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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title Prediction of Fatigue Indicator Parameter by Graph Neural Network
Author(s) Gyu-Jang Sim, Myoung-Gyu Lee, Marat Latypov
On-Site Speaker (Planned) Gyu-Jang Sim
Abstract Scope Fatigue in structural alloys is a critical issue due to its impact on material durability. Traditional fatigue testing methods are time-consuming and expensive. This study introduces a graph neural network (GNN) model to predict fatigue indicator parameters (FIPs) in polycrystalline materials. In this model, grains are represented as nodes and grain boundaries as edges. The GNN is trained on a comprehensive dataset of microstructural features derived from Crystal Plasticity Finite Element (CPFE) simulations, learning the complex relationships that influence FIP. Acting as a surrogate model for CPFE, it offers rapid predictions with similar accuracy. The model's ability to generalize across various sample sizes make it a robust tool for FIP prediction. This advancement offers a possibility of a faster, more reliable alternative for evaluating material fatigue, providing significant benefits for materials science and engineering applications.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Computational Materials Science & Engineering, Mechanical Properties

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