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Development of Manufacturing Ontology-based Quality Prediction Framework and System : Injection Molding Process

제조 온톨로지 기반 품질예측 프레임워크 및 시스템 개발 : 사출성형공정 사례

  • Lee, Kyoung-Hun (Department of Industrial and Systems Engineering, Dongguk University-Seoul) ;
  • Kang, Yong-Shin (u-SCM Research Center in Nano Information Technology Academy, Dongguk University) ;
  • Lee, Yong-Han (Department of Industrial and Systems Engineering, Dongguk University-Seoul)
  • 이경훈 (동국대학교 서울캠퍼스 산업시스템공학과) ;
  • 강용신 (동국대학교 나노정보기술연구원 u-SCM 연구센터) ;
  • 이용한 (동국대학교 서울캠퍼스 산업시스템공학과)
  • Received : 2012.01.09
  • Accepted : 2012.02.06
  • Published : 2012.03.01

Abstract

Today, many manufacturing companies realize that collaboration is crucial for their survival. Especially, in the perspective of quality, the importance of collaboration is emphasized because economic loss increases exponentially while defective parts go through the process in supply chain. However, the manufacturing companies are facing two main difficulties in implementing collaborative relationships with their suppliers. First, it is difficult for the suppliers to produce reliable products due to their obsolete facilities. The problem gets worse for second- or third-tire vendors. Second, the companies experience the lack of universally understandable set of terminology and effective methodologies for knowledge representation. Ontology is one of the best approaches to expressing and processing a domain knowledge. In this paper, we propose the manufacturing ontology-based quality prediction framework to represent and share the knowledge of industrial environment and to predict product quality in manufacturing processes. In addition, we develop the ontology-based quality prediction system based on the proposed framework. We carried out a series of experiments for an injection molding process at an automotive part supplier. The experimental results demonstrated that the proposed framework and system can be successfully applicable in manufacturing industry.

Keywords

References

  1. Borgo, S. and Leitao, P. (2004), The role of foundational ontologies in manufacturing domain applications, Lecture Notes in Computer Science, 3290/2004, 670-688.
  2. Chen, W. C., Tai, P. H, Wang, M. W., Deng W. J., and Chen, C. T. (2008), A neural network-based approach for dynamic quality prediction in a plastic injection molding process, Expert Systems with Applications, 35, 843-849. https://doi.org/10.1016/j.eswa.2007.07.037
  3. Choi, H. R., Hyun, S. Y., Lim, H. S., and Yoo, D. Y. (2006), Design of Collaborative Production and Supply Planning System based on ebXML, Information System Review, 8(1), 1-24.
  4. Chong, Z. S., Mohzani, M., Chin, J. F., and Yupiter, H. P. (2011), Approach to prediction of laser cutting quality by employing fuzzy expert system, Expert Systems with Applications, 38, 7558-7568. https://doi.org/10.1016/j.eswa.2010.12.111
  5. Gartner (2002), Semantic Web Technologies Take Middleware to Next Level, Gartner.
  6. Georgoudakis, M., Alexakos, C., Kalogeras, A. P., Gialelis, J., and Koubias S. (2007), Methodology for the efficient distribution a manufacturing ontology to a multiagent system utilizing a relevant Meta-Ontology, Emerging Technologies and Factory Automation, 2007. ETFA. IEEE Conference on, 25th-28th September, Rion, Greece, 1210-1216
  7. Gruber, T. R. (1993), A translation approach to portable ontology specifications, Knowledge Acquisition, 5, 199-220. https://doi.org/10.1006/knac.1993.1008
  8. 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.
  9. Horrideg, M., Konublauch, H., Rector, A., and Stevens, R. (2004), A Practical Guide To Building OWL Ontologies Using The Protege-OWL Pluging and CO-ODE Tool, University of Manchester and Stanford University.
  10. Huang, H. M. (2008), Ontological Perspectives for Autonomy Performance, Proceeding PerMIS Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems, 19th-21th August, Gaithersburg, MD, USA, 101- 107.
  11. 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 Industral and Systems Engineering, 30(4), 85-95.
  12. Kim, E. K. and Nam, Y. J. (2004), The Comparative Study on the Methodologies of Building Ontology toward Semantic Web, Korea Institute of Science and Technology Information, 35(2), 57-85.
  13. Lee, J. Y. (2006), Implementation Strategy on Ontology-based System for Task Management and Analysis, National Research Foundation of Korea.
  14. 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.
  15. Lee, S. C., Kim, C. S., and Lim, J. H. (2007), Solving the ambiguity of an Intention Reasoning using Context-Awareness Architecture based on Ontology, Korean Society for Internet Information, 8(5), 99-108.
  16. 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.
  17. Lin, H. K. and Harding, J. A. (2007), A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration, Computers in Industry, 58, 428-437. https://doi.org/10.1016/j.compind.2006.09.015
  18. Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A., and Schneider, L. (2003), The WonderWeb Library of Foundational Ontologies, Institute of Cognitive Sciences and Technology.
  19. 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.
  20. NIST (1999), An Analysis of Existing Ontological Systems for Applications in Manufacturing and Healthcare, National Institute of Standards and Technology.
  21. NIST (2009), Manufacturing Interoperability Program, a Synopsis, National Institute of Standards and Technology.
  22. Noy, N. F. and McGuinness, D. L. (2000), Ontology Development 101 : A Guide to Creating Your First Ontology, Stanford University.
  23. O'Connor, M., Knublauch, H., Tu, S., and Musen, M. (2004), Writing Rules for the Semantic Web Using SWRL and Jess, Stanford University.
  24. 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.
  25. Postawa, P. and Koszkul, J. (2005), Change in injection moulded parts shrinkage and weight as a function of processing conditions, Journal of Materials Processing Technology, 162, 109-115.
  26. Pouchard, L., Ivezic, N., and Schlenoff, C. I. (2000), Ontology Engineering for Distributed Collaboration in Manufacturing, Proceedings of the AIS Conference, 129, 2865-2872.
  27. Ryu, K., Cho, Y., Choi, H., and Lee, S. (2005), Collaborative Process Modeling for Embodying e-Manufacturing, IE Interfaces, 18(3), 221-233.
  28. Schneider, L. (2003), How to Build a Foundational Ontology : The Object-Centered High-level Reference Ontology OCHRE, University of Leipzig.
  29. Siegel, N., Goolsbey, K., Kahlert, R., and Matthews, G. (2004), The Cyc System : Notes on Architecture, Cycorp, Inc.
  30. SMBA (2009), An Annual Report on Korean Small and Medium-sized Business, Small and Medium Business Administration.
  31. Tsai, K. M., Hsieh, C. Y., and Lo, W. C. (2009), A study of the effects of process parameters for injection molding on surface quality of optical lenses, Journal of materials processing technology, 209, 3469-3477. https://doi.org/10.1016/j.jmatprotec.2008.08.006
  32. Tseng, T. L., Kwon, Y., and Ertekin, Y. M. (2005), Feature-based rule induction in machining operation using rough set theory for quality assurance, Robotics and Computer-Integrated Manufacturing, 21, 559-567. https://doi.org/10.1016/j.rcim.2005.01.001
  33. Vujasinovic, M., Ivezic, N., Kulvatunyou, B., Barkmeyer, E. J., Missikoff, M., Taglino, F., Marjanovic, Z., and Miletic, I. (2008), A Semantic-Mediation Architecture for Interoperable Supply-Chain Applications, International Journal of Computer Integrated Manufacturing, 22(6), 549-561.
  34. W3C (2004), OWL web ontology language overview, World Wide Web Consortium
  35. W3C (2004), SWRL : a semantic web rule language combining OWL and RuleML, World Wide Web Consortium
  36. Ye, F. and Ding, X. (2009), Manufacturing Enterprise Business Process Ontology Modeling for Knowledge Integration, Proceedings of 2009 IEEE International Conference on Grey Systems and Intelligent Services, 10th-12th November, Hangzhou, China, 1365-1369.
  37. Zeaiter, M., Knight, W., and Holland, S. (2010), Multivariate regression modeling for monitoring quality of injection moulding components using cavity sensor technology: Application to the manufacturing of pharmaceutical device components, Journal of Process Control, 21(1), 137-150.
  38. 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.

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