| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Data Science and Analytics for Materials Imaging and Quantification
|
| Presentation Title |
Advancements in EBSD Using Machine Learning |
| Author(s) |
Kevin Kaufmann, Chaoyi Zhu, Alexander S. Rosengarten, Daniel Maryanovsky, Tyler J. Harrington, Hobson Lane |
| On-Site Speaker (Planned) |
Kevin Kaufmann |
| Abstract Scope |
Electron backscatter diffraction (EBSD) is a powerful tool with the ability to collect diffraction patterns over large areas with relatively small step sizes, thus supporting multi-scale analysis. After EBSD pattern collection, current indexing techniques (whether Hough-based or dictionary pattern matching based) are capable of reliably differentiating between a user selected set of phases, if those phases contain sufficiently different crystal structures. Despite considerable efforts, the challenges of phase differentiation and identification remain. Recent improvements in EBSD detectors allow for unprecedented pattern collection rate and resolution, opening the door for implementing techniques from the data science field. This work demonstrates the application of convolutional neural networks for extracting crystallographic and chemical information from the information rich diffraction patterns and compares the results of this approach with solutions offered in commercial systems. Investigations into the internal mathematical operations of the “black box” algorithm operating on EBSD patterns will also be discussed. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Characterization, Computational Materials Science & Engineering |