Characterizing strip snap in cold rolling process using advanced data analytics

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

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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Among the undesirable quality incidents in the cold rolling process of strip products, strip snap could result in yield loss and reduced work speed. Therefore, it is necessary to reveal the factors influencing the occurrence of this failure for quality improvement. In this study, a data analytics approach was applied with the aim of determining relevant variables affecting snap occurrence. To validate this approach, a case study was conducted based on real-world data collected from an electrical steel reversing mill. The results suggested a selection of variables to characterize the quality issue of strip snap in the cold rolling process. This quality characterization study was performed as the preliminary stage of a quality improvement task.

Original languageEnglish
Pages (from-to)453-458
Number of pages6
JournalProcedia CIRP
Publication statusPublished - 20 Jun 2019
Externally publishedYes
Event52nd CIRP Conference on Manufacturing Systems, CMS 2019 - Ljubljana, Slovenia
Duration: 12 Jun 201914 Jun 2019


  • Cold rolling
  • Data analytics
  • Machine learning
  • Quality improvement
  • Strip breakage


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