Lamb wave-based damage assessment for composite laminates using a deep learning approach

•A convolutional sparse coding-based UNet (CSCUNet) is proposed to analyze the ultrasonic Lamb wave signal, which enables high-resolution imaging of damage in composite laminates.•The CSCUNet incorporates the multi-layer convolutional sparse coding block in its encoder, enhancing both performance an...

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Bibliographic Details
Published in:Ultrasonics Vol. 141; p. 107333
Main Authors: Zhang, Han, Wang, Fan, Lin, Jing, Hua, Jiadong
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.07.2024
Subjects:
Composite material
Damage assessment
Deep learning
Lamb wave
Size evaluation
Lamb wave
Deep learning
Damage assessment
Composite material
Size evaluation
ISSN:0041-624X, 1874-9968, 1874-9968
Online Access:Get full text
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Summary:•A convolutional sparse coding-based UNet (CSCUNet) is proposed to analyze the ultrasonic Lamb wave signal, which enables high-resolution imaging of damage in composite laminates.•The CSCUNet incorporates the multi-layer convolutional sparse coding block in its encoder, enhancing both performance and interpretability of the model.•The effectiveness of the proposed method has been verified through testing on both numerical simulations and experimental data obtained from the surfaces of composite specimens. With the increasing utilization of composite materials due to their superior properties, the need for efficient structural health monitoring techniques rises rapidly to ensure the integrity and reliability of composite structures. Deep learning approaches have great potential applications for Lamb wave-based damage detection. However, it remains challenging to quantitatively detect and characterize damage such as delamination in multi-layered structures. These deep learning architectures still lack a certain degree of physical interpretability. In this study, a convolutional sparse coding-based UNet (CSCUNet) is proposed for ultrasonic Lamb wave-based damage assessment in composite laminates. A low-resolution image is generated using delay-and-sum algorithm based on Lamb waves acquired by transducer array. The encoder-decoder framework in the proposed CSCUNet enables the transformation of low-resolution input image to high-resolution damage image. In addition, the multi-layer convolutional sparse coding block is introduced into encoder of the CSCUNet to improve both performance and interpretability of the model. The proposed method is tested on both numerical and experimental data acquired on the surface of composite specimen. The results demonstrate its effectiveness in identifying the delamination location, size, and shape. The network has powerful feature extraction capability and enhanced interpretability, enabling high-resolution imaging and contour evaluation of composite material damage.
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ISSN:0041-624X
1874-9968
1874-9968
DOI:10.1016/j.ultras.2024.107333