Deep learning for topology optimization of 2D metamaterials

Hunter T. Kollmann, Diab W. Abueidda, Seid Koric, Erman Guleryuz, Nahil A. Sobh

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Article number109098
JournalMaterials and Design
Volume196
DOIs
StatePublished - Nov 2020

Keywords

  • Architected materials
  • Auxetic materials
  • Homogenization
  • Machine learning
  • Microstructure
  • Periodic boundary conditions (PBCs)

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

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