Automated Classification of Helium Ingress in Irradiated X-750
This automates a tedious and labor-intensive process for analyzing structural materials in nuclear reactors, though it is incremental as it adapts an existing method to a specific domain.
The researchers tackled the problem of manually analyzing transmission electron microscopy images of helium bubbles in neutron-irradiated Inconel X-750 by adapting a region-based convolutional neural network, achieving similar accuracy and reproducibility to humans while being four orders of magnitude faster.
Imaging nanoscale features using transmission electron microscopy is key to predicting and assessing the mechanical behavior of structural materials in nuclear reactors. Analyzing these micrographs is often a tedious and labour intensive manual process. It is a prime candidate for automation. Here, a region-based convolutional neural network is adapted to detect helium bubbles in micrographs of neutron-irradiated Inconel X-750 reactor spacer springs. We demonstrate that this neural network produces analyses of similar accuracy and reproducibility to that produced by humans. Further, we show this method as being four orders of magnitude faster than manual analysis allowing for generation of significant quantities of data. The proposed method can be used with micrographs of different Fresnel contrasts and magnification levels.