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

arXiv: https://arxiv.org/abs/2512.13482 · HTML: https://mlsanity.com/paper/2512.13482/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 130 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling](https://mlsanity.com/paper/1710.11431/) (2017)
- [Real-time predictive maintenance for wind turbines using Big Data frameworks](https://mlsanity.com/paper/1709.07250/) (2017)
- [Physics-Informed Machine Learning for Smart Additive Manufacturing](https://mlsanity.com/paper/2407.10761/) (2024)
- [A Comprehensive Review of Diffusion Models in Smart Agriculture: Progress, Applications, and Challenges](https://mlsanity.com/paper/2507.18376/) (2025)

## Abstract (arXiv)

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.