Defect Detection in Aircraft Composites by Using a Neural Approach in the Analysis of Thermographic Images

Safety in aeronautics could be improved if continuous checks were guaranteed during the in-service inspection of aircraft. However, until now, the maintenance costs of so doing have proved prohibitive. For this reason there is a great interest for the development of low cost nondestructive inspection techniques that can be applied during normal routine tests. The analysis of the internal defects (not detectable by a visual inspection) of the aircraft composite materials is a difficult task unless invasive techniques are applied. In this paper, we have addressed the problem of inspecting composite materials by using automatic analysis of thermographic techniques. The analysis of the time/space variations in a sequence of thermographic images allows the identification of internal defects in composite materials that otherwise could not be detected. A neural network was trained to extract the information that characterises a range of internal defects in different types of composite materials. After the training phase the same neural network was applied to all the points of a sequence of thermographic images. The experimental results demonstrate the ability of the method to recognize regions containing defects but also to identify the contour regions that cannot be associated either with a defective or with a sound region.