A geometric error identification method for the swiveling axes of five-axis machine tools by static R-test

Abstract

This paper proposes a new method to identify the 12 geometric error components of the swiveling axes of five-axis machine tools by static R-test. First, the geometric error modeling of a five-axis machine tool including the error sources of the C-A swiveling head is briefly introduced based on the MBS method. With the assistance of an auxiliary fixture, the measuring strategy of the swiveling axes that consists of six inspection modes by static R-test is proposed. Then, a geometric error identification algorithm for the C- and A-axis is respectively proposed based on the least square method. Numerical simulation is conducted and the results show that the accuracy of the identification algorithm can reach 89 %. An optimization method for the auxiliary fixture is proposed by minimizing the condition number of the identification matrixes to further improve the identification accuracy. Finally, numerical simulation shows that the identification accuracy can be increased to 97 % after optimization. The theoretical analysis and numerical simulation results indicate that the proposed swiveling axes geometric error identification method is both efficient and effective.