Multi-response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis
UNSPECIFIED (2000) Multi-response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 16 (1). pp. 3-8. ISSN 0748-8017Full text not available from this repository.
Many industrial experiments based on Taguchi's parameter design (PD) methodology deal with the optimization of a single performance quality characteristic. Studies have shown that the optimal factor settings for one performance characteristic are not necessarily compatible with those of other performance characteristics. Multi-response problems have received very little attention among industrial engineers and Taguchi practitioners. Many Taguchi practitioners have employed engineering judgement for determining the final optimal condition when several responses are to be optimized. However, this approach always brings some level of uncertainty to the decisionmaking process and is very subjective in nature. In order to rectify this problem, the author proposes an alternative approach using a powerful multivariate statistical method called principal component analysis (PCA). The paper also presents a case study in order to demonstrate the potential of this approach. Copyright (C) 2000 John Wiley & Sons, Ltd.
|Item Type:||Journal Article|
|Subjects:||T Technology > TA Engineering (General). Civil engineering (General)
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
|Journal or Publication Title:||QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL|
|Publisher:||JOHN WILEY & SONS LTD|
|Number of Pages:||6|
|Page Range:||pp. 3-8|
Actions (login required)