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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Vydáno v:	Polymers Ročník 16; číslo 11; s. 1561
Hlavní autoři:	Liu, Yi, Yu, Qing, Liu, Kaixin, Zhu, Ningtao, Yao, Yuan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 31.05.2024 MDPI
Témata:	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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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Yu, Qing Zhu, Ningtao Liu, Kaixin Yao, Yuan Liu, Yi
AuthorAffiliation	3 Xi’an Zhanshi Testing & Engineering Co., Ltd., Xi’an 710000, China; zhuningtao270@163.com 2 The State Key Laboratory for Electronic Testing Technology, North University of China, Taiyuan 030051, China 4 Department of Chemical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan 1 Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China; yliuzju@zjut.edu.cn (Y.L.); 2112102164@zjut.edu.cn (Q.Y.)
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SubjectTerms	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
Title	Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites
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