A Preliminary Methodological Approach to Models for Manufacturing (MfM)

  • Fernando MasEmail author
  • Jesus Racero
  • Manuel Oliva
  • Domingo Morales-Palma
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)


To enhance multidisciplinary design and simulation of complex systems, MBSE (Model Based Systems Engineering) is a methodology where computer aided graphical modeling authoring tools are used to specify functions and behaviors of the systems. Simulation tools bring about a system behavioral abstraction required for the design of complex products. MBSE enables more robust systems engineering, because it results in models and their associated behavioral abstraction [1].

A research approach for modelling manufacturing systems in the aerospace industry, and in particular for FAL (Final Assembly Line), has been proposed by the authors in several research papers during the last years [2, 3]. Functional and data models have been published and deployed using data structures available from commercial PLM systems [4].

Recently a new approach for modelling manufacturing systems has been coined as an extension of the previous research to introduce MBSE in manufacturing. A new architecture based on 3-Layers Model (3LM) has been defined: a Data layer, an Ontology layer and a Service layer. Ontology layer is the core of the 3LM. The Ontology layer defines: Scope model, Data model, Behavior model and Semantic model, to further instance information from databases. Scope model is mandatory because manufacturing is a large and wide part of the artifact lifecycle and Data model can cover different several uses across it.

This paper presents a preliminary methodology for Models for Manufacturing (MfM) trends and issues that can be addressed in order to support the generation and management of manufacturing ontologies.


Models for Manufacturing (MfM) industrial DMU (iDMU) Ontologies 3-Layers Model (3LM) Model-Based Systems Engineering (MBSE) 



The authors would like to thank Ignacio Eguia, Jose Carlos Molina, Andres Padillo, and other Sevilla University colleagues, Julio Ortegon, and other Cadiz University colleagues, Rebeca Arista, Tamara Borreguero, and other AIRBUS colleagues for their support and contribution during the development of this work.


  1. 1.
    Holland, O.T.: Model-based systems engineering. In: Loper, M.L. (ed.) Modeling and Simulation in the Systems Engineering Life Cycle. SFMA, pp. 299–306. Springer, London (2015). Scholar
  2. 2.
    Mas, F., Rios, J., Menendez, J.L., Gomez, A.: A process-oriented approach to modeling the conceptual design of aircraft assembly lines. Int. J. Adv. Manuf. Technol. 67(1–4), 771–784 (2013). Scholar
  3. 3.
    Mas, F., Oliva, M., Rios, J., Gomez, A., Olmos, V., et al.: PLM based approach to the industrialization of aeronautical assemblies. Procedia Eng. 132, 1045–1052 (2015). Scholar
  4. 4.
    Gómez, A., Rios, J., Mas, F., Vizan, A.: Method and software application to assist in the conceptual design of aircraft final assembly lines. J. Manuf. Syst. 40, 37–53 (2016). Scholar
  5. 5.
    Friedenthal, S., Griego, R., Sampson, M.: INCOSE model based systems engineering (MBSE) initiative. In: INCOSE International Symposium (2007)Google Scholar
  6. 6.
    Dickerson, C.E., Mavris, D.: A brief history of models and model based systems engineering and the case for relational orientation. IEEE Syst. J. 7(4), 581–592 (2013)CrossRefGoogle Scholar
  7. 7.
    D’Ambrosio, J., Soremekun, G.: Systems engineering challenges and MBSE opportunities for automotive system design. In: 2017, IEEE International Conference on Systems and Cybernetics, Banff, AB, pp. 2075–2080 (2017)Google Scholar
  8. 8.
    Frechette, S.: Model based enterprise for manufacturing. In: 2011, 44th CIRP International Conference on Manufacturing Systems, Madison, WI, United States (2011)Google Scholar
  9. 9.
    Ivezic, N., Kulvatunyou, B., Brandl, D., Cho, H., Yan, L., et al.: Drilling down on Smart Manufacturing – enabling composable apps. Manuf. Lett. 08, 10 (2016)Google Scholar
  10. 10.
    Ivezic, N., Kulvatunyou, B., Lu, Y., Lee, Y., Lee, J., et al.: OAGi/NIST Workshop on Open Cloud Architecture for Smart Manufacturing. Report number: NISTIR 8124Google Scholar
  11. 11.
    Ríos, J., Hernández, J.C., Oliva, M., Mas, F.: Product avatar as digital counterpart of a physical individual product: literature review and implications in an aircraft. In: 22nd International Conference on Concurrent Engineering, vol. 2, pp. 657–666. IOS Press.
  12. 12.
    Bergenthal, J.: Final Report Model Based Engineering (MBE) Subcommittee. NDIA Systems Engineering Division, M&S Committee (2011). Accessed Mar 2018
  13. 13.
    European Commission: Aeronautics and Air Transport: Beyond Vision 2020 (Towards 2050). Official Publications of the European Communities, 2010, Luxembourg. Accessed Mar 2018
  14. 14.
    Mas, F., Menendez, J.L., Oliva, M., Rios, J., Gomez, A., et al.: iDMU as the Collaborative Engineering engine: research experiences in Airbus. In: International Conference on Engineering, Technology and Innovation (ICE), Bergamo, pp. 1–7 (2014).
  15. 15.
    Fiorini, S.R., Bermejo-Alonso, J., Gonçalves, P., de Freitas, E.P., Alarcos, A.O., et al.: A suite of ontologies for robotics and automation [industrial activities]. IEEE Robot. Autom. Mag. 24(1), 8–11 (2017)CrossRefGoogle Scholar
  16. 16.
    Mayer, S., Hodges, J., Dan, Yu., Kritzler, M., Michahelles, F.: An open semantic framework for the industrial Internet of Things. IEEE Intell. Syst. 32(1), 96–101 (2017)CrossRefGoogle Scholar
  17. 17.
    Perini, S., Arena, D., Kiritsis, D., Taisch, M.: An ontology-based model for training evaluation and skill classification in an Industry 4.0 environment. In: Lödding, H., Riedel, R., Thoben, K.-D., von Cieminski, G., Kiritsis, D. (eds.) APMS 2017. IAICT, vol. 513, pp. 314–321. Springer, Cham (2017). Scholar
  18. 18.
    Lundgren, M., Hedlind, M., Kjellberg, T.: Model driven manufacturing process design and managing quality. Procedia CIRP 50(1), 299–304 (2016)CrossRefGoogle Scholar
  19. 19.
    Uschold, M., Gruninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(2), 93–136 (1996)CrossRefGoogle Scholar
  20. 20.
    Kiritsis, D.: Closed-loop PLM for intelligent products in the era of the Internet of Things. Comput. Aided Des. 43(5), 479–501 (2011)CrossRefGoogle Scholar
  21. 21.
    Morales-Palma, D., Mas, F., Racero, J., Vallellano, C.: A preliminary study of Models for Manufacturing (MfM) applied to Incremental Sheet Forming. In: Chiabert, P., Bouras, A., Noël, F., Ríos, J. (eds.) PLM 2018. IFIP AICT, vol. 540, pp. 284–293. Springer, Cham (2018)Google Scholar
  22. 22.
    Slimani, T.: Ontology development: a comparing study on tools, languages and formalisms. Indian J. Sci. Technol. 8(24) (2015)Google Scholar
  23. 23.
    NIST: Integration Definition for Function Modeling (IDEF0). Computer Systems Laboratory of the National Institute of Standards and Technology, December 1993. Accessed Mar 2018
  24. 24.
    Cañas, A.J., et al.: Concept maps: integrating knowledge and information visualization. In: Tergan, S.-O., Keller, T. (eds.) Knowledge and Information Visualization. LNCS, vol. 3426, pp. 205–219. Springer, Heidelberg (2005). Scholar
  25. 25.
    Arista, R., Mas, F.: A preliminary model-based approach for Gender analysis of Airbus Research Organization. In: IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Stuttgart, pp. 1-6 (2018).

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Fernando Mas
    • 1
    Email author
  • Jesus Racero
    • 2
  • Manuel Oliva
    • 1
  • Domingo Morales-Palma
    • 2
  1. 1.AirbusSevilleSpain
  2. 2.Universidad de SevillaSevilleSpain

Personalised recommendations