Digital Twin Modeling Enabled Machine Tool Intelligence: A Review

Abstract

AbstractMachine tools, often referred to as the “mother machines” of the manufacturing industry, are crucial in developing smart manufacturing and are increasingly becoming more intelligent. Digital twin technology can promote machine tool intelligence and has attracted considerable research interest. However, there is a lack of clear and systematic analyses on how the digital twin technology enables machine tool intelligence. Herein, digital twin modeling was identified as an enabling technology for machine tool intelligence based on a comparative study of the characteristics of machine tool intelligence and digital twin. The review then delves into state-of-the-art digital twin modeling-enabled machine tool intelligence, examining it from the aspects of data-based modeling and mechanism-data dual-driven modeling. Additionally, it highlights three bottleneck issues facing the field. Considering these problems, the architecture of a digital twin machine tool (DTMT) is proposed, and three key technologies are expounded in detail: Data perception and fusion technology, mechanism-data-knowledge hybrid-driven digital twin modeling and virtual-real synchronization technology, and dynamic optimization and collaborative control technology for multilevel parameters. Finally, future research directions for the DTMT are discussed. This work can provide a foundation basis for the research and implementation of digital-twin modeling-enabled machine tool intelligence, making it significant for developing intelligent machine tools.