A node graph using SEM images for material properties prediction with a case study of composite material

Abstract

Establishing a mapping model between the microstructure and material properties of composite materials is crucial for material development. Scanning electron microscope (SEM) images are widely used for the prediction of material properties. However, the prediction from a single SEM image is independent and does not fully reflect the microstructure characteristics. To address this issue, this paper proposes a node graph construction strategy for SEM images and establishes a multi-graph-based graph attention network (GAT) material property prediction model to achieve the convergence of mutual complementation in microstructure features by using GAT. Firstly, multiple SEM images are constructed into node graphs by a microstructure feature encoder. Next, the microstructure features of multiple SEM images on the node graphs are mutually complemented and converged by using GAT. Finally, the prediction is carried out by using multiple SEM images. The experimental results show that the proposed method shows better performance than other methods.