✦ LIBER ✦

Improving principal component analysis (PCA) in automotive body assembly using artificial neural networks

✍ Scribed by Khi-young Jang; Kai Yang

Publisher: Society of Manufacturing Engineers
Year: 2001
Tongue: English
Weight: 1001 KB
Volume: 20
Category: Article
ISSN: 0278-6125
DOI: 10.1016/s0278-6125(01)80040-6

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✦ Synopsis

Principal Component Analysis (PCA) has been widely applied to identify the sources of dimenSional variation in automotive body assembly; however, current PCA methods based on a covariance matrix are not appropriate for dealing with high-dimensional data, noisy data, and missing data. Due to its adaptive nature and fast computational capabilii, an artificial neural network has been considered as a new alternative to ovemome the statistical analysis pmblerns. In this paper, an artificial neural network using a nonlinear transfer function will be introduced to overcome current data analysis problems in an auto assembly process. A case study is used to demonstrate the application of the proposed approach.

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