Lucy Todd, Matthew H. W. Chin and Marc-Olivier Coppens
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引用次数: 0
摘要
三维 Voronoi 支架广泛应用于增材制造领域,因为它们以轻质结构弹性著称,与各种自然环境(骨骼、肿瘤、淋巴结)和合成环境(泡沫、功能梯度多孔材料)具有许多拓扑相似性。遗憾的是,促进这些拓扑相似性的结构设计特征(如顶点数量)往往是不可预测的,需要反复试验不同的设计特征,才能实现理想的三维 Voronoi 结构。本文以 12,000 多个三维 Voronoi 结构为基础,提供了一个由方程式组成的工具包。通过这些方程,可以根据所需的结构参数(±3 G 以内)高效、准确地预测设计特征,如生成点数量 (G)。据我们所知,基于这些方程,我们提出了两个新的数学猜想,它们将顶点或边的数量以及平均边长与 Voronoi 结构中的 G 相关联。这些等式已在广泛的参数值和 Voronoi 网络大小中得到验证。提供的设计代码允许从超过 12,000 种结构中选择任何一种,并可根据用户要求轻松调整和 3D 打印。为说明设计代码,还介绍了与 T 细胞培养、骨支架和肾肿瘤相关的生物医学案例研究。
Two conjectures on 3D Voronoi structures: a toolkit with biomedical case studies
3D Voronoi scaffolds are widely applied in the field of additive manufacturing as they are known for their light weight structural resilience and share many topological similarities to various natural (bone, tumours, lymph node) and synthetic environments (foam, functionally gradient porous materials). Unfortunately, the structural design features that promote these topological similarities (such as the number of vertices) are often unpredictable and require the trial and error of varying design features to achieve the desired 3D Voronoi structure. This article provides a toolkit, consisting of equations, based on over 12 000 3D Voronoi structures. These equations allow design features, such as the number of generating points (G), to be efficiently and accurately predicted based on the desired structural parameters (within ±3G). Based on these equations we are proposing, to the best of our knowledge, two new mathematical conjectures that relate the number of vertices or edges, and the average edge length to G in Voronoi structures. These equations have been validated for a wide range of parameter values and Voronoi network sizes. A design code is provided allowing any of over 12 000 structures to be selected, easily adjusted based on user requirements, and 3D printed. Biomedical case studies relevant to T-cell culturing, bone scaffolds and kidney tumours are presented to illustrate the design code.
期刊介绍:
Molecular Systems Design & Engineering provides a hub for cutting-edge research into how understanding of molecular properties, behaviour and interactions can be used to design and assemble better materials, systems, and processes to achieve specific functions. These may have applications of technological significance and help address global challenges.