RBF network based surface shape modeling of stressed-lap in optical polishing process

Author

Wan, Yongjian ; Wu, Fan ; Fan, Bin ; Chen, Minyou ; Zhang, Xiaoju ; Wang, Mingyu ; Xie, Kaigui

Author_Institution

Inst. of Opt. & Electron., Chinese Acad. of Sci., Chengdu

fYear

2008

fDate

25-27 June 2008

Firstpage

5622

Lastpage

5626

Abstract

A neural network model for representing the surface shape of stressed-lap was developed to facilitate a computer controlled optical polishing process. The dynamic change of the surface shape of the stressed lap during the operating process of polishing a large and highly aspheric optical surface is investigated. The original data from the micro displacement sensor matrix were used to train the neural network model. The experiment showed that the proposed model can represent the surface shape of the stressed-lap accurately.

Keywords

control engineering computing; lapping (machining); learning (artificial intelligence); microsensors; neurocontrollers; optical fabrication; production engineering computing; radial basis function networks; RBF network; computer controlled optical polishing process; microdisplacement sensor matrix; neural network; stressed-lap; surface shape modeling; Computer networks; Neural networks; Optical computing; Optical control; Optical fiber networks; Optical sensors; Process control; Radial basis function networks; Shape control; Stress control; neural network modeling; optical manufacture; stressed-lap;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4593845

Filename

4593845