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Proceedings Paper

The intelligent controlling of active lap based on CMAC neural network
Author(s): Bin Fan; Li Yang; Zhige Zeng; Fan Wu; Shibin Wu; Yongjian Wan
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Paper Abstract

A tool named active lap could fit the aspheric optical surface to grit and polish controlled by CMAC neural networks is introduced. The solution to control the surface of active lap by CMAC neural networks is depicted based on the analyses of active lap control system. The structure and principle of CMAC neural networks model are introduced. A method to test and reconstruct the surface of active lap is put forward. We have the pulse voltage and Zernike polynomial coefficients of surface as input samples and output samples to train the CMAC neural networks until the output meet demand, then the deform of lap’s surface could be controlled by the trained CMAC neural networks. The original data from the micro displacement sensor matrix are interpolated and fitted to reconstruct the surface of active lap, the coefficient of Zernike polynomial fitted form reconstruct data by Gram-Schimdt method is looked as input samples to train the CMAC neural networks. The experiment of three units active lap was made to test the method mentioned above, the feasibility for multi units active lap controlling is discussed base on the simulation of 9 units and 18 units active lap, as well as the analysis of experiment and simulation results.

Paper Details

Date Published: 10 February 2005
PDF: 9 pages
Proc. SPIE 5638, Optical Design and Testing II, (10 February 2005); doi: 10.1117/12.574706
Show Author Affiliations
Bin Fan, Institute of Optics and Electronics, CAS (China)
Li Yang, Institute of Optics and Electronics, CAS (China)
Zhige Zeng, Institute of Optics and Electronics, CAS (China)
Fan Wu, Institute of Optics and Electronics, CAS (China)
Shibin Wu, Institute of Optics and Electronics, CAS (China)
Yongjian Wan, Institute of Optics and Electronics, CAS (China)


Published in SPIE Proceedings Vol. 5638:
Optical Design and Testing II
Yongtian Wang; Zhicheng Weng; Shenghua Ye; Jose M. Sasian, Editor(s)

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