Bayesian networks modeling for thermal error of numerical control machine tools
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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 wordsBayesian networks (BNs) Thermal error model Numerical control (NC) machine tool
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- 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
- 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
- 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
- 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