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

Authors:
J. C. Royer;A. Merle;C. de Sainte Marie
Affiliations:
CEA/CENG/D.LETI, 85 X, 38041 GRENOBLE Cedex, France;CEA/CENG/D.LETI, 85 X, 38041 GRENOBLE Cedex, France;3A.S.I. ltd, 4, Rue Chanaron, F 38000 GRENOBLE France
Venue:
IEA/AIE '90 Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2
Year:
1990

Citing 4
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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.