Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Study on Correlation-based Feature Selection in an Automatic Quality Inspection System using Support Vector Machine (SVM)

SVM 기반 자동 품질검사 시스템에서 상관분석 기반 데이터 선정 연구

  • Song, Donghwan (Department of System Design and Control Engineering, Ulsan National Institute of Science and Technology) ;
  • Oh, Yeong Gwang (Department of System Design and Control Engineering, Ulsan National Institute of Science and Technology) ;
  • Kim, Namhun (Department of System Design and Control Engineering, Ulsan National Institute of Science and Technology)
  • 송동환 (울산과학기술원 제어및설계공학과) ;
  • 오영광 (울산과학기술원 제어및설계공학과) ;
  • 김남훈 (울산과학기술원 제어및설계공학과)
  • Received : 2016.05.02
  • Accepted : 2016.11.29
  • Published : 2016.12.15
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Abstract

Manufacturing data analysis and its applications are getting a huge popularity in various industries. In spite of the fast advancement in the big data analysis technology, however, the manufacturing quality data monitored from the automated inspection system sometimes is not reliable enough due to the complex patterns of product quality. In this study, thus, we aim to define the level of trusty of an automated quality inspection system and improve the reliability of the quality inspection data. By correlation analysis and feature selection, this paper presents a method of improving the inspection accuracy and efficiency in an SVM-based automatic product quality inspection system using thermal image data in an auto part manufacturing case. The proposed method is implemented in the sealer dispensing process of the automobile manufacturing and verified by the analysis of the optimal feature selection from the quality analysis results.

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References

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