Automated Detection of Helium Bubbles in Irradiated X-750

Ultramicroscopy
Chris M AndersonLaurent Karim Béland

Abstract

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.

Citations

May 11, 2021·Micron : the International Research and Review Journal for Microscopy·Nik DennlerMarilyne Sousa

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