Digital twin tools for additive manufacturing (AM) are constrained by the underlying representations of component geometry that are currently in wide use. Mesh, voxel, and parametric surface representations require numerous conversions to intermediate representations at multiple points throughout the processing chain. Each conversion introduces additional error in the geometric representation and complicates comparison of in-situ process sensor data to the as-designed component. Additionally, the limited interoperability of the various representations produced throughout the process chain limit the insights available from current digital twin tools. We introduce a novel framework based on a unifying geometric representation that serves the complete AM digital thread. The presented GPU-accelerated, adaptively sampled distance function (ASDF) framework serves as a foundation for component design and path planning tools, especially for real-time path planning in AM, as well as provides a baseline representation of geometry from control systems, and enables rapid comparison of in-situ sensor data to the as-designed model without intermediate conversion, greatly reducing the burden of reducing such data to usable process insights.

中文翻译：

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自适应采样距离函数：用于先进制造的统一数字孪生表示


用于增材制造 （AM） 的数字孪生工具受到当前广泛使用的组件几何体的底层表示的限制。网格、体素和参数化表面表示需要在整个处理链中的多个点进行大量转换为中间表示。每次转换都会在几何表示中引入额外的误差，并使原位过程传感器数据与设计组件的比较复杂化。此外，在整个流程链中生成的各种表示的有限互操作性限制了当前数字孪生工具的洞察力。我们引入了一个基于统一几何表示的新型框架，该框架服务于完整的增材制造数字线程。所提出的 GPU 加速、自适应采样距离函数 （ASDF） 框架可作为组件设计和路径规划工具的基础，特别是用于增材制造中的实时路径规划，并提供来自控制系统的几何图形的基线表示，并支持将原位传感器数据与设计模型快速比较，而无需中间转换，大大减轻了将此类数据减少为可用过程见解的负担。