About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Neural Message Passing for Prediction of Abnormal Grain Growth in Monte Carlo Simulations of Materials Processing |
Author(s) |
Ryan Cohn, Elizabeth Holm |
On-Site Speaker (Planned) |
Ryan Cohn |
Abstract Scope |
Neural message passing allows deep learning to operate on irregular graph structures. This technique shows promise for modeling materials represented as graphs of connected grains. We apply neural message passing to predict the occurrence of abnormal grain growth (AGG) in materials processing simulations. AGG occurs when the growth rate of a small subset of grains far exceeds that of typical matrix grains during processing, significantly altering the properties and performance of various material systems. Despite this, AGG is not well understood and is therefore difficult to control during processing. Thus, there is significant interest in applying deep learning to predict the occurrence of AGG during processing. After generating a dataset of Monte Carlo simulations of grain growth, we train deep learning models to predict the occurrence of AGG. Preliminary results from this study indicate that a neural message passing model outperforms a comparable computer vision approach for this task. |
Proceedings Inclusion? |
Undecided |