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Proceedings Paper

Optimization study on the deformable mirror support structure of the hyperspectral imaging system for food detection
Author(s): Fu Zhao; Ping Wang; Yanjue Gong; Li Zhang; Chunling Meng
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Paper Abstract

The deformable mirror (DM) is a critical optoelectronics component of the hyperspectral imaging system for food detection. It is very significant for the deformable mirror to design the best support structure with high dynamic stiffness. Based on the finite element analysis method, this paper discusses the DM support structure's mechanical principle and carries out optimal design. Three kinds of DM support structures with different sections are selected to compare their resonate frequencies. The type of the support structure with larger resonant frequency is picked up, then an optimal design solution has been introduced to determine a group of rational structure parameters which improve the resonate frequency of the support. Finally, the validity simulation analyses including random vibration and harmonic response are carried out to demonstrate that the optimization method is effective to improve the performance of the DM support structure of the hyperspectral imaging system for food detection.

Paper Details

Date Published: 22 October 2010
PDF: 6 pages
Proc. SPIE 7657, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Design, Manufacturing, and Testing of Micro- and Nano-Optical Devices and Systems, 765718 (22 October 2010); doi: 10.1117/12.866147
Show Author Affiliations
Fu Zhao, Beijing Technology and Business Univ. (China)
Ping Wang, Beijing Institute of Technology (China)
Yanjue Gong, Beijing Technology and Business Univ. (China)
Li Zhang, Beijing Technology and Business Univ. (China)
Chunling Meng, Beijing Technology and Business Univ. (China)


Published in SPIE Proceedings Vol. 7657:
5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Design, Manufacturing, and Testing of Micro- and Nano-Optical Devices and Systems
Tianchun Ye; Sen Han; Masaomi Kameyama; Song Hu, Editor(s)

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