Lattice structures have been widely used in various applications of additive manufacturing due to its superior physical properties. If modeled by triangular meshes, a lattice structure with huge number of struts would consume massive memory. This hinders the use of lattice structures in large-scale applications (e.g., to design the interior structure of a solid with spatially graded material properties). To solve this issue, we propose a memory-efficient method for the modeling and slicing of adaptive lattice structures. A lattice structure is represented by a weighted graph where the edge weights store the struts' radii. When slicing the structure, its solid model is locally evaluated through convolution surfaces and in a streaming manner. As such, only limited memory is needed to generate the toolpaths of fabrication. Also, the use of convolution surfaces leads to natural blending at intersections of struts, which can avoid the stress concentration at these regions. We also present a computational framework for optimizing supporting structures and adapting lattice structures with prescribed density distributions. The presented methods have been validated by a series of case studies with large number (up to 100M) of struts to demonstrate its applicability to large-scale lattice structures.

中文翻译：

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大型自适应晶格结构的内存高效建模和切片

由于其优越的物理性能，晶格结构已广泛用于增材制造的各种应用中。如果用三角形网格建模，具有大量支柱的晶格结构将消耗大量内存。这阻碍了在大规模应用中使用晶格结构（例如，设计具有空间渐变材料属性的实体的内部结构）。为了解决这个问题，我们提出了一种用于自适应格结构的建模和切片的内存有效方法。晶格结构由加权图表示，其中边缘权重存储支柱的半径。切片结构时，其实体模型通过卷积曲面以流方式进行局部评估。这样，仅需要有限的存储器来生成制造的工具路径。也，卷积表面的使用导致在支杆的交点处自然融合，这可以避免应力集中在这些区域。我们还提出了一种计算框架，用于优化支撑结构和调整具有指定密度分布的晶格结构。所提出的方法已经通过大量（最多100M）支杆的一系列案例研究得到验证，以证明其适用于大规模晶格结构。