Digital twin (DT) enables smart manufacturing by leveraging real-time data, AI models, and intelligent control systems. This paper presents a state-of-the-art analysis on the emerging field of DTs in the context of milling. The critical aspects of DT are explored through the lens of virtual models of physical milling, data flow from physical milling to virtual model, and feedback from virtual model to physical milling. Live data streaming protocols and virtual modeling methods are highlighted. A case study showcases the transformative capability of a real-time machine learning-driven live DT of tool-work contact in a milling process. Future research directions are outlined to achieve the goals of Industry 4.0 and beyond.

翻译：数字孪生（DT）通过利用实时数据、人工智能模型和智能控制系统，赋能智能制造。本文对铣削领域新兴的数字孪生技术进行了前沿分析。通过物理铣削的虚拟模型、从物理铣削到虚拟模型的数据流，以及从虚拟模型到物理铣削的反馈这三个视角，探讨了数字孪生的关键方面。重点介绍了实时数据流协议和虚拟建模方法。一项案例研究展示了铣削过程中刀具-工件接触的实时机器学习驱动动态数字孪生的变革性能力。本文还概述了为实现工业4.0及更高目标所需的未来研究方向。