Keywords: Architected materials, microstructural design, topology optimization, material design, neural networks
TL;DR: Neural-network-based representation coupled with physics-based solver for architected material design.
Abstract: Microstructures, i.e., architected materials, are designed today, typically, by maximizing an objective, such as bulk modulus, subject to a volume constraint. However, in many applications, it is often more appropriate to impose constraints on other physical quantities of interest. In this paper, we consider such generalized microstructural optimization problems where any of the microstructural quantities, namely, bulk, shear, Poisson ratio, or volume, can serve as the objective, while the remaining can serve as constraints. In particular, we propose here a neural-network (NN) framework to solve such problems. The framework relies on the classic density formulation of microstructural optimization, but the density field is represented through the NN's weights and biases. The main characteristics of the proposed NN framework are: (1) it supports automatic differentiation, eliminating the need for manual sensitivity derivations, (2) smoothing filters are not required due to implicit filtering, (3) the framework can be easily extended to multiple-materials, and (4) a high-resolution microstructural topology can be recovered through a simple post-processing step. The NN-based representation, which is independent of analysis finite element mesh, provides for greater design freedom. The analytical representation also enables generation of high-resolution designs at no cost. The NN-based optimization is demonstrated to be more robust when compared to prior methods for generalized problems. The framework is illustrated through a variety of microstructural optimization problems.
Paper Track: Papers
Submission Category: AI-Guided Design
Supplementary Material: pdf