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Application of Principal Component Analysis in Automobile Body Assembly : Case Study  

Lee, Myung-D. (Research and Innovation Center, Ford Motor Co. MI)
Lim, Ik-Sung (Dept. of Industrial and Management Engineering, Namseoul University)
Kim, Eun-Jung (Research and Policy Development Team, Korea Agency for Digital Opportunity and Promotion)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.31, no.3, 2008 , pp. 125-130 More about this Journal
Abstract
Multivariate analysis is a rapidly expanding approach to data analysis. One specific technique in multivariate analysis is Principal Component Analysis (PCA). PCA is a statistical technique that linearly transform a given set of variables into a new set of composite variables. These new variables are orthogonal to each other and capture most of the information in the original variables. PCA is used to reduce the number of control points to be checked by measurement system. Therefore, the structure of the data set is simplified significantly It is also shown that eigenvectors obtained by conducting principal component analysis on the basis of the covariance matrix can be used to physically interpret the pattern of relative deformation for the points. This case study reveals the twisting deformation pattern of the underbody which is the largest mode of the total variation.
Keywords
Data reduction; Control Points; OCMM; PCA; MANOVA;
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