Thermal errors in high-speed motorized spindle: An experimental study and INFO-GRU modeling predictions

Abstract

As the accuracy requirements of machine tools increase, so do the accuracy requirements for electric spindles. Due to the compact internal structure of the spindle and poor heat dissipation, it is prone to serious thermal errors. Therefore, this study simulates the thermal characteristics of the A02 motorized spindle and builds a thermal error analysis experimental platform to monitor the temperature and thermal elongation data of key components in real time. Four key temperature measurement points are selected using k-means clustering and the Pearson correlation coefficient method. Finally, the INFO-GRU thermal error prediction model is established and compared with the WCA-GRU and GRU models. The results show that at 2000 r/min, the INFO-GRU model has a prediction accuracy of 95.5 %, which is better than WCA-GRU (91.5 %) and GRU (88.4 %); at 6000 rpm, the INFO-GRU model has a prediction accuracy of 95.9 %, which is also significantly higher than the other two models (91.7 % and 89.3 %, respectively). The novelty of this study lies in two improvements: firstly, the number of temperature measurement points is optimized by combining a clustering algorithm with a correlation coefficient method, reducing the amount of calculation and the risk of data coupling in the prediction; secondly, the GRU model optimized by the INFO algorithm is applied to the field of electric spindles for the first time, effectively analyzing the dynamic relationship between temperature and thermal expansion. The fluctuation of the model residual is controlled within 5 μm, providing a reliable prediction scheme for temperature compensation of high-speed electric spindles.