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Three-dimensional laser damage positioning by a deep-learning method
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DOI：10.1364/OE.387987.png)
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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 8 mu m. A high precision result with the lateral positioning error less than 38.5 mu m and axial positioning error less than 2.85mm illustrates the practicability for locating the damage sites at online damage inspection. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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IF:
3.3
论文数:
6.1W
被引数:
14.3W
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