{"ID":2838819,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.03050","arxiv_id":"2512.03050","title":"Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling","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.","short_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,...","url_abs":"https://arxiv.org/abs/2512.03050","url_pdf":"https://arxiv.org/pdf/2512.03050v1","authors":"[\"Peter Hedström\",\"Victor Lamelas Cubero\",\"Jón Sigurdsson\",\"Viktor Österberg\",\"Satish Kolli\",\"Joakim Odqvist\",\"Ziyong Hou\",\"Wangzhong Mu\",\"Viswanadh Gowtham Arigela\"]","published":"2025-11-21T22:16:07Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cond-mat.mtrl-sci\",\"physics.comp-ph\"]","methods":"[]","has_code":false}