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Meeting TMS Specialty Congress 2024
Symposium 2nd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2024)
Presentation Title Impact of Different Training Datasets on Machine Learning Based Grain Growth Model and Grain Growth Kinetics
Author(s) Vishal Yadav, Joseph Melville, Amanda Krause, Joel Harley, Michael Tonks, Yang Kang, Zhihui Tian
On-Site Speaker (Planned) Yang Kang
Abstract Scope In this study, we investigate the influence of training datasets generated from Monte Carlo Potts (MCP) and Phase-field (PF) simulations on the PRIMME (Physics-Regularized Interpretable Machine Learning Microstructure Evolution) model for isotropic grain growth prediction. Despite nearly identical initial conditions, the PRIMME model trained with MCP simulation data captures most normal grain growth characteristics but fails to reproduce the von Neumann-Mullins’ relationship and circular grain evolution. In contrast, the PRIMME model trained with PF simulation data successfully replicates all typical normal grain growth characteristics. Notably, both PRIMME models, whether trained with MCP or PF data, exhibit accelerated kinetics compared to the underlying training datasets, highlighting the significant impact of the simulation method on the model's learning behavior. This insight is invaluable for optimizing the PRIMME model's performance when training with experimental data.
Proceedings Inclusion? Definite: Other


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