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Sequential experimentation approach for robust design
Author(s) -
Ríos Armando J.,
Guerrero Guadalupe
Publication year - 2018
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2332
Subject(s) - taguchi methods , design of experiments , control (management) , competitor analysis , orthogonal array , computer science , engineering , machine learning , artificial intelligence , mathematics , statistics , management , economics
The Taguchi approach for robust design has been a common practice in industrial experimentation for many years. However, these designs possess serious disadvantages such as the inability to estimate control × control interactions. In this article, we propose the application of the R3 algorithm as an augmentation tool for Taguchi experiments. The augmented Taguchi designs were compared with its competitors, mixed resolution designs, and D‐optimal augmentation, using performance indicators . The results showed that Taguchi designs augmented with the R3 algorithm are capable of estimating control × control interactions, possess similar values for performance indicators when compared with other techniques, and in most cases require less runs.