A data-driven high-precision modeling method of machine tool spatial error under the influence of Abbe error

Abstract Due to the complex mechanism of the influence of Abbe error on spatial accuracy, the Abbe error accumulated in the traditional spatial accuracy model is hard to be identified and cannot be eliminated, which affects the modeling accuracy and restricts the effect of accuracy improvement. This paper presents a data-driven spatial accuracy modeling method for machine tool under the influence of Abbe error, using a three-axis coupling measurement optical path to directly measure the comprehensive spatial accuracy data of machine tool containing Abbe error. In addition, in order to effectively identify the Abbe error in the comprehensive spatial accuracy, the Abbe error quantization function is established to eliminate the Abbe error in the spatial accuracy data of machine tool by analyzing its formation mechanism in the measurement process. Further, aiming at the problem of small data samples after eliminating Abbe error, the data samples are extended based on the degradation mechanism of machine tool spatial accuracy at different coordinate positions, and a high-precision spatial error model for machine tool is given. Finally, the experiment is conducted on a three-axis CNC machine tool with the model accuracy of over 95%, and the example application verification shows that the developed model scheme is feasible and effective.