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Statistical Process Optimization Through Multi-Response Surface Methodology
Abstract:In recent years, response surface methodology (RSM) has brought many attentions of many quality engineers in different industries. Most of the published literature on robust design methodology is basically concerned with optimization of a single response or quality characteristic which is often most critical to consumers. For most products, however, quality is multidimensional, so it is common to observe multiple responses in an experimental situation. Through this paper interested person will be familiarize with this methodology via surveying of the most cited technical papers. It is believed that the proposed procedure in this study can resolve a complex parameter design problem with more than two responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready-made standard statistical packages.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083451Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4058
 Box, G.E.P., Hunter, W.G., and Hunter, J.S., 1978, Statistics for Experimenters, John Wiley & Sons, NY.
 Montgomery, D. C., Design and Analysis of Experiments, 2000, Wiley, New York. 5th Edition.
 D.C. Montgomery and G.C. Runger, Applied Statistics and Probability for Engineers, 1994, John Wiley and Sons, Inc, New York.
 J.A. Cornell, A.I. Khuri, Response surfaces: Designs and Analysis, 1987, Marcel Dekker, New York.
 C.Y. Tai, T.S. Chen, M.C. Wu, An enhanced Taguchi method for optimizing SMT processes, Journal of Electronics Manufacturing, vol. 2, 1992, pp. 91-100.
 J.J. Pignatiello, Strategy for robust multi-response quality engineering, IIE Transactions, vol. 25, no. 3, 1993, pp. 5-15.
 K.L. Layne, Method to determine optimum factor levels for multiple responses in the designed experimentation, Quality Engineering, vol. 7, no. 4, 1995, pp. 649-656.
 D.M. Byrne, S. Taguchi, The Taguchi approach to parameter design, Quality Progress, vol. 20, 1987, pp. 19-26.
 N. Logothetis, A. Haigh, Characterizing and optimizing multi-response processes by the Taguchi method, Quality and Reliability Engineering International, vol. 4, no. 2, 1988, pp.159-169.
 R.E. Ilhan, G. Sathyanarayanan, R.H. Storer, T.W. Liao, Off-line multi response optimization of electrochemical surface grinding by a multiobjective programming method, International Journal Machine Tools and Manufacture, vol. 32, no. 3, 1992, pp. 435-451.
 L. Zadeh, Optimality and non-scalar-value performance criteria, IEEE Transactions on Automatic Control, vol. 8, no. 59, 1963, pp. 59-60.
 T. Murata, H. Ishibuchi, H. Tanaka, Multi-objective genetic algorithm and its applications to flowshop scheduling, Computers and Industrial Engineering, vol. 30, no. 4, 1996, pp. 957-968.
 Myers, R.H.; Carter, W.H., Jr. Response Surface Techniques for Dual Response Systems. Technometrics, vol. 15, no. 2, 1973, pp. 301-317.
 Lee-Ing Tong, Chung-Ho Wang, Chih-Chien Chen, Chun-Tzu Chen, Decision Aiding Dynamic multiple responses by ideal solution analysis, European Journal of Operational Research, vol. 156, 2004, pp. 433-444.
 Onur Koksoy, Tankut Yalcinoz, Mean square error criteria to multiresponse process optimization by a new genetic algorithm, Applied Mathematics and Computation, 175, 2006, pp. 1657-1674.
 Lee-Ing Tong, Chung-Ho Wang, Jer-Yiing Houng and Jiao-Yan Chen, Optimizing Dynamic Multiresponse Problems Using the Dual- Response-Surface Method, Quality Engineering, vol. 14, no. 1, 2001-02, pp. 115-125.
 Hung-Chang Liao, Yan-Kwang Chen, A Data Envelopment Analysis Method for Optimizing Multi-Response Problem In The Taguchi Method, Computers and Industrial Engineering Special Issue on Selected papers form the 29th.International Conference on Computers and Industrial Engineering, vol. 46, no. 4, 2004, pp. 591-916
 Liao, H.C., "Using PCR-TOPSIS to optimize Taguchi-s multi-response problem", International Journal of Advanced Manufacturing Technology, vol. 22, 2003, pp. 649-55.
 Hsu, C.M., "An integrated approach to enhance the optical performance of couplers based on neural networks, desirability functions and tabu search", International Journal of Production Economics, vol. 92, 2004, pp. 241-54.
 Fung, C.P. and Kang, P.C., "Multi-response optimization in friction properties of PBT composites using Taguchi method and principle component analysis", Journal of Materials Processing Technology, Vol. 170 No. 3, pp. 602-10.
 Jiju Antony, Raj Bardhan Anand, Maneesh Kumar, M.K. Tiwari, Multiple response optimization using Taguchi methodology and neurofuzzy based model, Journal of Manufacturing Technology Management, vol. 17, vol. 7, 2006, pp. 908-925.
 E. Harrington, The desirability function, Industrial Quality Control, vol. 21, no. 10, 1965, pp. 494-498.
 G. Derringer, R. Suich, Simultaneous optimization of several response variables, Journal of Quality Technology, vol. 12, no. 4, 1980, pp. 214- 218.
 Kun-Lin Hsieh, Lee-Ing Tong, Hung-Pin Chiu and Hsin-Ya Yeh, Optimization of a multi-response problem in Taguchi-s dynamic system, Computers & Industrial Engineering, vol. 49, 2005, pp. 556-571.
 Myers, R.H.; Carter, W.H., Jr. Response Surface Techniques for Dual Response Systems. Technometrics 1973, vol. 15, no. 2, pp. 301-317.
 C.-P. Xu, S.-W. Kim, H.-J. Hwang, J.-W. Yun, Application of statistically based experimental designs for the optimization of exopolysaccharide production by Cordyceps milltaris NG3, Biotechnol. Appl. Biochem, vol. 36, 2002, pp. 127-131.
 M.J. Anderson, H.P. Anderson, Applying DOE to microwave popcorn, Process Ind. Quality, 1993, pp. 30-32.