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

热成像 纤维增强塑料 自编码 可视化 无损检测 材料科学 偏最小二乘回归 红外线的 计算机科学 主成分分析 人工智能 复合材料 光学 深度学习 机器学习 物理 量子力学
作者
Yubin Zhang,Changhang Xu,Pengqian Liu,Jing Xie,Yage Han,Rui Liu,L. Chen
出处
期刊:Composites Part B-engineering [Elsevier BV]
卷期号:272: 111216-111216 被引量:5
标识
DOI:10.1016/j.compositesb.2024.111216
摘要

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).
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