From microstructural images to properties - an interpretable deep learning approach to predict elastic-plastic properties of fiber composites

Abstract

The application of machine learning in the field of materials engineering can facilitate materials design and enable faster discovery of novel materials. This paper presents a deep learning approach for the prediction of the transverse elastic and plastic properties of unidirectional fibre reinforced composites directly from images of their microstructures. The training dataset consists of finite element predictions of the elastic-plastic properties of a set of 2D representative volume elements of unidirectional composites with different volume fraction and fiber diameter. Single-regression, Fully-connected Neural Networks (FcNNs) and a multi-regression Convolutional Neural Network (CNN) are designed and trained to predict 5 mechanical properties, using as input the images of the material’s microstructure. The performance of the FcNNs and of the CNN are compared; the CNN is shown to perform the most effective regression, predicting the properties of the composites with an accuracy of 99%. A key value addition in this research lies in the explainability of the otherwise ‘blackbox’ CNN model, guiding the reader through the model’s predictive process. We test the CNN on real, relatively low-resolution micrographs of composite microstructures and we find that this provides accurate predictions.

A Review of the Intelligent Optimization and Decision in Plastic Forming

“…In addition to this soft sensor method, it is also possible to predict the properties of the component directly using the metallographic images after forging. Emmanouil et al [ 53 ] proposed a deep learning method to predict materials’ properties directly from the microstructure images. Single regression, fully connected neural networks (FCNNs), and multiple regression CNN were designed and trained.…”