Abstract
An automatic classification method based on machine learning is proposed to distinguish between true and false laser-induced damage in large aperture optics. First, far-field light intensity distributions are calculated via numerical calculations based on both the finite-difference time-domain and the Fourier optical angle spectrum theory for Maxwell’s equations. The feature vectors are presented to describe the possible damage sites, which include true and false damage sites. Finally, a kernel-based extreme learning machine is used for automatic recognition of the true sites and false sites. The method studied in this paper achieves good recognition of false damage, which includes a variety of types, especially attachment-type false damage, which has rarely been studied before.
Highlights
A final optics assembly (FOA) is located at the output end of an inertial confinement fusion (ICF) experimental device
From the literature published in recent years, there are mainly two research teams in the field of damage online detection in ICF experiments for large aperture optics: the Lawrence Livermore National Laboratory (LLNL) and the China Academy of Engineering Physics (CAEP)
Each team has developed its own experimental version of the final optics damage inspection (FODI) system, namely, the NIF FODI developed for the United States National Ignition Facility (NIF) by LLNL scientists[8,9] and the SG-III FODI we developed for the SG-III laser facility at the CAEP.[10]
Summary
A final optics assembly (FOA) is located at the output end of an inertial confinement fusion (ICF) experimental device. DC-type false sites are caused by radiation or thermal noise generated by long-term operation in a vacuum environment and generally appear as randomly distributed isolated pixels with high gray values. These DC can be identified via machine learning or other pattern recognition methods. To solve this problem, we establish our FODI detection system in the SG-III laser facility and present a new solution for the classification of true and false sites. We use machine learning to perform high-accuracy classification experiments on false and true damage
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