Journal of Intelligent Manufacturing

, Volume 30, Issue 2, pp 905–916 | Cite as

Product design and manufacturing process based ontology for manufacturing knowledge reuse

  • Peter Chhim
  • Ratna Babu ChinnamEmail author
  • Noureddin Sadawi


This paper presents an effective product design and manufacturing process based ontology for manufacturing knowledge reuse. While a number of related efforts exist in the literature, there lacks a granular, interconnected product design and manufacturing process based ontology that can lead to greater industry adoption and knowledge reuse. In particular, the proposed ontology leverages an established industry standard to connect product design and manufacturing process knowledge in a logical and effective manner. The resulting effort is a general ontological framework that can be widely employed by the manufacturing industry. Additionally, the feasibility of implementing the ontology enabled knowledge reuse framework is demonstrated through a real-world case study.


APQP Knowledge management Manufacturing ontology Semantic web SPARQL 



We thank the two anonymous reviewers for their excellent inputs and suggestions that allowed us to significantly enhance the quality of the manuscript.


  1. AIAG. (2008). Advanced product quality planning and control plan (APQP). Southfield: Automotive Automotive Industry Action Group.Google Scholar
  2. Ameri, F., & Patil, L. (2012). Digital manufacturing market: A semantic web-based framework for agile supply chain deployment. Journal of Intelligent Manufacturing, 23(5), 1817–1832.CrossRefGoogle Scholar
  3. Bobrek, M., & Sokovic, M. (2005). Implementation of APQP—Concept in design of QMS. In 13th International scientific conference on achievements in mechanical and materials engineering. Gliwice-Wista, Poland.Google Scholar
  4. Borst, W. (1997). Construction of engineering ontologies. Ph.D. Thesis, University of Tweenty, Enschede, NL.Google Scholar
  5. Brachman, R., & Levesque, H. (1986). Knowledge representation and reasoning. Annual review of computer science., 1(1), 255–287.CrossRefGoogle Scholar
  6. Cassanelli, G., Mura, G., Fantini, F., Vanzi, M., & Plano, B. (2006). Failure analysis-assisted FMEA. Microelectronics Reliability, 46(9), 1795–1799.CrossRefGoogle Scholar
  7. Chandrasegaran, S., Ramani, K., Sriram, D., Horvath, I., Bernard, A., Harik, F., et al. (2013). The evolution, challenges, and future of knowledge representation in product design systems. Computer-Aided Design, 45(2), 204–228.CrossRefGoogle Scholar
  8. Chandrasekaran, B., Josephson, J., & Benjamins, V. (1999). What are ontologies and why do we need them? Intelligent Systems and their Applications IEEE, 14(1), 20–26.CrossRefGoogle Scholar
  9. Chungoora, N., Young, R., Gunendran, G., Palmer, C., Usman, Z., Anjum, A., et al. (2013). A model-driven ontology for manufacturing system interoperability and knowledge sharing. Computers in Industry, 64(4), 392–401.CrossRefGoogle Scholar
  10. Desouza, K., & Evaristo, R. (2003). Global knowledge management strategies. European Management Journal, 21(1), 62–67.CrossRefGoogle Scholar
  11. Dittmann, L., Rademacher, T., & Zelewski, S. (2004). Performing FMEA using ontologies. In 18th International workshop on qualitative reasoning (pp. 209-216). Evanston, USA.Google Scholar
  12. Ducharme, B. (2013). Learning SPARQL (2nd ed.). Sebastopol: O’Reilly Media Inc.Google Scholar
  13. Ebrahimimpour, V., Rezaie, K., & Shokravi, S. (2010). An ontology approach to support FMEA studies. Expert Systems with Applications., 37(1), 671–677.CrossRefGoogle Scholar
  14. Ettlie, J. E., & Kubarek, M. (2008). Design reuse in manufacturing and services. Journal of Product Innovation Management, 25(5), 457–472.CrossRefGoogle Scholar
  15. Fabian, B., Kunz, S., Konnegen, M., Muller, S., & Gunther, O. (2012). Access control for semantic data federations in industrial product-lifecyle management. Computers in Industry, 63(9), 930–940.CrossRefGoogle Scholar
  16. Hill, K., Menk, D., & Cooper, A. (2016). Contribution of the automotive industry to the economies of all fifty state and the United States—See more at: Retrieved from Center for Automotive Research:
  17. Hoffman, C., & Joan-Arinyo, R. (1998). CAD and the product master model. Computer-Aided Design, 30(11), 905–918.CrossRefGoogle Scholar
  18. Horridge, M. (2004). A practical guide to building OWL ontologies using the protege-OWL plugin and CO-ODE tools (1.3rd ed.). Manchester: The University Of Manchester.Google Scholar
  19. Iarovy, S., Ramis, B., Xiangbin, X., Sampath, A., Lobov, A., & Lastra, J. (2015). Representation of manufacturing equipment and services for OKD-MES: From service descriptions to ontology. In In 2015 IEEE 13th international conference on industrial informatics (INDIN) (pp. 1069–1074). IEEE.Google Scholar
  20. Imran, M., & Young, B. (2015). The application of common logic based formal ontologies to assembly knowledge sharing. Journal of Intelligent Manufacturing, 26, 139–158.CrossRefGoogle Scholar
  21. Iyer, N., Subramaniam, J., Kuiyan, L., Yagnanarayanan, K., & Karthik, R. (2005). Shape-based searching for product lifecycle applications. Computer-Aided Design, 37(13), 1435–1446.CrossRefGoogle Scholar
  22. Jardim-Goncalves, R., Grilo, A., & Popplewell, K. (2016). Novel strategies for global manufacturing systems interoperability. Journal of Intelligent Manufacturing, 27(1), 1–9.CrossRefGoogle Scholar
  23. Khadilkar, V., Kantarcioglu, M., Thuraisingham, B., & Castagna, P. (2012). Jena-HBase: A distributed, scalable and efficient RDF triple store. In Proceedings of the 11th international semantic web conference posters & demonstrations track 12 (pp. 85–88). ISWC-PD.Google Scholar
  24. Khilwani, N., & Harding, J. (2016). Managing corporate memory on the semantic web. Journal of Intelligent Manufacturing, 27(1), 101–118.CrossRefGoogle Scholar
  25. Laaroussi, A., Fies, B., Vankeisbelckt, R., & Hans, J. (2007). Ontology-aided FMEA for construction products. In 24th W78 Conference Maribor 26 (pp. 189–194).Google Scholar
  26. Lee, B. (2001). Using FMEA models and ontologies to build diagnostic models. AI EDAM, 15(04), 281–293.Google Scholar
  27. Liao, Y., Lezoche, M., Panetto, H., Boudjlida, N., & Loures, E. (2015). Semantic annotation for knowledge explicitation in a product lifecycle management context: A survey. Computers in Industry, 71(1), 24–34.CrossRefGoogle Scholar
  28. Lin, H., & Harding, J. (2007). A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration. Computers in Industry, 58(5), 428–437.CrossRefGoogle Scholar
  29. Lin, H., Harding, J., & Shahbaz, M. (2004). Manufacturing system engineering ontology for semantic interoperability across extended project teams. International Journal of Production Research, 42(24), 5099–5118.CrossRefGoogle Scholar
  30. Lin, L., Zhang, W., Lou, Y., Chu, C., & Cai, M. (2011). Developing manufacturing ontologies for knowledge reuse in distributed manufacturing environment. International Journal of Production Research, 49(2), 343–359.CrossRefGoogle Scholar
  31. Molhanec, M., Mach, P., Asamoah, D., & Mensah, B. (2011). The ontology based FMEA of lead free soldering process. In 34th International spring seminar (pp. 267–273). IEEE.Google Scholar
  32. Negri, E., Fumagalli, L., Garetti, M., & Tanca, L. (2016). Requirements and languages for the semantic representation of manufacturing systems. Computers in Industry, 81, 55–66.CrossRefGoogle Scholar
  33. Noy, N., & McGuinness, D. (2001). Ontology development 101: A guide to creating your first ontology. Stanford Medical Informatics.Google Scholar
  34. Pinto, H., & Martins, J. (2004). Ontologies: How can they be built? Knowledge and Information Systems, 6(4), 441–464.CrossRefGoogle Scholar
  35. Royce, W. (1970). Managing the development of large software systems. In Proceedings, IEEE Wescon (pp. 1–9).Google Scholar
  36. Sanya, O. I., & Shehab, M. E. (2015). A framework for developing engineering design ontologies within the aerospace industry. International Journal of Production Research, 53(8), 2383–2409.CrossRefGoogle Scholar
  37. Shearer, R., Motik, B., & Horrocks, I. (2008). HermiT: A highly-efficient OWL reasoner. OWLED, Vol. 432.Google Scholar
  38. Stanford Center for Biomedical Research. (2015). Protege. Retrieved from
  39. Swartout, B., Patil, R., Knight, K., & Russ, T. (1996). Toward distributed use of large scale ontologies. In Proceedings of 10th knowledge acquisition for knowledge—based systems workshop. Banff, Canada.Google Scholar
  40. Thisse, L. (1996). Advanced quality planning: A guide for any organization. Quality Progress, 31(2), 73–77.Google Scholar
  41. Tian, G., Yin, G., & Taylor, D. (2002). Internet-based manufacturing: A review and a new infrastructure for distributed intelligent manufacturing. Journal of Intelligent Manufacturing, 13(5), 323–338.CrossRefGoogle Scholar
  42. Tsarkov, D., & Horrocks, I. (2006). FaCT++ description logic reasonser: System description. Berlin: Springer.Google Scholar
  43. Tsia, C. a. (2014). The State of Knowledge Management: 2014. San Diego, CA: TSIA.Google Scholar
  44. Uschold, M., & King, M. (1995). Towards a methodology for building ontologies. In Workshop on basic ontological issues in knowledge sharing D (IJCAI’95), (pp. 6.1–6.10). Montreal, Canada.Google Scholar
  45. Young, B., Gunendran, A., Cutting-Decelle, A., & Gruninger, M. (2007). Manufacturing knowledge sharing PLM: A progression towards the use of heavy weight ontologies. Internatinoal Journal of Production Research, 45(7), 1505–1519.CrossRefGoogle Scholar
  46. yWorks GmbH. (2015). Retrieved from
  47. Zhao, X., & Zhu, Y. (2012). Application research of ontology–enabled process FMEA knowledge management method. International Journal of Intelligent Systems and Applications, 4(3), 34–40.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Industrial and Systems Engineering DepartmentWayne State UniversityDetroitUSA
  2. 2.Department of Surgery and CancerImperial College LondonLondonUK

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