| Civil & Mechanical Engineering |
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| Bayesian networks modeling for thermal error of numerical control machine tools |
| Xin-hua YAO, Jian-zhong FU, Zi-chen CHEN |
| College of Mechanical and Energy Engineering, Zhejiang University, Hangzhou 310027, China; Zhejiang Provincial Key Lab of Advanced Manufacturing Technology, Hangzhou 310027, China |
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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.
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Received: 03 May 2008
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