Prediction of Machine Tool Thermal Error Compensation Based on SVMR and ARM11

Zhi Peng Zhang; Kang Liu; Feng Guo

doi:10.4028/www.scientific.net/AMM.740.120

Paper Titles

Finite Element Analysis of Large Mining Height Hydraulic Support Top Beam
p.104

Finite Element Analysis of the Car Steering Knuckle Based on ANSYS
p.108

Modal Analysis of Axial Vibration of Long Pipeline Delivering Coal Slime in Power Plant
p.112

Numerical Analysis of Tension Force Subjected by Flexible Mattress
p.116

Prediction of Machine Tool Thermal Error Compensation Based on SVMR and ARM11
p.120

Research on Opening Holes in Cylindrical Shell Based on ANSYS Simulation
p.127

Structure and Fluid Dynamic Characteristics Analysis of Enclosed Nuclear Electric Valve
p.131

Study of the Vibratory Stress Relief for Large-Scale Parallel Welded Steel Truss
p.136

The Finite Element Analysis of the Floor Damper of High Speed Train
p.142

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 740Prediction of Machine Tool Thermal Error...

Prediction of Machine Tool Thermal Error Compensation Based on SVMR and ARM11

Abstract:

In order to improve the process precision of the machine tool, further development of SVMR was achieved by QT Creator. Support vector machine was applied to the ARM11 development board, SVMR model was online trained and real-time predicted the values of machine tool thermal error. Compared with the widely used BP neural network, this method has the characteristics of high compensation precision and strong generalization ability. Experiment research has proved that the stronger effectiveness and higher accuracy using this method.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 740)

Pages:

120-126

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.740.120

Citation:

Cite this paper

Online since:

March 2015

Authors:

Zhi Peng Zhang*, Kang Liu, Feng Guo

Keywords:

ARM11_Linux, Fanuc Machine Tool, Qt, SVMR, Thermal Error Compensation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] S. Yang,J. Yuan, J. Ni. Int J of Machine Tool &Manufacture. (1996), 36: 527-537.

Google Scholar

[2] Attia M H, Fraser S. Int J of Machine Tools& Manufacture. (1999), 39: 1001-1010.

Google Scholar

[3] Jianguo Yang. Hongtao Zhang. Hengchao Tong. Journal of Shanghai JiaoTong University. 2005(9).

Google Scholar

[4] A.C. Okafor. Yalcin M. Ertekin. International Journal of Machine Tool & Manufacture. ( 2000), 40(8): 1199~1213.

Google Scholar

[5] Bryan J. Annals of the CIRP, 1990, 39(2): 645-656.

Google Scholar

[6] VAPNIK V N. Statistical Learning Theory. New York:J. Wiley, (1998).

Google Scholar

[7] Yan Shi. liqun Han. Xiaoqin Lian: Neural Network Design Method and Example Analysis. Bejingjing University of Posts and Telecommunication Press. (2009): 275-278.

Google Scholar

[8] Jianhua Xu. Xuegong Zhang. Statistical Learning Theory. Publishing House of Electronics Industry, (2004. 6).

Google Scholar

[9] Chih-Chung Chang. Chih-Jen Lin. LIBSVM: A Library for Support Vector Machine (2004).

Google Scholar

[10] Hecht-Nielsen R. Kolmogorov's mapping neural network existence theorem. First IEEE International Conference on Networks, New York: [s. n. ], (1987).

Google Scholar