151. Machine learning generative models for automatic design of multi-material 3D printed composite solids
- Author
-
Yigit Menguc, Thomas J. Wallin, Tianju Xue, Maurizio M. Chiaramonte, and Sigrid Adriaenssens
- Subjects
Computer science ,Design flow ,Bioengineering ,02 engineering and technology ,010402 general chemistry ,Machine learning ,computer.software_genre ,01 natural sciences ,Chemical Engineering (miscellaneous) ,Representation (mathematics) ,Engineering (miscellaneous) ,business.industry ,Mechanical Engineering ,Bayesian optimization ,Metamaterial ,021001 nanoscience & nanotechnology ,Autoencoder ,0104 chemical sciences ,Generative model ,Geometric design ,Mechanics of Materials ,Representative elementary volume ,Artificial intelligence ,0210 nano-technology ,business ,computer - 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.
- Published
- 2020