The paper "Digital Twin Modeling Enabled Machine Tool Intelligence: A Review" by Lei Zhang, Jianhua Liu, and Cunbo Zhuang explores the integration of digital twin technology with machine tools to enhance their intelligence. The authors identify digital twin modeling as a key enabling technology for machine tool intelligence, based on a comparative analysis of the characteristics of both. The review delves into state-of-the-art digital twin modeling approaches, including data-based and mechanism-data dual-driven methods, and highlights three main challenges in the field. To address these challenges, the authors propose an architecture for a Digital Twin Machine Tool (DTMT) and detail three key technologies: data perception and fusion, mechanism-data-knowledge hybrid-driven digital twin modeling, and virtual-real synchronization technology. Additionally, they discuss dynamic optimization and collaborative control technology for multilevel parameters. The paper concludes by discussing future research directions for DTMTs, emphasizing the importance of digital twin modeling in developing intelligent machine tools. The review provides a comprehensive foundation for researchers and practitioners interested in advancing the field of intelligent machine tools.The paper "Digital Twin Modeling Enabled Machine Tool Intelligence: A Review" by Lei Zhang, Jianhua Liu, and Cunbo Zhuang explores the integration of digital twin technology with machine tools to enhance their intelligence. The authors identify digital twin modeling as a key enabling technology for machine tool intelligence, based on a comparative analysis of the characteristics of both. The review delves into state-of-the-art digital twin modeling approaches, including data-based and mechanism-data dual-driven methods, and highlights three main challenges in the field. To address these challenges, the authors propose an architecture for a Digital Twin Machine Tool (DTMT) and detail three key technologies: data perception and fusion, mechanism-data-knowledge hybrid-driven digital twin modeling, and virtual-real synchronization technology. Additionally, they discuss dynamic optimization and collaborative control technology for multilevel parameters. The paper concludes by discussing future research directions for DTMTs, emphasizing the importance of digital twin modeling in developing intelligent machine tools. The review provides a comprehensive foundation for researchers and practitioners interested in advancing the field of intelligent machine tools.