About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Chemistry and Physics of Interfaces
|
| Presentation Title |
Elucidating grain growth mechanisms using a combination of 3D non-destructive characterization, physics-based simulation, and machine learning |
| Author(s) |
Michael R. Tonks, Vishal Yadav, Joel Harley, Amanda Krause |
| On-Site Speaker (Planned) |
Michael R. Tonks |
| Abstract Scope |
While grain boundary migration and grain growth have been studied for many years, there are many unanswered questions regarding the correlation between velocity and curvature, abnormal grain growth, grain boundary anisotropy, and much more. In this presentation we summarize our efforts to gain new insights using a novel combination of 3D non-destructive characterization, physics-based grain growth simulation, and machine learning. Large 3D experimental datasets of grain structure evolution over time are providing new insights into how grain structures evolve in ceramics with different initial textures and doping. Various physics-based simulation approaches are being used to help interpret the experimental data and test hypothesized mechanisms. Machine learning models trained on simulated and experimental data are being used to predict when abnormal grain growth will occur, and to interpret the underlying mechanisms for local grain boundary migration. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Characterization |