Applying neural networks to analyse the properties and structure of composite materials

Abstract

In this paper, we show how a simple convolutional neural network (CNN) can be trained to predict homogenised elastic properties of a composite material based on its representative volume element (RVE). We consider both 2D and 3D composites featuring two components with fixed isotropic elastic properties. The dataset used to train the neural network is obtained in two stages. The first stage is the generation of random RVEs and computation of their homogenised elastic properties using finite element analysis (FEA) with periodic boundary conditions (PBC) applied to them, and the second stage is the application of a data augmentation scheme to that dataset obtained in the first stage. For the 2D and 3D cases, we present two neural networks and show that they are able to estimate the values of homogenised elastic properties fairly accurately. At the same time, we confirm that the data generation stage is computationally very expensive and is a major challenge for machine learning based techniques in computational mechanics. Indeed, it is expensive even for “low resolution” RVEs such as those considered in our work, i.e. squares (2D case) or cubes (3D case) uniformly subdivided into 8x8 (2D case) or 8x8x8 (3D case) domains. We discuss the flaws and drawbacks as well as the possible developments and uses of this approach in multiscale modeling of composite materials.

Keywords

• composite materials
• asymptotic homogenisation method
• numerical simulations
• finite element analysis
• multiscale modeling
• artificial intelligence
• convolutional neural networks