Full laser-based Lamb waves array imaging based on the two-dimensional multiple signal classification algorithm

Plates of aluminum have the advantages of low density, high strength and corrosion resistance, and are widely used in aviation, aerospace, shipbuilding and other industrial fields. Performing defect detection and structural health monitoring is of great significance for ensuring the health of facilities and maintaining normal production and use. Lamb waves can propagate long distances with low attenuation in metal plates to achieve long-distance non-destructive testing. Laser detection technology has been applied in the field of non-destructive testing and has the advantages of high spatial resolution and non-contact. In this study, Full laser-based Lamb waves array imaging based on the two-dimensional multiple signal classification algorithm is proposed to locate and image defects in the aluminum plate. A full laser ultrasonic detection system is used to excite and receive laser-based Lamb wave signals. The array is cross-shaped and is used to collect the data. Laser-based Lamb wave signals with wideband and low time domain resolution are processed by continuous wavelet transform to obtain a narrowband signal with certain central frequency. Finally, the two-dimensional multiple signal classification algorithm is used to realize the localization and imaging of defects in the aluminum plate.