Data mining from a hierarchical dataset for mechanical metamaterials composed of curved-sides triangles

Abstract

Mechanical metamaterials composed of curvilinear elements (MM-CEs) offer better programmability and functionality than their rectilinear counterparts. They thus hold promising potential for diverse applications, such as flexible electronics, impact protection, and deformation control. However, existing research on MM-CEs has primarily focused on specific curve shapes, neglecting the vast potential of the wider curve design space. Based on data-driven methods, this work builds a dataset with a hierarchical data structure, which transforms the infinite and difficult-to-conceptualize curve data into a finite number of potentially researchable curve sub-types. Moreover, methods are developed based on data mining to better understand the broad design space, excavate hidden connections of different curve sub-types and discover new rules for designing MM-CEs. The outcomes of this study offer novel strategies to investigate other structures and metamaterials with complex geometries as well.