| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Accelerated Discovery and Qualification of Nuclear Materials for Energy Applications
|
| Presentation Title |
Deep Learning for Automated Analysis of Cavities in Transmission Electron Microscopy Images |
| Author(s) |
Chun Yin Wong, Xing Wang, Zhe Fan, Karren L. More, Sergei V. Kalinin, Maxim Ziatdinov |
| On-Site Speaker (Planned) |
Chun Yin Wong |
| Abstract Scope |
The formation of cavities, including voids and bubbles, is a major threat to the integrity of materials under irradiation. Transmission electron microscope (TEM) is widely used for characterizing the size and density of cavities. Statistical analysis requires measuring hundreds of cavities, which is a time-consuming task if conducted manually. A robust and universal framework for automated cavity measurement can substantially accelerate the analysis. Here, such a framework was developed based on a custom convolutional neural network. The framework, trained using only five labeled images, was able to identify the locations and sizes of cavities in similar TEM images with an intersection-over-union (IoU) of 0.80 and 60 times faster than manual labeling. The framework was further extended to identify overlapping cavities via the Laplacian of Gaussian and Hough transform. Finally, the universality of the framework has also been demonstrated by applying to cavity analysis in TEM images acquired from different materials. |
| Proceedings Inclusion? |
Planned: |