物理引导的机器学习用于钢铁开发：一种计算框架和CCT图模型
Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling
AI总结：
本研究提出一种结合物理知识与机器学习的计算框架，用于构建钢铁的连续冷却转变图模型，实现了高效且准确的相变预测与材料设计。
摘要：
机器学习（ML）已经 emerged 作为一种强大的工具，用于加速材料的计算设计和生产。在材料科学中，ML 主要支持使用原理数据进行大规模发现新化合物，并通过数字孪生应用优化制造过程。然而，将通用ML框架应用于复杂的工业材料如钢铁仍然存在挑战。关键障碍是准确捕捉化学成分、加工参数和最终微观结构和性能之间的复杂关系。为此，我们引入了一个结合物理洞察与ML的计算框架，以开发一种用于钢铁的物理引导的连续冷却转变（CCT）模型。我们的模型在4100个图表的数据集上训练，并通过文献和实验数据验证。它展示了高计算效率，生成完整的CCT图，使用不到5秒的时间生成100条冷却曲线。它在所有合金钢上表现出强泛化能力，所有相的相分类F1分数均超过88%。对于相变温度回归，它在所有相上达到均方误差（MAE）低于20°C，除了贝氏体，其MAE略高为27°C。该框架可以扩展，通过添加通用和定制的ML模型，建立一个通用的热处理数字孪生平台。与互补的模拟工具和定向实验的整合将进一步支持加速的材料设计工作流程。
英文摘要：
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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