Design of process parameters using robust design techniques and multiple criteria optimization

This paper presents a methodology for the design of products/processes that makes use of the concepts of robust design and the techniques of multiple criteria optimization for simultaneously optimizing many quality characteristics. First, a systematic approach to the selection of an efficient matrix experiment for a design problem is presented. Appropriate performance measures are obtained so that their joint optimization results in the minimum variation of product characteristics. The use of transformations is highlighted as a useful technique to statistically validate the design process. A discrete multiple criteria optimization algorithm that incorporates the methods of dominated approximations and reference points is developed to obtain nondominated solutions for the design problem. The methodology is illustrated using a case study gleaned from the literature. >

[1]  George E. P. Box,et al.  Signal-to-noise ratios, performance criteria, and transformations , 1988 .

[2]  C. Nachtsheim.  Orthogonal Fractional Factorial Designs , 1985 .

[3]  Andrzej P. Wierzbicki,et al.  The Use of Reference Objectives in Multiobjective Optimization , 1979 .

[4]  Krishna R. Pattipati,et al.  Robust local experiments in sequential process improvement , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[5]  Krishna R. Pattipati,et al.  Multiple criteria optimization for the robust design of quantitative parameters , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[6]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[7]  Elsayed A. Elsayed,et al.  Optimal levels of process parameters for products with multiple characteristics , 1993 .

[8]  Neil R. Ullman,et al.  Signal-to-noise ratios, performance criteria, and transformations , 1988 .

[9]  G. Geoffrey Vining,et al.  Taguchi's parameter design: a panel discussion , 1992 .

[10]  H. MyersR.,et al.  Response surface methodology: 19661988 , 1989 .

[11]  Robert G. Launsby,et al.  Understanding industrial designed experiments (2nd ed.) , 1989 .

[12]  André I. Khuri,et al.  The Analysis of Multiresponse Experiments: A Review. , 1988 .

[13]  Yacov Y. Haimes,et al.  Multiobjective Decision Making: Theory and Methodology , 1983 .

[14]  N. Logothetis.  Box-Cox transformations and the Taguchi method , 1990 .

[15]  B. Roy.  The outranking approach and the foundations of electre methods , 1991 .

[16]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[17]  André I. Khuri,et al.  Response surface methodology: 1966–1988 , 1989 .

[18]  Saeed Maghsoodloo.  The Exact Relation of Taguchi's Signal-to-Noise Ratio to His Quality Loss Function , 1990 .

[19]  D. Hawkins.  Multivariate quality control based on regression-adjusted variables , 1991 .

[20]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[21]  A. Bendell,et al.  Taguchi methods : applications in world industry , 1989 .

[22]  C. R. Rao,et al.  Factorial Experiments Derivable from Combinatorial Arrangements of Arrays , 1947 .

[23]  Raghu N. Kacker,et al.  Performance measures independent of adjustment , 1987 .

[24]  Janusz Majchrzak.  A Methodological Guide to the Decision Support System DISCRET for Discrete Alternatives Problems , 1989 .