| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Data Science and Analytics for Materials Imaging and Quantification
|
| Presentation Title |
Dictionary Indexing of EBSD Patterns Assisted by Convolutional Neural Network |
| Author(s) |
Zihao Ding, Marc De Graef |
| On-Site Speaker (Planned) |
Zihao Ding |
| Abstract Scope |
Though accurate and noise-resistant, conventional Dictionary Indexing (DI) on EBSD patterns is a computation-intensive task. For each experimental pattern, the process needs to calculate the dot product of it and all patterns in a large dictionary. We use a convolutional neural network (CNN) to greatly cut down the workload. It predicts a customized smaller dictionary for each experimental pattern before calling DI. The system combines the efficiency of machine learning methods and the advantages of DI. It turns out the total time required is only 15% of pure DI, while the accuracy is at the same level. We also take this opportunity to release Python APIs for EMsoft, which are used in this project. With great flexibility and support for multiprocessing, it allows researchers to construct DI workflow conveniently and efficiently in the study. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Machine Learning, |