Modeling Manufacturing Resources: An Ontological Approach

  • Emilio M. SanfilippoEmail author
  • Sergio Benavent
  • Stefano Borgo
  • Nicola Guarino
  • Nicolas Troquard
  • Fernando Romero
  • Pedro Rosado
  • Lorenzo Solano
  • Farouk Belkadi
  • Alain Bernard
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)


Resource management is at the core of different manufacturing tasks, which need to be seamlessly integrated to optimize production in manufacturing environments. The development of knowledge-based systems led to the use of ontologies to systematically organize data. Unfortunately, ontologies for resource knowledge representation lack maturity and often rely on context-dependent modeling choices. As a result, the notion of manufacturing resource is treated in disparate, non-homogeneous ways at the expenses of communication and application systems interoperability. The purpose of the paper is to lay down a conceptual framework on manufacturing resources based on ontology engineering principles. By the end of the paper we will see how different approaches can be harmonized with the proposed approach.


Manufacturing resource Ontology Process planning 


  1. 1.
    El Kadiri, S., Kiritsis, D.: Ontologies in the context of product lifecycle management: state of the art literature review. Int. J. Prod. Res. 53(18), 5657–5668 (2015). Scholar
  2. 2.
    Monostori, L., et al.: Cyber-physical systems in manufacturing. CIRP Ann. 65(2), 621–641 (2016). Scholar
  3. 3.
    Weichhart, G., Molina, A., Chen, D., Whitman, L.E., Vernadat, F.: Challenges and current developments for sensing, smart and sustainable enterprise systems. Comput. Ind. 79, 34–46 (2016). Scholar
  4. 4.
    Lu, Y., Morris, K.C., Frechette, S.: Current standards landscape for smart manufacturing systems. Natl. Inst. Stand. Technol. NISTIR 8107, 22–28 (2016). Scholar
  5. 5.
    Chungoora, N., et al.: A model-driven ontology approach for manufacturing system interoperability and knowledge sharing. Comput. Ind. 64(4), 392–401 (2013). Scholar
  6. 6.
    Guizzardi, G.: Ontological foundations for structural conceptual models. Twente University (2005)Google Scholar
  7. 7.
    Guarino, N., Welty, C.A.: An overview of OntoClean. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 201–220. Springer, Heidelberg (2009). Scholar
  8. 8.
    Industrial Automation Systems and Integration – Industrial Manufacturing Management Data – General Overview: Part 1 (2004). ISO TC184/SC4, ISO IS 15531-1Google Scholar
  9. 9.
    Browning, T.R., Fricke, E., Negele, H.: Key concepts in modeling product development processes. Syst. Eng. 9(2), 104–128 (2006). Scholar
  10. 10.
    Solano, L., Romero, F., Rosado, P.: An ontology for integrated machining and inspection process planning focusing on resource capabilities. Int. J. Comput. Integr. Manuf. 29, 1–15 (2016). Scholar
  11. 11.
    Vichare, P., Nassehi, A., Newman, S.: A unified manufacturing resource model for representation of computerized numerically controlled machine tools. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 223, 463–483 (2009). Scholar
  12. 12.
    Labrousse, M., Bernard, A.: FBS-PPRE, an enterprise knowledge lifecycle model. In: Bernard, A., Tichkiewitch, S. (eds.) Methods and Tools for Effective Knowledge Life-Cycle-Management, pp. 285–305. Springer, Heidelberg (2008). Scholar
  13. 13.
    Bonfatti, F., Monari, P.D., Paganelli, P.: Resource-free and resource-dependent aspects of process modelling: a rule-based conceptual approach. Int. J. Comput. Integr. Manuf. 11(1), 60–76 (1998). Scholar
  14. 14.
    Solano, L., Rosado, P., Romero, F.: Knowledge representation for product and processes development planning in collaborative environments. Int. J. Comput. Integr. Manuf. 27, 787–801 (2014). Scholar
  15. 15.
    Newman, S.T., Nassehi, A.: Machine tool capability profile for intelligent process planning. CIRP Ann. Manuf. Technol. 58, 421–424 (2009). Scholar
  16. 16.
    ISO 15704. Industrial automation systems – requirements for enterprise-reference architectures and methodologies (2000)Google Scholar
  17. 17.
    Inden, U., Mehandjiev, N., Mönch, L., Vrba, P.: Towards an ontology for small series production. In: Mařík, V., Lastra, J.L.M., Skobelev, P. (eds.) HoloMAS 2013. LNCS (LNAI), vol. 8062, pp. 128–139. Springer, Heidelberg (2013). Scholar
  18. 18.
    Fadel, F.G., Fox, M.S., Gruninger, M.: A generic enterprise resource ontology. In: 3rd IEEE Workshop on Enabling Technologies, Morgantown, 17–19 April 1994.
  19. 19.
    ISO 18629-1. Industrial automation systems and integration – process specification language – part 1: overview and basic principles (2004)Google Scholar
  20. 20.
    Borgo, S., Masolo, C.: Foundational choices in DOLCE. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 361–381. Springer, Heidelberg (2009). Scholar
  21. 21.
    Masolo, C., et al.: Social roles and their descriptions. In: KR, pp. 267–277 (2004)Google Scholar
  22. 22.
    Röpke, H., Hell, K., Zawisza, J., Lüder, A., Schmidt, N.: Identification of “Industrie 4.0” component hierarchy layers. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8 (2016).

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Emilio M. Sanfilippo
    • 1
    Email author
  • Sergio Benavent
    • 4
  • Stefano Borgo
    • 2
  • Nicola Guarino
    • 2
  • Nicolas Troquard
    • 3
  • Fernando Romero
    • 4
  • Pedro Rosado
    • 4
  • Lorenzo Solano
    • 5
  • Farouk Belkadi
    • 1
  • Alain Bernard
    • 1
  1. 1.ECN, Laboratory of Digital Sciences of Nantes, UMR CNRS 6004NantesFrance
  2. 2.Laboratory for Applied Ontology ISTC-CNRTrentoItaly
  3. 3.Free University of Bozen-BolzanoBolzanoItaly
  4. 4.Universitat Jaume ICastellónSpain
  5. 5.Universitat Politècnica de ValènciaValenciaSpain

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