Multi-sourced modelling for strip breakage using knowledge graph embeddings

Chen, Zheyuan, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Valera Medina, Agustin ORCID: https://orcid.org/0000-0003-1580-7133 and Robinson, Fiona 2021. Multi-sourced modelling for strip breakage using knowledge graph embeddings. Presented at: 54th CIRP Conference on Manufacturing Systems (CMS 2021), Virtual, 22-24 September 2021. 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0. , vol.104 Elsevier, pp. 1884-1889. 10.1016/j.procir.2021.11.318

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Abstract

Strip breakage is an undesired production failure in cold rolling. Typically, conventional studies focused on cause analyses, and existing data-driven approaches only rely on a single data source, resulting in a limited amount of information. Hence, we propose an approach for modelling breakage using multiple data sources. Many breakage-relevant features from multiple sources are identified and used, and these features are integrated using a breakage-centric ontology which is then used to create knowledge graphs. Through ontology construction and knowledge embedding, a real-world study using data from a cold-rolled strip manufacturer was conducted using the proposed approach.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TN Mining engineering. Metallurgy T Technology > TS Manufactures
Publisher:	Elsevier
ISSN:	2212-8271
Date of First Compliant Deposit:	15 July 2021
Date of Acceptance:	13 July 2021
Last Modified:	27 Jan 2023 02:10
URI:	https://orca.cardiff.ac.uk/id/eprint/142556

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