Characterizing strip snap in cold rolling process using advanced data analytics

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. Characterizing strip snap in cold rolling process using advanced data analytics. Procedia CIRP 81 , pp. 453-458. 10.1016/j.procir.2019.03.078

Preview

PDF - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (728kB) | Preview

Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures
Publisher:	Elsevier
ISSN:	2212-8271
Date of First Compliant Deposit:	26 March 2019
Date of Acceptance:	13 March 2019
Last Modified:	02 May 2023 13:37
URI:	https://orca.cardiff.ac.uk/id/eprint/120659

Citation Data

Cited 6 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item