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

Hainan Zhang; Sitong Xiang; Chao Liu; Jianghao Sun; John Kobla Attifu

doi:10.1016/j.isatra.2022.08.018

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

ISA Trans. 2023 Mar:134:302-311. doi: 10.1016/j.isatra.2022.08.018. Epub 2022 Aug 24.

Authors

Hainan Zhang¹, Sitong Xiang², Chao Liu¹, Jianghao Sun¹, John Kobla Attifu¹

Affiliations

¹ Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China.
² Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China. Electronic address: xiangsitong@nbu.edu.cn.

PMID: 36058718
DOI: 10.1016/j.isatra.2022.08.018

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.

Keywords: Feature decomposition; Machine tool; Motion error; Reverse identification; Specimen design.