An efficient error prediction and compensation method for coordinated five-axis machine tools under variable temperature

Abstract

I n a dual-machine system, the positioning accuracy of coordinated machine tools is one of the most important performances. Considering the quasi-static errors of the developed system, the paper presents an effective error prediction and compensation method for coordinated five-axis machine tools under variable temperature. Firstly, for error prediction at a constant temperature, the workspace of coordinated machines is divided into the subspaces of translation and rotation axes, and an error prediction algorithm is presented with the known errors at sampled points. When the temperature changes, the thermal errors of the coordinated machines are analyzed by the simulation and error measurement, and an interpolation method is proposed to quickly determine the sampled data at any temperature. For error compensation, a recursive algorithm is proposed to compensate the absolute and relative pose error of dual machines. Validation experiments have been conducted to verify the proposed method on the developed system. The results have shown that, compared with previous method, the method presented in this paper is more accurate and time-efficient for error prediction and compensation under variable temperature conditions.