Author
Listed:
- Patrick Sapel
(Institute for Plastics Processing at RWTH Aachen University)
- Lina Molinas Comet
(Fraunhofer Institute for Applied Information Technology FIT)
- Iraklis Dimitriadis
(Fraunhofer Institute for Applied Information Technology FIT)
- Christian Hopmann
(Institute for Plastics Processing at RWTH Aachen University)
- Stefan Decker
(Fraunhofer Institute for Applied Information Technology FIT
Information Systems & Databases at RWTH Aachen University)
Abstract
One core concept of Industry 4.0 is establishing highly autonomous manufacturing environments. In the vision of Industry 4.0, the product leads its way autonomously through the shopfloor by communicating with the production assets. Therefore, a common vocabulary and an understanding of the domain’s structure are mandatory, so foundations in the form of knowledge bases that enable autonomous communication have to be present. Here, ontologies are applicable since they define all assets, their properties, and their interconnection of a specific domain in a standardized manner. Reusing and enlarging existing ontologies instead of building new ontologies facilitates cross-domain and cross-company communication. However, the demand for reusing or enlarging existing ontologies of the manufacturing domain is challenging as no comprehensive review of present manufacturing domain ontologies is available. In this contribution, we provide a holistic review of 65 manufacturing ontologies and their classification into different categories. Based on the results, we introduce a priority guideline and a framework to support engineers in finding and reusing existent ontologies of a specific subdomain in manufacturing. Furthermore, we present 16 supporting ontologies to be considered in the ontology development process and eight catalogs that contain ontologies and vocabulary services.
Suggested Citation
Patrick Sapel & Lina Molinas Comet & Iraklis Dimitriadis & Christian Hopmann & Stefan Decker, 2025.
"A review and classification of manufacturing ontologies,"
Journal of Intelligent Manufacturing, Springer, vol. 36(6), pages 3669-3693, August.
Handle:
RePEc:spr:joinma:v:36:y:2025:i:6:d:10.1007_s10845-024-02425-z
DOI: 10.1007/s10845-024-02425-z
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