Optimization of robust design for multiple quality characteristics

The Taguchi method has recently been widely applied to variability reduction for increased quality and lower cost in many different industries. The traditional Taguchi method was focused on optimizing a single quality characteristic. A real problem in a product or process possesses multiple quality characteristics. The optimization methods of multiple quality characteristics design have thus become crucial issues for industry. Several articles have presented approaches to optimizing the parameter design with multiple quality characteristics. Few have focused primarily on optimizing the correlated multiple quality characteristics problem. This research presents an approach to optimizing the correlated multiple quality characteristics with asymmetric loss function by a mathematical programming model. The goal is minimizing the total average quality loss for experiments. This proposed procedure is illustrated with data from nine previously published articles. A numerical analysis of the model is provided and the results are compared with those of prior approaches.

[1]  C. L. Lin,et al.  Optimisation of the EDM Process Based on the Orthogonal Array with Fuzzy Logic and Grey Relational Analysis Method , 2002 .

[2]  Saeed Maghsoodloo,et al.  Quadratic loss functions and signal-to-noise ratios for a bivariate response , 2001 .

[3]  Chao-Ton Su,et al.  The optimization of multi‐response problems in the Taguchi method , 1997 .

[4]  G. Geoffrey Vining.  A Compromise Approach to Multiresponse Optimization , 1998 .

[5]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[6]  Liang-Hsuan Chen.  Designing robust products with multiple quality characteristics , 1997, Comput. Oper. Res..

[7]  M. Hamada,et al.  Analyzing Experiments with Correlated Multiple Responses , 2001 .

[8]  Joseph J. Pignatiello,et al.  STRATEGIES FOR ROBUST MULTIRESPONSE QUALITY ENGINEERING , 1993 .

[9]  Jiju Antony,et al.  Optimization of multiple responses using a fuzzy-rule based inference system , 2002 .

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

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

[12]  N. Logothetis,et al.  Characterizing and optimizing multi‐response processes by the taguchi method , 1988 .

[13]  John Stufken,et al.  Taguchi Methods: A Hands-On Approach , 1992 .

[14]  Chao-Ton Su,et al.  OPTIMIZING MULTI‐RESPONSE PROBLEMS IN THE TAGUCHI METHOD BY FUZZY MULTIPLE ATTRIBUTE DECISION MAKING , 1997 .

[15]  Ful-Chiang Wu.  Optimisation of Multiple Quality Characteristics Based on Percentage Reduction of Taguchi’s Quality Loss , 2002 .

[16]  F. N. David,et al.  LINEAR STATISTICAL INFERENCE AND ITS APPLICATION , 1967 .

[17]  P.B.S. Reddy,et al.  Taguchi’s methodology for multi‐response optimization , 1998 .

[18]  C. Su,et al.  Multi-response robust design by principal component analysis , 1997 .

[19]  Kwok-Leung Tsui.  Robust design optimization for multiple characteristic problems , 1999 .

[20]  A. Khuri,et al.  Simultaneous Optimization of Multiple Responses Represented by Polynomial Regression Functions , 1981 .