开展了精密数控车床主轴系统热误差补偿的实验与建模方法的研究。建立了精密数控车床主轴系统轴向与径向偏转热误差补偿模型以增强其误差补偿能力, 并提高机床加工精度。构建了主轴系统热误差测试平台, 应用五点法测试主轴系统热误差, 使用热电偶与红外热像仪测量主轴系统温升关键点温度变化数据, 应用灰色综合关联分析法实现温度敏感测点辨识。构建了基于粒子滤波重采样粒子群算法的热误差预测模型, 对模型预测效果进行评价。结果表明: 基于粒子滤波重采样粒子群热误差补偿模型得到的轴向热误差预测残差为-1.29 μm～1.55 μm, 建模精度为95.04%; y向热偏转误差预测残差为-4.68×10-6°～9.66×10-6°, 建模精度为91.26%; z向热偏转误差预测残差为-5.83×10-6°～8.59×10-6°, 建模精度为93.24%。实验结果证明该热误差补偿模型具有较高的预测精度, 具有较强的工程应用价值。

The experiments and modeling of thermal error compensation for the spindle system of a Computer Numerical Control (CNC) lathe were researched. A thermal error compensation model for the Spindle system of CNC lathe at axial and radial directions was established to enhance its error compensation ability and to improve the machining precision. A test platform for the thermal error of the spindle system was built. The five point method was used to test the thermal error of the spindle system, and a thermocouple and a infrared thermal imager were taken to measure the temperature changes of the spindle system. Then the gray comprehensive correlation analysis method was used to identify the temperature-sensitive measurement points and to construct thermal error prediction model based on re-sampling step particle swam optimization to evaluate the model effect. The prediction results on the thermal error compensation model based on re-sampling step particle swam optimization show that the axial residual thermal error is -1.29 μm—1.55 μm, and the modeling accuracy is 9504%. The thermal residual error along y direction is -4.68×10-6°—9.66×10-6°, and the modeling accuracy is 91.26%. The thermal residual error along z direction is -5.83×10-6°—8.59×10-6°, and the modeling accuracy is 93.24%. The research shows that the thermal error compensation model has high precision and a strong engineering application value.