대한안전경영과학회지 (Journal of the Korea Safety Management & Science)

  • 제23권3호
  • /
  • Pages.83-88
  • /
  • 2021
  • /
  • 1229-6783(pISSN)
  • /
  • 2288-1484(eISSN)
대한안전경영과학회 (Korea Safety Management & Science)
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SVM 알고리즘을 활용한 선루프 실러도포 공정 품질검사 시스템 구축

The Construction of Quality Inspection System for Sunroof Sealer Application Process Using SVM Algorithm

  • 양희종 (주식회사 아이티엔제이) ;
  • 장길상 (울산대학교 경영대학 경영정보학과)
  • 투고 : 2021.09.13
  • 심사 : 2021.09.28
  • 발행 : 2021.09.30
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초록

Recently, due to the aging of workers and the weakening of the labor base in the automobile industry, research on quality inspection methods through ICT(Information and Communication Technology) convergence is being actively conducted. A lot of research has already been done on the development of an automated system for quality inspection in the manufacturing process using image processing. However, there is a limit to detecting defects occurring in the automotive sunroof sealer application process, which is the subject of this study, only by image processing using a general camera. To solve this problem, this paper proposes a system construction method that collects image information using a infrared thermal imaging camera for the sunroof sealer application process and detects possible product defects based on the SVM(Support Vector Machine) algorithm. The proposed system construction method was actually tested and applied to auto parts makers equipped with the sunroof sealer application process, and as a result, the superiority, reliability, and field applicability of the proposed method were proven.

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과제정보

본 연구는 울산대학교의 2016년도 연구비 지원 사업에 의하여 연구되었음.

참고문헌

  1. C. W. Hsu, C. J. Lin(2002), "A comparison of methods for multiclass support vector machines." Neural Networks, IEEE Transactions on, 13(20):415-425. https://doi.org/10.1109/72.991427
  2. Markets and Markets(2017), Machine vision market. R&D Special Zone Promotion Foundation. www.innopolis.or.kr
  3. E. M. Park(2013), "Classifying imbalanced data using an SVM ensemble with K-means clustering in semiconductor test process." Master's thesis, Sungkyunkwan University, Graduate School.
  4. J. Park, C. Hwang, K. Bae(2013), "Analysis of target classification performances of active sonar returns depending on parameter values of SVM kernel functions." Journal of the Korea Institute of Information and Communication Engineering, 17(5):1083-1088. https://doi.org/10.6109/JKIICE.2013.17.5.1083
  5. V. Vapnik(1995a), The nature of statistical learning theory. New York: Springer-Verlag.
  6. V. Vapnik(1995b), "Support-vector networks." Machine Learning, 20(3):273-279. https://doi.org/10.1007/BF00994018
  7. K. Veropoulos, C. Campbell, N. Cristianini(1999), "Controlling the sensitivity of support vector machines." Proceedings of the International Joint Conference on AI, 55-60.
  8. Wikipedia(2015), Support vector machine. https://ko.wikipedia.org/wiki/support_vector_machine (Accessed on 2017.10.20.).
  9. Y. Yang, I. Oh, R. Kang(2019), Data science with R. Hanbit Academy.