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CLC number: TN929.5

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Received: 2008-05-03

Revision Accepted: 2008-08-09

Crosschecked: 0000-00-00

Cited: 8

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.11 P.1524~1530


Bayesian networks modeling for thermal error of numerical control machine tools

Author(s):  Xin-hua YAO, Jian-zhong FU, Zi-chen CHEN

Affiliation(s):  College of Mechanical and Energy Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   yaoxinhua@hzcnc.com

Key Words:  Bayesian networks (BNs), Thermal error model, Numerical control (NC) machine tool

Xin-hua YAO, Jian-zhong FU, Zi-chen CHEN. Bayesian networks modeling for thermal error of numerical control machine tools[J]. Journal of Zhejiang University Science A, 2008, 9(11): 1524~1530.

@article{title="Bayesian networks modeling for thermal error of numerical control machine tools",
author="Xin-hua YAO, Jian-zhong FU, Zi-chen CHEN",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Bayesian networks modeling for thermal error of numerical control machine tools
%A Xin-hua YAO
%A Jian-zhong FU
%A Zi-chen CHEN
%J Journal of Zhejiang University SCIENCE A
%V 9
%N 11
%P 1524~1530
%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820337

T1 - Bayesian networks modeling for thermal error of numerical control machine tools
A1 - Xin-hua YAO
A1 - Jian-zhong FU
A1 - Zi-chen CHEN
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 11
SP - 1524
EP - 1530
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820337

The interaction between the heat source location, its intensity, thermal expansion coefficient, the machine system configuration and the running environment creates complex thermal behavior of a machine tool, and also makes thermal error prediction difficult. To address this issue, a novel prediction method for machine tool thermal error based on bayesian networks (BNs) was presented. The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques. Due to the effective combination of domain knowledge and sampled data, the BN method could adapt to the change of running state of machine, and obtain satisfactory prediction accuracy. Experiments on spindle thermal deformation were conducted to evaluate the modeling performance. Experimental results indicate that the BN method performs far better than the least squares (LS) analysis in terms of modeling estimation accuracy.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Bilmes, J.A., 2000. Dynamic Bayesian Multinets. Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA, p.38-45.

[2] Fu, J.Z., Chen, Z.C., 2004. Research on modeling thermal dynamic errors of precision machine based on fuzzy logic and artificial neural network. Journal of Zhejiang University (Engineering Science), 38(6):742-746 (in Chinese).

[3] Heckerman, D., 1997. Bayesian networks for data mining. Data Mining and Knowledge Discovery, 1(1):79-119.

[4] Heckerman, D., Breese, J.S., 1996. Causal independence for probability assessment and inference using Bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 26(6):826-831.

[5] Huang, Y., Liang, S.Y., 2005. Cutting temperature modeling based on non-uniform heat intensity and partition ratio. Machining Science and Technology, 9(3):301-323.

[6] Lin, W.Q., Fu, J.Z., Chen, Z.C., 2008. Thermal error modeling & compensation of numerical control machine tools based on on-line least squares support vector machine. Computer Integrated Manufacturing Systems, 14(2):295-299 (in Chinese).

[7] Lo, C.H., Yuan, J.X., Ni, J., 1999. Optimal temperature variable selection by grouping approach for thermal error modeling and compensation. International Journal of Machine Tools and Manufacture, 39(9):1383-1396.

[8] Pahk, H.J., Lee, S.W., 2002. Thermal error measurement and real time compensation system for the CNC machine tools incorporating the spindle thermal error and the feed axis thermal error. The International Journal of Advanced Manufacturing Technology, 20(7):487-494.

[9] Ramesh, R., Mannan, M.A., Poo, A.N., 2000. Error compensation in machine tools—A review Part 2: Thermal errors. International Journal of Machine Tool and Manufacture, 40(9):1235-1256.

[10] Yang, H., Ni, J., 2005. Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error. International Journal of Machine Tools and Manufacture, 45(4):455-465.

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