Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization

Abstract

Architected materials and metamaterials are a challenging frontier for the development of optimal design strategies targeted at the active and passive control of elastic wave propagation. Within this research field, the microstructural optimization of mechanical metamaterials for achieving desired spectral functionalities may require considerable computational resources. Based on this motivating framework, the present paper illustrates a machine learning methodology to attack the inverse design problem concerning the optimization of the dispersion properties characterizing a novel layered mechanical metamaterial, conceived starting from the bi-tetrachiral periodic topology. Specifically, an adaptive technique is adopted to surrogate and maximize the objective function purposely defined to determine the optimal beam lattice microstructure characterized by the largest stop bandwidth at the lowest centerfrequency (low-cutting mechanical metafilter). The technique is computationally efficient in identifying the existing optimal solution in the physically admissible parameter space. The designed bi-tetrachiral metamaterial provides satisfying broadband low-frequency filtering performances, not achievable by the component tetrachiral layers.