Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-ray CT scans and performance metrics. Despite the evolution of DT platforms, challenges remain in effectively inspecting them for actionable insights, either individually or in a multidisciplinary team setting. Quality assurance, manufacturing departments, pilot labs, and plant operations must collaborate closely to reliably produce parts at scale. This is particularly crucial in AM where complex structures require a collaborative and multidisciplinary approach. Additionally, the large-scale data originating from different modalities and their inherent 3D nature pose significant hurdles for traditional 2D desktop-based inspection methods. To address these challenges and increase the value proposition of DTs, we introduce a novel virtual reality (VR) framework to facilitate collaborative and real-time inspection of DTs in AM. This framework includes advanced features for intuitive alignment and visualization of multimodal data, visual occlusion management, streaming large-scale volumetric data, and collaborative tools, substantially improving the inspection of AM components and processes to fully exploit the potential of DTs in AM.

翻译：数字孪生是增材制造领域的一种新兴能力，有望彻底改变设计优化、检测、原位监控及根因分析。增材制造数字孪生通常整合多模态数据流，涵盖从机床刀具路径、过程成像到X射线CT扫描及性能指标等数据。尽管数字孪生平台已取得发展，但在有效检测这些平台以获取可操作见解方面仍存在挑战，无论是个人还是多学科团队场景均如此。质量保证、制造部门、试验工厂及工厂运营需紧密协作，才能可靠地规模化生产零件。这在增材制造中尤为关键，因其复杂结构要求采用协作式多学科方法。此外，源自不同模态的大规模数据及其固有的三维特性，对传统的二维桌面检测方法构成了重大障碍。为应对这些挑战并提升数字孪生的价值主张，我们提出一种新颖的虚拟现实框架，用于促进增材制造中数字孪生的协同实时检测。该框架包含先进功能，可实现多模态数据的直观对齐与可视化、视觉遮挡管理、大规模体数据流传输以及协作工具，从而显著改进增材制造组件与工艺的检测，充分挖掘增材制造中数字孪生的潜力。