Implementation of a digital twin framework for directed energy deposition additive manufacture

Digital twins (DTs) could support directed energy deposition-arc (DED-arc) manufacture by connecting process monitoring, data management and simulation, but most implementations address isolated tasks rather than extensible process-level integration. This study develops and demonstrates a modular DT framework for DED-arc using NVIDIA Omniverse as the virtual environment and data-management platform. The framework combines Omniverse Nucleus for asynchronous storage of experimental and simulation data with a custom MQTT-based streaming layer for time-sensitive robot and deposition signals. A DED-arc manufacturing cell using a KUKA KR70 robot was reproduced in the virtual environment, with live synchronisation of robot motion and arc state. The implemented data pipeline achieved an average streaming latency of approximately 520 ms for robot and arc-status data, while a 192 MB transducer data file was uploaded to Omniverse Nucleus in 18 s, equivalent to approximately 10.7 MB/s. These results demonstrate that the architecture can separate low-latency monitoring data from larger asynchronous experimental datasets while maintaining a unified digital thread. The framework also demonstrates extensibility by using DT-acquired thermocouple and as-built data to support thermo-mechanical finite element simulation workflows for residual stress and distortion assessment. The results show that the proposed architecture provides real-time process monitoring, traceable data storage and simulation coupling within a single platform, without replacing the existing manufacturing-cell communication infrastructure. This establishes a practical data backbone for future surrogate modelling, quality prediction and closed-loop control in DED-arc manufacturing.