Parametric modeling of CNC machine tool geometric errors based on low-order fourier polynomials

Abstract

To further improve the forecast accuracy of the geometric error of CNC machine tool, a low order Fourier polynomials fitting model (LFPFM) parametric modeling method based on Improved Gray Wolf Optimization(IGWO) algorithm is proposed. Straight-axis geometric error term is analyzed, and the introduction of piecewise attenuation factor can be adjusted to the GWO algorithm is Improved, to improve the scouting speed of the Optimization algorithm and precision, the error data in all parameters based on the IGWO algorithm to find the best fitting values, and get the error about low-order Fourier polynomial fitting polynomial, Use laser interferometer to measure the location errors of three straight line axes in the CNC machine tool, and parametric modeling was carried out. The experimental results show that the machine tool error parameterized model based on the LFPFM the IGWO algorithm can fit the error data well, and the residual error is smaller, the accuracy is higher, the fitting effect is better, so it is more suitable for the prediction and compensation model of CNC machine tool geometric error.