期刊
EXTREME MECHANICS LETTERS
卷 64, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.eml.2023.102078
关键词
Architected materials; Lattices; Optimisation; Auxetic materials; Machine learning mechanics
This study presents a computational framework that utilizes Bayesian optimization to design periodic mechanical metamaterials with improved mechanical properties. By varying the thickness of the struts, both the stiffness and strength of the metamaterials have been increased.
Periodic mechanical metamaterials, such as hexagonal honeycombs, have traditionally been designed with uniform cell walls to simplify manufacturing and modelling. However, recent research has suggested that varying strut thickness within the lattice could improve its mechanical properties. To fully explore this design space, we developed a computational framework that leverages Bayesian optimisation to identify configurations with increased uniaxial effective elastic stiffness and plastic or buckling strength. The best topologies found, representative of relative densities with distinct failure modes, were additively manufactured and tested, resulting in a 54% increase in stiffness without compromising the buckling strength for slender architectures, and a 63% increase in elastic modulus and a 88% increase in plastic strength for higher volume fractions. Our results demonstrate the potential of Bayesian optimisation and solid material redistribution to enhance the performance of mechanical metamaterials.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据