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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.13482 |
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| _version_ | 1866914201896747008 |
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| author | Liu, Wenyi Sharma, R. Guo, W. "Grace" Yi, J. Guo, Y. B. |
| author_facet | Liu, Wenyi Sharma, R. Guo, W. "Grace" Yi, J. Guo, Y. B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13482 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency Liu, Wenyi Sharma, R. Guo, W. "Grace" Yi, J. Guo, Y. B. Systems and Control Machine Learning 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. |
| title | Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2512.13482 |