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Detection of the Defected Regions in Manufacturing Process Data using DBSCAN

DBSCAN 기반의 제조 공정 데이터 불량 위치의 검출

  • Received : 2017.04.14
  • Accepted : 2017.05.25
  • Published : 2017.07.28

Abstract

Recently, there is an increasing interest in analysis of big data that is coming from manufacturing industry. In this paper, we use PCB (Printed Circuit Board) manufacturing data to provide manufacturers with information on areas with high PCB defect rates, and to visualize them to facilitate production and quality control. We use the K-means and DBSCAN clustering algorithms to derive the high fraction of PCB defects, and compare which of the two algorithms provides more accurate results. Finally, we develop a system of MVC structure to visualize the information about bad clusters obtained through clustering, and visualize the defected areas on actual PCB images.

Keywords

Manufacturing Data;PCB;Defected Region

Acknowledgement

Supported by : 산업통상자원부, 정보통신기술진흥센터

References

  1. 최종우, 이일우, "빅 데이터를 활용한 제조공정 결함 예측에 관한 연구," 한국데이터정보과학회지, 제26권, 제5호, pp.1141-1154, 2015.
  2. K. KOBARA, "Cyber Physical Security for Industrial Control Systems and IoT," IEICE Trans. Inf. & Syst., Vol.99, No.4, pp.787-795, 2016.
  3. A. Puhringer, "사물인터넷(IoT)과 인더스트리 4.0(Industry 4.0)이 기존의 산업용 통신에 미치는 영향," ICN, pp.22-27, 2015.
  4. J. Lee, E. Lapira, B. Behrad, and K. Hung-an, "Recent advances and trends in predictive manufacturing systems in big data environment," Manufacturing Letters, Vol.1, No.1, pp.38-41, 2013. https://doi.org/10.1016/j.mfglet.2013.09.005
  5. S. Gallo, T. Murino, and L. Santillo, "Time manufacturing prediction: preprocess model in neuro fuzzy expert system," Proceeding of The European Symposium on Intelligent Techniques, pp.1-11, 1999.
  6. S. Laschi, M. Franek, and M. Mascini, "Screen printed electrochemical immunosensors for PCB detection," Electroanalysis, Vol.12, Issue.16, pp.1293-1298, 2000. https://doi.org/10.1002/1521-4109(200011)12:16<1293::AID-ELAN1293>3.0.CO;2-5
  7. M. Ester, H. P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," In Kdd, Vol.96, No.34, 1996.
  8. D. Arthur and S. Vassilvitskii, "K-means++: The advantages of careful seeding," In Proceedings of the ACM-SIAM symposium on Discrete algorithms, pp.1027-1035, 2007.
  9. M. M. Deza and E. Deza, Encyclopedia of distances, Springer Berlin Heidelberg, 2009.
  10. C. C. Yang and T. D. Ng, "Analyzing content development and visualizing social interactions in web forum," IEEE International Conference on Intelligence and Security Informatics, pp.25-30, 2008.
  11. K. E. Krause, "Taxicab geometry," The Mathematics Teacher, Vol.66, No.8, pp.695-706, 1973.
  12. G. Chen, S. A. Jaradat, N. Banerjee, T. S. Tanaka, M. S. Ko, and M. Q. Zhang, "Evaluation and comparison of clustering algorithms in analyzing ES cell gene expression data," Statistica Sinica, pp.241-262, 2002.