The precision of machine tools is greatly constrained by errors either built into the machine tools or occurring on a periodic basis on account of temperature changes or variation in cutting forces, so it is essential to obtain these errors, and then eliminate or compensate for them. However, the interaction between many factors inducing errors, such as the heat source, thermal expansion coefficient, the machine system configuration and the running environment, creates complex behavior of a machine tool, and also makes synthetic error prediction difficult with traditional mathematics. Therefore, several modeling methods based on non-classical mathematics have been presented in recent years. The intelligent technology methods of neural network, support vector machines, Bayesian networks are the effective modeling and forecasting methods for machine errors. All these three methods were introduced in briefly in the paper, and the characteristics of them were discussed. A series of experiments were carried out to evaluate their merits and defects. Finally, some important conclusions about how to use these methods in different situations were provided. The works in this paper make a special summary of the error modeling with intelligent technology, and provide a useful guidance to further research on error compensation of NC machine tools.
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