A method for constructing digital twins of CNC machine tools feed systems based on hybrid mechanism-data

Traditional CNC machine tool feed system models suffer from low simulation accuracy and limited generalizability. These issues arise from simplified process replication and rigid optimization methods based on mechanical knowledge. To address these challenges, this study proposes a new hybrid mechanism data-driven digital twin (DT) modeling framework. Firstly, a nonlinear coupling characterization method was developed by combining fuzzy proportional integral (PI) control with mechanical system dynamics. This method achieves real-time adaptive parameter updating of the feeding system, and compared with traditional models, the maximum error is reduced by 32.79%. To further address the inherent simplification characteristics of the mechanism model, a WOA-CNN-LSTM-Attention algorithm was independently constructed for compensation, and experimental verification through physical machine operation confirmed that the maximum error was reduced from 0.0576mm to 0.0121mm. Finally, an online recognition system with a recursive least squares multiplication with a forgetting factor was used to achieve fast parameter convergence: tracking for 0.005 seconds in simple cases and 0.01 seconds in complex cases, achieving real-time DT synchronization. This study provides a systematic solution for building stable and efficient DT systems in precision manufacturing applications.