Digital twin technology in modern machining: A comprehensive review of research on machining errors

Abstract

With the evolution of intelligent manufacturing, high-precision CNC machining has become a crucial driver of industrial advancement. Modern manufacturing demands increasingly precise machining, yet traditional methods lack effective means for visualizing and compensating for machining errors in real-time. Digital twin technology offers a breakthrough solution by creating real-time mappings between physical and digital spaces, enabling visualized monitoring and intelligent prediction throughout the machining process. This technology serves five essential functions in machining errors management: real-time identification of high-precision errors, data-driven error modeling using multiple sources, error traceability and decoupling through causal reasoning, real-time error prediction and interaction, and closed-loop adaptive error control and compensation. This study provides a systematic review of digital twin technology's current applications in machining error management, focusing on machining errors identification, modeling, traceability, decoupling, control, and compensation. Future trends in digital twins focus on intelligent error prediction, real-time adaptive control, and multi-source data fusion for CNC machining. • A systematic review of digital twin-driven machining error research analyzes key aspects from identification to compensation, comparing traditional and digital twin approaches to demonstrate technological advancement. • Showcasing digital twin's edge: emphasizing unique capabilities such as visualization, real-time performance, full lifecycle control, and advanced multi-physics field simulation. • Critical analysis of current research challenges, focusing on high-fidelity model development, data fusion, real-time monitoring, feedback control, and emerging interdisciplinary integration with human-computer interaction, augmented reality, and AI technologies.

Topics & Concepts