Teruo Mori¹*, Soichiro Tanabe², Yoshiyuki Iwanaga³, Izuru Sadamatsu⁴, Takekazu Yamamoto⁵, Yuji Matsuoka⁵, Munetoshi Noda⁶, Shun Sato⁷, Tetsuya Sato⁷, Yoshiyuki Ukai⁸

¹Mori Consulting Office, Japan
²Chuo University, Research and Development Initiative, Japan
³Shikoku Polytechnic College, Machine Production Division, Japan
⁴Alps Alpine Corp, Fine Production Division, Japan
⁵Team Shizuoka for Optimizing, Japan
⁶Toyoda Gosei Chemical Corp, Japan
⁷UNIVANCE Corp, Machine Finishing Division, Japan
⁸Hoshizaki Corp, Electro controlling Division, Japan

*Corresponding Author:Teruo Mori, Mori Consulting Office, Japan.

Received: February 14, 2022; Published: March 22, 2022

Abstract

When optimizing using an orthogonal array, it is desirable to consider the various relationships between factors and assign many factors. Two-level orthogonal array can be assigned many factors. Three-level orthogonal array has the advantage of obtaining intermediate information on the level. For this reason, mixed type orthogonal arrays L18 (2137), L36 (211313) [1,2], etc are still used today. The response of these mixed typed orthogonal arrays is logarithmically converted to the SN ratio and sensitivity for optimization. This way also is called Taguchi methods [1].

Parameter design with a two-step procedure for predicting the optimum conditions is performed from this SN ratio and sensitivity with factor effect graph.

(1): The number of experiments will be increased proportional to the number of layout factors in the mixed type orthogonal array.

(2): In the first step of reducing the variation, select the combination of the levels that maximum levels the SN ratio of the factor effect graph as the optimum condition.

The confirmation value (b) had been expected as the optimum condition with minimized the variation. But, there are the problem that this confirmation value (b) is worse than the best value (a) of the SN ratio of the orthogonal array used for estimation" will be appeared for 62% of cases [3,4].

So, the prediction accuracy for the optimum conditions are poor. In order to improve these problems (1) and (2), this paper report will propose a new method to apply the conference matrix to the layout and the coefficient figure to the analysis to the row data. This report provides an easy-to-understand explanation that the conference matrix [5-11] reduces the number of experiments and improves prediction accuracy using the Coefficient of variation, especially for researchers.

We are sure our proposed ways to reduce the experimental number and the period and cost almost to 1/3~1/2 with the higher accuracy for optimizing, so we will recommend as the specific ways to solve the subjects of the Sustainable Development Goals. Especially it will contribute to create the effective countermeasures to Global Warning that has been requested immediately to take the actions to reduce the increasing temperature.

Keywords:Optimizing; Mixed Type Orthogonal Array; Conference Matrices; Coefficient Of Variation; Coefficient Graph; Regression Analysis; Taguchi Methods; Global Warning; Sustainable Development Goals

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Citation

Citation: Teruo Mori., et al. “Application Conference Matrices for Parameter Design".Acta Scientific Nutritional Health 6.4 (2022): 103-113.

Copyright

Copyright: © 2022 Teruo Mori., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.