Thermal error modeling of machine tool spindle based on the improved algorithm optimized BP neural network

Abstract

Thermal error of the machine tool spindle is one of the main factors affecting the machining accuracy. For the complex operating environment of the machine tool, the difficulty of thermal error prediction modeling, and the low accuracy of the traditional thermal error prediction model, a spindle thermal error prediction model based on the improved particle swarm optimization (IPSO) optimize back propagation (BP) neural network is established in this paper. The temperature measurement points are clustered by SOM neural network, and the correlation analysis method is used to explore the correlation between the thermal sensitive points and the thermal error of the spindle. The S-type function is used to improve the inertia weight coefficient of the IPSO algorithm so as to improve the particle optimization effect. IPSO is used to optimize the parameters of BP neural network, such as the initial weights and thresholds. Compared with the GA-BP prediction model, the modeling efficiency, robustness, and accuracy of IPSO-BP neural network prediction model are all superior GA-BP prediction model. Taking the thermal error of the electric spindle of precision CNC machining center as the research object, the intelligent temperature sensor and the laser displacement sensor are used to obtain the machine tool temperature values and the spindle thermal error values. The prediction accuracy of the GA-BP model for the spindle thermal error was 93.1%, and the prediction accuracy of the IPSO-BP model was 96.5%. The results show that the IPSO-BP model can accurately predict the thermal error of the spindle under different working conditions. The model can obtain higher thermal error prediction accuracy and is more suitable for the thermal error compensation model.