实时AI驱动的铣削数字孪生实现超低延迟，提升智能制造效率。
Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency
- 基于实时数据流与机器学习构建铣削过程数字孪生
- 实现铣刀-工件接触状态的实时追踪与反馈控制
- 适合智能制造、工业4.0及高精度加工领域研究者
数字孪生（DT）通过实时数据、AI模型与智能控制系统推动智能制造发展。本文针对铣削场景下的数字孪生前沿进展进行综述，从物理铣削过程的虚拟建模、物理到虚拟的数据流，以及虚拟向物理的反馈机制三方面展开分析。重点探讨了实时数据传输协议与虚拟建模方法。案例研究展示了一个基于实时机器学习的铣削刀具-工件接触状态数字孪生系统，具备显著的动态响应能力。未来研究方向旨在支持工业4.0及更高级别智能制造目标的实现。
原文摘要 · Abstract (English)
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.
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