Structural dynamics-driven digital twin framework for real-time vibration modelling of machine tools

Abstract

Structural vibration under dynamic machining conditions poses a significant challenge to the performance and reliability of machine tools. Traditional static and offline modeling approaches fail to adapt to real-time changes in structural dynamics. To address this, this paper proposed a structure dynamics-driven digital twin framework, enabling real-time monitoring, modeling, and feedback control of machine tool vibrations. Distinct from conventional digital twins that focus solely on geometric and kinematic synchronization, the proposed approach integrates physical dynamic characteristics as the core driver of digital twin evolution. To realize this concept, a digital twin platform for machine tool dynamics has been developed, featuring real-time feedback and interaction with the physical machine via bidirectional communication. Built upon this platform, a self-excitation strategy using servo feedforward injection is introduced to identify modal parameters during operation. Furthermore, variational mode decomposition (VMD) integrated with real-time modal information and modal superposition enables accurate full-field vibration twinning. Experiments conducted on a CHX-5240i vertical lathe verify the system’s accuracy, with modal frequency identification errors of less than 2% and a vibration twinning relative error of under 9%. Moreover, vibration thresholds derived from the digital twin are shown to correlate with surface roughness limits and machining stability boundaries, validating its utility in performance-oriented process control. This work achieves system-level advances in dynamic model integration, real-time modal updating, and vibration–performance mapping. By enabling real-time bidirectional interaction with the physical system, the proposed structure vibration digital twin framework offers a novel paradigm for intelligent manufacturing, with strong potential for deployment in closed-loop control and adaptive optimization of machining processes.