Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling

Peter Hedström; Victor Lamelas Cubero; Jón Sigurdsson; Viktor Österberg; Satish Kolli; Joakim Odqvist; Ziyong Hou; Wangzhong Mu; Viswanadh Gowtham Arigela

arXiv · 2512.03050

Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling

Peter Hedström·Victor Lamelas Cubero·Jón Sigurdsson·Viktor Österberg·Satish Kolli·Joakim Odqvist·Ziyong Hou·Wangzhong Mu·Viswanadh Gowtham Arigela

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Abstract

Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles data and digital twin applications for optimizing manufacturing processes. However, applying general-purpose ML frameworks to complex industrial materials such as steel remains a challenge. A key obstacle is accurately capturing the intricate relationship between chemical composition, processing parameters, and the resulting microstructure and properties. To address this, we introduce a computational framework that combines physical insights with ML to develop a physics-informed continuous cooling transformation (CCT) model for steels. Our model, trained on a dataset of 4,100 diagrams, is validated against literature and experimental data. It demonstrates high computational efficiency, generating complete CCT diagrams with 100 cooling curves in under 5 seconds. It also shows strong generalizability across alloy steels, achieving phase classification F1 scores above 88% for all phases. For phase transition temperature regression, it attains mean absolute errors (MAE) below 20 °C across all phases except bainite, which shows a slightly higher MAE of 27 °C. This framework can be extended with additional generic and customized ML models to establish a universal digital twin platform for heat treatment. Integration with complementary simulation tools and targeted experiments will further support accelerated materials design workflows.

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Peter Hedström, Victor Lamelas Cubero, Jón Sigurdsson, Viktor Österberg, Satish Kolli, Joakim Odqvist, Ziyong Hou, Wangzhong Mu, Viswanadh Gowtham Arigela. 2025-11-21. Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling. https://arxiv.org/abs/2512.03050

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