Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here we provide a detailed analysis of the heterogenous graph structures of spider webs, and use deep learning as a way to model and then synthesize artificial, bio-inspired 3D web structures. The generative AI models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation, 2) a discrete diffusion model with full neighbor representation, and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bio-inspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles towards integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties.
翻译:利用生成式深度学习和增材制造对异质分层仿生蜘蛛网结构进行建模和设计
翻译后的摘要:
蜘蛛网是无数薄且坚韧的丝纤维形成的复杂分层结构,具有卓越的机械性能,如轻量化、高强度和多样化的机械响应。然而,虽然简单的 2D 圆网可以轻易地模仿,但 3D 网络结构的建模和合成仍然具有挑战性,部分原因是设计特征 形式的丰富性。在本文中,我们详细分析了蜘蛛网的异质图形结构,并使用深度学习对人造仿生 3D 网络结构进行建模和合成。生成式模型是基于 关键几何参数进行条件化的(包括平均边长、节点数、平均节点度数等)。为了识别图形构建原则,我们使用归纳表示抽样形成了一个包含大量实验确定蜘蛛网图形的数据集,用于训练三个 条件生成式模型:1)受非平衡热力学启发的模拟扩散模型,具有稀疏的邻居表示, 2) 具有全邻居表示的离散扩散模型,和 3) 具有全邻居表示的自回归变换器体系结构。所有三个模型都具有可扩展性,可以产生复杂的 3D 的仿生蜘蛛网,成功构建满足设计目标的图形结构。我们进一步提出了一种算法,根据一系列几何设计目标,包括螺旋和参数形状,来组装由生成式模型产生的网络样本,从而将自然设计原则向发散的工程目标融合。利用 3D 打印技术制造了几种蜘蛛网并进行了测试以评估其力学性能。