Ultrasonic Phased Array Total Focusing Method of Imaging with Rayleigh Waves Based on Principal Component Analysis

Rayleigh waves can be used to detect surface defects effectively. However, the Rayleigh wave imaging quality is often poor due to the low reflected energy when using a single probe. To accomplish high-resolution imaging of surface defects, we propose a total focusing method with Rayleigh waves (TFMRW) combined with principal component analysis (PCA), in an approach called TFMRW-PCA. Firstly, the propagation characteristics of Rayleigh waves are analysed using simulation, and full matrix capture (FMC) data are obtained. Secondly, we use the Fermat principle to calculate the time of flight of the ultrasonic waves, and an improved TFM (TFMRW) algorithm is established to post-process the FMC data. Finally, PCA is used to separate the interference wave from the signal after processing by the TFMRW algorithm. In this paper, the effect of the quantity of elements and the location of the defect on the imaging results are analysed through simulation and experiment. The results show that TFMRW can accurately characterise the surface defects in the sample, with an average defect size error of 0.14 mm(2). Moreover, when combined with PCA, the average API value is reduced by 0.06 and the average signal-to-noise ratio (SNR) is increased by 6.26 dB.