Abstract
While AI and imaging technologies are dramatically transforming the process and machine condition monitoring, product inspection remains confined to probing the geometry and surface morphology. Subsurface and bulk inspection remain prohibitively slow and imprecise. This paper presents an explainable AI (XAI)-infused ultrasound imaging approach for rapid detection of artifacts including product defects. The approach led to the discovery of correlated spatial patterns in the images located away from the artifacts. This discovery enabled accurate (> 80%) detection of artifacts that are not discernible with the current image segmentation methods, and it could profoundly impact product quality and (cyber)security assurance technologies.
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