AI technology finding new flaws in vehicles

By CRM Staff 
Toronto, Ontario — August 1, 2018 — Researchers from the University of Waterloo have developed a power artificial intelligence (AI) model capable of improving the way vehicles are constructed. 

Devinder Kumar, a PhD candidate in systems design engineering at Waterloo, teamed up with the Fritz Haber Institute (FHI) in Berlin, to create a system that can accurately detect different atomic structures in metallic material. The model is capable of finding imperfections that went previously undetected. 
“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.
The idea was originally developed by FHI as a scenario to artificially create data that relates to the real world. This data was then used by Kumar and his team to generate approximately 80,000 images of potential defects and displacements. This information was then used to produce an effective AI model that can be used to identify various types of crystal structures in particular scenarios. Then data has since been released to the public in order to allow people to learn the algorithms on their own.  
“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 one percent defect.”
We have made an AI-based algorithm or model that can classify these kinds of symmetries even up to 40 percent of defect,” he said. 
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