Journal of the Korean Operations Research and Management Science Society (한국경영과학회지)

The Korean Operations Research and Management Science Society (한국경영과학회)
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Multiresponse Optimization Through A New Desirability Function Considering Process Parameter Fluctuation

공정변수의 변동을 고려한 호감도 함수를 통한 다중반응표면 최적화

  • 권준범 (포항공과대학교 산업경영공학과) ;
  • 이종석 (포항공과대학교 산업경영공학과) ;
  • 이상호 (포항공과대학교 산업경영공학과) ;
  • 전치혁 (포항공과대학교 산업경영공학과) ;
  • 김광재 (포항공과대학교 산업경영공학과)
  • Published : 2005.03.01
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Abstract

A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation which may amplify the variance of response. It is called POE (propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameter setting, a new desirability function is proposed by considering POE as well as distance-to-target of response and response variance. The proposed method is illustrated using a rubber product case in Ribeiro et al. (2000).

Keywords

References

  1. Ames, AE., N. Mattucci, S. Macdonald, G. Szonyi and D.M Hawkins, 'Quality Loss Function for Optimization across Multiple Response Surface,' Journal of Quality Technology, Vol.29(1997), pp.339-346
  2. Box, G.E.P. and K.B. Wilson, 'On the Experimental Attainment of Optimum Conditions,' Journal of the Royal Statistical Society-Series B, Vol.13(1951), pp,1-45
  3. Derringer, G.C., 'A Balancing Act : Optimizing a Product's Properties,' Quality Progress, (June 1994), pp.51-58
  4. Derringer, G.C. and R. Suich, 'Simultaneous Optimization of Several Response Variables,' Journal of Quality Technology, Vol.12, No.4 (1980), pp.214-219
  5. Fathi, Y, 'Nonlinear Programming Approach to the Parameter Design Problem,' European Journal of Operational Research, Vol.53(1991), pp.371- 381 https://doi.org/10.1016/0377-2217(91)90070-C
  6. Harrington, E.C., 'The Desirability Function,' Industrial Quality Control, Vol.4(1965), pp. 494-498
  7. Kim, K and D. Lin, 'Simultaneous Optimization of Multiple Responses by Maximizing Exponential Desirability Functions,' Journal of the Royal Statistical Society-Series C, Vol.43 (2000), pp.311-325
  8. Khuri, A and M Conlon, 'Simultaneous Optimization of Multiple Responses Represented by Polynomial Regression Functions,' Technometrics, Vol.23(1981), pp.363- 375 https://doi.org/10.2307/1268226
  9. Ko, Y, K. Kim and C. Joo. 'A New Loss Function-Based Method for Multiresponse Optimization,' Journal of Quality Technology, Vol.37, No.1(2005) , pp.50-59
  10. Lee, M. and K Kim, 'Expected Desirability Function : Consideration of Both Dispersion and Location Effects in Desirability Function Approach,' Working Paper, Department of Industrial Engineering, POSTECH, 2004
  11. Lin, D. and W. Tu, 'Dual Response Surface Optimization,' Journal of Quality Technology, Vol.27(1995), pp.34-39
  12. Myers, R.H. and D.C. Montgomery, Response Surface Methodology : Process and Product Improvement with Designed Experiments, 2nd Edition, John Wiley & Sons, New York, 2002
  13. Nocedal, J. and S.J. Wright, Numerical Optimization, Springer, 1999
  14. Pignatiello, J., 'Strategies for Robust to Multiresponse Quality Engineering,' lIE Transactions, Vol.25(1993), pp.5-15
  15. Plante, R., Process Capability : a Criterion for Optimizing Multiple Response Product and Process Design, lIE Transactions, Vol.33(2001), pp.497-509
  16. Ribeiro, J.L. and E.A. Elsayed, 'A Case Study on Process Optimization Using the Gradient Loss Function,' International Journal of Production Research, Vol.33, No.12(1995), pp. 3233-3248 https://doi.org/10.1080/00207549508904871
  17. Ribeiro, J,L., F. Fogliatto, and C.S.t. Caten, 'Minimizing Manufacturing and Quality Costs in Multiresponse Optimization,' Quality Engineering, Vol.13, No.2(2000), pp.191-201 https://doi.org/10.1080/08982110108918641
  18. Vining, G.G., 'A Compromise Approach to Multiresponse Optimization,' Journal of Quality Technology, Vol.30(1998), pp.309-313