http://www.cnr.it/ontology/cnr/individuo/prodotto/ID52567
Defect Detection in Aircraft Composites by Using a Neural Approach in the Analysis of Thermographic Images (Articolo in rivista)
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- Label
- Defect Detection in Aircraft Composites by Using a Neural Approach in the Analysis of Thermographic Images (Articolo in rivista) (literal)
- Anno
- 2005-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/j.ndteint.2005.04.005 (literal)
- Alternative label
T. DOrazio, C. Guaragnella, M. Leo, P. Spagnolo (2005)
Defect Detection in Aircraft Composites by Using a Neural Approach in the Analysis of Thermographic Images
in NDT & E international; Elsevier, Amsterdam (Paesi Bassi)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- T. DOrazio, C. Guaragnella, M. Leo, P. Spagnolo (literal)
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- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Istituto di Studi sui sistemi intelligenti per l'automazione (literal)
- Titolo
- Defect Detection in Aircraft Composites by Using a Neural Approach in the Analysis of Thermographic Images (literal)
- Abstract
- 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. (literal)
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