Deep Learning based Semantic Segmentation of µCT Images for Creating Digital Material Twins of Fibrous Reinforcements

Abstract In this study, a novel approach of processing μCT images to create digital material twins is presented. A deep convolutional neural network (DCNN) was implemented and used to segment μCT images of two different types of reinforcement (2D glass and 3D carbon). The DCNN successfully segmented the images based on multi-scale features extracted using data-driven convolutional filters. The network was trained using scanned μCT images, along with images extracted from computer-generated virtual models of the reinforcements. One of the convolutional layers of the trained network was utilized to extract features to be used in creating the machine learning-based model. The extracted features and the raw gray-scale data was used to train a supervised k-nearest neighbor (k-NN) model for pixel-wise classification. The performance of both approaches was evaluated by comparing the results with manually segmented images. The trained deep neural network was able to provide faster and superior predictions of different features of the reinforcements as compared to a conventional machine learning approach.