Laser ultrasonic inspection of surface and subsurface defects in materials based on deep learning

Abstract

Laser ultrasonic testing is an advanced non-destructive testing with high frequency and fast detection speed. However, most laser ultrasonic testing characterises defects by manually extracting the features of signals. To address this, deep learning is introduced to identify defects intelligently. Firstly, theoretical models based on finite elements are built to study the the surface and subsurface defects’ effects on surface acoustic wave (SAW) propagation. Experimental studies are also conducted on 5 samples with surface defects and 15 with subsurface defects. Then, Gaussian white noise is added to both simulation and experimental signals to expand the data set and imitate the complex conditions. The wavelet transform images of the reflected and transmitted signal are combined into a data set for training the convolutional neural network (CNN). The subsurface defects’ data set is similarly augmented. Finally, a total of 40,000 surface defect and 21,000 subsurface defect data are collected and split into training, test, and validation sets (6:2:2 ratio). The CNN accurately classifies surface defect penetration depth (minimum error 5.87%), while subsurface defect widths are determined with transfer learning (minimum error 5.6%). It proves that it is feasible to classify and identify defects by combining the laser ultrasonic method with deep learning.