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Journal of Zhejiang University-SCIENCE A

, Volume 9, Issue 11, pp 1524–1530 | Cite as

Bayesian networks modeling for thermal error of numerical control machine tools

Article

Abstract

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.

Key words

Bayesian networks (BNs) Thermal error model Numerical control (NC) machine tool 

CLC number

TN929.5 

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References

  1. Bilmes, J.A., 2000. Dynamic Bayesian Multinets. Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA, p.38–45.Google Scholar
  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).Google Scholar
  3. Heckerman, D., 1997. Bayesian networks for data mining. Data Mining and Knowledge Discovery, 1(1):79–119. [doi:10.1023/A:1009730122752]CrossRefGoogle Scholar
  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. [doi:10.1109/3468.541341]CrossRefGoogle Scholar
  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. [doi: 10.1080/10910340500196421]CrossRefGoogle Scholar
  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).Google Scholar
  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. [doi:10.1016/S0890-6955(99)00009-7]CrossRefGoogle Scholar
  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. [doi:10.1007/s001700200182]CrossRefGoogle Scholar
  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. [doi:10.1016/S0890-6955(00)00009-2]CrossRefGoogle Scholar
  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. [doi:10.1016/j.ijmachtools.2004.09.004]CrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH 2008

Authors and Affiliations

  1. 1.College of Mechanical and Energy EngineeringZhejiang UniversityHangzhouChina

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