Study on the Repair Technology of Laser Damage-Fused Silica Optics Based on the Neural Network Method
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.2. Methods
2.2.1. MRF Polishing Technique
2.2.2. Laser Scattering Test
2.2.3. Photothermal Absorption Test
3. Results
3.1. Damage Sample Preparation Result
3.2. MRF Polishing Experiment of Damaged Optics
3.3. Optimal Removal Depth Based on BP Neural Network
3.4. Experimental Verification of Damage Repair
4. Discussion
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Value |
---|---|
Polishing wheel speed | 280 r/min |
Flow rate | 120 L/min |
Electricity | 8 A |
Pressed depth | 0.25 mm |
Abrasive type | CeO2 |
Irradiation Times | Defects (<50 μm) Number | Defects (50 μm < × < 200 μm) Number | Defects (200 μm < × < 400 μm) Number | Defects (>400 μm) Number | Damage Growth Number |
---|---|---|---|---|---|
0 (initial) | 153 | 47 | 2 | 0 | 0 |
1 | 255 | 55 | 8 | 6 | 122 |
2 | 404 | 75 | 22 | 12 | 189 |
3 | 673 | 86 | 21 | 24 | 291 |
Initial Surface | Removal Depth 1.5 μm | Removal Depth 3 μm | Removal Depth 4.5 μm | Removal Depth 6 μm | |
---|---|---|---|---|---|
Defects number | 673 | 307 | 180 | 105 | 78 |
Number of Neurons: 17 | Number of Neurons: 19 | Number of Neurons: 21 | |||
---|---|---|---|---|---|
Steps | Error | Steps | Error | Steps | Error |
200 | 0.04756 | 200 | 0.03854 | 200 | 0.04325 |
400 | 0.01453 | 400 | 0.00959 | 400 | 0.01103 |
800 | 0.00456 | 800 | 0.00215 | 800 | 0.00359 |
1000 | 0.000973 | 1000 | 0.000431 | 1000 | 0.000758 |
Removal Depth/μm | Errors of Result (1) | Errors of Result (2) | Errors of Result (3) | Errors of Result (4) | Errors of Result (5) | Errors of Result (6) | Errors of Result (7) |
---|---|---|---|---|---|---|---|
1 | 0.748% | 0.97% | 3.26% | 0.98% | 1.53% | 0.42% | 0.18% |
2 | 0.358% | 0.46% | 3.02% | 0.6% | 4% | 0.32% | 0.26% |
3 | 1.47% | 0.64% | 3.15% | 1.05% | 0% (*) | 0% | 0.186% |
4 | 0.714% | 1.11% | 3.57% | 2% (*) | 10% | 1.39% | 0.261% |
5 | 0.568% | 1.92% | 2.63% | 3.84% | 25% | 3.03% (*) | 0.34% |
6 | 1.69% (*) | 5.12% (*) | 0% (*) | 5.8% | 50% | 11.1% | 1.05% |
7 | 2.816% | 4.76% | 5.26 | 12.5% | 100% | 12.5% | 0.819% (*) |
8 | 4.76% | 11.1% | 11.1% | 0% | 0% | 0% | 1.38% |
9 | 0% | 18.1% | 50% | 0% | 0% | 0% | 1.87% |
10 | 5.88% | 12.5% | 200% | 0% | 0% | 0% | 7.14% |
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Wang, B.; Zhang, W.; Shi, F.; Song, C.; Zhang, Y.; Sun, G.; Guo, S. Study on the Repair Technology of Laser Damage-Fused Silica Optics Based on the Neural Network Method. Materials 2022, 15, 5274. https://doi.org/10.3390/ma15155274
Wang B, Zhang W, Shi F, Song C, Zhang Y, Sun G, Guo S. Study on the Repair Technology of Laser Damage-Fused Silica Optics Based on the Neural Network Method. Materials. 2022; 15(15):5274. https://doi.org/10.3390/ma15155274
Chicago/Turabian StyleWang, Bo, Wanli Zhang, Feng Shi, Ci Song, Yaofei Zhang, Guoyan Sun, and Shuangpeng Guo. 2022. "Study on the Repair Technology of Laser Damage-Fused Silica Optics Based on the Neural Network Method" Materials 15, no. 15: 5274. https://doi.org/10.3390/ma15155274