| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
|
| Presentation Title |
Graph Neural Network Modeling of Deforming Polycrystals |
| Author(s) |
Darren C. Pagan |
| On-Site Speaker (Planned) |
Darren C. Pagan |
| Abstract Scope |
Here the applicability of using graph neural networks (GNNs) to predict grain-scale elastic response of polycrystalline metallic alloys is assessed. Using GNN surrogate models, the stresses within embedded grains in Low Solvus High Refractory Nickel (LSHR) Superalloy and Ti 7wt%Al (Ti-7Al) in uniaxial tension are predicted for both synthetic and measured 3D microstructures. A transfer learning approach is taken in which the GNN surrogate models are trained using crystal elasticity finite element modeling (FEM) simulations and then the trained surrogate models are used to predict the mechanical response of microstructures measured with high-energy X-ray diffraction microscopy (HEDM). The performance of using various microstructural and micromechanical descriptors for input nodal features to predict grain stresses is explored. The effects of elastic anisotropy on GNN model performance and outlooks for extension to plasticity will be discussed. |