NDLI: Using a principal components analysis for developing a robust design of electron beam welding

Content Provider	Springer Nature Link
Author	Jean, Ming Der Wang, Jen Ting
Copyright Year	2006
Abstract	This paper presents the application of principal components analysis for Taguchi orthogonal experiments to develop a robust electron beam welding treatment (EBWT) process with high efficiency multiple performance characteristics (MPCs). In this study, the principal components analysis (PCA) design incorporating the correlation matrix of tested trials is employed. In the first step, the MPCs are reduced to two independent components using PCA. Both components accounted for 98.8% of the total variance. The first principal component (PC), which refers to the integrated hardening capability index of the EBWT process, accounts for 70.7% of the total variation. The remaining 28.1% were contributed by the second PC, which can be interpreted as the penetration capability index. In the second step, we identify the most important PC loading vectors using PCA, and estimate the importance of the PCs. By using PCA, relationships between different MPCs can be investigated and the most important factors for the variance of the EBWT process can be identified.The experimental results show that redundant information could be eliminated by using principal components in conjunction with Taguchi’s orthogonal array experiments. This proposed approach is simple, effective, and efficient for developing a robust and high-efficiency EBWT process of high quality. In this study, the MPCs in the EBWT process are successfully optimized.
Starting Page	882
Ending Page	889
Page Count	8
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	28
Issue Number	9-10
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-01-01
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Electron beam welding treatment (EBWT) Multiple performance characteristic (MPC) Optimization Principal components analysis (PCA) Industrial and Production Engineering Production/Logistics Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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