Super-Resolution Ultrasound Lamb Wave NDE Imaging of Anisotropic Airplane Laminates via Deconvolutional Neural Network

Abstract

A deconvolutional neural network (DNN) is integrated into the ultrasound (UT) pulse-echo Lamb wave nondestructive evaluation (NDE) imaging technique to achieve subwavelength super-resolution defect images for the application of anisotropic composite airplane-laminated structures. First, numerical simulation has been performed to simulate the multilayer velocity–frequency dispersion relation of the symmetric and antisymmetric Lamb wave modes. Then, a super-resolution DNN is developed with all Lamb wave modes as the convolutional kernels to obtain the subwavelength defects image of the laminated structures. After that, the effectiveness of the pulse-echo Lamb wave NDE imaging technique is verified by the experimental test of a standard aluminum metal plate with three calibration holes of 2/3/5 mm diameters at the UT center frequency of 2 MHz and a line defect. Finally, the experiment of the pulse-echo Lamb wave NDE imaging technique is carried out with an L-joint coupon sample made of anisotropic graphite–epoxy airplane materials. The comparison between the experimental result and the conventional pitch-catch C-scan shows that the Lamb wave NDE technique can reveal more details of the defects, indicating its promising application in anisotropic layers’ structures defects inspection.