| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Data Science and Analytics for Materials Imaging and Quantification
|
| Presentation Title |
High Dimensional Analysis of Abnormal Grain Growth under Dynamic Annealing Conditions |
| Author(s) |
Matthew Higgins, Jiwoong Kang, Ning Lu, He Liu, Robert Suter, Ashwin Shahani |
| On-Site Speaker (Planned) |
Matthew Higgins |
| Abstract Scope |
Under specific annealing conditions, a few grains in polycrystalline structures may significantly outgrow other grains. This process is known as abnormal grain growth (AGG). Unfortunately, the phenomenological aspects of AGG remain a mystery. Synchrotron-based characterization methods such as high energy diffraction microscopy (HEDM) are eroding long-standing barriers to understand the temporal evolution of 3D microstructures. Here, we imaged via HEDM a Cu-17Al-11.4Mn alloy undergoing a cyclical, non-isothermal heat treatment. From the reconstructions, we measured the microstructural characteristics to understand the potential for a given grain to grow abnormally, and the complex interplay between precipitation, dissolution, and grain growth. We quantified a set of 14 independent microstructural features potentially contributing to the growth dynamics, thus necessitating dimensionality reduction. By applying principal component analysis and outlier/novelty detection methods, we discovered the key features that set abnormal grains apart, enabling them to eventually consume the microstructure. |
| Proceedings Inclusion? |
Planned: |