Taguchi's Parameter Design: A Panel Discussion

Nair, Vijayan N.; Abraham, Bovas; MacKay, Jock; Box, George E. P.; Kacker, Raghu N.; Lorenzen, Thomas J.; Lucas, James M.; Myers, Raymond H.; Vining, G. Geoffrey; Nelder, J. A.; Phadke, M. S.; Sacks, Jerome; Welch, William J.; Shoemaker, Anne C.; Tsui, Kwok Leung; Taguchi, Shin; Wu, Changbao

doi:10.1080/00401706.1992.10484904

Cited by 439 publications

(157 citation statements)

References 58 publications

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“…In more general settings, Taguchi and Wu (1985) used the SNR as an indicator of ''closeness to target'' or performance criteria (Box and Bisgaard 1987;Nair 1992;Nair and Pregibon 1986). For continuously distributed outcome variable, the SNR is defined as:…”

Section: Introductionmentioning

confidence: 99%

Signal-to-noise ratio (SNR) as a measure of reproducibility: design, estimation, and application

Elkum

Shoukri

2008

Health Serv Outcomes Res Method

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This paper proposes the use of signal-to-noise ratio (SNR) as another index of a measurement's reproducibility. We derive its maximum likelihood estimation and discuss confidence interval construction within the framework of the one-way random effect model. We investigate the validity of the approximate normal confidence interval by Monte-Carlo simulations. The paper also derives the optimal allocation for the number of subject and the number of repeated measurements needed to minimize the variance of the maximum likelihood estimator of the SNR. We discuss efficiency in estimation and cost considerations for the optimal allocation of the sample resources. The approach is illustrated on two examples: one from MRI data and the other on the WHO immunization coverage data.

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Section: Introductionmentioning

confidence: 99%

Signal-to-noise ratio (SNR) as a measure of reproducibility: design, estimation, and application

Elkum

Shoukri

2008

Health Serv Outcomes Res Method

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“…Phadke [4] summarizes the robust design of the Taguchi method into the following eight steps: (1) identify the main function, side effects and failure modes, (2) identify noise factors and testing conditions for evaluating the quality loss, (3) identify the quality characteristic to be observed and the objective function to be optimised, (4) identify the control factors and levels, (5) design the matrix experiment and define the data analysis procedure, (6) conduct the matrix experiment, (7) analyze the data, transfer the quality characteristic into signal-to-noise ratio, maximise signal-to-noise ratio to determine optimum levels for the control factors, and predict performance under these levels, and (8) conduct the verification experiment and plan future actions.…”

Section: Introductionmentioning

confidence: 99%

“…A number of investigations [5][6][7][8] have been conducted to understand the ideas, and to examine and improve the statistical techniques used in the Taguchi method after his introduction of robust design to several major American industries. Pignatiello [9] discusses the parameter design strategy of Taguchi for designing and improving products and processes from a statistical point of view.…”

Section: Introductionmentioning

confidence: 99%

The alpha risk of the Taguchi method for STB type quality characteristic

2004

Int J Adv Manuf Technol

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One of the basic assumptions of experimental design is that the error variances are equal for all treatment combinations. On the contrary, one of the basic assumptions of Taguchi's parameter design is that the error variances are not equal for the treatment combinations. Thus, the significant parameter levels are found by maximising the signal-to-noise ratio of the quality characteristic. In the analysis of variance (ANOVA) of the signal-to-noise ratio, the combination of column effects to better estimate error variance is referred to as pooling. Taguchi has suggested the strategy of "pooling-up". When using the poolingup strategy, there will be the tendency to make the alpha mistake more often. If the assumption of the former is true, then there is an alpha risk that judges some factor being significant when in fact it is not. The purpose of this paper is to investigate the alpha risk of the Taguchi method for the smaller-the-better (STB) type problem by simulation. The results show that the alpha risk is very high for several orthogonal arrays.

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“…In this study, electronic circuit design component values are an example of the first type of inputs which are tolerance requirements. Quality engineering uses robust design to reduce product or process quality variation by two actions [1,19,21]. One is parameter design, which adjusts the parameter value so that the output quality characteristic is less sensitive to variation.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Parameter and Tolerance Design for an Electronic Circuit Via Computer Simulation and Statistical Optimization

Jeang

2003

The International Journal of Advanced Manufacturing Technology

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Conventionally, parameter design is carried out prior to tolerance design for economic reasons. However, a combined parameter and tolerance design may be completed in one stage for further cost reduction and quality improvement. In this study, a simultaneous optimization of component parameter and component tolerance for an electronic circuit design is realized via computer simulation and Response Surface Methodology (RSM). The approach first generates a set of outputs (experimental data) through computer simulation based on various combinations of parameter and tolerance levels as inputs. Then these outputs are converted into a total cost as a response value before applying RSM for statistical analysis and optimization. The response value(total cost) includes quality loss, tolerance cost, and failure cost, which reflect the combined effect of the assigned parameter and tolerance values being assigned. In this study, computer simulation can be carried out by two methods. The first method uses Monte Carlo Simulation to generate the outputs with the condition that the transfer function is known. The second method uses a PSpice simulation program to generate outputs without necessarily knowing the transfer function. The results provide designers with the optimal component parameter and tolerance values, and the critical components, and the response function -all of which are very important during the early design activities as they enable designers to have a repeated application, accurate feedback and appropriate suggestions, particularly under uncertain design conditions.

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Taguchi's Parameter Design: A Panel Discussion

Cited by 439 publications

References 58 publications

Signal-to-noise ratio (SNR) as a measure of reproducibility: design, estimation, and application

Signal-to-noise ratio (SNR) as a measure of reproducibility: design, estimation, and application

The alpha risk of the Taguchi method for STB type quality characteristic

Simultaneous Parameter and Tolerance Design for an Electronic Circuit Via Computer Simulation and Statistical Optimization

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