机床热变形及热误差优化研究

为减少热变形对精密加工精度影响，对夏冬两季节机床主轴箱上温升和热变形及环境温度的影响进行了测试分析，并采用BP神经网络模型化的Volterra级数非线性系统实现热误差建模。分析结果表明：夏天环境温度受主轴箱散热影响而温度迅速升高；冬季机床散热较快，主轴箱上温升比较明显，环境温度几乎不变；同一台机床在夏季和冬季的热变形规律相似而变形量稍有不同。通过实验验证了该模型具有预测精度高的优点，为数控机床热误差实时补偿提供了参考。

Study on Optimization of Thermal Deformation and Thermal Error on Machine Tools

The test-analysis of effects on machine tool of motorized spindle temperature risings, thermal deformations and environment temperature in summer and in winter especially was presented, in order to reduce effects on machining precision by thermal deformation. Also the thermal error modeling based on using back propagation (BP) neural networking Volterra nonlinear system was realized. The analysis results indicate the environmental temperature effected by thermal diffusivities from headstock is raised rapidly in summer; thermal diffusivities of the machine tool is fast and the temperature is rising visibly on headstock in winter, and the environment temperature is almost in constant. The thermal deformations are similar in rules, but have some little difference in values between in summer and in winter for a same machine. It is validated by experiments that the model has merit of high predicative precision, which provides reference for compensation of real time thermal error in CNC machine tool.