TY - JOUR
T1 - Machine learning generative models for automatic design of multi-material 3D printed composite solids
AU - Xue, Tianju
AU - Wallin, Thomas J.
AU - Menguc, Yigit
AU - Adriaenssens, Sigrid
AU - Chiaramonte, Maurizio Maria
N1 - Funding Information:
This work is partially supported by the Princeton Catalysis Initiative at Princeton University.
Funding Information:
This work is partially supported by the Princeton Catalysis Initiative at Princeton University.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/11
Y1 - 2020/11
N2 - Mechanical metamaterials are artificial structures that exhibit unusual mechanical properties at the macroscopic level due to architected geometric design at the microscopic level. With rapid advancement of multi-material 3D printing techniques, it is possible to design mechanical metamaterials by varying spatial distributions of different base materials within a representative volume element (RVE), which is then periodically arranged into a lattice structure. The design problem is challenging, however, considering the wide design space of potentially infinitely many configurations of multi-material RVEs. We propose an optimization framework that automates the design flow. We adopt variational autoencoder (VAE), a machine learning generative model to learn a latent, reduced representation of a given RVE configuration. The reduced design space allows to perform Bayesian optimization (BayesOpt), a sequential optimization strategy, for the multi-material design problems. In this work, we select two base materials with distinct elastic moduli and use the proposed optimization scheme to design a composite solid that achieves a prescribed set of macroscopic elastic moduli. We fabricated optimal samples with multi-material 3D printing and performed experimental validation, showing that the optimization framework is reliable.
AB - Mechanical metamaterials are artificial structures that exhibit unusual mechanical properties at the macroscopic level due to architected geometric design at the microscopic level. With rapid advancement of multi-material 3D printing techniques, it is possible to design mechanical metamaterials by varying spatial distributions of different base materials within a representative volume element (RVE), which is then periodically arranged into a lattice structure. The design problem is challenging, however, considering the wide design space of potentially infinitely many configurations of multi-material RVEs. We propose an optimization framework that automates the design flow. We adopt variational autoencoder (VAE), a machine learning generative model to learn a latent, reduced representation of a given RVE configuration. The reduced design space allows to perform Bayesian optimization (BayesOpt), a sequential optimization strategy, for the multi-material design problems. In this work, we select two base materials with distinct elastic moduli and use the proposed optimization scheme to design a composite solid that achieves a prescribed set of macroscopic elastic moduli. We fabricated optimal samples with multi-material 3D printing and performed experimental validation, showing that the optimization framework is reliable.
KW - 3D printing
KW - Machine learning
KW - Mechanical metamaterial
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U2 - 10.1016/j.eml.2020.100992
DO - 10.1016/j.eml.2020.100992
M3 - Article
AN - SCOPUS:85091735018
SN - 2352-4316
VL - 41
JO - Extreme Mechanics Letters
JF - Extreme Mechanics Letters
M1 - 100992
ER -