| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets
|
| Presentation Title |
Filling Data Gaps in 3D Microstructure with Deep Learning |
| Author(s) |
Neal Brodnik, Devendra Jangid, Michael Goebel, Amil Khan, Saisidharth Majeti, McLean Echlin, B. S. Manjunath, Samantha Daly, Tresa Pollock |
| On-Site Speaker (Planned) |
Neal Brodnik |
| Abstract Scope |
Nonlinear machine learning tools such as network approaches are promising ways to rapidly infer complex material relationships. However, this ability depends on sufficient data for training, which presents challenges when experimental collection is difficult, such as for 3D microstructures. Here, we present the application of deep learning to 3D microstructure recognition and generation in ways that facilitate the learning of broad materials concepts and creation of more robust datasets. We demonstrate how publicly available 3D object datasets can teach distribution-based morphology recognition for application to microstructural features. We also show how image synthesis techniques like super-resolution can be adapted with physics-based constraints to function on crystallographic metadata such as indexed EBSD orientation maps. Physics-based training allows for faster, more accurate learning and presents opportunities where coarser datasets can be refined for future training approaches. Together, these approaches may also offer opportunities for deep learning to generate user-defined microstructures. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
ICME, Machine Learning, Modeling and Simulation |