Ultrasonic adaptive plane wave high-resolution imaging based on convolutional neural network

Abstract

Components with complex surfaces can pose a challenge for achieving phased array ultrasonic testing. Although coupling issues can be addressed by immersing or adding plexiglass wedges, obtaining high-quality images in a short time remains difficult under such conditions. This paper proposes an adaptive plane wave imaging scheme to address these challenges. First, the interface is reconstructed using a plane wave priority echo estimation method. Then, a beamforming algorithm adapted to the irregular interface is used to generate low-resolution (LR) images, which are then reconstructed into high-resolution (HR) images in a short time with convolutional neural network (CNN). The CNN is trained with simulation data, and the defects of test blocks and wheel rim are tested. Experimental results show that the reconstruction method combined with CNN significantly improves imaging quality and speed compared to the conventional adaptive plane wave beamforming algorithm based on the time-domain physical model.