Digital twins for machine tools: a systematic mapping study
Shengjian Chen, Carsten Ellwein, Lars Klingel, Rebekka Neumann, Jingxi Zhang, Oliver Riedel, Alexander Verl, Andreas Wortmann
Abstract
Machine tools are essential for modern manufacturing and drive the production of precise and high-quality components across various industries. For smart manufacturing, digital twins – virtual representations of cyber-physical entities – have emerged to enhance machine tools with intelligent capabilities, such as real-time monitoring, predictive maintenance, or performance optimisation. Despite their potential, the availability and application of digital twins in machine tools remain largely unexplored. This first systematic mapping study specifically on digital twins for machine tools investigates the purpose for their deployment to machine tools, the development methods to create such digital twins, and support for their connectivity to the machine tool and to other related systems. Our findings highlight the critical role of digital twins in improving the efficiency and reliability of machine tools.