dr Phil Harrison

Completed April 2011


SUMMARY

This report consists of analysis on three different types of textile composite materials. The

first step involved using an image processing software to create the primary information

required to input for Matlab analysis.

Using several pre-written Matlab codes, re-generating mesh images for the materials as well

as obtaining vital information i.e. shear angles and standard deviations was carried out.

The main aim of this project was to develop a technique to reproduce the inherent variability

in 'off-the-roll' engineering fabrics for each material analysed. This was done by applying a

pre-written genetic algorithm code with Matlab. The code should automatically create textile

sheets suitable for post thermoforming analysis where the resulting behaviour of the material

can be pre-determined. Difficulties such as the code‟s mathematics for accurately predicting

exact variability became apparent.

Within the materials laboratory spring stiffness experimentation was carried out on the

springs used within the pre-designed blank holder. Using the existing set thermoforming

station experiments were conducted firstly to calibrate the time for the radiant heater to

reach a desired temperature. Forming tests were then conducted on a number of specimens

to create shell hemispheres. The tests were set at various specifications to discover how the

material responds under deformation.


OBJECTIVES

● Cut-out and mark-out three large square samples each a different type of material then

divide each into 9 smaller squares (300x300mm)

● Analyse each sample using the program “ImageJ”

● Obtain shear angles, standard deviations and plot normal distributions for each specimen

using Matlab

● Predict the inherent variability for the large samples and thus the inherent variability in 'off-

the-roll' engineering fabrics.

● Run compression tests on springs

● Calibrate time to heat radiant heater

● Test specimens using thermoforming machine

ACKNOWLEDMENTS

I‟d like to give thanks to the help of the following people;

Dr. Phil Harrison for being my supervisor and giving his guidance throughout this project

Postgraduate Farag Abdiwi for his guidance throughout and providing me with Matlab codes

vital for this project

Technician John Davidson in aiding me with the various testing within the materials

laboratory

The technicians in the Electronics department for repairing the faulty apparatus required for

testing


CONTENTS

INTRODUCTION .................................................................................................................. 9

MATERIALS ....................................................................................................................... 11

Self-reinforced Polypropylene (SrPP/Armordon) .............................................................. 11

Plain Glass Woven Fabric ............................................................................................... 12

Pre-consolidated Twintex Twill ........................................................................................ 12

LITERATURE REVIEW

Textile composites

Predicting variability ....................................................................................................... 13

Fibre behaviour ............................................................................................................... 13

Modelling strategies ......................................................................................................... 14

Thermoforming ................................................................................................................ 14

PROJECT ANALYSIS ......................................................................................................... 15

Cutting out/marking out ................................................................................................... 15

IMAGE J ............................................................................................................................. 16

MATLAB ............................................................................................................................. 18

Further Analysis. ............................................................................................................. 24

MODELLING THE VARIABILITY. ....................................................................................... 26

Varifab ............................................................................................................................. 28

VarifabGA. ....................................................................................................................... 28

Predicted mesh results .................................................................................................... 29

EXPERIMENTAL RESULTS

Compression tests. ......................................................................................................... 31

Calibrating the radiant heater. ......................................................................................... 31

CONCLUSION .................................................................................................................... 33

APPENDIX .......................................................................................................................... 34

REFERENCES ................................................................................................................... 70

BIBLIOGRAPHY ................................................................................................................. 71


List of figures

Figure 1- Armordon material ............................................................................................... 11

Figure 2 - Glass fabric material . ........................................................................................ 12

Figure 3 - Twintex material .................................................................................................. 12

Figure 4 - SrPP sample and close-up of weave spacing ..................................................... 15

Figure 5 - Glass fabric sample and close-up of weave spacing ........................................... 15

Figure 6 - Twintex sample and close-up of weave spacing ................................................. 16

Figure 7 - ImageJ toolbar .................................................................................................... 17

Figure 8 - SrPP Sample with multi-point plot and close-up .................................................. 17

Figure 9 - SrPP mesh sample 1

Figure 10 - SrPP mesh sample 2 ........................................................................................ 18

Figure 11 - SrPP mesh sample 3 ........................................................................................ 18

Figure 12 - Glass fabric mesh sample 4

Figure 13 - Handled glass fabric mesh sample 4


Figure 15 - Handled glass fabric mesh sample 5


Figure 17 - Handled glass fabric mesh sample 6 ................................................................. 19

Figure 18 - Twintex mesh sample 7


Figure 20 - Twintex mesh sample 9 .................................................................................... 20

Figure 21 - SrPP distribution

Figure 22 - Glass fabric distribution

Figure 23 - Handled glass fabric distribution

Figure 24 - Twintex distribution ........................................................................................... 22

Figure 25 - SrPP area divided distributions ......................................................................... 25

Figure 26 - Glass fabric area divided distributions ............................................................... 25

Figure 27 - Twintex area divided distributions ..................................................................... 26

Figure 28 - Genetic Algorithm plot of standard deviation vs. average shear angle .............. 28

Figure 29 - Predicted mesh of SrPP sample 1

Figure 30 - Actual mesh of SrPP sample 1 .......................................................................... 29

Figure 31 - Predicted mesh of Glass sample 6

Figure 32 - Actual mesh of Glass sample 6 ......................................................................... 30

Figure 33 - Predicted mesh of Twintex sample 7

Figure 34 - Actual mesh of Twintex sample 7 . ................................................................... 30

Figure 35 - Image of single spring

Figure 36 - Image of 8 springs set-up on blankholder.......................................................... 31

Figure 37 - Radiant heater

Figure 38 - Graph of radiant heater calibration. ................................................................... 31

Figure 39 – Full set-up of blank holder with spring configuration ......................................... 32

Figure 40 – Image of Tracing attempt

Figure 41 - Large sample of SrPP

Figure 42 - Large sample of Glass fabric ............................................................................. 34

Figure 43 - Large sample of Twintex ................................................................................... 35

Figure 44 - Screenshot of ImageJ .................................................................................... 36

Figure 45 - Example excel file of ImageJ plot for SrPP sample 1 ........................................ 36


Figure 46 - SrPP sample 4 mesh

Figure 47 - SrPP sample 5 mesh ........................................................................................ 42




Figure 51 - SrPP sample 9 mesh ........................................................................................ 42

Figure 52 - Glass fabric sample 1 mesh





Figure 57 - Glass fabric sample 9 mesh .............................................................................. 43

Figure 58 - Twintex sample 1 mesh



Figure 61 - Twintex sample 4 mesh .................................................................................... 44



Figure 64 - Handled Glass sample 1 mesh



Figure 67 - Handled Glass sample 7 mesh ......................................................................... 44


Figure 69 - Handled Glass sample 8 mesh ......................................................................... 45

Figure 70 - SrPP sample 2 predicted mesh








Figure 78 - Glass sample 1 predicted mesh





Figure 83 - Glass sample 7 predicted mesh ........................................................................ 46



Figure 86 - Twintex sample 1 predicted mesh

Figure 87 - Twintex sample 2 predicted mesh ..................................................................... 48








Figure 94 - Graph of SrPP standard deviation vs. area

Figure 95 - Graph of Glass fabric standard deviation vs. area

Figure 96 - Graph of Twintex standard deviation vs. area

Figure 97 - Graph of SrPP average standard deviation vs. area

Figure 98 - Graph of Glass fabric average standard deviation vs. area

Figure 99 - Graph of Twintex average standard deviation vs. area ..................................... 52

Figure 100 - Graph of spring test 1...................................................................................... 55

Figure 101 - Graph of spring test 2...................................................................................... 56

Figure 102 - Graph of spring test 3

Figure 103 - Set-up of testing under Zwick Roell machine

Figure 104 - Graph of thermoforming test 1

Figure 105 - Outer view of test 1

Figure 106 - Inner view of test 1 .......................................................................................... 58


Figure 108 - Inner view of test 2

Figure 109 - Graph of thermoforming test 3 ........................................................................ 60





Figure 114 - Inner view of test 4














List of tables

Table 1 - SrPP dimensions and stats

Table 2 - Glass fabric dimensions and stats

Table 3 - Twintex dimensions and stats .............................................................................. 50

Table 4 - Large sample stats ............................................................................................... 51

Table 5 - Input parameters for varifab program ................................................................... 51

Table 6 - Spring stiffness data ............................................................................................. 57

List of equations

Equation 1 - Probability Density Function Where: ............................................................. 22

Equation 2 - Standard deviation equation . ......................................................................... 24

Equation 3 - Ellipse equation

Equation 4 - Eccentricity equation ....................................................................................... 27

Equation 5 - Sine function ................................................................................................... 28

Equation 6 - Spring stiffness equation ................................................................................. 56


INTRODUCTION

“Misalignment in textile composite‟s and dry fabrics‟ effects their material properties. Thus

understanding and predicting the effects of variability on the final part should allow variability

to be controlled.” 1

Textile Composites

“Textile composites can be defined as the combination of a resin system with a textile fibre,

yarn or fabric system. They can be flexible or quite rigid. The textile component provides

tensile strength and dimensional stability. Examples of inflexible or rigid textile composites

are found in a variety of products referred to as fibre reinforced plastic (FRP) systems.” 2

FRP products are now widely used as an alternative for metallic and wooden applications

such as; automotive/aircraft construction, hulls for boats, piping products, indoor/outdoor

containers and housing construction components.

The principal weaves are;

Plain for abrasion resistance and stability ideally suited to flat surfaces.

Twill for covering curved surfaces.

Satin more suitable for draping providing increased flexibility.

Available forms of reinforced plastics are:-

Glass fibre reinforced plastics (GFRP)

Carbon fibre reinforced plastics (CFRP)

Aramid fibre reinforced plastics (AFRP)

Natural fibre reinforced plastics (NFRP)

I have taken GFRP under consideration for this project.

“The characteristics, in addition to the choice of fibre and matrix material, are largely

determined by the orientation of the fibres in the textile fabric.” 3

1Long (2005)

2 Ko (1989)

3 http://www.zwick.co.uk/en/applications/composites/fiber-composites.html


Why choose GFRP?

● GFRP has a very high strength to weight ratio

● Low weights per square metre means faster installation, less structural framing,

and lower shipping costs

● Resistant to salt water, chemicals, and the environment - unaffected by acid rain,

salts, and most chemicals

● A seamless construction as domes and cupolas are resigned together to form a

one-piece, air/watertight structure

● Virtually any shape or form can be moulded

● Research shows no loss of laminate properties after 30 years so low maintenance

required

● Very durable, Stromberg GFRP stood up to category 5 hurricane Floyd with no

damage, while nearby structures were destroyed


MATERIALS

Self-reinforced Polypropylene (SrPP/Armordon)

Figure 1- Armordon material

The woven fabric is produced with Co-extruded tapes. Each individual co-extruded tape has

a high melting point polymer core, surrounded by a lower melting point copolymer “cap” coat.

Armordon is a composite material with unique properties. High impact strength and low

density makes Armordon the preferred choice where strength and weight saving priorities

are key. It offers important environmental benefits as it is 100% recyclable and with no glass

reinforcement, there are considerable advantages in both processing and machining. 4

Summary of properties:-

● High impact strength and abrasion resistant

● Low density

● 100% recyclable

● No glass fibre

● Non-toxic and inert

● Price

● Machineability

With prospects in ballistics and blast protection, passenger luggage, construction and

automotive and medical applications, Armordon can be developed to suit an extensive range

of product markets.

4 http://www.armordon.com/


Plain Glass Woven Fabric

Figure 2 - Glass fabric material

The plain woven glass fibre fabric is used for anticorrosion, corrosion resistance and

insulation of pipes and storage tank in power stations, oil fields, chemical plant, paper mill

and environment protection projection where highly corrosive mediums are present. Along

with high heat resistance qualities it can also be used in the construction work involving

reinforced plastics.

Pre-consolidated Twintex Twill

Figure 3 - Twintex material

“Made from commingled E-glass and polypropylene filaments, Twintex has a fast processing

cycle time. The dry prepreg is made by commingling the continuous glass and

Polypropylene (PP) filaments. Consolidation process is done by heating material above

melting temperature of PP matrix 180 C – 230 C) and applying a low pressure (1-30 bars)

before cooling under pressure.” 5

5 http://www.twintex.com/


LITERATURE REVIEW

Textile composites

“Textile composites have three structural levels:

1. The macro (M)-level defines the 3D geometry of the composite part and the distribution of

local reinforcements.

2. The meso (m)-level defines the internal structure of the reinforcement and variations of

the fibre direction and the fibre volume fraction inside the yarns and the fibrous plies.

3. The micro ( )-level defines the arrangement of the fibres in the representative volume

element (RVE) of the impregnated yarn or fibrous ply.” 6

Predicting variability

Dependable prediction of mechanical behaviour of composite materials is a primary

importance within textile composites. A study involving thermoforming experiments and

Finite Element ( FE) simulations of a commingled glass-Polypropylene(PP) woven

composite on a double dome was undertaken with the aim of assessing the comparison

between predicted and experimental shear angle data “The constitutive model is constituted

of a dedicated non orthogonal hypo-elastic shear resistance mode… It was concluded that

the shear angles were fairly well predicted for this particular case study, which could be

expected in view of the fact that no wrinkles had formed during the thermoforming

experiment.” 7

Fibre behaviour

The mechanisms of how a composite‟s inter-ply/in-plane shear is formed is an important

factor to how the fibres are aligned “The formed fibre pattern is governed mainly by the trellis

effect, i.e., local intra-ply shearing between initially orthogonal fibres” 8 Under biaxial testing

“a significant rise in shear resistance with an increase in tension was measured, but due to

the non-uniform shear distribution the measured shear force may be affected by the

variations in) yarn tensile load during the shear deformation” 9

6 Lomov, et al., 2006

7 Willems L. V. 8 Lin, Wang, Long, Clifford, & P.Harrison, 2007

9 Willems, Lomov, Verpoest, & D.Vandepitte, 2008


Modelling strategies

Creating accurate modelling and design tools for textile composites was conducted at the

University of Leuven in Belgium. They were able to construct numerous 2-Dimensional and

3-Dimensional weave structures using manufacturer‟s fabric and yarn data as a starting

point for modelling. It was concluded “the structures were easily constructed within TGP

tools, providing great flexibility of input data. WiseTex allows easy manipulation of fabric and

yarn data and visualisation tools.” 10

Another challenge undertaken at the same university was to use a technique to characterize

textile composites using Digital Image Correlation (DIC). Using a digital charge-coupled

device (CCD) camera tangential displacement and strains to the surface are calculated

bases on the comparison between subsequent images of an object during loading. The

loading is present due to biaxial/shear testing using picture frames. “It is concluded that

optical measurements are mandatory for reliable assessment of the textile deformation” 9

Thermoforming

After a study involving forming shell hemispheres it was deduced that “wrinkling is a result of

the interaction between shear deformation and compressive force.” 8

“Process parameters like the membrane stresses introduced by a blankholder, the mold and

preheating temperature, the blank shape and punch force can affect the drape behaviour,

the occurrence of wrinkling, and the consolidation and impregnation quality.” 7

10

Lomov, et al., 2001


PROJECT ANALYSIS

Cutting out/marking out

● For each of the materials a large square sample of roughly 900x900mm was cut-out

(Appendix A)

● The large samples were then divided up into nine smaller squares (3 rows of 3) of

approximately 300x300mm

● With a permanent marker horizontal and vertical lines were marked on every specimen to

create cells and nodes

● For the Armordon and Glass fabric the cell lines were spaced eight weaves apart shown

below

Figure 4 - SrPP sample and close-up of weave spacing

Figure 5 - Glass fabric sample and close-up of weave spacing


As the weaves for twintex are larger, the cell lines were drawn on 4 weaves apart

Figure 6 - Twintex sample and close-up of weave spacing


IMAGE J

“ImageJ is a public domain, Java-based image processing program developed at the

National Institutes of Health. User-written plug-ins makes it possible to solve many image

processing and analysis problems, from three-dimensional live-cell imaging to radiological

image processing multiple imaging system data comparisons to automated haematology

systems.” 11 Screenshot of imageJ is shown in Appendix B

With ImageJ‟s multi-point selection tool (Figure 7) points on every crossover point (nodes)

were plotted for each specimen (Figure 8).

Figure 7 - ImageJ toolbar

Using the analyze tool the program then automatically produces a table of coordinates for

every node in terms of pixels, which were converted to millimetres by multiplying each pixel

value by a scale. Using Microsoft excel the minimum x and y values were found. Each

column was then multiplied by their respective minimum values to create a node matrix table

to input into Matlab. Another table required for the Matlab program was a table of elements

to create the cell squares. This four column table represents each valid square cell in every

specimen, each column depicting the nodes that make up that cell. (Appendix B)

Figure 8 - SrPP Sample with multi-point plot and close-up

11

http://en.wikipedia.org/wiki/Image_J


MATLAB

A Matlab code created by Farag Abdiwi was devised to regenerate a mesh images for each

of the specimens. For each mesh the code named “ExpMesh1” Appendix C) was

programmed to find the shear angle for each node as well as the average shear angles and

standard deviations. The following figures show resulting mesh images generated from the

three types of textiles, with Farag‟s handled glass fabric samples also added for comparison.

SrPP meshes for bottom row:-

Figure 9 - SrPP mesh sample 1 Figure 10 - SrPP mesh sample 2

Figure 11 - SrPP mesh sample 3


Glass fabric and Handled Glass fabric meshes for middle row:-

Figure 12 - Glass fabric mesh sample 4 Figure 13 - Handled glass fabric mesh sample 4




Twintex meshes for top row:-

Figure 18 - Twintex mesh sample 7 Figure 19 - Twintex mesh sample 8



Ideally the cell lines (elements) should be perfectly parallel and perpendicular to each other

when it comes off the roll. This is somewhat accurate for the SrPP samples, but due to

minute handling for the Glass fabric there is slight variability in the angle between the

elements. Farag‟s glass fabric samples display increased variability due to frequent handling

of the material. As for the Twintex, specimen results show the variability is largely significant

with shear angles on the right side of the large sample dropping to well below 80˚ and shear

angles on the left side reaching above 100˚. Obvious reasons for this will be traced back to

the pre-consolidating process done during manufacturing of the roll. The results for a column

from each large sample material i.e. (samples1, 4 and 7) were similar. The rest of the

meshes are shown in Appendix D.


Histograms:-

Using a 2nd code developed by Farag named “histfit” Appendix C) a histogram for each

specimen was produced. The bell shaped graph portrays the normal distribution of

probability density versus shear angles which incorporates the probability density function

(PDF) (Equation 1)

Equation 1 - Probability Density Function 12

Where:

▪ μ is the mean

▪ σ 2 is the variance

▪ ɣ is the shear angle

The graphs below compares each material type‟s individual sample‟s distribution blue) with

the distribution for all samples collectively (red).

Figure 21 - SrPP distribution

12

Modern Engineering mathematics 4th edition


Figure 22 - Glass fabric distribution

Figure 23 - Handled glass fabric distribution

Figure 24 - Twintex distribution


It is clearly seen that these graphs correspond to their respective mesh results from earlier in

terms of the level of variability.

Further Analysis

The areas for each specimen were then divided to study the textile‟s fibre orientation over

several regions, firstly by half, then by a quarter and an eighth. The standard deviations for

these areas were plotted against their areas in graphs presented in Appendix F. Also within

this appendix lie graphs for average standard deviations against area. Results show

standard deviation increases with decreasing area.

Equation 2 - Standard deviation equation

The mesh generation process was repeated again for these new areas using the same

ExpMesh1 code as before. Again distributions of probability against shear angles for these

various areas were produced. By reconfiguring the “histfit” code from earlier to apply for

multiple average shear angles and standard deviations the code was renamed “histfit2” A

brief sample of the code is shown Appendix C.

The following graphs are the resulting distributions for histfit2 showing the different divisional

areas‟ probability density against the shear angle. A colour code is also supplied.

Single samples – Red

1/2 samples – Cyan

1/4 samples – Black

1/8 samples - Magenta


Figure 25 - SrPP area divided distributions

Figure 26 - Glass fabric area divided distributions


Figure 27 - Twintex area divided distributions

Once again these divisional distributions adopt the same characteristics as the predeceding

“hisfit” results, which show that the material acts similarly in terms of variability no matter the

size of the area.


MODELLING THE VARIABILITY

“Meshgen” a Matlab program written by Ikuo Koyama for his project on „Development of a

mesh generator including directional variability‟. 13 Based on ellipse and eccentricity

equations 3 and 4. Meshgen generates a „semi-discrete‟ or „meso-scale‟ element which is a

combination of truss and membrane elements.

Equation 3 - Ellipse equation

Equation 4 - Eccentricity equation

Where A is the major x-axis, C is the major y-axis, e is eccentricity, x and y are horizontal

and vertical displacements.

The program operates by the concept of; if there is progression of diagonals in every cell

then the positional relation between all the nodes can be found. In reality the variability that

takes place in the textile composites can look like curved lines, which is what limits Meshgen

as it can‟t re-represent the exact variability i.e. the formation of the curved weave. It can only

just model straight lines.

13

http://www.mech.gla.ac.uk/~pharriso/Teaching/index.html


Varifab

Equation 5 - Sine function

To be able to account for curved lines Farag included a sine function to the code (equation

5), which was thus named “Varifab” which is short for Variable fabric.

VarifabGA

Genetic algorithms GA‟s) are based on Charles Darwin‟s evolutionary theory „survival of the

fittest‟. The origin of species: “Preservation of favourable variations and rejection of

unfavourable variations.” 14 GA‟s are a technique for solving problems which need optimizing

(Choosing the best element from a set of available alternatives).

0 20 40 60 80 100 120 140 160 1800

1

2

3

4

5

6

7

8

9

Mean (Degree)

Sta

ndard

Devia

tion

Figure 28 - Genetic Algorithm plot of standard deviation vs. average shear angle

14

http://en.wikipedia.org/wiki/Survival_of_the_fittest


The code used to generate the following meshes is named “GARan4mfun25”, which was

created by Farag Abdiwi. There are a number of input variables that control the structure of

the generated mesh shown in Appendix E. The basis of the program can be explained from

Figure 28. There is a search space from which a target standard deviation is set for the

program to search for (the red circle). The program then selects a random number of

standard deviations (green circles) from the search space then plots a population these

possible solutions. In GA‟s programming these possible solutions are called chromosomes

and a group of chromosomes is called a population with every stage called a generation.

The best solution (The chromosome closest to our target standard deviation) is chosen and

the rest are discarded. The following are some of the resulting meshes from the program.

Predicted mesh results

Figure 29 - Predicted mesh of SrPP sample 1 Figure 30 - Actual mesh of SrPP sample 1


Figure 31 - Predicted mesh of Glass sample 6 Figure 32 - Actual mesh of Glass sample 6

Figure 33 - Predicted mesh of Twintex sample 7 Figure 34 - Actual mesh of Twintex sample 7

Although the predicted meshes don‟t exactly match their real-life equivalents, the results are

still fairly accurate and also give a good quality account of how the textile material could be

formed. The rest of the predicted meshes are presented in Appendix D.


EXPERIMENTAL RESULTS

Compression tests

Method and results for compression tests on blank holder springs are shown in Appendix G.

Figure 35 - Image of single spring Figure 36 - Image of 8 springs set-up on blankholder

Calibrating the radiant heater

A single test was carried out to determine the time required for the radiant heater (bought by

Laurent Maurel )15 to settle at a specific temperature.

With a set temperature of 180 ˚C, the set-up for the experiment was similar to that done by

Gordon Pettigrew in his report.16 Figure 38 shows the resulting graph.

Figure 37 - Radiant heater 17

15

Maurel, 2007 16

Pettigrew, 2007 17

Richards, 2009


Figure 38 - Graph of radiant heater calibration

Thermoforming tests

Using the current blank holder set-up with stainless steel springs, thermoforming of the

specimens was completed under the same method done in related projects by previous

undergraduate students i.e. Alana Richard 17 and Gail Gemmel 18

Results for the tests are presented in Appendix H.

Figure 39 – Full set-up of blank holder with spring configuration

18

Gemmel, 2008


CONCLUSION

ImageJ

Although the ImageJ software was very useful for processing and analysing images, the

stage using the multi-point plotting tool was time consuming. Improving the method for

solving this led me to briefly look into finding and/or modifying a Matlab code that can

automatically traces the images taken of the specimens, locate the coordinates of their

nodes and measure shear angles from the image files.

Figure 40 – Image of Tracing attempt

Figure 38 shows the best attempt achieved. The program uses greyscale values to trace the

lines, but configuring the code to identify the nodes, shear angles etc would take time and is

something that should be further looked into as the end result would be greatly beneficial for

the user.

Predicting Variability

The Genetic Algorithm code appears to be an optimum method for predicting the variability

in textile composites. It‟s accuracy although very close to what is desired isn‟t perfect.

Reasons for this could be down to human error from; initial marking/ cutting out of the

materials, plotting using ImageJ or perhaps the code itself may need slight reconfiguration.

Modelling a predicted mesh for the large square samples was also an objective for this

project but the program reported errors when inputting larger dimensions for the code to re-

model.

Thermoforming Testing

The forming of desired hemispherical shapes resulted in slight wrinkling for the SrPP

samples and increased wrinkling for the Twintex samples factors that affected this most

likely will have been the temperature of the blank holder o- ring at time of forming. The

edges of the specimen will not have been properly heated to the desired temperatures

causing buckling for the material and also for the blank holder itself.


APPENDIX

APPENDIX A - LARGE SAMPLES

Figure 41 - Large sample of SrPP

Figure 42 - Large sample of Glass fabric


Figure 43 - Large sample of Twintex


APPENDIX B - IMAGEJ

Figure 44 - Screenshot of ImageJ 19

Figure 45 - Example excel file of ImageJ plot for SrPP sample 1

19

http://en.wikipedia.org/wiki/File:ImageJScreenshot.png


APPENDIX C - MATLAB CODES

EXPMESH1

clear all

clf

clc

load ('InElementTable.txt');

load ('NodeMatrix.txt');

TrussElementTable = [];

for num1 = 1:size(InElementTable,1)

TrussElementTable =[TrussElementTable;[InElementTable(num1,2)

InElementTable(num1,3);InElementTable(num1,3) InElementTable(num1,4);InElementTable(num1,4)

InElementTable(num1,5);InElementTable(num1,5) InElementTable(num1,2)]];

end

TElementMatrix = [[1:size(TrussElementTable,1)]' TrussElementTable];

MElementMatrix = InElementTable;

for i=1:size(NodeMatrix,1)

NodeMatrixXX(i,1)=NodeMatrix(i,2);

end

for i=1:size(NodeMatrix,1)

NodeMatrixYY(i,1)=NodeMatrix(i,3);

end

Elementvaluexx=[];

num=1;

for i=1:size(MElementMatrix,1)

Elementvaluexx=[Elementvaluexx; num NodeMatrixXX(MElementMatrix(i,2),1)

NodeMatrixXX(MElementMatrix(i,3),1) NodeMatrixXX(MElementMatrix(i,4),1)

NodeMatrixXX(MElementMatrix(i,5),1)];

num=num+1;

end

Elementvalueyy=[];

num=1;


Elementvalueyy=[Elementvalueyy; num NodeMatrixYY(MElementMatrix(i,2),1)

NodeMatrixYY(MElementMatrix(i,3),1) NodeMatrixYY(MElementMatrix(i,4),1)

NodeMatrixYY(MElementMatrix(i,5),1)];

num=num+1;

end

aone=[];

atwo=[];


bone=[];

btwo=[];

num=1;


aone=[aone; num abs(Elementvaluexx(i,4)-Elementvaluexx(i,5))];

atwo=[atwo; num abs(Elementvalueyy(i,4)-Elementvalueyy(i,5))];

bone=[bone; num abs(Elementvaluexx(i,2)-Elementvaluexx(i,5))];

btwo=[btwo; num abs(Elementvalueyy(i,2)-Elementvalueyy(i,5))];

num=num+1;

end

P1=[];

P2=[];

P3=[];

P1=[P1; (Elementvaluexx(:,2)-Elementvaluexx(:,2)) (Elementvalueyy(:,2)-Elementvalueyy(:,2))]; %%

if theta=0 otherwise for example theta=45 P1=[P1; Elementvaluexx(:,2) Elementvalueyy(:,2)];


P2=[P2; Elementvaluexx(:,5) Elementvalueyy(:,5)];


P3=[P3; Elementvaluexx(:,3) Elementvalueyy(:,3)];

InitialShearAng=[];

i=[];

for i=1:size(P1,1)

InitialShearAng=[InitialShearAng; triangle_angles([P1(i,:);P2(i,:);P3(i,:)],'d')];

end

InitialShearAng=abs(InitialShearAng(:,:));

InitialShearAngmin=min (InitialShearAng (:,1));

InitialShearAngmax=max (InitialShearAng (:,1));

InitialShearAngminmax=[];

InitialShearAngminmax=[InitialShearAngminmax; InitialShearAngmin; InitialShearAngmax];

mu=mean (InitialShearAng (:,1));

stdiv=std (InitialShearAng (:,1));

figure (1)

hist(InitialShearAng(:,1),(1:size(InitialShearAng,1)))

hold on

xlabel('Shear Angle (Degrees)');

ylabel('Number of Cells');

h = findobj(gca,'Type','patch');

set(h,'FaceColor','b','EdgeColor','w');

hold off


figure (2)

hold on

HISTFIT

load 'xd.txt'

mu=mean(xd);

sigma=std(xd);

xdmin=min(xd);

xdmax=max(xd);

p1 = pdf ('Normal',xdmin:xdmax,mu,sigma);

figure (1)

plot (xdmin:xdmax,p1,'LineWidth',2,'Color','b');

hold on plot

title(' Sample 1 ')

xlabel( 'Shear Angle(Degrees)' ), ylabel( 'Probabilty Density (%)');

HISTFIT2

load 'xd.txt'

mu=mean(xd);

sigma=std(xd);

xdmin=min(xd);

xdmax=max(xd);

p1 = pdf ('Normal',xdmin:xdmax,mu,sigma);

figure (1)

plot (xdmin:xdmax,p1,'LineWidth',2,'Color','blue');

hold on plot

load 'xd1.txt'

mu1=mean(xd1);

sigma=std(xd1);

xd1min=min(xd1);

xd1max=max(xd1);

p1 = pdf ('Normal',xd1min:xd1max,mu,sigma);

figure (1)

plot (xd1min:xd1max,p1,'LineWidth',2,'Color','red');

hold on plot

load 'xd2.txt'

mu2=mean(xd2);

sigma=std(xd2);

xd2min=min(xd2);

xd2max=max(xd2);



figure (1)


hold on plot

load 'xd3.txt'

mu3=mean(xd3);

sigma=std(xd3);

xd3min=min(xd3);

xd3max=max(xd3);


figure (1)


hold on plot

load 'xd4.txt'

mu4=mean(xd4);

sigma=std(xd4);

xd4min=min(xd4);

xd4max=max(xd4);


figure (1)


hold on plot

load 'xd5.txt'

mu5=mean(xd5);

sigma=std(xd5);

xd5min=min(xd5);

xd5max=max(xd5);


figure (1)


hold on plot

load 'xd6.txt'

mu6=mean(xd6);

sigma=std(xd6);

xd6min=min(xd6);

xd6max=max(xd6);


figure (1)


hold on plot


load 'xd7.txt'

mu=mean(xd7);

sigma=std(xd7);

xd7min=min(xd7);

xd7max=max(xd7);


figure (1)


hold on plot

load 'xd8.txt'

mu=mean(xd8);

sigma=std(xd8);

xd8min=min(xd8);

xd8max=max(xd8);


figure (1)


hold on plot

load 'xd9.txt'

mu=mean(xd9);

sigma=std(xd9);

xd9min=min(xd9);

xd9max=max(xd9);


figure (1)


hold on plot


APPENDIX D – MESHES

Armordon

Figure 46 - SrPP sample 4 mesh Figure 47 - SrPP sample 5 mesh




Glass Fabric

Figure 52 - Glass fabric sample 1 mesh Figure 53 - Glass fabric sample 2 mesh




Twintex

Figure 58 - Twintex sample 1 mesh Figure 59 - Twintex sample 2 mesh




Figure 64 - Handled Glass sample 1 mesh Figure 65 - Handled Glass sample 3 mesh




Predicted Meshes

Figure 70 - SrPP sample 2 predicted mesh Figure 71 - SrPP sample 3 predicted mesh





Figure 78 - Glass sample 1 predicted mesh Figure 79 - Glass sample 2 predicted mesh





Figure 86 - Twintex sample 1 predicted mesh Figure 87 - Twintex sample 2 predicted mesh


APPENDIX E - TABLES

Table 1 - SrPP dimensions and stats

Table 2 - Glass fabric dimensions and stats

Table 3 - Twintex dimensions and stats


Table 4 - Large sample stats

Table 5 - Input parameters for varifab program


APPENDIX F - STANDARD DEVIATION VS AREA GRAPHS

Figure 94 - Graph of SrPP standard deviation vs. area

Figure 95 - Graph of Glass fabric standard deviation vs. area


Figure 96 - Graph of Twintex standard deviation vs. area

Figure 97 - Graph of SrPP average standard deviation vs. area


Figure 98 - Graph of Glass fabric average standard deviation vs. area

Figure 99 - Graph of Twintex average standard deviation vs. area


APPENDIX G - COMPRESSION TESTING

John and I ran three seperate compression tests on a single stainless steel spring used

within the current blankholder set-up modified by Farag Abdiwi consisting of eight springs.

Using the computer-aided Zwick Roell machine the top tool was lowered applying a force to

the spring at a designated speed compressing the spring to a desired distance.

At maximum compression the spring length was 17mm.

At full size(uncompressed) the spring length is 55mm.

Outer diameter: 11mm

Inner diameter: 9mm

Wire thickness: 1mm

Force limit of Zwick Roell machine: 2kN

Test 1:

No. of cycles = 3

Speed of Zwick Roell machine = 10mm/m

Desired spring distance = 27.5mm (50% compression)

Max Load = 22N



Test 2:

No. of cycles = 3


Desired spring distance = 38mm (69.1% compression)*MAXIMUM*

Max Load = 30.2N


Test 3:

No. of cycles = 3


Desired spring distance = 13.75mm (25% compression)

Max Load = 10.31N



Overall the graphs show ideal results of force being directly proportional to travel distance

(displacement).

The spring stiffness was found using equation 5

Equation 6 - Spring stiffness equation

X = displacement of spring‟s length

K = spring‟s stiffness

F = The force applied by the single spring on the pressure distribution plate.

Ftotal = The force applied by all eight springs on the pressure distribution plate.

Test No.

Free length

(mm) x (mm) k (Nmm) F(N) Ftotal(N)

1 55 27.5 0.8 22 176

2 55 38 0.794736842 30.2 241.6

3 55 13.75 0.750058182 10.3133 82.5064

Table 6 - Spring stiffness data


APPENDIX H - THERMOFORMING TESTS

Figure 103 - Set-up of testing under Zwick Roell machine

All springs where set to maximum compression for the following testing.

Test 1

1-layer using Armordon sample 1

No. of springs = 8

Total spring force = 241.6N

Set temperatures:-

Blankholder = 140˚C

Radiant heater = 140˚C

Top tool = 140˚C

Botom tool = 140˚C

Actual forming temperatures:-



Top tool = 135˚C

Botom tool = 138˚C



Figure 105 - Outer view of test 1 Figure 106 - Inner view of test 1

Test 2

1-layer using Twintex sample 2

No. of springs = 8


Set temperatures:-



Top tool = 185˚C

Botom tool = 185˚C





Top tool = 184˚C

Botom tool = 179˚C


Due to issues with the program, graphical results for test 2 couldnt be shown.

Test 3


No. of springs = 4


Set temperatures:-



Top tool = 140˚C

Botom tool = 140˚C




Top tool = 138˚C

Botom tool = 133˚C


Test 4

1-layerUsing Twintex sample#3

No. of springs = 4


Set temperatures:-



Top tool = 190˚C

Botom tool = 190˚C




Top tool = 187˚C

Botom tool = 182˚C




Test 5


No. of springs = 4


Set temperatures:-



Top tool = 140˚C

Botom tool = 140˚C




Top tool = 131˚C

Botom tool = 138˚C


Test 6

1-layer Using Twintex sample 4

No. of springs = 4


Set temperatures:-



Top tool = 190˚C

Botom tool = 190˚C




Top tool = 187˚C

Botom tool = 181˚C




Test 7

2-layers using Armordon samples 6 and 7

Orientation of samples at 0˚/90˚

No. of springs = 8


Set temperatures:-



Top tool = 120˚C

Botom tool = 120˚C




Top tool = 123˚C

Botom tool = 117˚C


Test 8

2-layers using samples 8 and 9

Orientation of samples at ± 45˚

No. of springs = 8


Set temperatures:-



Top tool = 120˚C

Botom tool = 124˚C




Top tool = 120˚C

Botom tool = 119˚C



REFERENCES

(Ko, 1989)

(Morozov, 2001)

(Hollaway, 1993)

(James, 2008)

(Long, 2005)

(Zwick UK)

(OCV Reinforcements)

(Wikipedia)

(Glasgow University )

(Richards, 2009)

(Wikipedia)

(McCrum, Buckley, & Bucknall, 1997)

(Powell, 1994)

(Willems, Lomov, Verpoest, & D.Vandepitte, 2008)

(Armordon Company)

(Lomov, et al., 2001)

(Lin, Wang, Long, Clifford, & P.Harrison, 2007)

(Lomov, et al., 2006)

(Willems L. V.)

(Wikipedia)

(Maurel, 2007)

(Pettigrew, 2007)

(Gemmel, 2008)


BIBLIOGRAPHY

Armordon Company. (n.d.). Armordon Home page. Retrieved from Armordon Web site:

http://www.armordon.com/

Gemmel, G. (2008). Design of a heated blank holder for a thermoforming station. Glasgow.

Glasgow University . (n.d.). Mechanical Engineering Department: Glasgow University.

Retrieved from Glasgow University Web site:

http://www.mech.gla.ac.uk/~pharriso/Teaching/index.html

Hollaway, L. (1993). Polymer Composites for Civil and Structural Engineering. Blackie

Academic & Professional.

James, G. (2008). Modern Engineering Mathematics. Pearson .

Ko, T.-W. C. (1989). Textile Structural Composites. Elsevier Science Publishing Company

Inc.

Lin, H., Wang, J., Long, A., Clifford, M., & P.Harrison. (2007). Predictive modelling for

optimization of textile composite forming. Elsevier.

Lomov, S., Huysmans, G., Luo, Y., Parnas, R., Prodomou, A., Verpoest, I., et al. (2001).

Textile composites; modelling strategies. Elsevier.

Lomov, V., Ivanov, D., Verpoest, I., Zako, M., Kurashiki, T., Nakai, H., et al. (2006). Meso-FE

modelling of textile composites: Road map, data flow and algorithms. Elsevier.

Long, P. A. (2005). EPSRC Platform Grant - Processing and Performance of Textile

Composites.

Maurel, L. (2007). Thermoforming station project. Glasgow.

McCrum, N., Buckley, C., & Bucknall, C. (1997). Principles of Polymer Engineering 2nd

edition. Oxford Science Publications.

Morozov, V. V. (2001). Mechanics and Analysis of Composite Materials. Elsevier Science

Publishing Company Inc.

OCV Reinforcements. (n.d.). Twintex: OCV Reinforcements. Retrieved from OCV

Reinforcements Web site: http://www.twintex.com/

Pettigrew, G. (2007). Thermoforming station project. Glasgow.

Powell, P. C. (1994). Engineering with Fibre-Polymer Laminates. Chapman & Hall.

Richards, A. (2009). Design and Manufacture of a Thermoforming Station.

Sulllivan, W., Wicks, E., & Koelling, C. (2009). Engineering Economy. Pearson Internaional

Edition.

Wikipedia. (n.d.). Retrieved from http://en.wikipedia.org/wiki/File:ImageJScreenshot.png

Wikipedia. (n.d.). Image J: Wikipedia. Retrieved from Wikipedia Web site:

http://en.wikipedia.org/wiki/Image_J


Wikipedia. (n.d.). Survival of the fittest: Wikipedia Web site. Retrieved from

http://en.wikipedia.org/wiki/Survival_of_the_fittest

Willems, A., Lomov, S., Verpoest, I., & D.Vandepitte. (2008). Drape-abilitiy characterization

of textile composite reinforcements using digital image correlation. Leuven: Elsevier.

Willems, L. V. Forming simulation of a thermoplastic commingled woven textile on a double

dome.

Zwick UK. (n.d.). Fibre-composites: Zwick UK. Retrieved from Zwick UK Web site:

http://www.zwick.co.uk/en/applications/composites/fiber-composites.html

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