RESUMO
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.
