Acta Scientific Nutritional Health (ASNH)(ISSN: 2582-1423)

Research Article Volume 6 Issue 4

Application Conference Matrices for Parameter Design

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.

However, this method has two problems (1) and (2).

(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

References

  1. Genichi Taguchi. “Quality Engineering Course 1. "Quality Engineering at the Development and Design Stage". Japanese Standards Association (1988): 138.
  2. Genichi Taguchi. "3rd Edition Design of Experiments" Maruzen Chapter 4 (1976): 3.3.
  3. Teruo Mori., et al. “First stage of robust design: prediction (reproducibility) accuracy of maximum signal-to-noise ratio and 62% problem". 48th Quality Control Society Kansai University (2018): 11-17.
  4. Teruo MORI. “The Inspection Report for the SN-Ratio Optimum Prediction Accuracy of Taguchi Two Step Design”. Acta Science Nutritional Health (2020): 88-98.
  5. V “Theorem of 2n-Terminal Networks with Application to Conference Telephony”. Electric Communication 26 (1950): 231-244.
  6. Goethals JM and Seidel JJ. “Orthogonal Matrices with Zero Diagonal”. Canadian Journal of Mathematics 19 (1967): 1001-1010.
  7. Xiao L., et al. “Constructing Definitive Screening Design Using Conference Matrices”. Journal of Quality Technology1 (2012): 2-8.
  8. Satoshi Suzuki., et al. “Additional trials in a three-level screening experiment using a conference matrix". Quality 4 (2014): 65-74.
  9. Kentaro Tanaka. “Conference Matrix and Design of Experiments”. Quality1 (2016): 51-54.
  10. Teruo Mori., et al. “Parameter design by orthogonal planning using conference matrix and two-level noise”. Quality 3 (2019): 66-78.
  11. NA Balonin and Jennifer Seberry. “A Review and new symmetric conference matrices”. Research online University of Wollongong (2014): 2-7.
  12. Teruo Mori., et al. “Study of confounding evaluation between two factors of mixed type orthogonal array L18 (2137) in parameter design”. Quality1 (2020): 71-78.
  13. Madhav S Phadke. “Quality Engineering using Robust design”. AT and T Bell Laboratories Prentice Hall USA. Chapter 7 (1989).
  14. RC Bose and KA Bush. “Orthogonal arrays of strength two and three”. Annals of Mathematical Statistics 23 (1952): 208-524.
  15. Genichi Taguchi. “Parameter Design in New Product Development". Japanese Standards Association (1984).

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.




Metrics

Acceptance rate30%
Acceptance to publication20-30 days
Impact Factor1.316

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