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Application of SVR, Taguchi loss function, and the artificial bee colony algorithm to resolve multiresponse parameter design problems: a case study on optimizing the design of a TIR lens

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Abstract

Multiresponse parameter design problems have become increasingly important and have received considerable attention from both researchers and practitioners since there are usually several quality characteristics that must be optimized simultaneously in most modern products/processes. This study applies support vector regression (SVR), Taguchi loss function, and the artificial bee colony (ABC) algorithm to develop a six-staged procedure that resolves these common and complicated parameter design problems. SVR is used to model the mathematical relationship between input control factors and output responses, and the ABC algorithm is used to find the optimal control factor settings by searching the well-constructed SVR models in which the Taguchi loss function is applied to evaluate the overall performance of a product/process. The feasibility and effectiveness of the proposed approach are demonstrated via a case study in which the design of a total internal reflection (TIR) lens is optimized while fabricating an MR16 light-emitting diode lamp. Experimental results indicate that the proposed solution procedure can provide highly robust design parameter settings for TIR lenses that can be directly applied in real manufacturing processes. Comparisons with the Taguchi method reveal that the Taguchi method is an undesirable and inappropriate method for resolving multiple-response parameter design problems, while the ABC algorithm can search the solution spaces in continuous domains modeled via SVR instead of in the limited discrete experiment levels, thus finding a more robust design than that obtained by the traditional analysis of variance. Consequently, the proposed integrated approach in this study can be considered feasible and effective and can be popularized as a useful tool for resolving general multiresponse parameter design problems in the real world.

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Acknowledgments

The author would like to thank the National Science Council, Taiwan, ROC for supporting this research under Contract No. NSC 101-2221-E-159-009. He would also like to thank Raymond Huang for his invaluable assistance during this study.

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  1. Department of Business Administration, Minghsin University of Science and Technology, 1 Hsin-Hsing Road, Hsin-Fong, Hsinchu, 304, Taiwan, ROC

    Chih-Ming Hsu

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Hsu, CM. Application of SVR, Taguchi loss function, and the artificial bee colony algorithm to resolve multiresponse parameter design problems: a case study on optimizing the design of a TIR lens. Neural Comput & Applic 24, 1293–1309 (2014). https://doi.org/10.1007/s00521-013-1357-3

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