Abstract
Data-driven models are rising as an auspicious method for the geometrical design of materials and structural systems. Nevertheless, existing data-driven models customarily address the optimization of structural designs rather than metamaterial designs. Metamaterials are emerging as promising materials exhibiting tailorable and unprecedented properties for a wide spectrum of applications. In this paper, we develop a deep learning (DL) model based on a convolutional neural network (CNN) that predicts optimal metamaterial designs. The developed DL model non-iteratively optimizes metamaterials for either maximizing the bulk modulus, maximizing the shear modulus, or minimizing the Poisson's ratio (including negative values). The data are generated by solving a large set of inverse homogenization boundary values problems, with randomly generated geometrical features from a specific distribution. Such s data-driven model can play a vital role in accelerating more computationally expensive design problems, such as multiscale metamaterial systems.
Original language | English (US) |
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Article number | 109098 |
Journal | Materials and Design |
Volume | 196 |
DOIs | |
State | Published - Nov 2020 |
Keywords
- Architected materials
- Auxetic materials
- Homogenization
- Machine learning
- Microstructure
- Periodic boundary conditions (PBCs)
ASJC Scopus subject areas
- General Materials Science
- Mechanics of Materials
- Mechanical Engineering