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Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title	Physical Metallurgy and Machine Learning Guide the Prediction of Continuous Cooling Phase Transformation in Steels
Author(s)	Wentao Zhao, Ziyong Hou, Xiaoxu Huang
On-Site Speaker (Planned)	Wentao Zhao
Abstract Scope	The continuous cooling transformation (CCT) diagram play an important role in alloy and heat treatment design and optimization, especially in iron-based alloys. In a long history, the CCT diagram determined by experiments is costly and time-consuming. While, the traditional physical metallurgy model is less accuracy and low universality in prediction the CCT diagram. In this work, a machine learning method guided by physical metallurgy principle is successfully established to predict the CCT behavior, in which physical metallurgy model together with thermal processing parameters is taken into consider. Furthermore, The validation and accuracy of the prediction model was compared and discussed with the CCT diagram of the thermal expansion experiment and the microstructure observation in steels.
Proceedings Inclusion?	Planned:
Keywords	Phase Transformations, Machine Learning, Iron and Steel

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