发表机构
University of Oulu; University of North Carolina at Charlotte（奥卢大学; 北卡罗来纳大学夏洛特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种分层数字孪生框架，扩展现有信息物理机床框架，集成多类数据实现机床与加工过程同步，经实验验证性能达标，为AI辅助加工应用奠定基础。
AI 中文摘要
数字孪生（DTs）已成为提升制造系统监控、优化与自动化水平的关键技术。然而，现有信息物理机床（CPMT）实现方案主要针对机床本身建模，加工过程仅与物理实体部分同步。本文在已有CPMT框架基础上扩展，提出一种分层DT框架，同时维护机床与加工过程的数字孪生。该框架集成实时数控（CNC）运行数据、基于体素的工件表示、同步的过程振动测量数据，以及用于过程回放、可追溯性和未来合成数据生成的持久零件DT仓库。实验评估显示，该框架可实现20Hz加工状态更新率的实时运行、超过100帧/秒的交互式可视化，以及0.16mm的平均深度重建误差。该实现为AI辅助加工应用奠定基础，同时保留机床监控与远程操作能力。
英文摘要
Digital Twins (DTs) have emerged as a key technology for improving the monitoring, optimization, and automation of manufacturing systems. However, existing Cyber-Physical Machine Tool (CPMT) implementations primarily represent the machine tool, while the machining process remains only partially synchronized with its physical counterpart. This paper extends a previously presented CPMT framework by introducing a hierarchical DT framework that simultaneously maintains DTs of both the machine tool and the machining process. The proposed framework integrates real-time CNC operational data, a voxel-based workpiece representation, synchronized process vibration measurements, and a persistent part DT repository for process replay, traceability, and future synthetic data generation. Experimental evaluation demonstrated real-time operation at a 20 Hz machining-state update rate, interactive visualization exceeding 100 frames per second, and a mean depth reconstruction error of 0.16 mm. The implementation provides a foundation for AI-assisted machining applications while preserving the machine tool monitoring and teleoperation capabilities.
