AUTOMATIC DETECTION OF FLAWS FROM WAVEFIELD DATA USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Ultrasonic nondestructive visible testing using wavefield imaging has been applied to nondestructive testing by acquiring full-waveform data over a region of interest. Using the wavefield visualization, we can detect surface and sub-surface flaws in the target object. In this case, it would be useful that flaws can be automatically classified. In this study, a deep learning method based on the convolutional neural network (CNN) is applied to the classification of the flaw from the wavefield data. It is not easy to acquire a lot of the wavefield data in advance in actual fields. Therefore, we make numerical wavefield datasets as a substitute for the measured data using simulation by the elastodynamic finite integration technique. After building the CNN model, we used test data to verify the performance of the model. The CNN model using both simulation and measured dataset showed a high accuracy rate for the flaw classification.