An application of machine learning to the problem of parameter setting in non-destructive testing

Abstract

This article presents an aid system for the setting of non-destructive testing instruments. Some problems inherent in this field are briefly discussed, before showing how they led us to introduce machine learning techniques into the system. The approach uses learning from examples. The goal of the learning module is to determine dependencies between parameters of different experiments in order to automatically generate a set of rules. A prototype, called MANDRIN, has been implemented and is being evaluated on a real application: an x-ray tomograph. The first results are presented in the last section.