Journal article 22143 views 50 downloads
A core reference ontology for steelmaking process knowledge modelling and information management
Computers in Industry, Volume: 135, Start page: 103574
Swansea University Authors:
Qiushi Cao, Sadeer Beden, Arnold Beckmann
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DOI (Published version): 10.1016/j.compind.2021.103574
Abstract
Following the trend of Industry 4.0, the business model of steel manufacturing is transforming from a historical inwardly focused supplier/customer relationship to one that embraces the wider end-to-end supply chain and improves productivity more holistically. However, the data and information requi...
| Published in: | Computers in Industry |
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| ISSN: | 0166-3615 |
| Published: |
Elsevier BV
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa59000 |
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| Abstract: |
Following the trend of Industry 4.0, the business model of steel manufacturing is transforming from a historical inwardly focused supplier/customer relationship to one that embraces the wider end-to-end supply chain and improves productivity more holistically. However, the data and information required for supply chain planning and steelmaking process modelling are normally distributed over scattered sources across organisation boundaries and research communities. This leads to a major problem concerning semantic interoperability. To address this issue, this paper introduces a Common Reference Ontology for Steelmaking (CROS). CROS serves as a shared steelmaking resource and capability model that aims to facilitate knowledge modelling, knowledge sharing and information management. In contrast to most of the existing steelmaking ontologies which merely focus on conceptual modelling, our work pays special attention to the real-world implementation and utilisation aspects of CROS. The functionality and usefulness of CROS is evaluated and tested on a real-world condition-based monitoring and maintenance task for cold rolling mills at Tata Steel in the United Kingdom. |
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| Keywords: |
Industry 4.0; Steelmaking; Knowledge graph; Ontology; Ontology-based data access; Condition-based maintenance |
| College: |
Faculty of Science and Engineering |
| Funders: |
EP/S018107/1, EP/T517537/1]and Tata Steel. |
| Start Page: |
103574 |