An analytical predictor machine learning corrector scheme for modeling lateral flow in hot strip rolling
Amirali HASHEMZADEH, Frederic E. BOCK, Camile HOL, Koen SCHUTTE, Antonella COMETA, Celal SOYARSLAN, Benjamin KLUSEMANN, Ton VAN DEN BOOGAARD
Abstract. Rolling is a metal forming process where slabs are passed through rollers to produce strips with specific dimensions and mechanical properties. This process is performed in hot or cold formats. In hot rolling, the workpiece is initially heated above its recrystallization temperature. During the hot rolling process, plastic deformation occurs as the material’s thickness decreases and elongation takes place along the longitudinal axis of the workpiece. Due to the incompressibility of plastic deformation, the material also expands in the transverse direction, a phenomenon known as spread or lateral flow. Modeling spread is crucial for sustainability considerations and meeting customer expectations regarding the quality of the final product. Current prediction methodologies, such as the accurate but slow Finite Element (FE) method or the fast but inaccurate analytical metal forming analysis, are impractical for optimal control. To tackle these challenges, hybrid frameworks have emerged as a promising alternative. The present work aims to develop a fast and accurate model for predicting spread in hot rolling. Specifically, machine learning improves analytical models by leveraging data from a high-fidelity FE model. Initially, we review analytical models for spread, which address key aspects of the problem’s physics. To generate the ground truth (GT) space, an automated FE model for hot strip rolling is created. Moreover, the model’s sensitivity to both process and material parameters is investigated. In the Analytical Predictor Machine Learning Corrector scheme, the analytical models generate initial predictions of GT. In the correction step, a data-driven machine learning model is used to refine these predictions by compensating for deviations from high-fidelity FE simulations. The proposed hybrid framework improves the accuracy of the existing analytical models while preserving their computational efficiency.
Keywords
Hot Rolling, Finite Element Model, Predictor-Corrector Modeling, Machine Learning
Published online 5/7/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Amirali HASHEMZADEH, Frederic E. BOCK, Camile HOL, Koen SCHUTTE, Antonella COMETA, Celal SOYARSLAN, Benjamin KLUSEMANN, Ton VAN DEN BOOGAARD, An analytical predictor machine learning corrector scheme for modeling lateral flow in hot strip rolling, Materials Research Proceedings, Vol. 54, pp 2002-2011, 2025
DOI: https://doi.org/10.21741/9781644903599-215
The article was published as article 215 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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