Abstract
As a core component enabling multi-axis precision machining in CNC machine tools, the CNC rotary table’s precision state directly affects machining quality and production efficiency. Real-time and accurate prediction of its precision remaining useful life (PRUL) is of great significance for optimizing maintenance costs, ensuring production safety, and extending equipment service life. To this end, a digital twin framework for PRUL prediction of CNC rotary tables was first established, defining a five-dimensional digital twin model. Then, based on the Meta-action theory, an initial precision model of the CNC rotary table was constructed. Time-varying errors were quantitatively characterized using thermal error simulation and the wear model, and combined with the Wiener process model, theoretical PRUL prediction within the virtual mirror was realized. Finally, the virtual mirror data and physical entity data were fused to generate corrected model parameters, yielding a real-time PRUL prediction model for the CNC rotary table driven by digital twin data. The case study results demonstrate that the proposed method, which integrates the mechanistic model with real-time data, achieves an improvement of at least 16% in both prediction accuracy and stability compared to methods based solely on either virtual mirror data or monitoring data.