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.
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Notes
Some cold work tool steels form an exception to this definition.
In case any of the expressions in the denominator are zero the MCC is not defined
The fourth publication, Vannucci et al. (2011) applies two use cases and is therefore discussed one paragraph below
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Cemernek, D., Cemernek, S., Gursch, H. et al. Machine learning in continuous casting of steel: a state-of-the-art survey. J Intell Manuf 33, 1561–1579 (2022). https://doi.org/10.1007/s10845-021-01754-7
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DOI: https://doi.org/10.1007/s10845-021-01754-7