Neural Network-Based Guided Wave Detection Method for Defects in CFRP Truss Joints

To address the challenges of poor accessibility in contact detection and difficulty in water immersion scanning path planning for defects at the corners of carbon fiber reinforced polymer (CFRP) truss joints, a guided wave detection method was proposed. The method employed a pitch-catch configuration to excite and receive guided waves. Wavelet packet decomposition was applied to the guided wave signals to generate feature maps, and a neural network was utilized for defect identification. A dedicated detection system for CFRP truss joint defects was developed. Experiments were conducted on CFRP specimens with prefabricated flat-bottom holes and delamination defects at the corners. A dataset was constructed to train a convolutional neural network model, and validation tests were conducted on the detection system. Results demonstrated detection accuracies of above 99% for delamination defects of 6 mm or larger and flat-bottom hole defects of 2 mm or larger, validating the effectiveness of the method in addressing defect detection challenges for CFRP truss joints.