Four 20 mm cubic 3D braided carbon/carbon(C/C)composite specimens were scanned by micro X-ray computed tomography(XCT)to obtain internal microstructure images with a voxel resolution of 18.27 μm.A deep learning based semantic segmentation algorithm was then used to train a large number of 2D XCT images to achieve intelligent identification and segmentation of rods,fiber bundles,matrix,pores,delamination and cracks of these specimens.The results show that:(1)The XCT scanning can characterize the distribution and morphology of the above components and defects with high resolutions,and the dominant defect is delamination between adjacent fiber bundle layers;(2)Since the grey values in the CT images of all micro components of C/C composites are very close,it is impossible for the traditional threshold segmentation method to segment the different com-ponents,whereas the deep learning based algorithm is able to effectively filter noise and artifacts and segment all the components and defects with high accuracy and at a prediction speed of about two orders faster than manual image labelling.This deep learning algorithm thus provides a promising tool to construct high-resolution numerical models for further studies such as performance optimization of C/C composites.
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