Strip snap analytics in cold rolling process using machine learning

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PublisherIEEE Computer Society
Pages368-373
Number of pages6
ISBN (Electronic)978-1-7281-0355-6, 978-1-7281-0356-3 , 978-1-7281-0357-0
DOIs
Publication statusPublished - Aug 2019
Externally publishedYes
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: 22 Aug 201926 Aug 2019

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Country/TerritoryCanada
CityVancouver
Period22/08/1926/08/19

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