@article{REN2026, 
author = {Yinghui REN and Kai FENG and Luyao LIU and Zhaohui DENG and Xipeng XU and Fei TAO and Bo YE and Qiang ZHAO and Haitao LIU and Wei LI and Hongrui ZHAO and Qiding YANG},
title = {Artificial intelligence and digital twin enabled intelligent grinding: Progress and opportunities},
year = {2026},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {47},
number = {19},
keywords = {intelligent grinding, artificial intelligence, digital twin, fusion-driven, full-task chain, closed-loop optimization},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2026.33815},
doi = {10.7527/S1000-6893.2026.33815},
abstract = {Traditional grinding is affected by random abrasive cutting, time-varying contact, abrasive tool wear, and force-thermal coupling, resulting in bottlenecks in process-state perception, abnormality and risk identification, quality prediction, and closed-loop parameter regulation. Consequently, it is difficult to meet the requirements of high-quality and high-efficiency intelligent manufacturing characterized by process self-generation, state self-perception, parameter self-decision-making, and process self-adaptation. First, focusing on four typical task-dominant scenarios, namely difficult-to-machine materials, complex surfaces, low-rigidity structures, and high-consistency manufacturing, this paper systematically reviews the research progress of intelligent grinding enabled by Artificial Intelligence (AI) methods and Digital Twin (DT) technology across the three-stage full task chain of pre-grinding planning, in-process execution, and post-grinding feedback. Second, five common supporting capabilities for intelligent grinding are discussed, including multi-source data infrastructure and unified state representation, mechanism-data fusion modeling, virtual-physical synchronization and digital thread, industrial deployment and trustworthy closed-loop operation, and system integration architecture. Furthermore, future opportunities for intelligent grinding are discussed from four aspects: open data and unified evaluation, small-sample learning and cross-condition generalization, safety and trustworthiness with human-in-the-loop collaboration, and platform-based operation and maintenance and capability development. Finally, the research trend of AI- and DT-enabled intelligent grinding is summarized as an evolution from single-source perception, local modeling, and offline optimization toward systematic closed-loop intelligence covering four typical scenarios, spanning three stages, and supported by five common capabilities. This review provides a reference for intelligent perception, stability prediction, closed-loop regulation, and continuous evolution of grinding processes under complex working conditions.}
}