Research on Axial Thermal Error Modeling Method of CNC Machine Tool Spindle Based on GA-ARMA*

Abstract

In order to improve the prediction accuracy of the thermal error model of CNC machine tools based on time series and reduce the time of model parameter identification, a time series thermal error modeling method based on intelligent optimization (GA-ARMA) was proposed. Using the reciprocal of the residual between the actual value and the predicted value as the genetic algorithm (GA) individual fitness value function, select the best individual obtained by evolution for several generations as the parameter of the ARMA model, quickly identify the parameters of the ARMA model, and establish the GA-ARMA spindle axial thermal error model. Through experiments to compare the prediction effects of the time series thermal error model based on intelligent optimization and the time series thermal error model, taking a certain type of three-axis CNC machine tool as the object, the prediction and comparison are carried out under different working conditions. The experimental results show that the model prediction average residual error reaches 1.28 $\mu$m, and the modeling efficiency is improved by 544%.