Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency
It addresses smart manufacturing challenges for Industry 4.0, but appears incremental as it focuses on analysis and a case study without claiming major breakthroughs.
This paper tackles the problem of enabling real-time digital twins for milling processes by analyzing critical aspects like virtual models, data flow, and feedback, and demonstrates a case study with a machine learning-driven digital twin for tool-work contact.
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.