Laser ultrasonic imaging of complex defects with full-matrix capture and deep-learning extraction

Abstract

Phased array-based full-matrix ultrasonic imaging has been the golden standard for the non-destructive evaluation of critical components. However, the piezoelectric phased array cannot be applied in hazardous environments and online monitoring due to its couplant requirement. The laser ultrasonic technique can readily address these challenging tasks via fully non-contact inspection, but low detection sensitivity and complicated wave mode conversion hamper its practical applications. The laser-induced full-matrix ultrasonic imaging of complex defects was displayed in this study. Full matrix data acquisition and deep learning method were adapted to the laser ultrasonic technique to overcome the existing challenges. For proof-of-concept demonstrations, simulations and experiments were conducted on an aluminum sample with representative defects. Numerical and experimental results showed good agreement, revealing the excellent imaging performance of proposed method. Compared with the total focusing method based on ray-trace model, the deep learning method could create superior images with additional quantitative information through end-to-end networks, which use the hierarchical features and generate details from all the relevant imaging and physical characteristics information. The proposed method may help assess defect formation and development at the early stage in a hazardous environment and understand the in-situ manufacturing process due to its couplant-free nature.