Version 1
: Received: 9 April 2024 / Approved: 15 April 2024 / Online: 15 April 2024 (17:53:44 CEST)
How to cite:
Hu, X.; Wei, Z.; Guo, H.; Huang, X.; Zhao, B.; Zhong, W.; Zhu, Q.; Chen, Z. Prediction for Incremental Damage on Optics from Final Optics Assembly in ICF High Power Laser Facility. Preprints2024, 2024040908. https://doi.org/10.20944/preprints202404.0908.v1
Hu, X.; Wei, Z.; Guo, H.; Huang, X.; Zhao, B.; Zhong, W.; Zhu, Q.; Chen, Z. Prediction for Incremental Damage on Optics from Final Optics Assembly in ICF High Power Laser Facility. Preprints 2024, 2024040908. https://doi.org/10.20944/preprints202404.0908.v1
Hu, X.; Wei, Z.; Guo, H.; Huang, X.; Zhao, B.; Zhong, W.; Zhu, Q.; Chen, Z. Prediction for Incremental Damage on Optics from Final Optics Assembly in ICF High Power Laser Facility. Preprints2024, 2024040908. https://doi.org/10.20944/preprints202404.0908.v1
APA Style
Hu, X., Wei, Z., Guo, H., Huang, X., Zhao, B., Zhong, W., Zhu, Q., & Chen, Z. (2024). Prediction for Incremental Damage on Optics from Final Optics Assembly in ICF High Power Laser Facility. Preprints. https://doi.org/10.20944/preprints202404.0908.v1
Chicago/Turabian Style
Hu, X., Qihua Zhu and Zhifei Chen. 2024 "Prediction for Incremental Damage on Optics from Final Optics Assembly in ICF High Power Laser Facility" Preprints. https://doi.org/10.20944/preprints202404.0908.v1
Abstract
High-power laser facilities necessitate predicting incremental damage to final optics to identify evolving damage trends. In this study, we propose a surface damage detection method utilizing image segmentation employing ResNet-18, and a damage area estimation network employing U-Net++. Paired sets of online and offline images of optics obtained from a large laser facility is used to train the network. The trends of varying damage could be identified by incorporating additional experimental parameters. A key advantage of the proposed method is that the network can be trained end-to-end on small samples, eliminating the need for manual labeling or feature extraction. The software developed based on these models can facilitate the daily inspection and maintenance of optics in large laser facilities.
Keywords
Optics image processing; deep learning; ResNet-18; UNet++
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.