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The development of an ontology for describing the capabilities of manufacturing resources

Author

Listed:
  • Eeva Järvenpää

    (Tampere University of Technology)

  • Niko Siltala

    (Tampere University of Technology)

  • Otto Hylli

    (Tampere University of Technology)

  • Minna Lanz

    (Tampere University of Technology)

Abstract

Today’s highly volatile production environments call for adaptive and rapidly responding production systems that can adjust to the required changes in processing functions, production capacity and dispatching of orders. There is a desire to support such system adaptation and reconfiguration with computer-aided decision support systems. In order to bring automation to reconfiguration decision making in a multi-vendor resource environment, a common formal resource model, representing the functionalities and constraints of the resources, is required. This paper presents the systematic development process of an OWL-based manufacturing resource capability ontology (MaRCO), which has been developed to describe the capabilities of manufacturing resources. As opposed to other existing resource description models, MaRCO supports the representation and automatic inference of combined capabilities from the representation of the simple capabilities of co-operating resources. Resource vendors may utilize MaRCO to describe the functionality of their offerings in a comparable manner, while the system integrators and end users may use these descriptions for the fast identification of candidate resources and resource combinations for a specific production need. This article presents the step-by-step development process of the ontology by following the five phases of the ontology engineering methodology: feasibility study, kickoff, refinement, evaluation, and usage and evolution. Furthermore, it provides details of the model’s content and structure.

Suggested Citation

  • Eeva Järvenpää & Niko Siltala & Otto Hylli & Minna Lanz, 2019. "The development of an ontology for describing the capabilities of manufacturing resources," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 959-978, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-018-1427-6
    DOI: 10.1007/s10845-018-1427-6
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    References listed on IDEAS

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    1. Nicola Guarino & Christopher A. Welty, 2009. "An Overview of OntoClean," International Handbooks on Information Systems, in: Steffen Staab & Rudi Studer (ed.), Handbook on Ontologies, pages 201-220, Springer.
    2. J. Backhaus & G. Reinhart, 2017. "Digital description of products, processes and resources for task-oriented programming of assembly systems," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1787-1800, December.
    3. York Sure & Steffen Staab & Rudi Studer, 2009. "Ontology Engineering Methodology," International Handbooks on Information Systems, in: Steffen Staab & Rudi Studer (ed.), Handbook on Ontologies, pages 135-152, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Russell Tatenda Munodawafa & Satirenjit Kaur Johl, 2019. "Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies," Sustainability, MDPI, vol. 11(15), pages 1-21, August.
    2. Chenxi Yuan & Guoyan Li & Sagar Kamarthi & Xiaoning Jin & Mohsen Moghaddam, 2022. "Trends in intelligent manufacturing research: a keyword co-occurrence network based review," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 425-439, February.
    3. Wei Nie & Katharina Vita & Tariq Masood, 2024. "An ontology for defining and characterizing demonstration environments," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3501-3521, October.
    4. Hien Nguyen Ngoc & Ganix Lasa & Ion Iriarte, 2022. "Human-centred design in industry 4.0: case study review and opportunities for future research," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 35-76, January.
    5. Colin Reiff & Matthias Buser & Thomas Betten & Volkher Onuseit & Max Hoßfeld & Daniel Wehner & Oliver Riedel, 2021. "A Process-Planning Framework for Sustainable Manufacturing," Energies, MDPI, vol. 14(18), pages 1-28, September.
    6. Kai Zhang & Zhiying Tu & Dianhui Chu & Xiaoping Lu & Lucheng Chen, 2024. "Aic: an industrial knowledge graph with Abstraction-Instance-Capability reasoning abilities for personalized customization," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3419-3440, October.
    7. Amon Göppert & Lea Grahn & Jonas Rachner & Dennis Grunert & Simon Hort & Robert H. Schmitt, 2023. "Pipeline for ontology-based modeling and automated deployment of digital twins for planning and control of manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2133-2152, June.
    8. Xiaochen Zheng & Xiaodu Hu & Rebeca Arista & Jinzhi Lu & Jyri Sorvari & Joachim Lentes & Fernando Ubis & Dimitris Kiritsis, 2024. "A semantic-driven tradespace framework to accelerate aircraft manufacturing system design," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 175-198, January.

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