Joint Optimization of Mean and Standard Deviation Using Response Surface Methods

Köksoy, Onur; Doganaksoy, Necip

doi:10.1080/00224065.2003.11980218

Cited by 104 publications

(59 citation statements)

References 17 publications

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“…The errors are typically assumed to be NID(0, σ 2 ). Myers, Khuri and Vining 29 (henceforth referred to as MKV) pointed out that the model proposed by Welch et al 27 could be used to formulate dual response surfaces. The choice of optimum conditions x * could then be obtained via the joint exploration of the response surfaces generated by the mean and variance of the response.…”

Section: Single Model Containing Control and Noise Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Robust Parameter Design: A Review

Robinson

Borror

Myers

2003

Quality & Reliability Eng

185

107

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Parameter design is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The assumption is that there are both controllable factors (control variables) and uncontrollable/difficult to control factors (noise variables) that operate on the quality characteristic of a process. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic while minimizing the variation imposed on the process via the noise variables. Parameter design was popularized in the mid 1980s by Japanese quality consultant Genichi Taguchi. A panel discussion edited by Nair summarized important responses to Taguchi's ideas and methodology. In the last decade, there have been many applications and new developments in this important area. This review paper focuses largely on the work done since 1992, but a historical perspective of parameter design is also given.

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Section: Single Model Containing Control and Noise Variablesmentioning

confidence: 99%

“…The response surface for the process mean is still given by (27) but the process variance is now represented by…”

Section: Use Of Generalized Linear Modelsmentioning

confidence: 99%

Robust Parameter Design: A Review

Robinson

Borror

Myers

2003

Quality & Reliability Eng

185

107

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show abstract

“…This approach shows that, while allowing some process bias, the resulting process variance would be less than or, at most, equal to the variance of the dual response approach; hence, the MSE approach provides better (or at least equal) settings of design factors than previous approaches. Further research in this area has been discussed in the literature [19][20][21][22][23][24].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming

Kovach

Cho

Antony

2006

Int J Adv Manuf Technol

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The well-known quality improvement methodology, robust design, is a powerful and cost-effective technique for building quality into the design of products and processes. Although several approaches to robust design have been proposed in the literature, little attention has been given to the development of a flexible robust design model. Specifically, flexibility is needed in order to consider multiple quality characteristics simultaneously, just as customers do when judging products, and to capture design preferences with a reasonable degree of accuracy. Physical programming, a relatively new optimization technique, is an effective tool that can be used to transform design preferences into specific weighted objectives. In this paper, we extend the basic concept of physical programming to robust design by establishing the links of experimental design and response surface methodology to address designers' preferences in a multiresponse robust design paradigm. A numerical example is used to show the proposed procedure and the results obtained are validated through a sensitivity study.

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“…A membership function in fuzzy set theory is used to measure the decision-maker's degree of satisfaction concerning the mean and standard deviation 61 of the responses. Kim and Cho (2002), Tang and Xu (2002), Koksoy and Doganaksoy (2003) presented various extensions for solving the dual response approach.…”

Section: 1mentioning

confidence: 99%

Robust Parameter Design Using the Weighted Metric Method—The Case of ‘the Smaller the Better’

Ardakani¹,

Noorossana²,

Niaki³

et al. 2009

International Journal of Applied Mathematics and Computer Science

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In process robustness studies, it is desirable to minimize the influence of noise factors on the system and simultaneously determine the levels of controllable factors optimizing the overall response or outcome. In the cases when a random effects model is applicable and a fixed effects model is assumed instead, an increase in the variance of the coefficient vector should be expected. In this paper, the impacts of this assumption on the results of the experiment in the context of robust parameter design are investigated. Furthermore, two criteria are considered to determine the optimum settings for the control factors. In order to better understand the proposed method and to evaluate its performances, a numerical example for the case of 'the smaller the better' is included.

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Joint Optimization of Mean and Standard Deviation Using Response Surface Methods

Cited by 104 publications

References 17 publications

Robust Parameter Design: A Review

Robust Parameter Design: A Review

Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming

Robust Parameter Design Using the Weighted Metric Method—The Case of ‘the Smaller the Better’

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