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Multi-sourced modelling for strip breakage using knowledge graph embeddings

Chen, Zheyuan, Liu, Ying ORCID:, Valera Medina, Agustin ORCID: 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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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: 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

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