Robotic additive manufacturing using a cold spray deposition head attached to a robotic arm can deposit material in a solid state with deposition rates in kilogrammes per hour. Under such a high deposition rate, the complicated interplay between the robot’s motion, gun standoff distance, spray angle, overlapping, and the interaction of supersonic powder particles with a growing structure could cause overabundance or deficiency of material build-up. Over time, the accumulation of these discrepancies can negatively affect the overall shape and size of the final manufactured object. In-process spatio-temporal 3D reconstruction, also known as 4D reconstruction, could allow for early detection of deviations from the design, thus providing the opportunity to rectify at an early stage, making the process more robust, efficient and productive. However, in-process model reconstruction is challenging due to the dynamic nature of the scene (e.g. sensor and object relative movements), the three-dimensional growth of a time-varying build object, the textureless nature of build surfaces, and its computational complexity. We propose a real-time, in-process 4D reconstruction framework for free-form additive manufacturing processes, such as cold spray that deals with a real-time dynamic and evolving scene built by incremental deposition of materials. In our approach, temporal point clouds from three cameras are acquired and segmented to extract the region of interest (build object). The subsequent multi-temporal and multi-camera registration of the segmented 3D data is addressed by combining geometrically constrained Fiducial marker tracking and plane-based registration without drift accumulation. Finally, the registered point clouds are fused via voxel fusion of growing parts to reconstruct the 3D model of the object with smoothened surfaces. The proposed solution is deployed and verified in a robotic cold spray cell with different test scenarios and shape complexities.

中文翻译：

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机器人增材制造中的过程中 4D 重建

使用连接到机械臂的冷喷涂沉积头的机器人增材制造可以以公斤/小时的沉积速率沉积固态材料。在如此高的沉积速率下，机器人的运动、喷枪间隔距离、喷射角度、重叠以及超音速粉末颗粒与生长结构的相互作用之间复杂的相互作用可能会导致材料堆积的过多或不足。随着时间的推移，这些差异的积累会对最终制造物体的整体形状和尺寸产生负面影响。过程中时空 3D 重建（也称为 4D 重建）可以尽早发现设计偏差，从而提供早期纠正的机会，使过程更加稳健、高效和富有成效。然而，由于场景的动态性质（例如传感器和对象相对运动）、随时间变化的构建对象的三维增长、构建表面的无纹理性质及其计算复杂性，过程中模型重建具有挑战性。我们提出了一种用于自由形式增材制造工艺的实时、过程中 4D 重建框架，例如处理通过材料增量沉积构建的实时动态和不断变化的场景的冷喷涂。在我们的方法中，获取并分割来自三个摄像机的时间点云以提取感兴趣的区域（构建对象）。通过结合几何约束基准标记跟踪和基于平面的配准（无漂移累积），可以解决分段 3D 数据的后续多时相和多摄像机配准问题。最后，通过生长部分的体素融合来融合配准的点云，以重建具有平滑表面的物体的 3D 模型。所提出的解决方案在具有不同测试场景和形状复杂性的机器人冷喷涂单元中进行部署和验证。