Mei Yujian, Chen Jian, Zeng Yike, Wu Lu, Fan Zheng
The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.
The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.
Ultrasonics. 2023 Mar;129:106915. doi: 10.1016/j.ultras.2022.106915. Epub 2022 Dec 20.
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.
基于相控阵的全矩阵超声成像一直是关键部件无损评估的黄金标准。然而，由于需要耦合剂，压电相控阵无法应用于危险环境和在线监测。激光超声技术可以通过完全非接触检测轻松解决这些具有挑战性的任务，但检测灵敏度低和波模式转换复杂阻碍了其实际应用。本研究展示了复杂缺陷的激光诱导全矩阵超声成像。全矩阵数据采集和深度学习方法被应用于激光超声技术以克服现有挑战。为了进行概念验证演示，对具有代表性缺陷的铝样品进行了模拟和实验。数值和实验结果显示出良好的一致性，揭示了所提方法出色的成像性能。与基于射线追踪模型的全聚焦方法相比，深度学习方法可以通过端到端网络创建具有额外定量信息的 superior 图像，该网络使用分层特征并从所有相关成像和物理特征信息中生成细节。所提方法因其无需耦合剂的特性，可能有助于在危险环境中早期评估缺陷的形成和发展，并了解原位制造过程。
