Temperature Rise Experiment and Prediction Modeling for Key Position of Preload Adjustable Motorized Spindle

Abstract

High-speed motorized spindle is one of the key components of CNC machine tools. The temperature rise prediction and control of motorized spindle is an important technology for the development of intelligent machine tools. The influence of gas supply pressure and spindle speed on the temperature rise of the outer ring of the front bearing of the motorized spindle was experimentally studied by using the established test platform for online regulation of the motorized spindle under pre-load. The results show that the steady-state temperature rise of the high-speed motorized spindle increases with the increase of its speed and gas supply pressure. It further indicates that the online control of the pre-load of the bearing can realize the active control of the thermal characteristics of the motorized spindle. The BP neural network temperature rise prediction model was established to predict the temperature rise at the key position of the high-speed motorized spindle under specific rotational speed, air supply pressure and ambient temperature. The reliability and accuracy of the model are verified by comparing the predicted results with the experimental results. The predicted results can be used as the basis for studying the thermal characteristics of high-speed motorized spindle.