About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data informatics: Design of Structural Materials
|
Presentation Title |
Machine Learning Approach to Understanding Abnormal Grain Growth |
Author(s) |
Ryan Cohn, Megna Shah, Adam Pilchak, Eric Payton, Anthony Rollett, Elizabeth Holm |
On-Site Speaker (Planned) |
Ryan Cohn |
Abstract Scope |
Abnormal grain growth occurs in metals and ceramics when one grain achieves a significant size advantage over its neighbors due to a faster growth rate during grain growth. Controlling abnormal grain growth, either to limit or encourage it, is desirable for many structural and functional materials. However, the abnormal growth initiation process is not well understood. Previous studies employed Kinetic Monte Carlo methods to simulate abnormal grain growth in polycrystals with nonuniform grain boundary mobility. The results successfully replicated the statistics of abnormal grain growth, and the simulations often agreed with experiments as well. In this work, we apply machine learning and data science techniques to these data to discover how local grain environments influence the abnormal growth process. Machine learning enables both prediction of the growth mode on a grain-by-grain basis as well as determination of the geometric, topological, and crystallographic factors that encourage or inhibit abnormal growth. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation |