Regression predictive modeling of high-speed motorized spindle using POA-LSTM

Abstract

With the increasing importance of motorized spindles in high-end machining, the accuracy requirements for motorized spindles have been increasing. Therefore, the accuracy problem caused by thermal deformation of the motor spindle during machining has become a prominent and current topic of interest. The problem of thermal deformation of the spindle is due to the fact that a large amount of heat is accumulated inside the spindle during the high-speed rotation of the electric spindle, which has no timely temperature compensation measures and can only rely on the conditions of heat conduction, heat convection and heat radiation to transfer the heat to the outside world, which leads to the thermal expansion of the spindle. Aiming at this problem, this paper takes the A02 electric spindle as the research object, and collects and analyzes the temperature information of the current temperature rise of the key parts of the electric spindle and the thermal elongation of the spindle by constructing the experimental platform for thermal simulation and analysis; and then through the COMSOL steady-state simulation cloud diagram combined with the K-means clustering and gray correlation analysis, it filters out the four temperature points with the highest correlation degree of the temperature data, and constructs the POA-LSTM thermal error model to predict the thermal elongation of the spindle at different rotational speeds, and compared the accuracy of SSA-LSTM and LSTM thermal error models, at a high rotational speed of 10,000 r/min, the accuracies of Early LSTM, SSA-LSTM, and LSTM were 97.44 %, 90.27 %, and 86.66 %, respectively, and the model accuracy of POA-LSTM was about 98.257 %; thus the POA-LSTM thermal error model has high prediction accuracy and robustness.