| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Automated Defect Identification for Tristructural Isotropic Fuels |
| Author(s) |
Joseph Oncken, Nancy Lybeck, Jeffrey Phillps, Scott Niedzialek, Justin Coleman |
| On-Site Speaker (Planned) |
Joseph Oncken |
| Abstract Scope |
During the manufacture of tristructural-isotropic-coated nuclear fuel particles, internal fissure defects can form in the uranium oxycarbide (UCO) kernels. These fissures result in a defective fuel particle that can fracture during subsequent fuel processing. Therefore, it is necessary to detect fissured kernels in a batch to determine if the batch meets specification prior to blending with other batches and upgrading processes. Previous attempts at identifying fissures involved the manual inspection of micrographs of UCO fuel kernel cross sections. This process is tedious, time-consuming, and may introduce counting errors, making it a good candidate for automation. This work presents an automated detection method for fissures in UCO kernels, using image segmentation to extract relevant features from micrographs, which then serve as the input to a convolutional neural network used to automatically distinguish between fissured and non-fissured kernels. |