A High-Precision Defect Quantification Method Using a Unilateral Oblique Focusing Guided Wave EMAT and a Theory-Informed Explainable Progressive Residual Deep Convolutional Network

Using ultrasonic guided wave technology to quantify defects has always been challenging, especially for nonarray electromagnetic acoustic transducers (EMATs). This is because EMATs are always implemented with complicated defect feature extraction and signal processing algorithms and have a low signal-to-noise ratio. Therefore, we propose a high-intensity unilateral oblique focusing EMAT (UOF-EMAT) to address these problems. Based on this new EMAT, the principle of guided wave defect quantification was studied, and a theory-informed progressive residual deep convolutional network (ProResNet) was developed. The results demonstrate that the proposed UOF-EMAT can achieve unilateral focusing of the guided wave signal and increase the signal intensity at the preset focal position by 11 dB compared with the traditional bilateral focusing EMAT. Moreover, compared with the quantification results of general shallow and deep neural networks (NNs), the defect quantification error of ProResNet is reduced by approximately 9-10 and 1.6-5.2 times, respectively. Furthermore, to explain the internal functions of ProResNet visually and verify the training logic of the network, the Grad-CAM++ method was used. Compared with the unexplainable direct visual method, the Grad-CAM++ method can effectively interpret the function of ProResNet visually, and the effectiveness of the theory-informed deep ProResNet in defect quantification is verified. The source code (with description) and database used in this article are available at (https://github.com/ShyTHU/ProResNet.git).