A Data-Efficient Multiobjective Machine Learning Method for 3D-Printed Architected Materials Design

Abstract

Architected materials that consist of multiple subelements arranged in particular orders can demonstrate a much broader range of properties than their constituent materials. However, the rational design of these materials generally relies on experts’ prior knowledge and requires painstaking effort. Here, we present a data efficient method for the multiproperty optimization of 3D-printed architected materials utilizing a machine learning (ML) cycle consisting of the finite element method (FEM) and 3D neural networks. Specifically, we applied our method to orthopedic implant design. Compared to expert designs, our experience-free method designed microscale heterogeneous architectures with a biocompatible elastic modulus and higher strength. Furthermore, inspired by the knowledge learned by the neural networks, we developed machine-human synergy, adapting the ML-designed architecture to fix a macroscale, irregularly shaped animal bone defect. Such adaptation exhibits 20 % higher experimental load-bearing capacity than the expert design. Thus, our method opens a new paradigm for the fast and intelligent design of architected materials with tailored mechanical, physical, and chemical properties.