Deep learning-based microstructure analysis of multi-component heterogeneous composites during preparation

Monitoring microstructure evolution during the preparation has always been a difficult problem in the modification studies of SiC composite matrix. Here, we used X-ray tomography microscopy to observe the microstructure of SiC f /SiC-W-ZrB 2 composites at different fabrication stage. Based on deep learning, the tracking of the densification process of matrix-modified SiC f /SiC composites was achieved and its suitability for microstructure reconstruction was also verified. The results showed that the average errors of reconstructed SiC f /SiC, pore and Metal (W/ZrB 2 ) are respectively 7.53%, 8.31% and 0.96% by comparison with the segmentation results. Compared with the experimental results, the average error and the average relative error of reconstructed SiC f /SiC is less than 3% and 3.74%.