Machine learning generative models for automatic design of multi-material 3D printed composite solids. (November 2020)

• Record Type: Journal Article
• Authors: Xue, Tianju; Wallin, Thomas J.; Menguc, Yigit; Adriaenssens, Sigrid; Chiaramonte, Maurizio
• Is Part Of: Extreme mechanics letters. Volume 41(2021)
• Publication Date: 2020-11
• Subjects: Mechanical metamaterial -- Machine learning -- 3D printing; Mechanics -- Periodicals; Mechanics, Applied -- Periodicals; Mechanics; Electronic journals; Periodicals; 531.05
• DOI: 10.1016/j.eml.2020.100992
• Languages: English
• ISSNs: 2352-4316

Abstract

Abstract: 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.