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Proceedings Paper

Dynamic errors modeling of CMM based on generalized regression neural network
Author(s): Weihong Zhong; Hongwei Guan; Yingdao Li; Xiushui Ma
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Paper Abstract

The development of modern manufacturing requires a higher speed and accuracy of coordinate measuring machines (CMM). The dynamic error is the main factor affecting the measurement accuracy at high-speed. The dynamic error modeling and estimation are the basis of dynamic error correcting. This paper applies generalize regression neural network (GRNN) to establish and estimate dynamic error model. Compared with BP neural network (BPNN), GRNN has less parameters, only one smoothing factor parameter should to be adjusted. So that it can predict the network faster and with greater computing advantage. The running speed of CMM axis is set through software. Let it running for the X axis motion. The values of the grating and the dual frequency laser interferometer are gained synchronously at the same measure point. The difference between the two values is the real-time dynamic measurement error. The 150 values are collected. The first 100 values of the error sequence are used as training data to establish GRNN model, and the next 50 values are used to test the estimation results. When the smooth factor is set at 0.5, the estimation of GRNN training data is better.The simulation with the experimental data shows that GRNN method obtains better error estimation accuracy and higher computing speed compared with BPNN. GRNN can be applied to dynamic error estimation of CMM under certain conditions.

Paper Details

Date Published: 26 May 2011
PDF: 6 pages
Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79971L (26 May 2011); doi: 10.1117/12.888558
Show Author Affiliations
Weihong Zhong, Zhejiang Univ. (China)
Hongwei Guan, Zhejiang Univ. (China)
Yingdao Li, Zhejiang Univ. (China)
Xiushui Ma, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 7997:
Fourth International Seminar on Modern Cutting and Measurement Engineering
Jiezhi Xin; Lianqing Zhu; Zhongyu Wang, Editor(s)

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