Strip snap analytics in cold rolling process using machine learning

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 2019. Strip snap analytics in cold rolling process using machine learning. Presented at: 2019 IEEE 15th International Conference on Automation Science and Engineering, Vancouver, BC, Canada, 22-26 August 2019. -.

Preview

PDF - Accepted Post-Print Version Download (344kB) | Preview

Abstract

Strip snap, also known as strip breakage or belt tearing, is an undesirable quality incident which results in yield loss and reduced work speed in the cold rolling process of strip products. Therefore, it is necessary to reveal a functional relationship between certain selected variables and strip snap event for the aim of quality improvement. In this study, the probability of strip snap occurrence was quantified by a selected measured variable. Several machine learning algorithms were adopted to predict this target probability. To validate this approach, a case study was conducted based on real-world data collected from an electrical steel reversing mill. The excessively good performance indicates several variables which are strongly correlated with the target.

Item Type:	Conference or Workshop Item (Paper)
Status:	In Press
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TS Manufactures
Date of First Compliant Deposit:	11 July 2019
Date of Acceptance:	16 May 2019
Last Modified:	27 Jan 2023 02:07
URI:	https://orca.cardiff.ac.uk/id/eprint/124101

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item