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Proceedings Paper

Research on the technology of correcting the transmitted wavefront GRMS of large-diameter spatial filter lens by CCOS
Author(s): Zhigang Li; Zhenjun Bao; Heng Zhu; Jianpeng Cui; Dilong Wu; Ping Ma
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Paper Abstract

For large-scale high-power laser devices, the transmitted wavefront gradient root mean square (GRMS) is one of the most important indexes for evaluating the low-frequency distortion of spherical lenses. In this paper, for the low-frequency error index requirement of large-diameter spatial filter lens, a conformed polishing method based on composite tool is proposed in the spherical CCOS polishing stage. Based on the detection results of the optical components, the GRMS distribution is separated by low-pass filtering, and the high value of GRMS is partitioned by K-means algorithm to determine the polishing path and dwelling time. A composite conformed polishing tool is designed and the simulation analysis of the polishing tool structure and polishing stress distribution are carried out to optimize the parameters, in order to obtain an ideal GRMS convergence removal function. After experiments and production verifications of several 440×440 sized spatial filter lenses, the results show that the GRMS can be quickly converged below 7nm/cm, and the convergence rate is improved by about 50% compared with the traditional CNC polishing.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 1142743 (31 January 2020);
Show Author Affiliations
Zhigang Li, Chengdu Fine Optical Engineering Research Ctr. (China)
Zhenjun Bao, Chengdu Fine Optical Engineering Research Ctr. (China)
Heng Zhu, Chengdu Fine Optical Engineering Research Ctr. (China)
Jianpeng Cui, Chengdu Fine Optical Engineering Research Ctr. (China)
Dilong Wu, Chengdu Fine Optical Engineering Research Ctr. (China)
Ping Ma, Chengdu Fine Optical Engineering Research Ctr. (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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