Machine learning in continuous casting of steel: a state-of-the-art survey

David Cemernek (), Sandra Cemernek (), Heimo Gursch (), Ashwini Pandeshwar (), Thomas Leitner (), Matthias Berger (), Gerald Klösch () and Roman Kern ()
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David Cemernek: Know-Center GmbH - Research Center for Data-Driven Business & Big Data Analytics
Sandra Cemernek: Technical University Graz - CAMPUSonline
Heimo Gursch: Know-Center GmbH - Research Center for Data-Driven Business & Big Data Analytics
Ashwini Pandeshwar: Know-Center GmbH - Research Center for Data-Driven Business & Big Data Analytics
Thomas Leitner: voestalpine Stahl Donawitz GmbH
Matthias Berger: voestalpine Stahl Donawitz GmbH
Gerald Klösch: voestalpine Stahl Donawitz GmbH
Roman Kern: Know-Center GmbH - Research Center for Data-Driven Business & Big Data Analytics

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 6, No 1, 1579 pages

Abstract: Abstract Continuous casting is the most important route for the production of steel today. Due to the physical, mechanical, and chemical components involved in the production, continuous casting is a very complex process, pushing conventional methods of monitoring and control to their limits. In recent years, this complexity and the increasing global competition created a demand for new methods to monitor and control the continuous casting process. Due to the success and associated rise of machine learning techniques in recent years, machine learning nowadays plays an essential role in monitoring and controlling complex processes. This publication presents a scientific survey of machine learning techniques for the analysis of the continuous casting process. We provide an introduction to both the involved fields: an overview of machine learning, and an overview of the continuous casting process. Therefore, we first analyze the existing work concerning machine learning in continuous casting of steel and then synthesize the common concepts into categories, supporting the identification of common use cases and approaches. This analysis is concluded with the elaboration of challenges, potential solutions, and a future outlook of further research directions.

Keywords: Continuous casting; Machine learning; Intelligent manufacturing; Process industry; Quality prediction; Steel manufacturing; Steel quality control (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10845-021-01754-7

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