Towards an Interoperable Digital Shadow System for Continuous Steel Casting using Explainable AI and Kubernetes

Continuous casting of steel is a complex process that requires a large amount of data, which can be difficult to interpret by humans. This paper presents a digital shadow system for continuous steel casting, which integrates advanced technologies to achieve a scalable, interpretable, and efficient solution. The system consists of applications designed to model ladle movements and predict temperature drops in liquid steel during transportation. The implementation of the system leverages a modular and scalable architecture to enable effective monitoring and validation of the continuous casting process. The digital shadow framework combines predictive modeling, explainability, and orchestration tools such as Kubernetes and Argo Workflows to ensure a high degree of automation, reducing manual intervention, and ensuring consistency and reliability in steel production. The framework provides a detailed and real-time representation of the casting process, facilitating a deeper understanding of the operational dynamics and contributing to improving the quality of the steel billets.

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Publication Details

Published
2025-03-17
DOI
https://doi.org/10.1109/percomworkshops65533.2025.00049
Primary Topic
Manufacturing Process and Optimization
Type
article
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article

Towards an Interoperable Digital Shadow System for Continuous Steel Casting using Explainable AI and Kubernetes

Marek� Wilkus, Krzysztof Regulski, Krzysztof Bzowski, Piotr Hajder et al.
Manufacturing Process and Optimization
article

Towards an Interoperable Digital Shadow System for Continuous Steel Casting using Explainable AI and Kubernetes

Marek� Wilkus, Krzysztof Regulski, Krzysztof Bzowski, Piotr Hajder, Łukasz Sztangret, Lucyna Hajder, Kazimierz Michalik, Mateusz Mojżeszko, Łukasz Rauch, Krzysztof Banaś, R. Nadolski, Andrzej Opaliński, Monika Pernach, Danuta� Szeliga, Adam Mrózek, Tomasz Jażdżewski, Filip Hallo, Wojciech Jędrysik
article en

Abstract

Continuous casting of steel is a complex process that requires a large amount of data, which can be difficult to interpret by humans. This paper presents a digital shadow system for continuous steel casting, which integrates advanced technologies to achieve a scalable, interpretable, and efficient solution. The system consists of applications designed to model ladle movements and predict temperature drops in liquid steel during transportation. The implementation of the system leverages a modular and scalable architecture to enable effective monitoring and validation of the continuous casting process. The digital shadow framework combines predictive modeling, explainability, and orchestration tools such as Kubernetes and Argo Workflows to ensure a high degree of automation, reducing manual intervention, and ensuring consistency and reliability in steel production. The framework provides a detailed and real-time representation of the casting process, facilitating a deeper understanding of the operational dynamics and contributing to improving the quality of the steel billets.

AGH University of Krakow (PL)
Openalex Percentile: Top 80%
Manufacturing Process and Optimization
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