Near-Surface Defect Detection Using Convolutional Neural Network and Laser Ultrasound Testing
A novel method for near-surface defect detection combining deep convolutional neural network(CNN)and support vector machine ensemble learning was proposed to address the challenge of quantitative recognition of surface defect depth and angle in laser ultrasonic testing.First,a finite element model was developed to simulate laser ultrasonic signals with various defect depths and angles.Second,wavelet transform was applied to perform time-frequency analysis of the signals,generating spectra that capture both time-and frequency-domain features.Finally,the time-frequency spectra were input into deep CNN and support vector machine models to predict defect depths and angles.Results reveal that the proposed model achieves high-precision predictions of defect depth and angle,with regression coefficients exceeding 0.98 and an average relative error between the true and predicted values below 13%.Notably,this prediction accuracy surpasses that of individual network models in ensemble learning,and other widely used CNN models and VRD ensemble neural network models.
measurement and metrologylaser ultrasoundfinite element simulationwavelet transformneural network