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Multi-faceted modelling for strip breakage in cold rolling using machine learning

Chen, Zheyuan, Liu, Ying ORCID:, Valera-Medina, Agustin ORCID:, Robinson, Fiona and Packianather, Michael ORCID: 2021. Multi-faceted modelling for strip breakage in cold rolling using machine learning. International Journal of Production Research 59 (21) , pp. 6347-6360. 10.1080/00207543.2020.1812753

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In the cold rolling process of steel strip products, strip breakage is an undesired production failure which can lead to yield loss, reduced work speed and equipment damage. To perform a root cause analysis, conventional physics-based approaches which focus on mechanical and metallurgical principles have been applied in a retrospective manner. With the advancement of data acquisition technologies, numerous process monitoring data is collected by various sensors deployed along this process; however, conventional approaches cannot take advantage of these data. In this paper, a machine learning-based approach is proposed to characterise and model strip breakage in a predictive manner. First, to match the temporal characteristic of strip breakage which occurs instantaneously, historical multivariate time-series data of a cold rolling process were extracted in a run-to-failure manner, and a sliding window strategy was adopted for data annotation. Second, breakage-centric features were identified from three facets – physics-based approaches, empirical knowledge and data-driven features. Finally, these features were used as inputs for strip breakage modelling using recurrent neural networks (RNNs), which are specialised in discovering underlying patterns embedded in time-series data. An experimental study using real-world data collected from a cold-rolled electrical steel strip manufacturer revealed the effectiveness of the proposed approach.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Taylor & Francis
ISSN: 0020-7543
Date of First Compliant Deposit: 5 August 2020
Date of Acceptance: 4 August 2020
Last Modified: 09 Nov 2023 23:07

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