Research on classification and recognition of CFRP cylinder fiber layer fracture based on Wigner-Ville distribution images and HRNet

Song Huang; Shutao Xiong; Fei Wang; Longyi Zhang; Dingding Fan

doi:10.1117/12.3050550

24 October 2024 Research on classification and recognition of CFRP cylinder fiber layer fracture based on Wigner-Ville distribution images and HRNet

Song Huang, Shutao Xiong, Fei Wang, Longyi Zhang, Dingding Fan

Author Affiliations +

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 1339604 (2024) https://doi.org/10.1117/12.3050550
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

Fiber layer fracture in CFRP (Carbon Fiber Reinforced Polymer) cylinders is one of the most common damages during the usage of these cylinders. Once this damage occurs during the cylinder's load operation, it can develop and eventually lead to extremely serious consequences. Currently, there is very little research on the classification of the degree of fiber layer fracture. In this paper, we utilize acoustic emission response signals under artificially simulated scenarios of different degrees of fiber fracture, perform Wigner-Ville analysis to obtain feature images, and use an improved HRNet for damage degree recognition. The improved HRNet method introduces ASPP (Atrous Spatial Pyramid Pooling) into the classification network, which effectively resists environmental noise, and provides a stable and sensitive response to fiber fracture. In the experiments, it effectively recognizes three different degrees of fiber fracture damage.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Song Huang, Shutao Xiong, Fei Wang, Longyi Zhang, and Dingding Fan "Research on classification and recognition of CFRP cylinder fiber layer fracture based on Wigner-Ville distribution images and HRNet", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 1339604 (24 October 2024); https://doi.org/10.1117/12.3050550

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