Geometric deep learning for enhanced quantitative analysis of microstructures in X-ray computed tomography data

Quantitative microstructural analysis of XCT 3D images is key for quality assurance of materials and components. In this paper we implement a Graph Convolutional Neural Network (GCNN) architecture to segment a complex Al-Si Metal Matrix composite XCT volume (3D image). We train the model on a synthetic dataset and we assess its performance on both synthetic and experimental, manually-labeled, datasets. Our simple GCNN shows a comparable performance, measured via the Dice score, to more standard machine learning methods, but uses a greatly reduced number of parameters (less than 1/10 of parameters), features low training time, and needs little hardware resources. Our GCNN thus achieves a cost-effective reliable segmentation. We present a novel Graph Convolutional Neural Network approach to segment a 3D image of a complex Al-Si Metal Matrix composite. Training on synthetic datasets shows a semantic understanding of the ground truth labels of manually-labeled experimental images. Our simple Graph Neural Network shows a comparable performance to standard methods, using a reduced number of parameters, low training time, towards cost-effective reliable segmentation.