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

TL;DR

This paper presents a physics-informed machine learning framework for modeling steel microstructure transformations, enabling rapid and accurate CCT diagram predictions to accelerate steel development.

Contribution

It introduces a novel physics-informed ML model for CCT diagrams that is efficient, generalizable, and validated against experimental data, advancing computational steel design.

Findings

Generated 100 cooling curves in under 5 seconds

Achieved phase classification F1 scores above 88%

MAE below 20°C for phase transition temperature predictions

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 {\deg}C across all phases except bainite, which shows a slightly higher MAE of 27 {\deg}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.