A machine learning platform developed by researchers at the University of Michigan's Michigan Ion Beam Laboratory uses augmented reality (AR) to detect and quantify radiation-induced defects in parts and testing materials in nuclear reactors.
The researchers tested samples of iron, chromium, and aluminum with a krypton beam, which creates radiation defects when the krypton ions hit the sample.
This allows for instantaneous quantification of radiation-induced defects, eliminating the need to download video and manually count every defect in selected frames.
University of Michigan's Kevin Field said, "The software displays the results in graphics overlaid on the electron microscope imagery, which labels the defects—giving their size, number, location, and density—and summarizes this information as a measure of structural integrity."
View Full Article
Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA
No entries found