Lamb wave SCF-TFM super resolution imaging based on deep learning
Corrosion and cracks are common defects in structural plates.The mode conversion of Lamb waves at these non-perforating damages is a primary factor limiting the quality of Lamb wave imaging.Meanwhile,acoustic diffraction adheres to the Rayleigh criterion,leading to resolution limits in ultrasonic imaging.This paper designed a fully convolutional network to segment and reconstruct the received signals,enabling the automatic extraction of target modes and eliminating interference from clutter and mode conversions.Additionally,a sign coherence factor-total focusing method(SCF-TFM)is proposed,where the symbolic coherence factor is applied during the total focus method imaging process,suppressing the interference from scattered waves in non-target regions.By considering both amplitude and phase information of the signals,it can partially overcome the limitations of the Rayleigh criterion,achieving super-resolution imaging.Experimental results demonstrate that for a single blind-hole defect,the lateral resolution of the imaging result using this method is 62.41％higher than that of total focus method,and the signal-to-noise ratio(SNR)is increased by 58.23％.For multiple asymmetric blind-hole defects,when the spacing between defects exceeds the Rayleigh resolution limit,the signal-to-noise ratio improves by 92.89％using this method.When the spacing is below the Rayleigh resolution limit,this method can achieve super-resolution imaging.
万方数据
维普
