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, Roman Kern

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftReview eines Fachbereichs (Review article)

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.
Originalspracheenglisch
FachzeitschriftJournal of Intelligent Manufacturing
Frühes Online-DatumMär 2021
DOIs
PublikationsstatusElektronische Veröffentlichung vor Drucklegung. - Mär 2021

ASJC Scopus subject areas

  • Software
  • Artificial intelligence
  • !!Industrial and Manufacturing Engineering

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