Machine Learning For Nondestructive Evaluation

This paper reports on the current status of a collaborative project exploring applications of machine learning methods to Nondestructive Evaluation (NDE). It presents initial results of applying AI methods for inductive learning, feature extraction, and functionfinding to support the ultrasonic diagnosis of defective metal parts. Experience with a simple classification approach using ID3 has led us to develop an adaptation of a machine vision technique to obtain a more abstract feature representation. Learning from examples expressed in the new representation is expected to be less sensitive to noise. In addition, the new representation better supports function-finding and more knowledgeintensive classification processes.