Intelligent damage recognition of composite materials based on deep learning and ultrasonic testing

作者：Caizhi Li, Weifeng He, Xiangfan Nie, Xiaolong Wei, Hanyi Guo, Xin Wu, Haojun Xu, Tiejun Zhang, Xinyu Liu · 发表于：AIP Advances · 年份：2021 · DOI：10.1063/5.0063615 · 被引用次数：30 · 研究领域：Thermography and Photoacoustic Techniques、Ultrasonics and Acoustic Wave Propagation、Non-Destructive Testing Techniques

Ultrasonic non-destructive testing can effectively detect damage in aircraft composite materials, but traditional manual testing is time-consuming and labor-intensive. To realize the intelligent recognition of aircraft composite material damage, this paper proposes a 1D-YOLO network, in which intelligent fusion recognizes both the ultrasonic C-scan image and ultrasonic A-scan signal of composite material damage. Through training and testing the composite material damage data on aircraft skin, the accuracy of the model is 94.5%, the mean average precision is 80.0%, and the kappa value is 97.5%. The use of dilated convolution and a recursive feature pyramid effectively improves the feature extraction ability of the model. The effectively used Cascade R-CNN (Cascade Region-Convolutional Neural Network) improves the recognition effect of the model, and the effectively used one-dimensional convolutional neural network excludes non-damaged objects. Comparing our network with YOLOv3, YOLOv4, cascade R-CNN, and other networks, the results show that our network can identify the damage of composite materials more accurately.