Three-dimensional laser damage positioning by a deep-learning method

PLUTO / PLUTO-2 Spatial Light Modulators
Beam Shaping / Beam Steering Misc.: Speckle / Characterization / Metrology
Published on:
Authors: Zhan Li and Lu Han and Xiaoping Ouyang and Pan Zhang and Yajing Guo and Dean Liu and Jianqiang Zhu
Abstract:

“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.”

Open Access

Publication: Optics Express
Issue/Year: Vol. 28, Issue 7, pp. 10165-10178
DOI: 10.1364/OE.387987
Link: https://doi.org/10.1364/OE.387987

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