산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제31권3호
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  • Pages.125-130
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  • 2008
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
권호 표지

자동차 차체 조립공장에서 주성분 분석의 응용 : 사례 연구

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

  • 이명득 (미국 포드자동차 연구혁신센터) ;
  • 임익성 (남서울대학교 산업경영공학과) ;
  • 김은정 (한국정보문화진흥원 조사연구팀)
  • 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)
  • 발행 : 2008.09.30
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초록

이 논문은 자동차 차체 조립과정에서, 품질관리의 일환으로써, 비접촉 자동측정시스템을 이용하여 검사해야 하는 수많은 비독립적인 검사점을 다변량분산분석과 주성분분석을 이용하여 효율적으로 검사점을 감소시키는 방법을 설명하고 있다. 이 연구의 목적은 다변량분산분석, 주성분 분석의 개념과 이러한 기법들을 산업체 제조분야에서 응용하는 방법을 설명하여 독자의 사례 응용 이해를 돕는데 있으며, 또한 특히 주성분분석을 이용하여 수 많은 비독립적인 검사점을 어떻게 유효하게 줄여나가는지를 보여주고자 한다. 독자의 이해를 돕기 위하여 위와 같은 절차를 순서대로 설명하였으며, 실제 자동차 조립공장에서 발생하는 사례를 수치 예를 들어 설명하였다.

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.

키워드

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