IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
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The Development of Taguchi and Response Surface Method Combined Model

Taguchi-RSM 통합모델 제시

  • 이상복 (서경대학교 산업공학과) ;
  • 김연수 (연세대학교 응용통계학과) ;
  • 윤상운 (연세대학교 응용통계학과)
  • Received : 2010.02.16
  • Accepted : 2010.07.30
  • Published : 2010.09.01
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Abstract

Taguchi defined a good quality as 'A correspondence of product characteristic's expected value to the objective value satisfying the minimum variance condition.' For his good quality, he suggested Taguchi Method which is called Robust design which is irrelevant to the effect of these noise factors. Taguchi Method which has many success examples and which is used by many manufacturing industry. But Optimal solution of Taguchi Method is one among the experiments which is not optimal area of experiment point. On the other hand, Response Surface Method (RSM) which has advantage to find optimal solution area experiments points by approximate polynomial regression. But Optimal of RSM is depended on initial point and RSM can not use many factors because of a great many experiment. In this paper, we combine the Taguchi Method and the Response Surface Method with each advantage which is called Taguchi-RSM. Taguchi-RSM has two step, first step to find first solution by Taguchi Method, second step to find optimal solution by RSM with initial point as first step solution. We give example using catapults.

Keywords

References

  1. Jang, Hyung-Cheol (1998), Method of Taguchi DOE and Analysis of Response Surface Method, Journal of Pyungtack University, 10(2), 287-304.
  2. Jung, Hey-Jin and Koo, Bon-Cheol (2007), Optimization of Robust Design Model using Data Mining, Journal of the Society of Korea Industrial and Systems Engineering, 30(2), 99-105.
  3. KSA (1991), Lecture of Quality Engineering(Quality Engineering Application Analysis) 1-7, Korean Standard Association.
  4. Lee, Kihoon (2000), Introduce of Robust Statistics Methods in Data Miming, Journal of Industry Management in Cheonju University, 18(1), 1-10.
  5. Leon, R. V., Shoemaker, A. C., and Kackar, R. N. (1987), Performance Measures Independent of Adjustment(with discussion), Technometrics, 29(3), 253-265. https://doi.org/10.2307/1269331
  6. Myers, R. H. and Carter, W. H. (1973), Response Surface Techniques for Dual Response Systems, Technometrics, 15(1), 301-307. https://doi.org/10.1080/00401706.1973.10489044
  7. Myers, R. H., Khuri, A. I., and Vining, G. (1992) Response Surface Alternatives to the Taguchi Robust Parameter Design Approach, The American Statistics, 46(1), 131-139.
  8. Park, Byung-Jun and Cho, Byung-Yup (1999), A Method of Simultaneous Optimization of Central Composite Design in Using Robust Design, Journal of Statistical Institute in Chosun University, 1(1), 61-79.
  9. Ree, Sangbok (2000), Easy Taguchi Method-From Basic to Application, Eretech.
  10. Ree, Sangbok (2004), DOE with Using Minitab, Eretech.
  11. Ree, Sangbok (2006), Taguchi Method Application wih Using Minitab version, 14, Eretech.
  12. Vining, G. G. and Myers, R. H. (1990), Combining Taguchi and Response Surface Philosophies; A Dual Response Approach, Journal of Quality Technology, 22(1), 38-45.
  13. Yoo, Jungbin (1992), A Study of Taguchi Quality Engineering, Journal of Application of Science in Seowon University, 1(1), 123-133.