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Proceedings Paper

Modeling of CMM dynamic error based on optimization of neural network using genetic algorithm
Author(s): Qu Ying; Luo Zai; Lu Yi
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Paper Abstract

By analyzing the dynamic error of CMM, a model is established using BP neural network for CMM .The most important 5 input parameters which affect the dynamic error of CMM are approximate rate, length of rod, diameter of probe, coordinate values of X and coordinate values of Y. But the training of BP neural network can be easily trapped in local minimums and its training speed is slow. In order to overcome these disadvantages, genetic algorithm (GA) is introduced for optimization. So the model of GA-BP network is built up. In order to verify the model, experiments are done on the CMM of type 9158. Experimental results indicate that the entire optimizing capability of genetic algorithm is perfect. Compared with traditional BP network, the GA-BP network has better accuracy and adaptability and the training time is halved using GA-BP network. The average dynamic error can be reduced from 3.5μm to 0.7μm. So the precision is improved by 76%.

Paper Details

Date Published: 31 December 2010
PDF: 6 pages
Proc. SPIE 7544, Sixth International Symposium on Precision Engineering Measurements and Instrumentation, 75445Q (31 December 2010); doi: 10.1117/12.885603
Show Author Affiliations
Qu Ying, China Jiliang Univ. (China)
Luo Zai, China Jiliang Univ. (China)
Lu Yi, China Jiliang Univ. (China)

Published in SPIE Proceedings Vol. 7544:
Sixth International Symposium on Precision Engineering Measurements and Instrumentation
Jiubin Tan; Xianfang Wen, Editor(s)

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