Abstract
In the context of Industry 4.0, the paradigm of manufacturing has shifted from autonomous to intelligent by integrating advanced communication technologies. However, to enable manufacturers to respond quickly and accurately to the complex environment of manufacturing, knowledge of manufacturing required suitable representation. Ontology is a proper solution for knowledge representation, which is used to describe concepts and attributes in a specified domain. This paper proposes an ontology-based industrial model and significantly improves the interoperability of the models. Firstly, we conceptualize the attribute of the industrial models by providing concept and their properties in the schema layer of the ontology. Then, according to the data collected from the manufacturing system, several instances are created and stored in the data layer. In addition, we present a prototype distributed computing application. The result suggests that the ontology can optimize the management of industrial models and achieve interoperability between models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wei, H.L., Mukherjee, T., Zhang, W., Zuback, J.S., Knapp, G.L., De, A., DebRoy, T.: Mechanistic models for additive manufacturing of metallic components. Progress Mater. Sci. 116, 100703(2021)
Ghahramani, M., Qiao, Y., Zhou, M.C., d O’Hagan, A., Sweeney, J.: AI-based modeling and data-driven evaluation for smart manufacturing processes. IEEE/CAA J. Automatica Sinica 7(4), 1026–1037(2020)
Noy, F.N., McGuinness, D.L.: Ontology development 101: a guide to creating your first ontology. Stanford knowledge systems laboratory technical report KSL-01-05 (2001)
Järvenpää, E., Siltala, N., Hylli, O., Lanz, M.: The development of an ontology for describing the capabilities of manufacturing resources. J. Intell. Manuf. 30(2), 959–978 (2019)
Dinar, M., Rosen, D.W.: A design for additive manufacturing ontology. J. Comput. Inf. Sci. Eng. 17(2) (2017)
Saha, S., Li, W.D., Usman, Z., Shah, N.: Core manufacturing ontology to model manufacturing operations and sequencing knowledge. Service Oriented Computing and Applications, 1–13 (2023)
Sanfilippo, E.M., Belkadi, F., Bernard, A.: Ontology-based knowledge representation for additive manufacturing. Comput. Ind. 109, 182–194 (2019)
Lemaignan, S., Siadat, A., Dantan, J.Y., Semenenko, A.: MASON: a proposal for an ontology of manufacturing domain. In: IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS’06), pp. 195–200 (2006)
Elkan, C., Greiner, R.: Building large knowledge-based systems: representation and inference in the cyc project: DB Lenat and RV Guha. Artificial Intelligence (1993)
Mayer, R.J.: Information integration for concurrent engineering (IICE). In: IDEF3 Process description capture method report (1995)
López, M.F., Gómez, P.A., Sierra, J.P., Sierra, A.P.: Building a chemical ontology using methontology and the ontology design environment. IEEE Intell. Syst. Appl. 14, 37–46 (1999)
Farazi, F.: OntoKin: an ontology for chemical kinetic reaction mechanisms. J. Chem. Inf. Model. 60(1), 108–120 (2019)
Tim, B.L., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 34–43 (2001)
Bitsch, G., Senjic, P., Askin, J.: Dynamic adaption in cyber-physical production systems based on ontologies. Procedia Comput. Sci. 200, 577–584 (2022)
Zhao, Y.Y., Liu, Q., Xu, W.J., Yuan, H.Q., Lou, P.: An ontology self-learning approach for CNC machine capability information integration and representation in cloud manufacturing. J. Ind. Inf. Integr. 25, 100300 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gao, C. et al. (2024). An Ontology for Industrial Intelligent Model Library and Its Distributed Computing Application. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_6
Download citation
DOI: https://doi.org/10.1007/978-981-99-8145-8_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8144-1
Online ISBN: 978-981-99-8145-8
eBook Packages: Computer ScienceComputer Science (R0)