AUTHORS: Ikuo Tanabe

Download as PDF

ABSTRACT: In recent years, the Design of Experiments (hereafter, DOE) and the Taguchi Methods have been used to decide optimum processing conditions with narrow dispersion and achieve robust designs. However, when large interactions between several control factors are present, since they behave as confounding variables, the estimation accuracy is significantly reduced and making practical use of the DOE and Taguchi Methods can be extremely difficult in some cases. As a common countermeasure, calculation accuracy is confirmed by comparing, through the S/N ratio and sensitivity results, the best and worst gain results. This can be of great harm in terms of time and labor and, if the difference between the best and worst gain results is large, could result in the Taguchi Methods estimations being ignored. In this study, a tool for the easy determination of control factor interactions in the DOE and the Taguchi Methods was developed and evaluated. Then a software of the tool was a developed and evaluated under several mathematical models. It was concluded that: (1) a usable tool for the easy determination of control factor interactions in the DOE and the Taguchi Methods was developed; (2) The tool was able to determine control factor interactions in DOE or the Taguchi Methods.

KEYWORDS: Design of Experiments, Taguchi Methods, innovation, innovative tool, software, optimum condition

REFERENCES:

[1] Fujikawa S., Optimum parameter design using the Taguchi method for Finite-element analysis of 3D forging deformation, Journal of Japan Society for Technology of Plasticity, Vol. 40, No. 466, 1999, pp. 1061-1065 (in Japanese).

[2] Mizutani J., Hamamoto S., Mukaiyama T., Tanabe I and Yamada Y., Functional evaluation of a micro-polishing process by a combined method of physical and chemical treatments, Journal of Quality Engineering Society,Vol. 10, No. 1, 2002, pp. 84-90 (in Japanese).

[3] Makino T., Optimization of exhaust port using computer simulation, Proceedings of 13th Quality Engineering Society Conference, 2005, pp.6-9 (in Japanese).

[4] Tatebayashi, K., Computer Aided Engineering Combined with Taguchi methods, Proceeding of the 2005 Annual Meeting of the Japan Society of Mechanical Engineering, No. 05-1 Vol. 8, 2005, pp. 224-225.

[5] Sakamoato H., Tanabe I. and Takahashi S., Development for sound quality optimisation by Taguchi methods, Journal of Machine Engineering, Vol. 15, No.2, 2015, pp. 69-81.

[6] Sugai H., Tanabe I., Mizutani J., Sugii S. and Katayama S., Predictions of optimum forming conditions in press forming using Taguchi method and FEM simulation, Transactions of Japan Society of Mechanical Engineers (Series C), Vol. 72, No.721, 2006, pp. 3044-3050 (in Japanese).

[7] Yano H., Calculation Method for Taguchi Methods, Nikkan Kogyo Shinbun, 2006, pp. 9-149 (in Japanese).

[8] Arizono I., Kanagawa A., Ohta H., and Watanabe K., Variable samplig plans for normal distribution indexed by Taguchi’s loss function, Naval Research Logistic, Vol. 44, No.6, 1997, pp. 591-603 (in Japanese).

[9] Tanabe I., Iyama T., Yanagisawa T., and Mizutani J., Development of simulation system on CAE without trial working for products (Manufacture of high precision system using dispersion of error factors), Transactions of Japan Society of Mechanical Engineers (Series C), Vol. 75, No.749, 2009, pp. 203-209 (in Japanese).