Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites

Ultrasonic testing is widely used for defect detection in polymer composites owing to advantages such as fast processing speed, simple operation, high reliability, and real-time monitoring. However, defect information in ultrasound images is not easily detectable because of the influence of ultrasou...

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Veröffentlicht in:Polymers Jg. 16; H. 11; S. 1561
Hauptverfasser: Liu, Yi, Yu, Qing, Liu, Kaixin, Zhu, Ningtao, Yao, Yuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 31.05.2024
MDPI
Schlagworte:
Analysis
Carbon fiber reinforced plastics
Deep learning
Defects
Dimensional stability
Fiber reinforced polymers
Fourier transforms
Machine learning
Methods
Neural networks
Nondestructive testing
Performance evaluation
Polymer matrix composites
Polymeric composites
Three dimensional composites
Time series
Ultrasonic testing
Wavelet transforms
Taiwan
carbon-fiber-reinforced polymer
receptive field
3D convolution
deep autoencoder
ultrasonic testing
ISSN:2073-4360, 2073-4360
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Zusammenfassung:Ultrasonic testing is widely used for defect detection in polymer composites owing to advantages such as fast processing speed, simple operation, high reliability, and real-time monitoring. However, defect information in ultrasound images is not easily detectable because of the influence of ultrasound echoes and noise. In this study, a stable three-dimensional deep convolutional autoencoder (3D-DCA) was developed to identify defects in polymer composites. Through 3D convolutional operations, it can synchronously learn the spatiotemporal properties of the data volume. Subsequently, the depth receptive field (RF) of the hidden layer in the autoencoder maps the defect information to the original depth location, thereby mitigating the effects of the defect surface and bottom echoes. In addition, a dual-layer encoder was designed to improve the hidden layer visualization results. Consequently, the size, shape, and depth of the defects can be accurately determined. The feasibility of the method was demonstrated through its application to defect detection in carbon-fiber-reinforced polymers.
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ISSN:2073-4360
2073-4360
DOI:10.3390/polym16111561