Predicting elastic modulus of porous La0.6Sr0.4Co0.2Fe0.8O3-δ cathodes from microstructures via FEM and deep learning. (18th June 2021) - Liu, Xuhao; Yan, Zilin; Zhong, Zheng
Abstract: In this work, a deep learning accelerated homogenization framework is developed for prediction of elastic modulus of porous materials directly from their inner microstructures. The finite element method (FEM) and the homogenization theory are used to obtain the macroscopic properties of materials based on their microstructures. Based on a large dataset consisting of various microstructures and corresponding elastic properties via FEM, a deep convolutional neural network (CNN) is trained to capture the nonlinear functional relationship between the microstructure features and their macroscopic elastic properties. The deep learning model is finally well validated against extra new samples with excellent predictive performances. This demonstrates that the CNN deep learning model can be trusted as a surrogate model for the FEM based homogenization method, with the computation time being reduced by several orders of magnitude. The proposed deep learning framework is highly extendable for prediction of various macroscopic properties from microstructures. Graphical abstract: Image 1 Highlights: Meso scale finite element model (FEM) is built for calculation of anisotropic elastic properties of a porous SOFC cathode. Convolutional neural network based deep learning model is built on basis of the large simulation data by FEM. The deep learning framework is highly extendable for linking more macroscopic material properties to microstructures.