Denis Giordana
Stiffness Prediction of Hierarchical Voronoi-like Metamaterials Using Graph Neural Networks.
Rel. Andrea Tridello. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Meccanica, 2025
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Abstract
Natural cellular materials—e.g., bone, wood, and honeycomb—are known for their extraordinary mechanical performance such as strength-to-weight and damage tolerance. One of common geometrical features of these natural cellular materials is their Voronoi-like structure, which has been demonstrated as a critical mechanism to improve mechanical performance by studies in recent decades. Inspired by these interesting structures, scientists and engineers have developed Voronoi-like mechanical metamaterials with tunable mechanical properties for various industrial applications. However, most studies focus on designing fully random or fully ordered Voronoi-like structures, which are not common in Nature. Moreover, natural materials also show hierarchy in Voronoi-like designs. Therefore, to fill this knowledge gap and develop tunable architectures, an algorithm which controls the finite randomness of Voronoi-like structures and achieves associated hierarchical design has been elaborated.
Then, these architectures have been simulated through finite element (FE) analyses, by using the commercial FE software Abaqus, to obtain the effective stiffness of designed Voronoi-like mechanical metamaterials
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