Reverse identification of dynamic and static motion errors for five-axis machine based on specimen feature decomposition

Abstract

The dynamic and static error motions dramatically influence the accuracy of five-axis machine tools. We propose a new approach of reverse identification for dynamic and static motion error of five-axis machine tools through specimen cutting and feature decomposition. The error mapping between the workpiece features and the corresponding errors is established, and a feature specimen is designed accordingly. By measuring the specimen with the on-machine measurement and CMM, 15 static motion errors and dynamics-induced errors can be identified and separated. The error estimates are verified by the direct measurement via the interferometer, autocollimator and ballbar tests. These errors are compensated using a volumetric error model, and the specimen accuracy after compensation is effectively increased, which confirms the feasibility and accuracy of this method.