<title>Automatic defects recognition in composite aerospace structures from experimental and theoretical analysis as part of an intelligent infrared thermographic inspection system</title>

An evaluation of artificial neural networks applied to infrared thermography inspection of composite aerospace structures

“…The results obtained from various and multidisciplinary developments in image processing, geometrical (CAD-CAM) and theoretical modelling have allowed the setting up of a data base necessary for the development of a fast, evolutive and friendly user expert system. However the developed concept based on spatial analysis first and a linear process, in which all the different processing stages hang together without feed back, have shown some limits, mainly lack of versatility [1][2]. A new approach was defined, which has been to classify in a first step the temperature versus time curves corresponding to each pixel of a sequence of images, and in a second step to proceed to spatial analysis.…”

“…This model developped by LEMTA has been used to realize a software program named © Multicouche, giving the evolution of the front or rear temperature of the considered part, submitted to a very short heat pulse. The validity of the model has been checked elsewhere [1] by comparing the theoretical results with the experimental ones.…”

“…If rapid inspection can be expected, the size of the data and the complexity of the thermograms make the interpretation difficult. So in order to help the operator in the fulfilment of his job to achieve rapid, reliable and repeatable non destructive evaluation, we have caried out for the last four years a project named SEQUOIA, in which Artificial Intelligence has been integrated.The first approach presented at QNDE 93 was based on spatial analysis which revealed itself to be encouraging but insufficient and with not enough versatility [1]. A complementary approach is presented, it is based on the use of multi-layered Neural Networks.…”