Study on the Repair Technology of Laser Damage-Fused Silica Optics Based on the Neural Network Method

As a key component of a high-power laser device, fused silica optics needs to bear great laser energy, and laser damage is easily generated on the optical surface. In order to improve the service life and availability of optics, it is necessary to repair the damaged optics. In this work, the repair technique of damaged, fused silica optics was studied. The neural network method was mainly used to establish the correlation between the number of small-scale damage points and the repair depth. The prediction accuracy of the model is better than 90%. Based on the neural network model, the removal depth parameters were optimized with the suppression coefficient of the damage points. The processing effect of the optimized parameters was verified by magnetorheological polishing experiments. In this paper, a repair technique based on a neural network was proposed, which avoids the low efficiency caused by processing iterations in the repair process, and can accurately what was expected. The method proposed in this work has an important reference value in the repair process of fused silica optics.