Digital Twin-Enabled Intelligent Stability Management in Ultra-Precision Machining

Abstract

Abstract Ultra‑precision machining (UPM) is essential for producing components with sub‑micron accuracy and superior surface finish, yet its industrial adoption is constrained by low efficiency, complex stability management, and insufficient intelligent monitoring. To address these challenges, this study develops a holistic digital twin framework for UPM. The architecture is organized into three layers: physical, communication, and service. MTConnect provides standardized data exchange across these layers, while mechanistic modelling is combined with machine learning to enable real‑time monitoring, adaptive control, and intelligent process planning. The intelligent stability management strategy incorporates a transfer‑learning‑based chatter detection model with an Elastic Weight Consolidation (EWC)-based continuous learning algorithm to adaptively update the stability boundary (SLD) to reflect evolving machining states. Experimental validation on a micro‑milling platform confirmed the effectiveness of the framework, with the chatter detection model achieving 96.6% accuracy and the adaptive SLD updating method successfully refining decision boundaries to represent dynamic machining conditions. Immersive VR/AR visualization further enhanced operator collaboration and transparency. Distinguishing itself from prior task‑specific approaches, this work establishes an integrated stability management strategy that unifies sensing, modelling, adaptive optimization, and human–machine interaction. This study advances theoretical insight and practical implementation in ultra‑precision machining, providing a scalable foundation for intelligent and resilient manufacturing systems.