Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Prediction for Incremental Damage on Optics from Final Optics Assembly in ICF High Power Laser Facility

Xueyan Hu
ORCID logo ,
Zhou Wei
,
Huaiwen Guo
,
Xiaoxia Huang
,
Bowang Zhao
,
Wei Zhong
,
Qihua Zhu
,
Zhifei Chen
*
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. 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. Preprints 2024, 2024040908. 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.

Download PDF

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0
Get PDF Cite Share 0
Bookmark BibSonomy Mendeley Reddit Delicious
×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.