4.7 Article

A black-box optimization strategy for customizable global elastic deformation behavior of unit cell-based tri-anti-chiral metamaterials

期刊

ADVANCES IN ENGINEERING SOFTWARE
卷 186, 期 -, 页码 -

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ELSEVIER SCI LTD
DOI: 10.1016/j.advengsoft.2023.103553

关键词

Finite Element Method; Homogenization; Numerical; Simulation; Interpolation

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A Finite Element simulation-based optimization framework is proposed to create a predefined deformation behavior in metamaterials. The framework includes numerical homogenization, interpolation, and black-box optimization. The framework is tested on tri-antichiral metamaterials, exploring various combinations of optimization objectives and constraints.
Metamaterials are a class of materials with a distinctive unit cell-based periodic architecture, often resulting in unique mechanical properties. The potential of metamaterials can be further improved by using gradients of unit cell parameters and thereby creating a specific distribution of material properties in a part. This design freedom comes with the challenge of finding new suitable design and optimization strategies. In this study, a Finite Element simulation-based optimization framework to create a predefined deformation behavior of unit cell-based metamaterials is presented. The framework consists of a numerical homogenization method to create an efficient linear elastic material representation, an interpolation scheme for a fast correlation of unit cell parameters with homogenized material properties and a black-box based optimization part. The framework is tested on a tri-antichiral metamaterial with additional geometric parameters and a newly developed transition unit cell. Several specified lateral deformations of simple 2D rectangular test specimens under tensile load are used as primary optimization targets and the homogenized results are validated against Finite Element simulations of fully modeled tri-anti-chiral structures. In addition, the effect of the design space and the number of design variables is studied and several combinations of optimization objectives and constraints are tested.

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