Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency

TL;DR

This paper reviews the development of real-time digital twins for milling, emphasizing AI-driven models, data flow, and live feedback to enable low-latency, smart manufacturing processes.

Contribution

It introduces a real-time, machine learning-based digital twin framework for milling, highlighting live data streaming and virtual modeling techniques for industrial applications.

Findings

Demonstrated a live digital twin for tool-work contact in milling

Highlighted data flow protocols for real-time updates

Outlined future research directions for Industry 4.0

Abstract

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.