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ICT 기술을 융합한 자동차 실러도포 공정 모니터링 시스템

Car Sealer Monitoring System Using ICT Technology

  • 김호연 (한국기술교육대학교 컴퓨터 공학부) ;
  • 박종섭 (한국기술교육대학교 컴퓨터 공학부) ;
  • 박요한 (한국기술교육대학교 컴퓨터 공학부) ;
  • 조재수 (한국기술교육대학교 컴퓨터 공학부)
  • 투고 : 2018.06.21
  • 심사 : 2018.09.19
  • 발행 : 2018.09.30

초록

In this paper, we propose a car sealing monitoring system combined with ICT Technology. The automobile sealer is an adhesive used to bond inner and outer panels of doors, hoods and trunks of an automobile body. The proposed car sealer monitoring system is a system that can accurately and automatically inspect the condition of the automobile sealer coating process in the general often factory production line where the lighting change is very severe. The sealer inspection module checks the state of the applied sealer using an area scan camera. The vision inspection algorithm is adaptive to various lighting environments to determine whether the sealer is defective or not. The captured images and test results are configured to send the task results to the task manager in real-time as a smartphone app. Vision inspection algorithms in the plant outdoors are very vulnerable to time-varying external light sources and by configuring a monitoring system based on smart mobile equipment, it is possible to perform production monitoring regardless of time and place. The applicability of this method was verified by applying it to an actual automotive sealer application process.

키워드

참고문헌

  1. Kim, G.S., Y.H. Park, J.S. Park, and J.S. Cho, "Auto Parts Visual Inspection in Severe Changes in the Lighting Environment", Robotics and Systems, Vol.21, No.12, 2015, 1109-1114.
  2. Babaud, J., A.P. Witkin, M. Baudin, and R.O. Duda, "Uniqueness of the Gaussian Kernel for Scale-Space Filtering", IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol.1, 1986, 26-33.
  3. Kumar, A., Y. Bar-Shalom, and E. Oron, "Precision Tracking Based on Segmentation with Optimal Layering for Imaging Sensors", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.17, No.2, 1995, 182-188. https://doi.org/10.1109/34.368171
  4. Otsu, N., "A Threshold Selection Method from Gray-Level Histograms", IEEE Transactions on Systems, Man, and Cybernetics, Vol.9, No.1, 1979, 62-66. https://doi.org/10.1109/TSMC.1979.4310076
  5. Perez, L. and J. Wang, "The Effectiveness of Data Augmentation in Image Classification using Deep Learning", arXiv preprint arXiv, 1712.04621, Dec 2017.
  6. Thames, L. and D. Schaefer, "Cybersecurity for Industry 4.0 : Analysis for Design and Manufacturing", 2017, 255.