Automated image segmentation of 3D printed fibrous composite micro-structures using a neural network

Abstract

A new, automated image segmentation method is presented that effectively identifies the micro-structural objects (fibre, air void, matrix) of 3D printed fibre-reinforced materials using a deep convolutional neural network. The method creates training data from a physical specimen composed of a single, straight fibre embedded in a cementitious matrix with air voids. The specific micro-structure of this strain-hardening cementitious composite (SHCC) is obtained from X-ray micro-computed tomography scanning, after which the 3D ground truth mask of the sample is constructed by connecting each voxel of a scanned image to the corresponding micro-structural object. The neural network is trained to identify fibres oriented in arbitrary directions through the application of a data augmentation procedure, which eliminates the time-consuming task of a human expert to manually annotate these data. The predictive capability of the methodology is demonstrated via the analysis of a practical SHCC developed for 3D concrete printing, showing that the automated segmentation method is well capable of adequately identifying complex micro-structures with arbitrarily distributed and oriented fibres. Although the focus of the current study is on SHCC materials, the proposed methodology can also be applied to other fibre-reinforced materials, such as fibre-reinforced plastics. The micro-structures identified by the image segmentation method may serve as input for dedicated finite element models that allow for computing their mechanical behaviour as a function of the micro-structural composition.