Abstract：
The extraordinary quasi-static mechanical properties of nacre-like composite metamaterials, such as high specific strength, stiffness, and toughness, are due to the periodic arrangement of two distinct phases in a \"brick and mortar\" structure. It is also theorized that the hierarchical periodic structure of nacre structures can provide wider band gaps at different frequency scales. However, the function of hierarchy in the dynamic behavior of metamaterials is largely unknown, and most current investigations are focused on a single objective and specialized applications. Nature, on the other hand, appears to develop systems that represent a trade-off between multiple objectives, such as stiffness, fatigue resistance, and wave attenuation. Given the wide range of design options available to these systems, a multidisciplinary strategy combining diverse objectives may be a useful opportunity provided by bioinspired artificial systems. This paper describes a class of hierarchically-architected block lattice metamaterials with simultaneous wave filtering and enhanced mechanical properties, using deep learning based on artificial neural networks (ANN), to overcome the shortcomings of traditional design methods for forward prediction, parameter design, and topology design of block lattice metamaterial. Our approach uses ANN to efficiently describe the complicated interactions between nacre geometry and its attributes, and then use the Bayesian optimization technique to determine the optimal geometry constants that match the given fitness requirements. We numerically demonstrate that complete band gaps, that is attributed to the coupling effects of local resonances and Bragg scattering, exist. The coupling effects are naturally influenced by the topological arrangements of the continuous structures and the mechanical characteristics of the component phases. We also demonstrate how we can tune the frequency of the complete band gap by modifying the geometrical configurations and volume fraction distribution of the metamaterials. This research contributes to the development of mechanically robust block lattice metamaterials and lenses capable of controlling acoustic and elastic waves in hostile settings.
摘要：
珍珠层复合超材料的非凡准静态力学性能，如高比强度，刚度,和韧性，是由于“砖和砂浆”结构中两个不同阶段的周期性排列。从理论上讲，珍珠质结构的分层周期性结构可以在不同的频率范围内提供更宽的带隙。然而,层次结构在超材料动态行为中的作用在很大程度上是未知的，目前大多数调查都集中在单一目标和专门应用上。自然,另一方面,似乎开发了代表多个目标之间权衡的系统，如刚度，抗疲劳性，和波衰减。鉴于这些系统提供了广泛的设计选项，结合不同目标的多学科策略可能是生物启发人工系统提供的有用机会。本文介绍了一类具有同步滤波和增强机械性能的分层结构块晶格超材料，使用基于人工神经网络(ANN)的深度学习，为了克服传统设计方法进行前向预测的缺点，参数设计,块晶格超材料的拓扑设计。我们的方法使用人工神经网络来有效地描述珍珠质几何形状与其属性之间的复杂相互作用，然后利用贝叶斯优化技术确定符合给定适应度要求的最优几何常数。我们通过数值证明了完整的带隙，这归因于局部共振和布拉格散射的耦合效应，存在。耦合效应自然受到连续结构的拓扑布置和组成相的机械特性的影响。我们还演示了如何通过修改超材料的几何构型和体积分数分布来调整完整带隙的频率。这项研究有助于开发机械坚固的块状晶格超材料和透镜，能够在恶劣的环境中控制声波和弹性波。
