A holographic and deep learning-based method is presented for three-dimensional laser damage location. The axial damage position is obtained by numerically focusing the diffraction ring into the conjugate position. A neural network Diffraction-Net is proposed to distinguish the diffraction ring from different surfaces and positions and obtain the lateral position. Diffraction-Net, which is completely trained by simulative data, can distinguish the diffraction rings with an overlap rate greater than 61% which is the best of results reported. In experiments, the proposed method first achieves the damage pointing on each surface of cascade slabs using diffraction rings, and the smallest inspect damage size is 8µm. A high precision result with the lateral positioning error less than 38.5µm and axial positioning error less than 2.85mm illustrates the practicability for locating the damage sites at online damage inspection.

中文翻译：

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深度学习法进行三维激光损伤定位

提出了一种基于全息和深度学习的三维激光损伤定位方法。通过将衍射环数值聚焦到共轭位置来获得轴向损坏位置。提出了一种神经网络Diffraction-Net来区分不同表面和位置的衍射环并获得横向位置。由模拟数据完全训练的衍射网可以区分出重叠率大于61％的衍射环，这是所报告的最好结果。在实验中，该方法首先使用衍射环实现了指向级联平板每个表面的损伤，最小检查损伤尺寸为8μm。横向定位误差小于38.5µm，轴向定位误差小于2的高精度结果。