Laser ultrasonic surface defects detection method based on 2D-CNN

In the process of the laser ultrasonic surface defects detection, the quantitative characterization of the defects mainly depends on the operator's judgment, and it is easily interfered by the human factors, which leads to the unstable detection results. To solve this problem, an defects automatic classification detection method based on the two-dimensional convolutional neural network (2D-CNN) for image recognition was proposed. The finite element method was used to simulate the laser ultrasonic detection process, and the ultrasonic signal data was collected for training the classification model; the continuous wavelet transformation (CWT) was used to process the ultrasonic signal to obtain the wavelet time-frequency images, and the images were used as inputs to train the convolutional neural network (CNN) classification model to realize the automatic classification of the surface defects depth. The verification results show that the proposed detection method can accurately classify the defects of different depths, and the average accuracy rate of the test reaches 97.3%; the constructed CNN classification model can independently learn the defects features of the input images and complete the classification, which improves the stability of the test results, and provides a new idea for the automatic analysis and processing of laser ultrasonic defects detection.