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

Due to the intricate mechanism of Abbe error's impact on spatial accuracy, the accumulated Abbe error in the traditional spatial accuracy model is difficult to identify and eliminate, resulting in reduced modeling accuracy and limiting accuracy improvement. This paper proposes a data-driven spatial accuracy modeling approach for machine tools under the influence of Abbe error, using a three-axis coupling measurement optical path to directly measure the comprehensive spatial accuracy data of the machine tool containing Abbe error. Firstly, to effectively identify Abbe error in the comprehensive spatial accuracy, an Abbe error quantization function is established, which analyzes its formation mechanism in the measurement process to eliminate Abbe error in the machine tool's spatial accuracy data. Furthermore, to address the issue of limited data samples after eliminating Abbe error, data samples are extended based on the degradation mechanism of machine tool spatial accuracy at different coordinate positions, resulting in a high-precision spatial error model for machine tools. Finally, the experiment is conducted on a three-axis CNC machine tool, with a model accuracy of over 95%. The example application verification demonstrates that the developed model scheme is feasible and effective. [ABSTRACT FROM AUTHOR]