High-speed electric spindle is an important part of computer numerical control (CNC) machining equipment, and the thermal displacement generated by the electric spindle during operation affects the electric spindle machining stability and machining accuracy. Error compensation for the high-speed motorized spindle can well compensate for the influence of thermal displacement on machine tool processing. Therefore, the prediction of thermal displacement of electric spindle is particularly important. In this paper, firstly, the temperature field information is obtained by simulating and analyzing the calculation of the heat generation and heat exchange inside the electric spindle. Set up the experimental platform with reasonable temperature measurement points according to the simulation results; secondly, the temperature sensitive points are screened by fuzzy C-mean clustering algorithm and Pearson correlation coefficient, which can effectively improve the covariance and correlation between temperature variables; finally, based on the screened data for thermal error modeling, an optimized extreme learning machine based on marine predator algorithm (MPA-ELM) is provided to predict the thermal displacement of electric spindles model. And comparing the model accuracy of extreme learning machine (ELM), MPA-ELM and extreme learning machine optimized by genetic algorithm (GA-ELM), the experimental data show that MPA-ELM has better prediction accuracy.

高速电主轴是计算机数控（CNC）加工设备的重要组成部分，电主轴在运行过程中产生的热位移会影响电主轴的加工稳定性和加工精度。高速电主轴的误差补偿可以很好地补偿热位移对机床加工的影响。因此，电主轴热位移的预测就显得尤为重要。本文首先通过模拟分析电主轴内部的发热和热交换计算得到温度场信息。根据仿真结果搭建具有合理测温点的实验平台；其次，通过模糊C均值聚类算法和Pearson相关系数对温度敏感点进行筛选，可以有效提高温度变量之间的协方差和相关性；最后，基于筛选的热误差建模数据，提供了一种基于海洋捕食者算法（MPA-ELM）的优化极限学习机，用于预测电主轴模型的热位移。并比较极限学习机（ELM）、MPA-ELM和遗传算法优化的极限学习机（GA-ELM）的模型精度，实验数据表明MPA-ELM具有更好的预测精度。基于筛选的热误差建模数据，提供了一种基于海洋捕食者算法（MPA-ELM）的优化极限学习机，用于预测电主轴模型的热位移。并比较极限学习机（ELM）、MPA-ELM和遗传算法优化的极限学习机（GA-ELM）的模型精度，实验数据表明MPA-ELM具有更好的预测精度。基于筛选的热误差建模数据，提供了一种基于海洋捕食者算法（MPA-ELM）的优化极限学习机，用于预测电主轴模型的热位移。并比较极限学习机（ELM）、MPA-ELM和遗传算法优化的极限学习机（GA-ELM）的模型精度，实验数据表明MPA-ELM具有更好的预测精度。