THERMAL ERROR MODELING AND FORECASTING FOR NC MACHINE TOOLS BASED ON INTELLIGENT TECHNOLOGY

摘要

One of the major errors in machine tools namely thermal error that occurs due to extended usage of the machine was analyzed in this paper. The thermal error caused by expansion of the various structural elements of the machine will induce machining inaccuracy, so that it is necessary to predict its value and make error compensation.rnModeling and forecasting thermal errors is one of the difficulties in error compensation for machine tools. 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 modeling difficult with traditional mathematics. Therefore, some 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 thermal 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.rnThe works in this paper make a special summary of the thermal error modeling with intelligent technology, and provide a useful guidance to further research on error compensation of NC machine tools.