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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title ANN Based Modeling of Thermodynamic, Transport, and Transformation Related Properties
Author(s) Hai-Lin Chen, Yunpeng Ma, Qing Chen, Paul Mason
On-Site Speaker (Planned) Paul Mason
Abstract Scope Predicting thermodynamic, thermophysical, and transformation‑related properties is essential for materials design and process optimization. CALPHAD provides accurate predictions for multicomponent systems based on descriptions of low‑order systems, but its applicability is limited when experimental data for critical low‑order subsystems are scarce or missing. Moreover, CALPHAD is not generally formulated to predict processing‑dependent properties such as steel hardness. This work presents an ANN‑based framework for modeling materials properties across different physical regimes. For single‑phase thermodynamic and transport properties, ensembles of ANN models were constructed using elemental features and their interactions to capture composition‑dependent behavior. In contrast, steel hardness was treated as a processing‑dependent, multi‑phase transformation response using specially designed ANN architectures in conjunction with phase‑fraction information, incorporating composition, austenitization temperature and time, and cooling rates as input features. The results demonstrate that ANN models can effectively complement CALPHAD for data‑limited systems and for properties beyond conventional thermodynamic descriptions.
Proceedings Inclusion? Undecided

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