Multi-sourced modelling for strip breakage using knowledge graph embeddings

Zheyuan Chen, Ying Liu, Agustin Valera Medina, Fiona Robinson

Research output: Contribution to journalConference articlepeer-review

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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.

Original languageEnglish
Pages (from-to)1884-1889
Number of pages6
JournalProcedia CIRP
Volume104
Early online date31 Jul 2021
DOIs
Publication statusPublished - 26 Nov 2021
Event54th CIRP Conference on Manufacturing Systems. 2021: Towards Digitalized Manufacturing 4.0 - Virutal
Duration: 22 Sep 202124 Sep 2021

Keywords

  • Strip Breakage
  • Cold Rolling
  • Multi-sourced data
  • Ontology
  • Knowledge Graph

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