Digital Twin Modelling Method of Five-Axis Machine Tool for Predicting Continuous Trajectory Contour Error

Abstract

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.

Research on the construction and fidelity evaluation of digital twins for CNC machine tools

“…DT technology has demonstrated enormous potential in various fields, including production logistics [9], urban planning [10], aerospace [11], energy [12], and automotive manufacturing [13]. With the introduction of machine tool DTs by Liu et al [14], the application of the system in machine tool condition monitoring and augmented reality assistance on the network has demonstrated the advantages and potential of the proposed method. Luo et al [15] established a multi-domain unified modeling method for numerical control machine tools, explored mapping strategies between physical space and digital space, and proposed autonomous strategies for numerical control machine tools.…”