Carbon Fiber Composites Defect Recognition Based on BP Neural Network in Ultrasonic Testing

Based on signal of carbon fiber composites defect such as lamination, porosity, looseness in ultrasonic testing , this paper performs wavelet packet transform on ultrasonic testing signals for carbon fiber composites that contain defect information, extracts sample-features from approximation coefficients and detail coefficients. it builds and trains a BP neural network for defect identification. The network uses Levenberg-Marquardt algorithm to quickly process the data. It identifies the defect type by means of BP neural and achieves good effect.