考虑几何误差和跟随误差的五轴联动轨迹误差预测方法

The cause of the trajectory error of the five-axis CNC machine tool is complex and often unpredictable before processing, and it is difficult to determine in advance whether the selected machine tool can meet the requirements of the contour error of the parts. As a result, more conservative processing parameters have to be set to meet the processing accuracy requirements during on-site processing, which greatly sacrifices the processing efficiency of the parts. In this regard, this article proposes a five-axis trajectory error prediction method that considers geometric errors and tracking errors. It predicts the trajectory error of a certain trajectory on a selected machine tool before or during processing, and judges in advance whether the selected machine tool can meet the requirements of part contour error, which is of great significance to ensure the machining accuracy of the part and improve the machining efficiency. In the proposed method, 30 position-dependent geometric errors dependent to position (PDGES) are characterized as functions, and 11 position-independent geometric errors independent to position (PIGES) are characterized as constants. The setpoint position sequence of each axis and the raster detection position sequence are read, and the tool axis vector trajectory including the tracking errors and 41 geometric errors are synthesized to realize the prediction of the five-axis tool center position trajectory error and the tool axis posture trajectory error. And an AC double-turntable vertical five-axis machine tool is used to process S-shaped specimens to verify the proposed five-axis trajectory error prediction method. The results show that both the setpoint trajectory and the predicted trajectory present an S shape, and the predicted trajectory has a certain deviation relative to the setpoint trajectory, which presents the influence of geometric errors and following errors; the measured trajectory and the predicted trajectory have the same variation trend, and both curves reflect some of the peak characteristics, which shows the correctness of the prediction algorithm.