Taguchi MethodsComputer Aided Engineering Assignment

Department of Computer EngineeringCurtin University of Technology

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Introduction

The following article is an introduction to the basic principles and philosophies behind the Taguchi approach to quality control. It will attempt to give an overview on the Taguchi techniques used in quality control. These include, the concept of Parameter design, Orthogonal arrays and the process of Brainstorming.

Table of ContentsIntroductionThe Taguchi PhilosophyThe Fundamental Taguchi ConceptsThe Total Loss FunctionTotal Loss Function Case StudyTaguchi Quality StrategyTaguchi Techniques using Quality Control

BrainstormingOrthogonal ArraysParameter Design

ConclusionReferencesAuthors

INTRODUCTIONThe word quality cannot have a specific meaning when applied to the manufacturing industry. This is basically because the word quality changes within the context it is being used. For a long time, manufacturing industries in European and American countries have worked from the basis of a tolerance. This tends to suggests that the manufactured item would be passed as acceptable if its quality was within the specified tolerance range.

Taguchi's response to quality differs rather greatly from the goalpost philosophy of the European and American countries. The Japanese implementation of Taguchi's concept

sees them working on the principle that when designing a product, it should be designed with minimum loss, with the relative product being designed as close to the optimum value as is feasibly possible. This would result in the product being manufactured in regards to its life cycle and customer satisfaction from the design stages. It would also mean that less repair work would be required in the long run.

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2.0 Taguchi PhilosophyIt is being increasingly recognised that the high quality of a product or service and the associated customer satisfaction are the key for enterprise survival. Also recognised is the fact that pre-production experiments, assuming properly designed and analysed, can contribute significantly towards quality improvements of a product. A traditional (but still very popular) method of improving the quality of a product is the method of adjusting one factor at a time during pre-production experimentation. In this method, the engineer observes the result of an experiment after changing the setting of only one factor (parameter). This method has the major disadvantages of being very costly and unreliable. The Japanese were the first to realise the potential of another method using statistical design of experiments (SDE) - originally developed by R. Fisher [1]. SDE, in contrast to the one factor method, advocates the changing of many factors simultaneously in a systematic way (ensuring an independent study of the product factors). In either method, once factors have been adequately characterised, steps are taken to control the production process so that causes of poor quality in a product are minimised.

In the manufacturing industry, one area of current development is concerned with the application of modern off-line quality control techniques (pre-production experimentation and analysis) to product and process engineering. Most of the ideas for these quality control techniques are derived from W. E. Deming [2]. These ideas were built upon by Professor Genichi Taguchi. While Deming's main achievements was to convince companies to shift quality improvements to statistical control of the production process [3], Taguchi makes a further step back from production to design, to make a design robust against variability in both production and user environments.

Five major points of the Taguchi quality philosophy are :

In a competitive market environment, continual quality improvements and cost reductions are necessary for business survival.

An important measurement of the quality of a manufactured product is the total loss generated by that product to the society.

Change the pre-production experimental procedure from varying one factor at a time to varying many factors simultaneously (SDE) , so that quality can be built into the product and the process.

The customer's loss due to poor quality is approximately proportional to the square of the deviation of the performance characteristic from its target or nominal value. Taguchi changes the objectives of the experiments and the definition of quality from "achieving conformance to specifications" to "achieving the target and minimising the variability.

A product (or service) performance variation can be reduced by examining the non-linear effects of factors (parameters) on the performance characteristics. Any deviation from a target leads to poor quality.

Taguchi's main objectives are to improve process and product design through the identification of controllable factors and their settings, which minimise the variation of a product around a target response. By setting factors to their optimal levels, a product can be manufactured more robust to changes in operation and environmental conditions. Taguchi removes the bad effect of the cause rather than the cause of a bad effect, thus obtaining a higher quality product.

3.0 The Fundamental Taguchi Concepts The definition of quality given by the Taguchi methodology is customer orientated. Taguchi defines quality in a negative manner - "Quality is the loss imparted to society from the time the product is shipped" [3]. This "loss" would include the cost of customer dissatisfaction that leads to the loss of company reputation. This differs greatly from the traditional producer-orientated definition which includes the cost of re-work, scrap, warranty and services costs as measures of quality. The customer is the most important part of the process line, as quality products and services ensure the future return of the customer and hence improves reputation and increased market share. In general, there are four quality concepts devised by Taguchi :

1. Quality should be designed into the product from the start, not by inspection and screening. (Details of Concept 1)

2. Quality is best achieved by minimising the deviation from the target, not a failure to confirm to specifications. . (Details of Concept 2)

3. Quality should not be based on the performance, features or characteristics of the product. . (Details of Concept 3)

4. The cost of quality should be measured as a function of product performance variation and the losses measured system- wide. (Details of Concept 4)

The above concepts are detailed below:

3.1 Quality Concept One

Quality should be designed into the product from the start, not by inspection and screening. Quality improvements should occur during the design stages of a product or process, and continue through to the production phase. This is often called an "off-line" strategy. Poor quality can't be improved by the traditional process of inspection and screening (on the production line). According to Taguchi, no amount of inspection can put quality back into a product; it merely treats the symptom [3]. Quality concepts should therefore be developed by the philosophy of prevention ; problems are tackled at the source and not down stream. Taguchi emphasises that quality is something that is designed into a product, to make it robust and immune to the uncontrollable environmental factors in the manufacturing phase. This leads us to the next quality concept of minimising variation in a product. Return to Quality Concepts

3.2 Quality Concept Two

Quality is best achieved by minimising the deviation from the target, not a failure to confirm to specifications. The product should be designed so that it is robust or immune to uncontrollable environmental factors - eg. noise, temperature and humidity. This concept mainly deals with actual methods of affecting quality. Reducing variation is the key to improving quality. By specifying a target value for critical parameters, and ensuring manufacturing meets the target value with little deviation, the quality may be greatly improved. Return to Quality Concepts

3.3 Quality Concept Three

Quality is not based on the performance, features or characteristics of the product. Adding features to a product is not a way of improving quality, but only of varying its price and the market it is aimed at. The performance and characteristics of a product, can be related to quality, but should not be the basis of quality. Instead, performance is a measure of product capability. Return to Quality Concepts

3.4 Quality Concept Four

The cost of quality should be measured as a function of product performance variation and the losses measured system-wide. From given design parameters, the deviations from a target are measured in terms of the overall life cycle costs of the product. This includes costs or re-work, inspection, warrantry servicing, returns and product replacement. It is these costs that provide some guidance as to which major parameters need controlling. Return to Quality Concepts

4.0 The Total Loss FunctionIn the early 1980s, Dr Taguchi proposed the following statement relating to the quality of a product:

"Quality is the financial Loss to society after the article is shipped". This is one of the many concepts which was developed by Dr Taguchi. However, the above statement somehow depicts inequality, since a loss to society is not a desirable characteristic. The idea of quality is related to something which is new, beautiful and good which in the engineering sense, must have many features or functionalities. We can rearrange the above definition and still retain the basic concept of Quality to denote a positive attribute as follows: "Quality is the avoidance of financial loss to society after the product is shipped"The important point here is the fact that quality is related to monetary loss and not to any other factors or conditions. Even though the actual loss maybe the loss of functionality to the product, or other losses such as pollution, time, noise, etc., the overall effect is a financial loss. It can also be expanded to include the development, and manufacturing phases of a product.

A poorly designed product begins to impart losses to society from the very start of the production stage, and continues to do so, until steps are taken to improve its functionality and performance. There are two major categories of loss to society with respect to the product quality:

The first category relates to the losses incurred as a result of harmful effects to society. ( e.g. pollution ).

The second category relates to the losses arising because of excessive variation in functional performances.

The second category has a dominant impact to the design stages of the product and will be discussed here.

The conventional method of computing the cost of quality is based on the number of parts rejected and reworked. This method of quality evaluation is incapable of distinguishing between two samples, that are both within the specification limits, but with different distributions of targeted properties.

The following figure illustrates the difference between the conventional method and Taguchi's view of the loss function.

Fig 4.1 Taguchi and The Conventional Loss Functions.

The graph depicts the loss function as a function of deviation from an ideal, or the target value of a given design parameter. Here m represents the target value, or the most desirable value, of the parameter under consideration. This parameter may be critical dimension, colour of the product, surface finish or any other characteristics that contributes to the customer's conception of quality.

UAL and LAL in the figure 4.1 represent upper and lower acceptable limits of a design parameter, respectively. Normally, the product is functionally acceptable if the value of the specified parameter is within the range between the UAL and LAL limits. No societal loss is assumed to occur and the product is accepted for further processing. However, if the product lies outside these limits, it is either discarded or subjected to salvage operations. Every attempt is made to control the manufacturing process to maintain the product within these limits.

However, according to Taguchi, there is no sharp cut-off in the real world. Performance begins to gradually deteriorate as the design parameter deviates from its optimum value. Therefore, he proposed that the loss function be measured by the deviation from the ideal value. This function is continuous as shown by the dotted line in Figure 4.1. Product performance begins to suffer when the design parameters deviate from the ideal or the target value. This loss function takes the following basic quadratic form:

L(x) = k(x - m)²

Where L is the loss in dollars (money), m is the point at the which the characteristic should be set, x is where the characteristic actually is set, and k is a constant that depends on the magnitude of the characteristic and the monetary unit involved.

This basic loss function is used if no other function based on data is available. However, when no market research data is available, the next best option is to use the quadratic loss function.

From the quadratic loss function, the total loss increases parabolically as the deviation from the target value increases. This loss represents a continuous function. This indicates that by making a product within the specification limits, it does not necessary mean that the product is of good quality, since good quality is now defined as keeping the product characteristic on target with low variation. Taguchi emphasis on optimum customer satisfaction is by developing products which meet the target value on a consistent basis. Thus, the most important aspect of Taguchi's quality control philosophy is the minimisation of variation around the target value.

5.0 Total loss function Case Study

To illustrate the advantages of the loss function, a case study which places emphasis on low variations of the target value to minimise cost and loss of the product will be examined.

5.1 Loss function in Developmental Design

The loss function can be used to compute the advantages of being on target with low variation for the distribution of a product characteristic. The following figures describe the relationship between the output voltage and the gain of a power transistor in a regulated power supply circuit. This information is commonly available from transistors manuals and data sheets which are published by the manufacturers of the components.

Fig 5.1.0

From the figure, it can be seen that for a design specification of 115V, it would be necessary to use a transistor with a gain of 20, which would cost approxiamateley 25 cents. The cost of the electronic component depends on the tolerance and the power

handling capability. The 25 cent transistor has a tolerance of ±30% which will be assumed to be three standard deviation away from the target value. Hence one standard deviation is equivalent to 10% in tolerance.

In figure 5.1.0, it can also be seen that the variation in the gain, is transmitted to the variation in the voltage. If a normal distribution of the gain is assumed , then a normal distribution of voltage will be obtained. While it is centred around the target of 115V, it is also possible to have a voltage as low as 109 and as high as 121V. If a higher tolerance transistor is used then the tolerance would be reduced. However, this would mean a higher cost in the overall product. To produce the true regulated power supply could then cost up to four times the original design cost.

However, a more cost effective approach is to use the portion of the voltage Vs gain curve that is less steep. In this way, the large variation in gain to the output voltage is not transmited. This part of the curve is around the 40-HFE point. Even with the ±30% tolerance range, the variation in voltage around this point is ± 2 Volts. Hence, it is seen that designing in the constant region of the relationship will reduce the variation.

The distributions for transistor A and transistor B are shown in the following diagram:

Figure 5.1.1 Distribution Curve for Transistor A and B

The figure shows the output of both transistors, with the output of transistor B shifted and superimposed over the output of transistor A. From inspection, transistor A, with is wide and flat distribution curve, will exhibit a large variation in its output voltage. Whereas transistor B will exhibit the opposite characteristics.

To calculate the expected loss, integration of the area of the loss function with the area of the distribution must be preformed. This may be done numerically, point by point, or by combing the distribution function with the loss function.

The results shows that the expected loss (EL) which is related to the standard deviation k and the location of the average of the distribution to be:

EL = k[ (AL - m )² + S² ]

The losses for the two transistors can then be calculated as follows:

For transistor A:

EL = 0.444[ ( 115 -115)² + 2² ]EL = $1.78For transistor B: EL = 0.444[ (124 -115)² + 0.33² ]EL = $36.01

The loss for transistor B is excessive because it is 9 volts of the target. This loss can be minimised by using a higher current limiting resistor which would shift the entire voltage versus gain curve as follows:

Figure 5.1.2 New Gain Vs Voltage Curve

The loss for transistor B now becomes:

EL = 0.444[ (115 -115 )² + 0.33² ]EL = $0.048

Hence the above case study has shown how Taguchi quality engineering by design methodology can be put into practice to minimise the output variation. Since loss is a function of the variation, reducing the variation will ultimately reduce the loss.

6.0 Taguchi Quality Strategy

Taguchi viewed poor quality as a lack of consistency in the ingredients of the product. Because of this inconsistency, the manufactured products may not satisfy the

quality(product variation) and specification(target value) demands of the customers. The product mean value is off target and the variation around the mean is large(Total Loss function.) Thus, methodologies and techniques have been developed by Taguchi to reduce the elements of variation:

Deviation from the targeted value, these include the total loss function and parameter design techniques and

Variations with respect to others in the group. Which is dealt with in Orthogonal Arrays

6.1 Quality Strategy

Taguchi observed that variation in product specification, is the primary cause for rejection of a product. The cure for this quality loss is to reduce the variation, and effort should be directed toward zero variation, zero defect. The Taguchi approach for reducing variation in the product is a two-step process :

Manufacture the product in the best manner most of the time. (less deviation from the target)

Produce all products as identically as possible. (less variation between the products)

This approach is a general methodology by Taguchi in quality control. Since the quality of a product, or process, may be difficult to define in quantitative terms.

Quality is what the customers perceive it to be.

Thus, quality varies from customer to customer, and from product to product. But, in general, lack of product consistency is the major factor in the perception of poor quality. Taguchi quality strategy is to improve quality in the product design stage by :

Making the design less sensitive towards influence of uncontrollable factors.

Optimizing the product design.

Taguchi Techniques in Quality ControlThe following techniques are used in Taguchi quality control.

Brainstorming

Orthogonal Arrays

Parameter Design

Brainstorming

The brainstorming stage is perhaps the most important stage of the whole Taguchi procedure. At this stage, clear statements of the problems are established, the objectives, the desired output characteristics, the methods of measurement and the appropriate experiments are designed. Brainstorming is an activity which promotes group participation, encourages creative thinking and generates many ideas in a short period of time.

The experiment stage is where resulting data is examined and an interpretation of results is conducted. The variability control factors are determined and the optimal values are selected so that the variability of the product is minimised. The target control parameters are also determined during this stage, and their settings are selected for a desired mean response. Predictions are also made at this stage of the Taguchi procedure.

To confirm the predicted results, a confirmatory experiment is conducted. This is an essential stage in order to confirm that the new parameter settings do provide optimal performance. Confirmation removes any concerns of the incorrect choice of parameters, experimental design or assumptions of product response. After which, corrective action can then be followed. A study of the new system should take place and any improvements require standardising. After successful improvements, the optimal settings will, from then on, become a standard.

Orthogonal Arrays

A product can be designed and manufactured based on a set of specifications demanded by the customer. Each specification has a required parameter value or values, which the

manufactured product must be able to satisfy. Thus, the manufacturing process must be capable of producing the designed parameters, which is termed as the targeted value, according to the customer's specifications. Unfortunately in reality, manufacturing processes are far from ideal. Products manufactured tends to give a distribution that has a mean value slightly different from the targeted value. Thus, one of the main technique used in Taguchi's quality control is to reduce the variation around the targeted value. According to Taguchi, the quality of a group of products can be improved by achieving its end product specifications distribution as close to the target value as possible. This concept can be realised by designing and building the quality into the product itself. Hence, Taguchi employs design experiments using specially constructed table, known as "Orthogonal Arrays (OA)" to treat the design process, such that the quality is build into the product during the product design stage.

Discussions of the various aspects of Orthogonal Arrays(OA) can be found in the following links:

The Approach of OAComparison to the Conventional ApproachOA AnalysisApplication of OAAdvantages and Disadvantages of OA

The Approach of Orthogonal Arrays

An experiment during the product design stages, involves the materials used in manufacturing the experimental product which affects the final quality outcome. Factors such as variations in the chemical ratio, the level of ingredients used, and how the product is formed together, will contribute to the variation in the targeted value of the final product.

Orthogonal Arrays(OA) are a special set of Latin squares, constructed by Taguchi to lay out the product design experiments. By using this table, an orthogonal array of standard procedure can be used for a number of experimental situations. Consider a common 2-level factors OA as shown in table 1 below :

Table 1. An orthogonal array of L8.

The array is designated by the symbol L8, involving seven 2-level factors, zeros and ones. The array has a size of 8 rows and 7 columns. The number (zeros/ones) in the row indicate the factor levels (be it a fluid viscosity, chemical compositions, voltage levels, etc.) and each row represents a trial condition. The vertical columns represents the experimental factors to be studied. Each of the assigned columns contain four levels of zeros(0), and four levels of ones(1), these conditions, can combine in four possible ways, such as (0,0), (0,1), (1,0), (1,1,), with 27 possible combinations of levels. The columns are said to be orthogonal or balanced, since the combination of the levels occurred the same number of times, when two or more columns, of an array are formed. Thus, all seven columns of an L array, are orthogonal to each other.

The OA facilitates the experimental design process by assigning factors to the appropriate columns. In this case, referring to table 1, there are at most seven 2-level factors, these are arbitrarily assigned factors A, B, C, D, E, F, and G to columns 1, 2, 3, 4, 5, 6, 7 and 8 respectively, for an L8 array. From the table, eight trials of experiments are needed, with the level of each factor for each trial-run as indicated on the array. The experimental descriptions are reflected through the condition level. For example, 0 may indicates the factor is not applied, and 1 represents the factor that is fully applied. The factors may be variation in chemical concentration, material purity, mechanical pressure and so on. The experimenter may use different designators for the columns, but the eight trial-runs will cover all combinations, independent of column definition. In this way, the OA assures consistency of the design carried out by different experimenters. The OA also ensures that factors influencing the end product's quality are properly investigated and controled during the initial design stage.

Comparison to the Conventional Approach

The method of investigating all possible combinations and conditions in an experiment(involving multiple factors) is traditionally known as factorial design. The factorial design is based on the theory, that for a full factorial design, the number of possible designs, N (number of trails), is :

N = Lm

where L = number of levels for each factor m = number of factors involved

Thus, if the qualities for a given product depended on three factors, the variation of 2-level conditions can be limited to a number of design experiments of 23, which equals 8 trials. If the same method is carried out on the conditions based on table 1, for 27, then 128 trials would be needed. Moreover, the method of level combinations laid out is not specified in the factorial design process. This may lead to different results on the same experimental subject each time a trial is conducted. Thus, Taguchi's Orthogonal Array is able to simplify and standardised the factorial designs, in a manner that will yield consistent data results and similar outcomes, even though the trials are carried out by

different experimenters. Thus, two different investigators will have similar conclusion and a standard design methodology.

The concept of standard design methodology and uniform results through OA analysis is very important, since it allows the manufacturer to produce two products of the same quality standards, using the same materials, but with differences in the manufacturing process. This is possible since, through OA experimental analysis, the quality influencing factors of a product can be identified, controlled, and hence compensated during the early product design stage. Thus, the quality of the product itself, rather than depending on the manufacturing process, is able to "adapt" to the manufacturing process.

Taguchi's OA is considered to be more superior than the traditional factorial design method since :

The factorial design experiment is not efficient in handling large number of factor variables.

Taguchi's OA experiments, on a product design yield similar and consistent results, although the experiment can be carried out by different experimenters.

The OA table allows determination of the contribution, of each quality influencing factor.

OA allows easy interpretation of experiments with a large number of factors.

Orthogonal Array Analysis

The results obtained from the orthogonal array are then analysed to achieved the following objectives :

To estimate the contribution of individual quality influencing factors in the product design stage.

To gain the best, or optimum, condition for a process, or a product, so that good quality characteristics can be sustained.

To approximate the response of the product design parameters under the optimum conditions.

The contribution of individual quality influencing factors, is the deciding key of the control to be enforced on the product design. A commonly applied statistical treatment - The Analysis of Variance (ANOVA) - is used to analyse the results of the OA experiment in product design, and to determine how much variation each quality influencing factor has contributed. By studying the main effects of each of the factors, the general trends of the influence factors, towards the product, or process, can be characterised. The characteristics can be controlled, such that a lower, or a higher, value in a particular

quality influencing factor, produces the preferred result. Thus, the levels of influencing factors , to produce the best results, can be predicted.

There are two different methodologies in carrying out the complete OA analysis. A common approach is to analyse the average result of repetitive runs, or a single run, through ANOVA analysis as discussed. Another approach, which is a better method for multiple runs, is to use signal (S) to noise (N) ratio (S/N) for the same steps in the analysis. The objective of S/N analysis, is to determine the most optimum set of the operating conditions, from variations of the influencing factors within the results. The signals, in this case, will be those factors which are invariant. The noise are those influencing factors which are active. Details regarding the methods of OA results analysis using ANOVA and signal-to-noise ratio can be referred to article [1].

Application of Orthogonal Array

Taguchi's OA analysis is used to produce the best parameters for the optimum design process, with the least number of experiments. The OA manages to transform a quality concept into the product design. The OA method is able to treat quality influencing factors at discrete levels, and often this method save time, and indirectly reduces the cost of hardware testing. Thus, the OA is usually applied in the design of engineering products, test and quality development, and process development. All applications involved have a common objective, that is to use Taguchi's OA method to build the quality into a product at the initial design stage.

Advantages and Disadvantages of Orthogonal Array

The advantages of OA, are such that they can be applied to experimental design involving a large number of design factors. The OA design experiments, analysis, and cost guidance based on the loss function have made this approach more attractive. The limitation of OA is that it can only be applied at the initial stage of the product/process design system. There are some situations whereby OA techniques are not applicable, such as a processes involving influencing factors that vary in time and cannot be quantified exactly.

Parameter DesignWhen a product is said to be optimum, it implies that the product has achieved most of the target values set out by the quality measure. Taguchi tries to reduce the variation around the target, not by eliminating the cause of variation, since totally removing the cause of variation can be expensive in an industrial setting. The variation is reduced by

adjusting the levels of the influencing factors, and controlling the variation of other factors, which is the approach of the parameter design technique.

To illustrate this concept, consider an example involving only one factor.

An electronic device which controls the luminance of a light bulb, was influenced significantly by the applied voltage. The investigator, wishing to select the right voltage, investigate the luminance of the light bulb at several input voltages. The influence of the voltage variation on the luminance of the light bulb can be shown in the figure .

Fig 3. Voltage variation on luminance

If the working range of the applied voltage is between VC and VD as shown in figure 3, obviously, voltage B to B" would give a variation around targeted value B, but would minimally affect the intensity of the luminance. Voltage B is attractive, since small fluctuations in the applied voltage ( B to B" ), will have no significant effects on the quality of the luminance as perceived by the user. So, through the parameter design process, the investigator is able to choose a parameter, which is least influenced by the variation factors.

Thus, the performance characteristics of a product can be affected by two factors, namely design parameters, and sources of noise. The design parameters are those nominal product values which are selected by the engineers. Sources of noise are those variables that cause the deviations of actual nominal product value. The objective of parameter design experiments are to identify the settings of the design parameters, at which the noise factor influence is at its minimum. The detail analysis on the parameter design experiments can be found in reference [1].

In summary, the following points were investigated:

The concept of poor quality from the Taguchi's perspective.

The types of variations possible.

Taguchi's quality characteristics.

The quality characteristics evaluation criteria.

Taguchi's quality strategy in the product design process.

ConclusionsThis article has investigated the Taguchi philosophy in quality control. And discussed the difference between Taguchi's new concepts, in quality control, and the traditional goalpost philosophy. It has shown that in Taguchi's view, the measurement of the quality of a manufactured product, is the total loss generated by that product to the society. Taguchi defines the quality control of a given product as "achieving the targeted value and minimising the variability around the target value", instead of "achieving conformance to the specification".

The Taguchi quality strategies discussed are derived from several experiment techniques used in the product design stage to implement the quality concepts into the product. Taguchi proposed the idea of brainstorming to define the processes and factors which create the product, followed by a series of experimental implementation, outlined by Taguchi's quality concepts, to determine the optimum parameters to be implemented in the parameter design stage of the product.

Taguchi suggested the use of Orthogonal Arrays(OA), in the experiment implementation stage, to investigate and predict noise factors which might affect the quality of a given product during the product manufacturing phase. Through OA experiment analysis, the quality influencing factors of a product can then be identified, controlled, and hence compensated during early product design stage.

Glossary Terms

Total Loss Function The loss function is a function of deviation from the ideal value of a given design parameter.

Orthogonal Arrays

An important analysis tool in designing quality into the design process.

Parameter Design The approach whereby the influential factors are adjusted to control the variation

of other factors.

REFERENCES.[1] Howard Gitlow, Shelly Gitlow, Alan Oppenheim and Rosa Oppenheim.1989. "Tools And Methods For The Improvement Of Quality". Von Hoffman Press, Inc. Boston.

[2] Claire G. Meisonheime. 1992 "Improving Quality : A Guide To Effective Programs". Aspen Publishers, Inc. Gaitherburg. .

[3] A.V. Feigenbaum.1991 "Total Quality Control". 3rd edition. Mcgraw Hill, Inc. New York.

[4] Ranjit Roy. "A Primer On The Taguchi Method". Van Nostrand Reinhold. New York. 1990.

[5] Thomas B. Barker. 1990 "Engineering Quality By Design : Interpreting The Taguchi Approach". Marcel Dekker, Inc. New York. .Figure 5.1.0(14), 5.1.1(16), 5.1.2(13)

[6] Genichi Taguchi.1988 "Introduction To Quality Engineering : Designing Quality Into Products And Processes". Asian Productivity Organisation. Tokyo. .

[7] R. Kacker. 1986 "Taguchi's Quality Philosophy : Analysis And Commentary". Quality Progress. December. PP 21-29.

[8] Kwok-Leung Tsui, 1988, "Strategies for Planning Experiments using Orthogonal Arrays and Confounding Tables", Quality and Reliability Engineering International, Vol. 4. pp 113-122.

[9] K. Dehnad, 1989 "Quality Control, Robust Design, and the Taguchi method" , Wadsworth & Brooks/Cole, California.

[10] 1988"Introduction to Taguchi methods", Engineering, Jan.

[11] G. Taguchi,1988, "Introduction to Quality Engineering: Designing Quality into Products and Processes", Asian Productivity Organisation, Japan.

[12]T.B. Barker, 1990 "Engineering Quality by Design", Marcel Dekker, Inc. N.Y.

[13] R. Roy, 1990"A primer on the Taguchi Method", Van Nostrand Reinhold, N.Y.

[14] C.Maynard,1995 "Quality Engineering", Curtin University Handout Notes for Computer Aided Engineering.

AuthorsThe Authors of this paper are all currently studying at Curtin University Of Technology in Western Australia. We are Final Years Students doing the Information and Communications Engineering Degree.

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Research for article provided byYeow Nam NgDon BlackKhanh LuuLatest Revision done by :Hartono Tedjokusumo (2nd yr Computer Technology)

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