| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Data Science and Analytics for Materials Imaging and Quantification
|
| Presentation Title |
Quantitative EBSD Image Analysis and Prediction via Deep Learning |
| Author(s) |
Yi Han, Joey Griffiths, Yunhui Zhu, Hang Yu |
| On-Site Speaker (Planned) |
Yi Han |
| Abstract Scope |
In this work, we demonstrate a deep learning based approach to quantitatively analyze and characterize the variation of microstructure from a large dataset of material imaging. Metal samples processed via the Additive Manufacturing (AM) technique known as the additive friction stir deposition (AFSD) are used to validate our approach. The microstructure images obtained from electron backscatter diffraction (EBSD) are processed through a deep neural network called VGG16 to generate high-dimensional features, then a set of low-dimensional principal microstructure descriptors are extracted to represent the key differences among the analyzed microstructures, allowing for quantitative comparison between existing microstructures as well as prediction of new microstructure within the domain spanned by the principal descriptors. This allows us to explore the physical insights into the implicitly expressed microstructure descriptors by mapping the regenerated microstructures as a function of principal component values. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Machine Learning, |