Ultrasonic defect detection and classification system using machine learning

Abstract

In an automated defect detection and classification system, one or more computing devices access scan data acquired in an ultrasonic scan of an object. A first input feature map, including a two-dimensional (2D) scan image, is built from the scan data and input to a first deep neural network to generate a first output feature map. A second input feature map, including an image of a defect-free object, is input to a second deep neural network, having the same structure and weight values as first deep neural network, to produce a second output feature map. The scanned object is determined to contain a defect when a distance between first and second output feature maps is large. In an alternative approach, the 2D scan image and one or more images of the defect-free object are input to different channels of neural network trained using color images.