Automated Detection of Helium Bubbles in Irradiated X-750
Autor: | C.D. Judge, Jacob Klein, Laurent Karim Béland, Heygaan Rajakumar, Chris M. Anderson |
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Rok vydání: | 2020 |
Předmět: |
010302 applied physics
Reproducibility Materials science Structural material Orders of magnitude (temperature) business.industry chemistry.chemical_element Magnification 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Convolutional neural network Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Optics chemistry 0103 physical sciences 0210 nano-technology Inconel business Instrumentation Nanoscopic scale Helium |
Zdroj: | Ultramicroscopy. 217 |
ISSN: | 1879-2723 |
Popis: | 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. |
Databáze: | OpenAIRE |
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