Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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Inspection of Automotive Oil-Seals Using Artificial Neural Network and Vision System

인공신경망과 비전 시스템을 이용한 자동차용 오일씰의 검사

  • 노병국 (한성대학교 기계시스템공학과) ;
  • 김기대 (대구가톨릭대학교 기계자동차공학부)
  • Published : 2004.08.01
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Abstract

The Classification of defected oil-seals using a vision system with the artificial neural network is presented. The artificial neural network fur classification consists of 27 input nodes, 10 hidden nodes, and one output node. The selection of the number of the input nodes is based on an observation that the difference among the defected, non-defected, and smeared oil-seals is greatly pronounced in the 26 step gray-scale level thresholding. The number of the hidden nodes is chosen as a result of a trade-off between accuracy and computing time. The back-propagation algorithm is used for teaching the network. The proposed network is capable of successfully classifying the defected from the smeared oil-seals which tend to be classified as the defected ones using the binary thresholding. It is envisaged that the proposed method improves the reliability and productivity of the automotive vision inspection system.

Keywords

References

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