This paper presents a digital twin-assisted online monitoring and control system for machining operations. Process information including Cutter-Workpiece Engagement (CWE) maps are extracted from the virtual model of the process and fused with the CNC internal data as well as the measured external sensor data. Several applications are illustrated to demonstrate the system. Adaptive control of cutting load based on directly measured forces or reconstructed forces from an accelerometer-integrated tool holder is demonstrated. The spindle current data integrated with CWE information from the virtual model is utilized for the estimation of tool wear progression and tool breakage detection. Digital twin-assisted chatter detection and avoidance are presented by avoiding false alarms at the cutter's entry into and exit from work material which triggers transient vibrations. It is shown that by utilization of the digital twin data and information fusion, robust process monitoring and control can be achieved while anomalies and false alarms are avoided.

中文翻译：

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数字孪生辅助智能加工过程监控

本文提出了一种用于加工操作的数字孪生辅助在线监测和控制系统。从流程的虚拟模型中提取包括刀具与工件啮合 (CWE) 映射在内的流程信息，并将其与 CNC 内部数据以及测得的外部传感器数据融合。举例说明了几个应用程序来演示该系统。演示了基于直接测量的力或来自加速度计集成刀架的重建力的切削负载的自适应控制。主轴电流数据与虚拟模型中的 CWE 信息相结合，用于估计刀具磨损进程和刀具破损检测。通过避免刀具进入和退出工件材料时触发瞬态振动的误报，提出了数字双辅助颤振检测和避免。结果表明，通过利用数字孪生数据和信息融合，可以实现鲁棒的过程监测和控制，同时避免异常和误报。