Defect detection and identification of point-focusing shear-horizontal EMAT for plate inspection

As a kind of nondestructive testing (NDT) method, shear-horizontal (SH)-guided wave detection technology is widely used on an electromagnetic acoustic transducer (EMAT). Although ultrasonic-guided waves perform well in defect location, it is difficult to obtain detailed information about defects, and the low efficiency of EMAT energy conversion still reduces the EMAT’s performance. Therefore, in this work, the defect detection method of different shapes and sizes by point-focusing shear-horizontal (PFSH)-guided wave EMAT with the use of periodic permanent magnet (PPM) is investigated through simulation and experiment. For the purpose of defect classification and quantification, the extraction principles of defect features are obtained through simulation based on the circumferential scatter diagrams, and the neural network (NN) is used to process the features extracted from the experimental data. The results show that by extracting effective defect features from the scatter diagram, high-accuracy classification and high-precision quantification of defects under the influence of the focusing transducer can be achieved