experimental design for process optimization

An application ofexperimental designfor processoptimisation

Hefin Rowlands,

Jiju Antony and

Graeme Knowles

Introduction

Of key importance in any step towards

improvement in operation and efficiency is

the move from a reactive approach to a

proactive approach (Kolarik and Pan, 1991).

The traditional reactive approach is about

detecting and correcting problems that

already exist. It is a historic approach and is

always focussed on past events. It manifests

itself as an inspection based philosophy where

quality is inspected into products at the

expense of high rework. Under this approach

no effort is put in to understand and solve the

cause of the rework or rejects since the

company is always under pressure to deliver

usually by fire fighting the problems. It can be

very difficult to break out of this scenario.

A proactive approach places emphasis on

measurement, analysis, prediction and

prevention. A greater emphasis is also needed

at the design stage in order to prevent defects

and errors appearing later in the product's life

cycle. To this end, quality must be designed

into products and processes rather than

inspected into them. An important and well-

recognised tool to achieve this goal is the

Taguchi method (Ross, 1988). The Taguchi

approach is aimed at minimising variation in

product and process performance and thereby

achieves continuous quality improvement of

products and processes.

Higher education has a role to play to

educate industry in the application of

powerful problem solving techniques such as

Taguchi methods. In other words, it is

important to have a proper communication

between the industrial and academic world

for tackling new challenging problems in

industry. This paper briefly outlines the

results of Taguchi case studies carried out by

part-time engineering students at the

University of Wales College Newport

(UWCN). The results of the study have

provided a greater stimulus among the

engineering fraternity for the wider

application of such applied statistical methods

in industry. Typical applications of the

Taguchi method include:. minimising the defects on a steel strip that

has been electrolytically coated with a tin

based coating;. to optimise the production process of

retaining a metal ring in a plastic body in

a braking system;. to investigate an over-adjustment

problem in a braking system;

The authors

Hefin Rowlands is Head of the Department of

Engineering, University of Wales College Newport,

Newport, UK.

Jiju Antony and Graeme Knowles are senior teaching

fellows at the International Manufacturing Centre,

University of Warwick, Coventry, UK

Keywords

Optimization, Taguchi methods, Design of experiments,

United Kingdom

Abstract

Dr Taguchi is a Japanese engineer and an international

quality consultant who has made breakthrough

improvements in product and process quality through the

use of statistical design of experiments (SDOE). The

Taguchi method became popular in the West in the 1980s

as a means to design robust products and processes.

Although many companies and industries have used the

method with success, the real benefits of the approach

were not realised and fully understood in many cases.

This lack of success could be attributed to a number of

factors, but mainly because the experiments were treated

in isolation and not integrated into a continuous

improvement strategy. This paper briefly presents the

results of the application of the Taguchi methodology in

the UK industry. The paper also illustrates the application

of the Taguchi method for optimising the production

process of retaining a metal ring in a plastic body in a

braking system.

Electronic access

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quality.asp

The current issue and full text archive of this journal is

available at

http://www.emerald-library.com

Techniques

78

The TQM Magazine

Volume 12 . Number 2 . 2000 . pp. 78±83

# MCB University Press . ISSN 0954-478X

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. to improve the weld strength of an

electrolytic welding process in the joining

of steel coils;. optimisation of a spot welding process;. to improve the resistance of conductive

plastic tracks.

Overview of the Taguchi method

In today's modern global market, quality is a

key issue for companies wishing to keep their

customers and remain competitive in

business. Quality can no longer be simply the

result of an inspection process, but needs to

be a company-wide management philosophy.

Quality improvement programmes are very

much part of the strategic planning process of

successful companies (McKeown, 1992).

Alongside the strategic planning issues are the

importance of design and the idea of

designing quality into products and processes.

By investing more effort in design, quality can

be designed into a product, thus reducing the

need to rely on inspection to ensure quality.

However, the task of optimising the design

becomes increasingly difficult as products

become more complex. A method which has

received much attention, both positive and

negative, over recent years as a quality

improvement tool is the Taguchi method.

The Taguchi method is based on statistical

design of experiments and is applied at the

parameter design stage to establish optimum

process settings or design parameters. The

following are the objectives of Taguchi's

parameter design:. making products and processes

insensitive to environmental variations

(e.g. humidity, ambient temperature,

dust, electrical supply voltage);. making product and processes insensitive

to manufacturing variations or

imperfections;. making products insensitive to product

deterioration (reliability degradation, tool

wear, etc.); and. making products insensitive to unit-to-

unit variations (component-to-

component variation, shift-to-shift

variations, machine-to-machine

variations, material-to-material variation

and so on).

Taguchi's philosophy of quality improvement

is to place effort into reducing variation in

products and processes at source. Rather than

reduce variation in individual components by

specifying tighter tolerances (tolerance

design), Taguchi's method addresses the

issue by careful selection of design parameters

(called factors). Reduction in variation in the

final product is achievable without the

additional cost of specifying tighter tolerance

components. This approach of parameter

design results in a more robust design that is

capable of withstanding variations from

unwanted sources such as raw materials,

components, manufacturing processes and

the environment.

The Taguchi philosophy and its associated

experimental design method has been

extensively used in the manufacturing

environment to improve production

processes, for example a metal injection

moulding process (Fox and Lee, 1990) and a

plasma deposition process in device

fabrication (Logothetis et al., 1990). In such

environments, careful planning of the

experiment is important if the full benefits of

the experimental methods are to be realised

(Coleman and Montgomery, 1993). Other

examples of manufacturing related

applications of the Taguchi method include

scheduling (Dooley and Mahmoodi, 1992)

and optimisation of a robot's performance

capability for continuous path operation (Wu

et al., 1991).

Despite the successful applications of the

Taguchi method, a wider use of the approach

and its associated techniques is only possible

by gaining a better understanding of the

method and its analysis. The successes and

failings of the Taguchi approach to parameter

design have been widely discussed (Nair,

1992; Lochner, 1991; Pignatiello and

Ramberg, 1991; Antony, 1996). In summary,

Taguchi's main successes have been to

emphasise the importance of quality in design

and to simplify the use of experimental design

as a general purpose tool for quality

engineers. Amongst the many criticisms of the

Taguchi method is the use of the signal-to-

noise (S/N) ratio as a performance measure

statistic. S/N ratio measures the functional

robustness of products and processes. The S/

N ratios have been criticised as providing

misleading results in certain cases. Although

the classical experimental design has a much

wider appeal than the Taguchi method, the

Taguchi method does provide the practical

engineer with a useful starting point for

quality improvement. This is fundamentally

because the former is more focused on the

79

An application of experimental design for process optimization

Hefin Rowlands, Jiju Antony and Graeme Knowles

The TQM Magazine

Volume 12 . Number 2 . 2000 . 78±83

statistical aspects whereas the latter is

primarily focused on the engineering aspects

of quality. The beauty of Taguchi method lies

in the fact that it integrates statistical methods

into the powerful engineering process.

Potential applications of Taguchimethods in industry

Taguchi methods have intensive applications

in many manufacturing companies. The

following section illustrates the applications of

Taguchi methods in various manufacturing

and service industry sectors. The

manufacturing sectors are classified into

plastics, automotive, metal fabrication,

process and electronics and semi-conductors.

Plastics. Process type: injection moulding process.. Nature of the problem: high scrap and

rework rate due to excessive process

variability.. Size of the experiment: eight trials or runs.. Benefits: zero defects were achieved.

Annual savings were estimated to be

above £40,000.

Automotive. Product type: diesel injector.. Nature of the problem: high rework rate.. Size of the experiment: sixteen trials or

runs.. Benefits: annual savings were estimated to

be over £10,000.

Metal fabrication. Type of process: welding.. Nature of the problem: low welding

strength of tin coated wires to a

connector.. Size of the experiment: sixteen trials or

runs.. Benefits: process capability index

increased from 0.50 to 2.5. Annual

savings were estimated to be £16,000.

Process. Type of process: chemical process.. Nature of the problem: low process yield.. Size of the experiment: eight trials.. Benefits: process yield was improved by

over 10 per cent.

Electronics and semi-conductors. Type of process: wire bonding process.. Nature of the problem: low wire pull

strength and therefore large customer

returns were experienced by the

company.. Size of the experiment: 16 trials.. Benefits: the average pull strength has

increased by 30 per cent and therefore

customer returns have decreased from 18

per cent to nearly 2 per cent. Annual

savings were estimated to be over

£30,000.

Applications in service industry. Minimising the time to respond to

customer complaints.. Minimising errors on service orders. Reducing the service delivery time to

customers.. Reducing the length of stay in an

emergency room in hospitals and health

care institutions.. Comparing competitive strategies of

launching new products.

Getting into Taguchi

In order to apply Taguchi methods in

industry, one may require planning,

engineering, communication, statistical and

teamwork skills. Moreover, right people and

right environment are crucial for the effective

application of Taguchi methods for tackling

process and product quality problems. The

participation and commitment of top

management are also vital for the successful

implementation. The following key points

must be taken into account when introducing

Taguchi methodology into design and

production:. Do you get excessive variability in your

processes?. Do you understand your product and

processes?. Is your process performance on target?. Is your product performance robust

under various environmental conditions?. Do you have to set up tolerances on the

critical parts to minimise variability?

Role of Taguchi methods in total qualitymanagement

There are many definitions of quality but the

definition of quality proposed by Dr Taguchi

(Kolarik, 1995) is more relevant in terms of

working towards target performance of

product/process. Indeed working towards the

target performance reflects the continuous

improvement attitude. Although not explicitly

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stated, Deming's definition implies that the

needs of the customer may change. Also with

the view that new customers may be added in

the future, this suggests a dynamic definition

of quality. Quality issues do not stay still and

therefore we must be prepared to adapt our

ideas and views on quality in order to meet

the ever-changing needs of our customers.

This idea is consistent with the approach of

continuous improvement.

The ethos of total quality management is

continuous improvement. In order to improve

our product/process quality, we need to

measure appropriate quality characteristic(s),

which are most critical to our customers. In

otherwords, we cannot simply manage any

process without measurement. The role of

Taguchi methods in TQM is to identify and

optimise the critical quality characteristics

which affect the final product/process

performance.

Taguchi case study

This section details one of the Taguchi

experiments that the students have

implemented at their workplace. In many

cases this would have been the first

application of such a technique in the

company. The results of the study have

provided a greater stimulus in terms of the

wider application of the Taguchi methods in

other core processes. The objective of the

study was to optimise the production process

of retaining a metal ring in a plastic body in a

braking system by a hot forming method.

The production process consisted of a

heated die, which was forced down by air

pressure onto a valve body forming a plastic

lip into which a retaining metal ring was

inserted. Although the process was fairly

straightforward it was felt that the maximum

strength of the product was not being

achieved. A test rig was designed to simulate

the production process and to enable a series

of experiments to be performed. A Taguchi

style experiment was carried out to identify

the process variables that would provide the

greatest and consistent pull-out strength. This

was tested on a standard tensometer.

A brainstorming session consisting of chief

development engineers, senior design

engineers, plastic engineers, identified a list of

factors which were thought to affect the pull-

out strength. These factors were then

categorised as control or noise factors as

shown below:. Control factors: die temperature, hold

time, batch number, maximum force

during hot forming and force application

rate.. Noise factors: dimensional variation of

valve body, depth of stake, material

variation and rate of hot forming.. Control/noise factor: strain rate during pull-

out.

All five control factors were used for the

experiment. The strain rate pull-out was used

as a noise factor and controlled at two levels

for the experiment to simulate a varying load

on the product when in use. Due to the

difficulties and expense of manufacturing

valve bodies to different dimensions and with

variations in material, it was decided not to

include these in the experiment.

The levels for each factor were selected

systematically by the engineers based on their

knowledge and experience of the process. The

levels chosen are shown in Table I. As a

reference for the results of the experiment,

based on the experience of the engineers, the

following levels were considered to give the

best performance: A2, B1, C1, D1, E1.

The experiment was carried out using a

modified L8 orthogonal array (Taguchi and

Konishi, 1987) to account for the four levels

of factor A. The S/N calculations are based on

a larger the better S/N ratio.

Table II illustrates the average pull-out

values and average S/N ratio values

corresponding to each level of the chosen

factors.

Figure 1 illustrates the mean response

graph which simply plots the average pull-out

strength values at each level of the selected

factors. Figure 2 illustrates the S/N ratio plot

which basically shows the average S/N ratio

values at each factor level.

In order to determine which of the effects

are statistically significant, it was decided to

perform the Analysis of Variance (ANOVA).

ANOVA is a powerful technique which

Table I Factor levels

Factor Level 1 Level 2 Level 3 Level 4

A Die temperature (deg. C) 180 200 220 240

B Hold time (sec) 5 15

C Batch no. 1 2

D Maximum force (KN) 6 7

E Force application rate (KN/sec) 5 1

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sub-divides the total variation in the data into

useful and meaningful components of

variation. The results of the ANOVA are

shown in Tables III and IV respectively.

In order to determine the significant effects,

the calculated F-ratios are compared with the

tabled F-ratios. For the S/N analysis, from F

tables, F0.05,1,2 = 18.51, F0.05,3,2 = 19.16,

F0.10,1,2 = 8.53 and F0.10,3,2 = 9.16. This

indicates that only factor B is statistically

significant at the 90 per cent confidence level.

For the mean analysis, F0.05,1,17 = 4.45,

F0.05,3,17 = 3.20, F0.10,1,17 = 3.03 and

F0.10,3,17 = 2.44. This clearly indicates that

factors A, B, C and D have significant effect

on the mean pull-out strength.

Determination of optimal factorsettings

The optimal settings are those which provide

the best process/product performance based

on the obtained data from the experiment. In

this case, the objective was to maximise the

pull-out strength with minimum variation. As

only factor B has significant effect on the S/N

ratio, we have chosen level 2 of factor B, as it

provides a higher S/N ratio. It is important to

note that the higher the S/N ratio, the greater

the process robustness and product

performance. Having analysed the S/N ratio,

the next step was to determine the factor

settings that yielded the maximum pull-out

strength. In this case, we have again chosen

those factor settings with higher mean pull-

out strength values. The analysis of mean

pull-out strength values has provided the

following factor settings:. Factor A ± level 4;. Factor B ± level 2;. Factor C ± level 2; and. Factor D ± level 2.

The predicted average pull-out strength

obtained based on the optimal factor settings

is approximately 4.5KN. A confirmation run

gave results close to the prediction. This

shows a significant improvement on the pull-

out strength compared to the average pull-out

strength value of 3.26, i.e. an increase of

about 38 per cent.

Table II Response table ± mean pull-out strength and mean S/N ratio

Factor levels Average (KN) Average S/N ratio

A1 2.72 8.44

A2 2.93 9.27

A3 3.29 10.30

A4 4.12 12.26

B1 2.92 9.09

B2 3.60 11.04

C1 3.15 9.65

C2 3.38 10.49

D1 3.15 9.68

D2 3.38 10.45

E1 3.26 10.02

E2 3.27 10.12

Figure 1 Mean reponse plot for the experiment

Figure 2 Mean S/N ratio plot for the experiment

Table III Results of ANOVA on the raw data

Factor Sum of squares

Degree of

freedom Mean square F-ratio

Per cent

contribution

B 2.77 1 2.77 87.08 25.35

A 6.87 3 2.29 71.88 62.63

C 0.34 1 0.34 10.55 2.81

D 0.29 1 0.29 9.25 2.43

Pooled error 0.54 17 0.03 ± ±

Total 10.82 23 0.47 ± 100

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Conclusions

This paper has illustrated a practical

application of Taguchi method in industry.

The study has shown a significant

improvement (approximately 38 per cent) in

pull-out strength and thereby encouraged the

company in a wider application of the method

in other processes. The main feature, which

resulted in the success of this study, was the

careful experimental planning. A wide range

of personnel was involved in the

brainstorming exercise to identify the most

appropriate parameters for the experiment.

The potential benefit of the Taguchi method

to industry is vast. The paper demonstrates

the potential applications of Taguchi methods

in both manufacturing and service industry

sectors. It is important to bear in mind that

the successful application of Taguchi

methods requires planning, engineering,

statistical, communication and teamwork

skills. Management commitment and active

participation are crucial for the

implementation of such methods for tackling

product and process quality problems. The

real benefits are achievable when the method

is used in conjunction with other tools and

techniques and integrated into the continuous

improvement programme of the company.

For companies which have not yet started on

this path, now is the time to start. It is hoped

that this paper will stimulate more companies

to take up the challenge and implement a

programme of Taguchi experiment as part of

their continuous improvement programme.

References

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systematic approach to planning for a designed

industrial experiment'', Technometrics, Vol. 35

No. 1, pp. 1-27.Dooley, K.J. and Mahmoodi, F. (1992), `̀ Identification

of robust scheduling heuristics: application of

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Table IV Results of ANOVA on the S/N ratio data

Factor Sum of squares

Degree of

freedom Mean square F-ratio

Per cent

contribution

B 7.64 1 7.64 12.52 26.56

A 16.28 3 5.43 8.90 54.41

D 1.42 1 1.42 2.33 3.04

Pooled error 1.22 2 0.61 ± 15.99

Total 26.56 7 3.8 ± 100

Commentary

A detailed exploration of the Taguchi method and its role in process optimization.

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experimental design for process optimization

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