Digital Twins for Metal-Cutting Machine Tools: A Systematic Review

Abstract

Digital twin (DT) technology has become a transformative engineering paradigm for metal-cutting machine tool systems and materials-processing equipment, enabling real-time synchronisation, predictive analytics, and intelligent decision-making throughout the entire production cycle. This systematic review summarises the latest advances in the engineering-oriented implementation of digital twins, with a focus on their modelling frameworks, tool condition monitoring, compensation for geometric, kinematic, thermal and dynamic errors, fault diagnosis and adaptive control, and on the application of this technology to lathes, milling machines and grinding machines. The search for and selection of literature were carried out in accordance with the PRISMA 2020 guidelines. In total, 680 records were identified in Google Scholar in May 2026, of which 197 studies met the inclusion criteria and were synthesised narratively within six thematic sections. The analysis demonstrates that modern digital twin architectures integrate multiphysics modelling, multi-sensor data fusion, and machine learning to create high-precision virtual replicas of physical assets. Predictive maintenance and fault diagnosis systems use machine learning and deep learning to detect incipient faults in feed systems, spindles, and other critical components before failure. The review also analyses existing challenges and outlines future research directions for reliable industrial digital twins.