Aspheric optical surface profiling based on laser scanning and auto-collimation

Xie, Hongbo and Jiang, Min and Wang, Yao and Pang, Xiaotian and Wang, Chao and Su, Yongpeng and Yang, Lei (2017) Aspheric optical surface profiling based on laser scanning and auto-collimation. Review of Scientific Instruments, . ISSN 0034-6748. E-ISSN 1089-7623. (doi:https://doi.org/10.1063/1.4995685) (Full text available)

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Abstract

Nowadays the utilization of aspheric lens has become more and more popular, enabling highly increased degree of freedom for optical design and simultaneously improving the performance of optical system. Hence this also entails a stringent requirement for fast and accurate measuring the shape of these aspheric components. In this paper, the instrument is greatly developed to satisfy the growing need to test axially symmetric aspheric surface, which is implemented by converting the pose of reflective mirror in optical path to the coordinate of reflection point on the surface when laser rapidly scans . At each movement position managed by grating-rule sensor, the rotating angle of reflective mirror is defined using position sensitive detector based on the laser auto-collimating and beam center-fitting principle. Testing a convex and a concave surfaces with highly reproducible results, including coefficient of determination better than 0.999 and RMSE less than =10, validates the feasibility of this method. In comparison to the conventional computer-generated hologram tester or interferometer, the present instrument—essentially builds on the pure geometrical optics technology—is a powerful tool to measure the aspheric surface quickly and accurately with simple structure and algorithm.

Item Type: Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunications > TK5103.59 Optical communications, Fibre-optics
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Broadband & Wireless Communications
Depositing User: Chao Wang
Date Deposited: 10 Nov 2017 15:10 UTC
Last Modified: 14 Nov 2017 16:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/64347 (The current URI for this page, for reference purposes)
Wang, Chao: https://orcid.org/0000-0002-0454-8079
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