Journals Books 2687-5527
Latest Issue Archive Future Issues About Us
Conference Proceedings

SETSCI - Volume 4 (6) (2019)
ISAS WINTER-2019 (ENS) - 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences, Samsun, Turkey, Nov 22, 2019

Development of a Dual Response Optimization Model under Non-standard Experimental Design Situations
Akın Özdemir1*
1Bayburt University, Bayburt, Turkey
* Corresponding author:
Published Date: 2019-12-22   |   Page (s): 316-319   |    179     7

ABSTRACT The design of experiments is a highly effective offline quality improvement method to optimize the existing and new processes or products. In the literature, standard experimental situations have been paid a lot of attention. In a number of non-standard experimental situations, special experimental design techniques should be considered in order to conduct an experiment for design factors. Indeed, an I-optimal design, a computer-generated special experimental design, is a good choice to predict the mean and variance responses under non-standard experimental design situations. In this research work, an I-optimal design is selected to generate experimental design points for a non-standard experimental situation. Then, an I-optimal design-based dual response optimization model is proposed in order to obtain an optimum operating condition for design factors while minimizing the process variance as small as possible. Comparison studies are also conducted. Finally, a numerical example is conducted in order to illustrate the effectiveness of the proposed optimization model.
KEYWORDS Quality improvement, Dual Response Model, Non-standard Experimental Design, I-optimal design, Optimization
REFERENCES [1] J. J. Borkowski, “A comparison of prediction variance criteria for response surface designs,” J. Qual. Technol., vol. 35(1), pp. 70-77, 2003.
[2] G. E. P. Box and N. R. Draper, “The choice of a second order rotatable design,” Biometrika, vol. 50(3/4): pp. 335-352, 1963.
[3] N. R. Draper, “Center points in second-order response surface designs,” Technometrics, vol. 24(2), pp. 127-133, 1982.
[4] G. E. P. Box and N. R. Draper, “A basis for the selection of response surface design,” J. Am. Stat. Assoc., vol. 54(287): pp. 622-654, 1959.
[5] T. T. Allen and S. H. Tseng, “Variance plus bias optimal response surface designs with qualitative factors applied to stem choice modeling,” Qual. Reliab. Eng. Int., vol. 27(8), pp. 1199-1210, 2011.
[6] H. H. Toro Díaz HH, H. L. Chan HL, and B. R. Cho, “Optimally designing experiments under non-standard experimental situations,” International Journal of Experimental Design and Process Optimisation, vol. 3(2), pp. 133-158, 2012.
[7] R. H. Myers, D. C. Montgomery, and C. M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley Series in Probability and Statistics), Hoboken, NJ: Wiley, 2016.
[8] G. Taguchi, Introduction to Quality Engineering, UNIPUB/Kraus International: White Plains, NY; 1986.
[9] G. G. Vining and R. H. Myers, “Combining Taguchi and response surface philosophies: a dual response approach,” J. Qual. Technol., vol. 22(1), pp. 38-45, 1990.
[10] Y. Fathi, “A nonlinear programming approach to the parameter design problem,” Eur. J. Oper. Res., vol. 53(3), pp. 371-381, 1991.
[11] E. Del Castillo and D. C. Montgomery, “A nonlinear programming solution to the dual response problem,” J. Qual. Technol., vol. 25(3), pp. 199-204, 1993.
[12] D. K. Lin and W. Tu, “Dual response surface optimization,” J. Qual. Technol., vol. 27(1), pp. 34-39, 1995.
[13] K. A. Copeland and P. R. Nelson, “Dual response optimization via direct function minimization,” J. Qual. Technol., vol. 28(3), pp. 331-336, 1996.
[14] D. M. Steinberg and D. Bursztyn, “Noise factors, dispersion effects, and robust design,” Stat. Sinica, vol. 8(1), pp. 67-85, 1998.
[15] T. J. Robinson and C. M. Anderson-Cook, “A closer look at D-optimality for screening designs,” Qual. Eng., vol. 23(1), pp. 1-14, 2010.
[16] G. J. Park, T. H. Lee, K. H. Lee, and K. H. Hwang, “Robust design: an overview,” AIAA J., vol. 44(1), pp. 181-191, 2006.
[17] M. Arvidsson and I. Gremyr, “Principles of robust design methodology,” Qual. Reliab. Eng. Int., vol. 24(1), pp. 23-35, 2008.
[18] L. Ouyang, Y. Ma, J. H. Byun, J. Wang, and Y. Tu, “An interval approach to robust design with parameter uncertainty,” Int. J. Prod. Res., vol. 54(11), pp. 3201-3215, 2016.
[19] A. Ozdemir and B. R. Cho, “A Nonlinear integer programming approach to solving the robust parameter design optimization problem,” Qual. Reliab. Eng. Int., vol. 32(8), pp. 2859-2870, 2016.
[20] A. Ozdemir and B. R. Cho, “Response surface-based robust parameter design optimization with both qualitative and quantitative variables.” Eng. Optimiz., vol. 49(10), pp. 1796-1812, 2017.
[21] A. Ozdemir and B. R. Cho, “Response surface optimization for a nonlinearly constrained irregular experimental design space,” Eng. Optimiz., vol. 51(12), pp. 2030-2048, 2019.
[22] T. N. Tsai and M. Liukkonen, “Robust parameter design for the micro-BGA stencil printing process using a fuzzy logic-based Taguchi method,” Appl. Soft Comput., vol. 48, pp. 124-136, 2016.
[23] A. Hot, T. Weisser, and S. Cogan, “An info-gap application to robust design of a prestressed space structure under epistemic uncertainties,” Mech. Syst. Signal Pr., vol. 91, pp. 1-9, 2017.
[24] Y. Lu, S. Wang, C. Yan, and Z. Huang, “Robust optimal design of renewable energy system in nearly/net zero energy buildings under uncertainties,” Appl. Energ., vol. 187, pp. 62-71, 2017.
[25] K. Chatterjee, K. Drosou, S. D. Georgiou, and C. Koukouvinos, “Response modelling approach to robust parameter design methodology using supersaturated designs,” J. Qual. Technol., vol. 50(1), pp. 66-75, 2018

SET Technology - Turkey

eISSN  : 2687-5527    

E-mail :
+90 533 2245325

Tokat Technology Development Zone Gaziosmanpaşa University Taşlıçiftlik Campus, 60240 TOKAT-TURKEY
©2018 SET Technology