Research on Thermal Error Modeling Method of Machine Tool Spindle Based on Optimized BP Neural Network

Abstract

Abstract Addressing the limitations of the single-temperature measurement point monitoring for detecting the temperature changes in the CNC machine tool spindle, and the shortcomings of the thermal error model based on back propagation neural network (BP) in accuracy, convergence and robustness. This paper studies the thermal error identification model and method of spindle based on multiple temperature sensors. An Adaptive particle swarm algorithm-back-propagation neural network (IAPSO-BP) model for thermal error identification of principal axes is proposed. To enhance modeling accuracy and comprehensively monitor the temperature information of the machine tool spindle, the input of this model is generated by processing the data collected through five temperature sensors. The IAPSO algorithm is employed for the automatic identification of BP parameters, reduce manual intervention, and enhancing the model’s capacity for generalization.