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

Liu, Wenyi (ORCID:0009000902963517); Sharma, R; “Grace”_Guo, W; Yi, J; Guo, YB

doi:10.1016/j.eng.2025.12.028

skip to main content

An official website of the United States government Here's how you know

Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 5:00 PM ET until 8:00 PM ET on Friday, September 11 due to maintenance. We apologize for the inconvenience.

Citation Details

This content will become publicly available on January 1, 2027

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

Award ID(s):: 2328260

PAR ID:: 10685801

Author(s) / Creator(s):: Liu, Wenyi; Sharma, R; “Grace”_Guo, W; Yi, J; Guo, YB

Publisher / Repository:: Elsevier

Date Published:: 2026-01-01

Journal Name:: Engineering

ISSN:: 2095-8099

Format(s):: Medium: X

Sponsoring Org:: National Science Foundation

Free Publicly Accessible Full Text
This content will become publicly available on January 1, 2027
Journal Article:
https://doi.org/10.1016/j.eng.2025.12.028

More Like this