FAN MO Fan.Mo@nottingham.ac.uk
Interdisciplinary Research Fellow in Intelligent Manufacturing Systems
Semantic models and knowledge graphs as manufacturing system reconfiguration enablers
Mo, Fan; Chaplin, Jack C.; Sanderson, David; Martínez-Arellano, Giovanna; Ratchev, Svetan
Authors
JACK CHAPLIN Jack.Chaplin@nottingham.ac.uk
Assistant Professor
DAVID SANDERSON DAVID.SANDERSON@NOTTINGHAM.AC.UK
Senior Research Fellow
GIOVANNA MARTINEZ ARELLANO Giovanna.MartinezArellano@nottingham.ac.uk
Anne Mclaren Research Fellow
Professor SVETAN RATCHEV svetan.ratchev@nottingham.ac.uk
Cripps Professor of Production Engineering & Head of Research Division
Contributors
FAN MO Fan.Mo@nottingham.ac.uk
Researcher
JACK CHAPLIN Jack.Chaplin@nottingham.ac.uk
Researcher
DAVID SANDERSON DAVID.SANDERSON@NOTTINGHAM.AC.UK
Researcher
GIOVANNA MARTINEZ ARELLANO Giovanna.MartinezArellano@nottingham.ac.uk
Researcher
Professor SVETAN RATCHEV svetan.ratchev@nottingham.ac.uk
Project Leader
Abstract
Reconfigurable Manufacturing System (RMS) provides a cost-effective approach for manufacturers to adapt to fluctuating market demands by reconfiguring assets through automated analysis of asset utilization and resource allocation. Achieving this automation necessitates a clear understanding, formalization, and documentation of asset capabilities and capacity utilization. This paper introduces a unified model employing semantic modeling to delineate the manufacturing sector's capabilities, capacity, and reconfiguration potential. The model illustrates the integration of these three components to facilitate efficient system reconfiguration. Additionally, semantic modeling allows for the capture of historical experiences, thus enhancing long-term system reconfiguration through a knowledge graph. Two use cases are presented: capability matching and reconfiguration solution recommendation based on the proposed model. A thorough explication of the methodology and outcomes is provided, underscoring the advantages of this approach in terms of heightened efficiency, diminished costs, and augmented productivity.
Citation
Mo, F., Chaplin, J. C., Sanderson, D., Martínez-Arellano, G., & Ratchev, S. (2024). Semantic models and knowledge graphs as manufacturing system reconfiguration enablers. Robotics and Computer-Integrated Manufacturing, 86, Article 102625. https://doi.org/10.1016/j.rcim.2023.102625
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 19, 2023 |
Online Publication Date | Sep 8, 2023 |
Publication Date | 2024-04 |
Deposit Date | Sep 28, 2023 |
Publicly Available Date | Sep 28, 2023 |
Journal | Robotics and Computer-Integrated Manufacturing |
Print ISSN | 0736-5845 |
Electronic ISSN | 1879-2537 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 86 |
Article Number | 102625 |
DOI | https://doi.org/10.1016/j.rcim.2023.102625 |
Keywords | Semantic models; Knowledge graphs; Reconfigurable manufacturing systems; Capability matching |
Public URL | https://nottingham-repository.worktribe.com/output/25383246 |
Publisher URL | https://www.sciencedirect.com/science/article/pii/S073658452300100X?via%3Dihub |
Additional Information | This article is maintained by: Elsevier; Article Title: Semantic models and knowledge graphs as manufacturing system reconfiguration enablers; Journal Title: Robotics and Computer-Integrated Manufacturing; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.rcim.2023.102625; Content Type: article; Copyright: © 2023 The Author(s). Published by Elsevier Ltd. |
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