Share Email Print
cover

Proceedings Paper

Grinding precision forecasting in optical aspheric grinding using artificial neural network and genetic algorithm
Author(s): Chen Jiang; Yinbiao Guo; Qingqing Yang; Chunguang Han
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A new approach based on an artificial neural network (ANN) was presented for the prediction of machining precision of optical aspheric grinding. The ANN model is based on Globally Convergent Adaptive Quick Back Propagation algorithm (GCAOBP). A genetic algorithm (GA) was then applied to the trained ANN model to predict the gridding precision. The integrated GCAOBP-GA algorithm was successful in predicting the Root Mean Square of profile error (RMS) of optical aspheric workpiece in parallel grinding method using machining parameters. The results of experiments have shown that RMS of machined workpiece in parallel grinding can be predicted effectively through this approach.

Paper Details

Date Published: 6 October 2010
PDF: 5 pages
Proc. SPIE 7655, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies, 76551L (6 October 2010); doi: 10.1117/12.864454
Show Author Affiliations
Chen Jiang, Xiamen Univ. (China)
Yinbiao Guo, Xiamen Univ. (China)
Qingqing Yang, Ningbo Dahongying Univ. (China)
Chunguang Han, Xiamen Univ. (China)
Ningbo Dahongying Univ. (China)


Published in SPIE Proceedings Vol. 7655:
5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies
Li Yang; Yoshiharu Namba; David D. Walker; Shengyi Li, Editor(s)

© SPIE. Terms of Use
Back to Top