Composite defect location based on Graph-in-Graph Convolutional Network
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摘要: 针对复合材料层合板结构缺陷的快速检测定位，提出了一种基于超声导波的复合材料缺陷检测图嵌图卷积神经网络模型（G-GCN）。G-GCN通过构建导波信号相互关系的时空特征高级表征图，由局部-全局变换构建局部图，以表征单个导波信号内的相互关系信息；再基于局部图构建全局图，表征多个导波信号之间的相互关系信息；然后利用全局图输入图卷积神经网络模型训练学习，输出相应的复合材料缺陷预测，实现极少量传感器条件下的快速精准缺陷检测与定位。最后搭建了超声导波复合材料检测试验平台，验证了G-GCN的先进性和可靠性。Abstract: This paper proposed a Graph-in-Graph Convolutional Network (G-GCN) model based on ultrasound-guided waves for the rapid detection and localisation of structural defects in composite laminates. G-GCN was constructed an advanced representation map of the spatio-temporal characteristics of guided wave signals. The local map was constructed by local global transformation to represent the relationship information in a single guided wave signal. Then, the global map was constructed based on the local map to represent the relationship information between multiple guided wave signals. The global map input map was used to convolve neural network model training and learning, and the corresponding composite defect prediction was output, It realized fast and accurate defect detection and location with very few sensors. Finally, an experimental platform for nondestructive testing of ultrasonic guided wave composite materials was built to verify the advancement and reliability of G-GCN.
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