The aim of this study is to reduce the influence of machine tools spindle thermal error on machining accuracy in load machining state. The temperature and thermal error measuring system and experiments of the 3-axis vertical milling machine tool spindle in idling and load machining state were established and carried out. The differences in temperature and thermal error between the idling and load machining states were analyzed. Upon combining the fuzzy clustering and gray correlation algorithm, the temperature-sensitive points were optimized. The thermal error prediction models of machine tool spindle system in load machining state with the optimal specific cutting energy were established based on the adaptive chaotic particle swarm optimization algorithm, and the model prediction effects were evaluated. The results showed that the spindle system temperature and thermal error in load machining were higher than the idle state. Two temperature-sensitive points were selected that not only reduced the redundancy of temperature measuring points but also ensured the model prediction accuracy. The thermal error models prediction accuracy was above 90%, and the root mean square error and residual error were better than PSO and regression. The experimental results showed that the thermal error prediction models have a high prediction accuracy and engineering application value.

本研究的目的是减少负荷加工状态下机床主轴热误差对加工精度的影响。建立并进行了三轴立式铣削机床主轴在空载和负荷加工状态下的温度和热误差测量系统及实验。分析了空载和负载加工状态之间温度和热误差的差异。将模糊聚类和灰色关联算法相结合，对温度敏感点进行了优化。基于自适应混沌粒子群优化算法，建立了具有最佳切削能量的负荷加工状态下机床主轴系统的热误差预测模型，并对模型的预测效果进行了评估。结果表明，负荷加工中主轴系统的温度和热误差均高于空载状态。选择了两个温度敏感点，不仅减少了温度测量点的冗余度，而且确保了模型预测的准确性。热误差模型的预测精度在90％以上，均方根误差和残差均优于PSO和回归。实验结果表明，热误差预测模型具有较高的预测精度和工程应用价值。热误差模型的预测精度高于90％，均方根误差和残差均优于PSO和回归。实验结果表明，热误差预测模型具有较高的预测精度和工程应用价值。热误差模型的预测精度在90％以上，均方根误差和残差均优于PSO和回归。实验结果表明，热误差预测模型具有较高的预测精度和工程应用价值。