Dynamic and static error identification and separation method for three-axis CNC machine tools based on feature workpiece cutting

Abstract

CNC machine tools play an extremely important role in the equipment manufacturing industry. Because a machine tool has multiple geometric errors which are highly coupled, the error identification process is difficult. Most direct and indirect measurement methods do not consider the dynamic errors in the actual cutting process. This study aims to optimize the error identification process of three-axis CNC machine tools, and proposes an efficient identification and separation method for the dynamic error and the quasi-static error based on feature workpiece cutting. In this study, a stepped feature workpiece that can reflect geometric errors is designed, and the mapping relationship between the geometric error components and the features is established. After the feature workpiece is cut, it performs two tests of on-machine measurement and CMM calibration sequentially. It can realize the identification and separation of 7 geometric errors and corresponding dynamic errors based on the two sets of test data. This method takes the actual cutting workpiece as the error source and can identify and separate the dynamic error and the quasi-static error simultaneously. Thus, the measurement efficiency of this method is higher than direct measurements. The geometric error identification result is highly consistent with the laser interferometer measurement, which proves the method in this study is feasible, efficient, and accurate.