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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

A Novel Approach for Predicting Mechanical Properties of Weld Metals Using a Physics-Informed Neural Network With Reduced Data Dependency Under Small-Data Regime

Author(s)

Jaeheon Lee, Sangbyuk Lee, Yeongcheol Shin, Seung Hwan Lee

On-Site Speaker (Planned)

Jaeheon Lee

Abstract Scope

Predicting the mechanical properties of weld metals is challenging due to complex and nonlinear interactions among alloying elements. Conventional experimental approaches require substantial time and cost, while data-driven methods based on statistical analysis or machine learning typically require large datasets and are prone to overfitting under limited data conditions. To address these limitations, this study proposes a physics-informed neural network (PINN) that incorporates governing equations derived from metallurgical strengthening mechanisms into the loss function, thereby regulating the training process. The proposed model integrates four strengthening mechanisms that determine the yield strength of weld metals and utilizes them for training. Generalization performance was evaluated using k-fold cross-validation by comparing the proposed PINN with a benchmark neural network without governing equations. Prediction accuracy was also analyzed as a function of the training data ratio (TDR). The results demonstrate that the proposed PINN improves generalization performance and reduces dependence on large training datasets.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Mechanical Properties, Iron and Steel

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