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

Phased array-based full-matrix ultrasonic imaging has been the golden standard for the non-destructive evalu-ation of critical components. However, the piezoelectric phased array cannot be applied in hazardous environ-ments 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 experi-mental 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 hierar-chical 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 envi-ronment and understand the in-situ manufacturing process due to its couplant-free nature.

Automated summary

Phased array-based full-matrix ultrasonic imaging is the golden standard for non-destructive evaluation of critical components, but it has limitations in hazardous environments. This study demonstrated the laser-induced full-matrix ultrasonic imaging of complex defects using a couplant-free approach. The proposed method overcame existing challenges through full matrix data acquisition and deep learning.