Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data

Abstract

A novel method to predict the mechanical responses of arbitrary microstructures from the deep learning of microstructures and their stress-strain response is presented in this work. Two-phase microstructural images that consist of different grain sizes and compositions are generated and quantified using the two-point statistical homogenisation scheme. Finite element (FE) simulations are used to predict the in-plane elastoplastic response of the generated microstructures. To minimize the computational efforts, microstructures and the stress-strain data are projected into the lower order orthogonal spaces by using the principal component analysis (PCA). Effective methods to visualise and understand the distribution of microstructure-response data in the transformed dimensional space are presented in detail. The reduced order statistically homogeneous microstructures along with the reduced stress-strain data are learned by using the convolutional neural networks (CNN). A new set of randomly generated microstructures are fed into the trained convolutional network to predict the stress-strain response. The derived failure strength and modulus of the predicted response curves are showing a scatter index of 1.74% and 10.53% against the true FE predicted values. The mechanical responses of randomly generated two-phase fibre reinforced plastic (FRP) composite microstructures are predicted using the developed deep learning model. Thus, the proposed strategy can predict the mechanical properties of arbitrary microstructural design with better accuracy and minimal computational effort. • A novel deep machine learning strategy that uses the statistical quantification and dimensionality reduction techniques • Enhanced data visualisation methods that improve the user understanding and domain knowledge • The training of dominant lower-dimensional features simplifies the CNN architecture • Effective methodology to predict the inelastic response of the random non-periodic FRP composite microstructures