TY - CHAP

T1 - An analytical predictor machine learning corrector scheme for modeling lateral flow in hot strip rolling

AU - Hashemzadeh, Amirali

AU - Bock, Frederic E.

AU - Hol, Camile

AU - Schutte, Koen

AU - Cometa, Antonella

AU - Soyarslan, Celal

AU - Klusemann, Benjamin

AU - VAN DEN BOOGAARD, Ton

N1 - Publisher Copyright: © 2025, Association of American Publishers. All rights reserved.

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - Finite Element Model

KW - Hot rolling

KW - Machine Learning

KW - Predictor-corrector Modeling

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=105008073113&partnerID=8YFLogxK

U2 - 10.21741/9781644903599-215

DO - 10.21741/9781644903599-215

M3 - Article in conference proceedings

AN - SCOPUS:105008073113

SN - 9781644903599

T3 - Materials Research Proceedings

SP - 2002

EP - 2011

BT - 28th International ESAFORM Conference on Material Forming, ESAFORM 2025

A2 - Carlone, Pierpaolo

A2 - Filice, Luigino

A2 - Umbrello, Domenico

PB - Association of American Publishers

T2 - 28th International ESAFORM Conference on Material Forming, ESAFORM 2025

Y2 - 7 May 2025 through 9 May 2025

ER -