关键词: 智能磨削, 人工智能, 数字孪生, 融合驱动, 全任务链, 闭环优化

Abstract: Traditional grinding is affected by random abrasive cutting, time-varying contact, abrasive tool wear, and force–thermal coupling, leading to bottlenecks in process-state perception, abnormal-risk identification, quality-result prediction, and closed-loop parameter regulation. As a result, 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. 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 full task chain of pre-grinding planning, in-process execution, and post-grinding feedback. Five common supporting capabilities for intelligent grinding are discussed, including multi-source data foundation 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. Future opportunities for intelligent grinding are further 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. This review provides a reference for intelligent perception, stable prediction, closed-loop regulation, and continuous evolution of grinding processes under complex working conditions.

Key words: intelligent grinding, artificial intelligence, digital twin, fusion-driven, full-task chain, closed-loop optimization