As for the machine tool with high-speed ball screw feed system, the quite large friction force may change the real contact state of kinematic joints, which would further affect the dynamic characteristics of the whole system. In this article, an equivalent dynamic model of high-speed ball screw feed system was established using hybrid element method. The equivalent axial stiffness of individual kinematic joint and system transmission stiffness were all derived considering the influence of feed rate. The variation in the system natural frequency with feed rates was analyzed using the model proposed and also verified by experiments. The results show that the system natural frequency in motion state is larger than that in static state and behaves differently in different feed rates. The equivalent axial stiffness of the rear-end support bearing unit can reduce to 0 as the feed rate increases, which further leads to a critical value for system natural frequency. In the high-speed motion of the feed system, resonance may occur when the system natural frequency is almost same to the torque ripple harmonic frequency of the servo motor.

As for the gantry-type machine tools, the forces of the slide blocks supporting the beam are different for different saddle locations, which would further affect the stiffness of the slider–guide joints and the dynamic characteristics of the whole machine tools. Therefore, the beam kinematic joints (the slider–guide joints) on the gantry-type machine tools are crucial for the dynamics prediction of traveling bridge systems. In this article, considering the effect of axis coupling force on the slider–guide joints’ stiffness, an equivalent dynamic model of traveling bridge systems for the gantry-type machine tools is established using hybrid element method. The additional load variation in the four slider blocks is analyzed for different locations of the saddle, and the variation in the slider–guide joints’ stiffness and traveling bridge system natural frequency are also studied. Finally, validation experiments are conducted on the traveling bridge systems of the gantry-type milling machine tools, and the results show that the dynamic modeling proposed in this article can reach a higher accuracy.

The ball-screw feed drive has varying high-order dynamic characteristics due to flexibilities of the slender screw spindle and joints between components, and an obvious feature of non-collocated control when a direct position measurement using a linear scale is employed. The dynamic characteristics and non-collocated situation have long been the source of difficulties in motion and vibration control, and deteriorate the achieved accuracy of the axis motion. In this study, a dynamic model using a frequency-based substructure approach is established, considering the flexibilities and their variation. The position-dependent variation of the dynamic characteristics is then fully investigated. A corresponding control strategy, which is composed of a modal characteristic modifier (MCM) and an intelligent adaptive tuning algorithm (ATA), is then developed. The MCM utilizes a combination of peak filters and notch filters, thereby shaping the plant dynamics into a virtual collocated system and avoiding control spillover. An ATA using an artificial neural network (ANN) as a smooth parameter interpolator updates the parameters of the filters in real time in order to cope with the feed drive’s dynamic variation. Numerical verification of the effectiveness and robustness of the proposed strategy is shown for a real feed drive

The CNC machine tool is the passive executor of machining code. It cannot predict the machining accuracy during machining. If the error is found to be out of tolerance after processing, it will not only scrap the parts, but also greatly affect the processing efficiency. This phenomenon is very prominent when machining sculptured surface parts with five-axis machine tools. Therefore, this paper proposes a Digital Twin (DT) modeling method of five-axis machine tools for predicting Continuous Trajectory Contour Error (CTCE) caused by tracking errors and geometric errors. The DT consists of three parts: the Setpoints Trajectory (ST) model, the Actual Trajectory (AT) model considering tracking errors and geometric errors and the CTCE model. For a specific machine tool, according to the basic geometric information of the machine tool (tool length, kinematic chain information, etc.) and 41 geometric errors, the DT can be established. Inputting the Setpoints Positions (SPs) and the Linear Encoder Detection Positions (LEDPs), the DT can be used to predict the Tool-Tip Position Trajectory (TTPT) contour error and the Tool Orientation Trajectory (TOT) contour error. In order to verify the proposed method experimentally, the KMC400S U five-axis machine tool is selected to establish its DT by which the contour error of the S-shaped trajectory are predicted offline. Then, the DMU50 five-axis machine tool is selected to establish its DT to predict the contour error of the circular trajectory in real time. Combined with the deep motion mechanism, this paper proposes a DT modeling method for the vertical application scene of parts machining accuracy prediction, which is of great significance to developing the DT application theory and ensuring the machining accuracy of parts.