| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
3rd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2025)
|
| Presentation Title |
Topological Analysis of Processing to Microstructure Mappings |
| Author(s) |
Zachary Varley, Megna N Shah, Jeff P Simmons, Veera Sundararaghavan |
| On-Site Speaker (Planned) |
Zachary Varley |
| Abstract Scope |
The relationship between processing parameters and resultant microstructures remains a fundamental challenge in materials science. We explore a neural network approach to map between processing parameter space and microstructure morphology space, with particular emphasis on quantifying topological faithfulness in latent representations. We evaluate several complementary approaches for measuring topology preservation, combining techniques from computational topology, manifold learning, and dimensionality estimation. These metrics assess both local neighborhood structure and global topological features of the paired point clouds representing processing conditions, input data, and learned latent representations. From a materials development perspective, maintaining accurate topological relationships in the learned representations could enable more reliable navigation of processing parameter space and facilitate materials optimization. We compare the effectiveness and computational efficiency of these metrics in the context of autoencoder architectures, providing insights for developing more robust computational tools for materials design and processing-structure relationship analysis. |
| Proceedings Inclusion? |
Undecided |