Dynamic prediction method for contour error of CNC machine tools driven by mechanism-data hybrid

For multi-axis CNC machining of complex parts, the dynamic prediction of contour error is the basis and guarantee for on-line adjustment of machining parameters and high-quality machining. During the operation of the machine tool, the change of the pose of each feed shaft makes the machine tool show obvious time-varying dynamic characteristics, which makes the contour error difficult to predict. Aiming at this problem, a dynamic prediction method of contour error of multiaxis machine tool driven by mechanism-data hybrid is proposed. Taking the two-axis feed system of a five-axis machining center as the specific object, the dynamic modeling and solution are carried out, and the rigid-flexible coupling transfer function matrix is constructed as the mechanism model. The data set of feed shaft pose and natural frequency is obtained by modal experiment, and the data model is constructed by machine learning algorithm. The time-varying rigid-flexible coupling transfer function matrix is obtained by combining the mechanism model with the data model. Taking it as the controlled object, a time-varying servo feed system is built in MATLAB/Simulink to realize the dynamic prediction and verification of contour error. Finally, the above model is integrated into VE² to develop the digital twin model of the machine tool, so as to realize the dynamic presentation of contour error and tracking error. The research results have reference value for constructing the contour error model under the influence of time-varying dynamic characteristics.