Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지
DOI QR코드

DOI QR Code

A Product Quality Prediction Model Using Real-Time Process Monitoring in Manufacturing Supply Chain

실시간 공정 모니터링을 통한 제품 품질 예측 모델 개발

  • 오영광 (울산과학기술대학교 디자인 및 인간공학부) ;
  • 박해승 (울산과학기술대학교 디자인 및 인간공학부) ;
  • 유아름 (울산과학기술대학교 디자인 및 인간공학부) ;
  • 김남훈 (울산과학기술대학교 디자인 및 인간공학부) ;
  • 김영학 ((주)아이티스타) ;
  • 김동철 ((주)아이티스타) ;
  • 최진욱 ((주)아이티스타) ;
  • 윤성호 ((주)아이티스타) ;
  • 양희종 ((주)아이티스타)
  • Received : 2012.11.04
  • Accepted : 2013.03.19
  • Published : 2013.08.15
Download PDF
⟨ Previous Next ⟩

Abstract

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.

Keywords

References

  1. An, D. W., Ko, H. H., Back, J. G., and Kim, S. S. (2009), A Final Test Yields Prediction Methodology in the Semiconductor Manufacturing Process Using Stepwise Support Vector Machine, Joint Spring Conference of MS/IE, 5, 676-683.
  2. Bernardo, N. Yahya, Park, J. H., Bae, H. R., and Mo, J. K. (2011), Similarity Measurement Using Ontology in Vessel Clearance Process, Journal of the Korean Institute of Industrial Engineers, 37(2), 153-162. https://doi.org/10.7232/JKIIE.2011.37.2.153
  3. Cherkassky, V. and Ma, Y. (2004), Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks, 17(1), 113-126. https://doi.org/10.1016/S0893-6080(03)00169-2
  4. Cho, D. (2010), Mixed-effects LS-SVM for longitudinal data, Journal of Korean Data and Information Science Society, 21, 363-369.
  5. Cortes, C. and Vapnik, V. (1995), Support-vector networks, Machine Learning, 20(3), 273-297.
  6. Cristianini, N. and Taylor, J. (2000), An Introduction to Support Vector Machines, Cambridge University Press.
  7. Gomez-Perez, A., Corcho, O. and Fernandez-Lopez, M. (2003), Ontological Engineering : with Examples from the Areas of Knowledge Management, e-Commerce and the Semantic Web, Springer Verlag, London.
  8. Gu, X. (2010), Toyota Recalls : revealing the value of secure supply chain, Massachusetts institute of technology, Massachusetts Institute of Technology, USA.
  9. Hearst, M. A., Dumais, S. T., Osman, E., Platt, J., and Scholkopf, B. (1998), Support vector machines, IEEE Intelligent System, 13(4), 18-28.
  10. Herre, H., Heller, B., Burek, P., Hoehndorf, R., Loebe, F., and Michalek, H. (2007), General Formal Ontology (GFO) : A Foundational Ontology Integrating Objects and Processes, Part I : Basic Principles. Research Group Ontologies in Medicine (Onto-Med), University of Leipzig.
  11. Joachims, T. (1998), Test catergorization with support vector machines, Proceedings of the European Conference on Machine Learning, 10th European Conference on Machine Learning, 137-142.
  12. Kim, T. M. and Shin, H. J. (2007), A Study on the Forward Method of 'Single PPM Quality Innovation' for Special type : Focused on Mold Industry, Journal of the Society of Korea Industrial and Systems Engineering, 30(4), 85-95.
  13. Kyoung-Hun, L., Yong-Shin, K., and Yong-Han, L. (2012), Development of Manufacturing Ontology-based Quality Prediction Framework and System : Injection Molding Process, Journal of the Society of Korea Industrial and Systems Engineering, 25(1), 40-51. https://doi.org/10.7232/IEIF.2012.25.1.040
  14. Lee, C. J., Song, S. O., and Yoon, E. S. (2004), The Monitoring of Chemical Process using The Support Vector Machine, Korean Chem. Eng. Res., 42(5), 538-544.
  15. Lee, M. H., Kim, H. S., and Kim, N. H. (2006), Design and Implementation of the Web Service Based Collaborative Production Management System, Journal of the Society of Korea Industrial and Systems Engineering, 29(3), 78-86.
  16. Lee, M. J., Kim, W. J., and Kim, H. J. (2012), SWCL extension for knowledge representation of piecewise linear constraints on the semantic web, Korea Management Education Association, 37(4), 9-36. https://doi.org/10.7737/JKORMS.2012.37.4.019
  17. Lemaignan, S., Siadat, A., Dantan, J. Y., and Semenenko, A. (2006), MASON : a proposal for an ontology of manufacturing domain, IEEE Workshop on Distributed Intelligent System, Collective Intelligence and Its Application, 15th-16th June, Metz, France, 195-200.
  18. Niles, I. and Pease, A. (2001), Towards a Standard Upper Ontology, Proceedings of the international conference on Formal Ontology in Information Systems, 17th-19th October, Maine, USA.
  19. NIST, (2009), Manufacturing Interoperability Program, a Synopsis, National Institute of Standards and Technology.
  20. Park, S. J. and Lee, G. B. (2003), Concept of the Next Generation Manufacturing System and consideration for its Embodiment in Manufacturing Industries, Journal of the Korean Society of Precision Engineering, 20(9), 27-31.
  21. Pontil, M. and verri, A. (1997), Properties of Support Vector Machines, A. I. Memo # 1612; CBCL paper # 152, Massachusetts Institute of Technology Cambridge.
  22. Shin, J., Park, H. and Seok, K. H. (2009), Variance function estimation with LS-SVM for replicated data, Journal of Korean Data and Information Science Society, 20, 925-931.
  23. The Joongang Ilbo (2011), http://article.joinsmsn.com/news/article/article. asp?total_id = 6585982&ctg = 1100.
  24. W3C (2004), OWL web ontology language overview, World Wide Web Consortium.
  25. Zhou, J. and Rose, D. (2004), Manufacturing ontology analysis and design : towards excellent manufacturing, Industrial Informatics 2004 2nd IEEE International Conference on, 26th June, Sophia Antipolis, France, 39-45.

Cited by

  1. SVM-Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0 vol.2018, pp.1530-8677, 2018, https://doi.org/10.1155/2018/5795037