About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Comparison of U-Net and Mask R-CNN Neural Network for Detection of Helium Bubbles and Voids in Nuclear Reactor Materials |
Author(s) |
Shradha Agarwal, Sydney Elizabeth Copp, July Zacarias Reyes, Steven Zinkle |
On-Site Speaker (Planned) |
Shradha Agarwal |
Abstract Scope |
Analysing micrographs of microstructural features using transmission electron microscopy is key for predicting the performance of structural materials in nuclear reactors. Analysing micrographs is often a very tedious manual process, therefore recently many researchers have tried to automate the process by using various types of neural network, however, these networks still require lot of manual work. This paper compares two state-of-the-art neural networks, Mask-RCNN and U-Net to maximize the automation of tasks such as counting of microstructural features like helium bubbles and voids.
To better understand the accuracies, performance and limitation of each model, we conducted robust hyperparameter validation test including suite of random splits and datasetsize-dependent and domain-targeted cross-validation tests. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Nuclear Materials, Characterization |