Researchers at the University of Waterloo in Canada have found a better way to identify atomic structures, an essential step in improving materials selection in the aviation, construction and automotive industries. Their findings should allow the integrity of metals to be determined with greater confidence.
Devinder Kumar, a PhD candidate in systems design engineering at Waterloo, collaborated with researchers at the Fritz Haber Institute (FHI) in Berlin, Germany, to develop a powerful artificial intelligence (AI) model that can accurately detect different atomic structures in metallic materials. This model, which is described in a paper in Nature Communications, can find imperfections in the metal that were previously undetectable.
"Anywhere you have metals you want to know the consistency, and that can't be done in current practical scenarios because current methods fail to identify the symmetry in imperfect conditions," said Kumar, who is a member of the Vision and Image Processing Research Group under the supervision of Alexander Wong, a professor at Waterloo and Canada Research Chair in the area of AI. "So, this new method of evaluating metallic material will lead to better material design overall and has the potential to affect all the industries where you need material designing properties."
"This new method of evaluating metallic material will lead to better material design overall and has the potential to affect all the industries where you need material designing properties."Devinder Kumar, University of Waterloo
FHI came up with a new method for artificially creating data that relates to the real world. Kumar, along with his collaborators, was able to use this method to generate about 80,000 images of different kinds of defects and displacements. Using these images, he was then able to produce a very effective AI model to identify various types of crystal structures in practical scenarios. This data has been released to the public so other researchers can actually develop their own algorithms.
"In theory, all metallic materials have perfect symmetry and all the items are in the correct place, but in practice because of various reasons such as cheap manufacturing there are defects," Kumar said. "All these current methods fail when they try to match actual ideal structures – most of them fail when there is even 1% defect. We have made an AI-based algorithm or model that can classify these kinds of symmetries even up to 40% of defect."
This story is adapted from material from the University of Waterloo, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.