One-dimensional deep convolutional autoencoder active infrared thermography: Enhanced visualization of internal defects in FRP composites

Fiber-reinforced polymer (FRP) composites have been widely applied in different industrial fields, thereby necessitating the employment of non-destructive testing (NDT) methods to ensure structural integrity and safety. Active infrared thermography (AIRT) is a fast and cost-efficient NDT technique f...

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Vydáno v:	Composites. Part B, Engineering Ročník 272; s. 111216
Hlavní autoři:	Zhang, Yubin, Xu, Changhang, Liu, Pengqian, Xie, Jing, Han, Yage, Liu, Rui, Chen, Lina
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.03.2024
Témata:	Active infrared thermography Feature extraction Fiber-reinforced polymer composites One-dimensional deep convolutional autoencoder Thermal image sequence processing Active infrared thermography Feature extraction Thermal image sequence processing Fiber-reinforced polymer composites One-dimensional deep convolutional autoencoder
ISSN:	1359-8368, 1879-1069
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Fiber-reinforced polymer (FRP) composites have been widely applied in different industrial fields, thereby necessitating the employment of non-destructive testing (NDT) methods to ensure structural integrity and safety. Active infrared thermography (AIRT) is a fast and cost-efficient NDT technique for inspecting FRP composites. However, this method is easily affected by factors such as inhomogeneous heating, leading to a low level of visualization of defects. To address this issue, this study proposes a novel method called one-dimensional deep convolutional autoencoder active infrared thermography (1D-DCAE-AIRT) to enhance the visualization of internal defects in FRP composites. This method first preprocesses the thermal image sequence acquired by AIRT inspections. Subsequently, high-level thermal features at the pixel level are extracted from the aforementioned preprocessed thermal image sequence using a designed one-dimensional deep convolutional autoencoder (1D-DCAE) model. Finally, the extracted high-level thermal features are employed to generate enhanced visualization results that exhibit improved defect visibility. The results of three kinds of AIRT (eddy current pulsed thermography, flash thermography, and vibrothermography) experiments on FRP composite specimens with artificially introduced defects show that 1D-DCAE-AIRT can effectively enhance the visualization of internal defects. The enhancement effect is better than the conventional techniques of fast Fourier transform (FFT), principal component analysis (PCA), independent component analysis (ICA), and partial least-squares regression (PLSR).
ISSN:	1359-8368 1879-1069
DOI:	10.1016/j.compositesb.2024.111216