Abstract

This paper presents a complete framework aimed to nondestructive inspection of composite materials. Starting from the acquisition, performed with lock-in thermography, the method flows through a set of consecutive blocks of data processing: input enhancement, feature extraction, classification and defect detection. Experimental results prove the capability of the presented methodology to detect the presence of defects underneath the surface of a calibrated specimen made of Glass Fiber Reinforced Polymer (GFRP). Results are also compared with those obtained by other techniques, based on different features and unsupervised learning methods. The comparison further proves that the proposed methodology is able to reduce the number of false positives, while ensuring the exact detection of subsurface defects.

Autore Pugliese

• Stella Ettore , Marani Roberto , D'orazio Tiziana Rita

Tutti gli autori

• Marani R.; Palumbo D.; Galietti U.; Stella E.; D'Orazio T.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2016

ISSN

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ISBN

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Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

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Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

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