| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Design of Structural Materials
|
| Presentation Title |
Machine Learning for the Recognition and Synthesis of Polycrystalline Metal Microstructures |
| Author(s) |
Neal Brodnik, Devendra Jangid, Amil Khan, Michael Goebel, McLean Echlin, B. S. Manjunath, Samantha Daly, Tresa M. Pollock |
| On-Site Speaker (Planned) |
Neal Brodnik |
| Abstract Scope |
One major challenge in the development of materials is establishing representative behavior from different processing conditions, which often demands extensive physical testing. However, computational models can replace or augment physical tests and process modeling to save time and cost. This work explores the use of convolutional, adversarial, and graph neural networks to recognize and generate polycrystalline metal microstructures based on prior experimental information. Networks are trained on microstructural morphologies and arrangements gathered using a 3D serial sectioning technique known as the Tribeam. This information is then used to produce new microstructural features that are distinct from the ground truth data while still bearing similarity in a physical and statistical capacity. These approaches can also be used to mitigate imaging artifacts and explore relationships between microstructure and mechanical response. With sufficient fidelity, network generated microstructures could be used to supplement experimental approaches and greatly accelerate materials development. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Modeling and Simulation, Titanium |