Development of Probability Based Defect Verification Algorithm for Automatic Visual Inspection

자동외관검사를 위한 확률기반 불량 확인 알고리즘 개발

  • Kim, Youngheub (Dept. of Mechanical Engineering, Dongyang Mirae University) ;
  • Ryu, Sun-Joong (Dept. of Mechanical Engineering, Dongyang Mirae University)
  • 김영흡 (동양미래대학교 기계공학과) ;
  • 유선중 (동양미래대학교 기계공학과)
  • Received : 2017.04.06
  • Accepted : 2017.06.21
  • Published : 2017.06.30

Abstract

The visual inspection of electronic parts consists of two steps: automatic visual inspection and verification inspection. In the stage of a verification inspection, the human inspector sequentially inspects all the areas which detected in the automatic inspection. In this study, we propose an algorithm to determine the order of verification inspection by Bayes inference well known in the field of machine learning. This is a method of prioritizing a region estimated to have a high probability of defect using experience data of past inspection. This algorithm was applied to the visual inspection of ultraviolet filters to verify its effectiveness. As a result of the comparison experiment, it was confirmed that the verification inspection can be completed 30% of the conventional method by adapting proposed algorithm.

Keywords

References

  1. S. Yoo, "System Design for High-speed Visual Inspection of Electronic Components", Journal of the Semiconductor & Display Technology, Vol. 11, No. 3. September, pp. 39-44, 2012.
  2. J. H. Lee, "Study on the Optical Analysis Equipment Control System for Electronic Parts Inspection", Journal of the Semiconductor & Display Technology, Vol. 14, No. 4. December, pp. 67-71, 2015.
  3. K. W. Ko, Y. J. Lee, B.-W. Choi and J.-H. Kim," Development of Automatic Visual Inspection for the Defect of Compact Camera Module," ICCAS2005, pp. 2414-2417, 2005.
  4. David M. Eagleman, "Visual illusion and neurobiology", Nature, 2, pp. 920-926, 2001.
  5. Russell, S., Norvig, P., Artificial Intelligence: A Modern Approach, 2nd ed., Prentice Hall, 2003.
  6. H. Zhang, "The Optimality of Naive Bayes", The 17th International FLAIRS Conference, Miami Beach, Florida, May 17-19, 2004.
  7. J. H. George; P. Langley, "Estimating Continuous Distributions in Bayesian Classifiers", Proc. Eleventh Conf. on Uncertainty in Artificial Intelligence. Morgan Kaufmann. pp. 338-345, 1995.
  8. A. McCallum, K. Nigam, "A comparison of event models for Naive Bayes text classification", AAAI-98 workshop on learning for text categorization. 1998.