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Predicting Lateral Flow in Hot Sheet Metal Rolling Using Symbolic Regression

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Abstract:

This work investigates the use of symbolic regression (SR) to address the trade-off between predictive accuracy and computational efficiency in modeling physical phenomena by constructing compact, closed-form expressions directly from data. In this study, SR is applied to develop fast and accurate models for predicting lateral spread in the hot rolling of steel slabs. The SR models are trained on high-fidelity finite element simulation data and evaluated against established analytical models. Model selection is guided by a parsimony-based optimization strategy that balances predictive accuracy and expression complexity. The results show that the SR-derived formulations achieve lower prediction errors with reduced complexity compared to traditional analytical models. Moreover, SR maintains strong predictive performance even when trained on limited datasets, demonstrating its robustness. Overall, the findings of this work highlight the suitability of symbolic regression for computationally efficient and accurate modeling of complex physical phenomena.

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Periodical:

Key Engineering Materials (Volume 1050)

Pages:

157-169

DOI:

https://doi.org/10.4028/p-5QE7ks

Citation:

Cite this paper

Online since:

April 2026

Authors:

Daniel Luis Mederos, Amirali Hashemzadeh, Antonella Cometa*, Celal Soyarslan, Frederic Bock, Ton van den Boogard, Benjamin Klusemann

Keywords:

Data-Driven Modeling, Finite Element Modelling, Hot Rolling, Lateral Spread, Symbolic Regression

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Creative Commons CC BY 4.0

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* - Corresponding Author

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