Comparison of performances of five prospective approaches for the multi-response optimization

The Taguchi method of experimental design is widely used for optimization of process performance. However, this method has been developed to optimize single-response processes. But, in many situations, the engineers are required to determine the process settings that can simultaneously optimize multiple responses. In the recent past, researchers have proposed several systematic procedures for multi-response optimization. Most of these methods use complicated statistical/mathematical models and are, therefore, not easily comprehendible to the engineers who do not have a strong background in mathematics. Only a few methods, e.g. weighted signal-to-noise (WSN) ratio, Grey relational analysis, multiple-response signal-to-noise ratio, VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian), and weighted principal component methods, use relatively simpler procedures. In this paper, the computational procedures for these five methods are standardized. Three sets of experimental data are analyzed using these standardized procedures and the predicted optimization performances of the five methods are compared. The results show that no method can give better optimization than the WSN method.

[1]  P. Shahabudeen,et al.  Simultaneous optimization of multi-response problems in the Taguchi method using genetic algorithm , 2006 .

[2]  Douglas M. Hawkins,et al.  Quality Loss Functions for Optimization across Multiple Response Surfaces , 1997 .

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

[4]  R. Ramakrishnan,et al.  Multi response optimization of wire EDM operations using robust design of experiments , 2006 .

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

[6]  Shankar Chakraborty,et al.  Optimisation of multiple responses for WEDM processes using weighted principal components , 2009 .

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

[8]  G. Geoffrey Vining,et al.  Combining Taguchi and Response Surface Philosophies: A Dual Response Approach , 1990 .

[9]  Hm Hsu,et al.  A genetic algorithm for the optimal design of microwave filters , 1999 .

[10]  Hung-Chang Liao,et al.  Multi-response optimization using weighted principal component , 2006 .

[11]  Douglas C. Montgomery,et al.  A Nonlinear Programming Solution to the Dual Response Problem , 1993 .

[12]  Margaret A. Nemeth,et al.  Applied Multivariate Methods for Data Analysis , 1998, Technometrics.

[13]  Lee-Ing Tong,et al.  Optimization of multiple quality responses involving qualitative and quantitative characteristics in IC manufacturing using neural networks , 2001, Comput. Ind..

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

[15]  Chao-Ton Su,et al.  Optimization of TQFP molding process using neuro-fuzzy-GA approach , 2003, Eur. J. Oper. Res..

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

[17]  R. N. Kackar.  Off-Line Quality Control, Parameter Design, and the Taguchi Method , 1985 .

[18]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[19]  Lee-Ing Tong,et al.  Optimization of multi-response processes using the VIKOR method , 2007 .

[20]  Muh-Cherng Wu,et al.  An enhanced Taguchi method for optimizing SMT processes , 1992 .

[21]  C. Wang,et al.  Optimizing multiple quality characteristics via Taguchi method-based Grey analysis , 2007 .

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

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

[24]  Chao-Ton Su,et al.  Applying neural network approach to achieve robust design for dynamic quality characteristics , 1998 .

[25]  Jiju Antony,et al.  Multi‐response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis , 2000 .

[26]  T. R. Bement,et al.  Taguchi techniques for quality engineering , 1995 .

[27]  Amar Patnaik,et al.  Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method , 2007 .

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

[29]  Lee-Ing Tong,et al.  A NOVEL MEANS OF APPLYING NEURAL NETWORKS TO OPTIMIZE THE MULTIRESPONSE PROBLEM , 2001 .

[30]  A. Haq,et al.  Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method , 2008 .