Intelligent identification of micro components and defects of 3D braided C/C composites based on deep learning of X-ray CT images
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摘要: 首先采用微观X射线计算断层扫描(Micro X-ray computed tomography, XCT)对四枚20 mm立方体三维编织碳/碳(Carbon fiber reinforced carbon, C/C)复合材料试件进行扫描，获得精度为18 μm的内部微观结构图像；然后采用基于深度学习的语义分割算法，对大量二维XCT图像进行训练，实现对试件三维微观组分(碳棒、碳纤维束和基体)和缺陷(孔洞、分层和裂纹)的智能识别和分割。结果表明：(1) 微观XCT扫描能够高精度表征三维编织C/C复合材料内部组分和缺陷的分布和形态，主要缺陷为相邻纤维束层之间的分层；(2)由于C/C复合材料各微观组分均为碳材料，在CT图像中灰度值相同(或十分接近)，难以采用传统阈值算法进行分割；深度学习算法能够有效过滤噪声与伪影并自动精准分割各组分和缺陷，且预测速度比人工图像标注高约两个数量级。本工作对三维编织C/C复合材料后续微细观建模和性能优化奠定了基础。Abstract: 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 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 components, 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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图 1 三维编织C/C复合材料三维四向预制体结构图例
Figure 1. Illustration of 3D four-directional preform architecture in 3D braided C/C composite
图 5 基于语义分割的深度学习流程 (左下掩膜图像中的白色为各组分或缺陷，黑色为背景；右上预测结果图像中绿色代表碳棒、蓝色代表碳基体、黄色代表碳纤维束、红色代表缺陷)
Figure 5. Process of deep learning based on semantic segmentation (The white areas in the lower left masks represent the components or defects and the black areas are the background; the green, blue, yellow and red areas represent rods, matrix, fiber bundles and defects in the upper rightpredicted results, respectively)
图 6 碳纤维复材微观CT图像自动识别与分类软件
Figure 6. Software flow chart of automatic identification and classification software for carbon fiber composite micro-CT images (AICCT)
图 7 三维编织C/C复合材料C1试件微观结构模型
Figure 7. Microstructure model of 3D braided C/C composite C1 specimen
图 8 三维编织C/C复合材料C1试件分层现象
Figure 8. Delamination in 3D braided C/C composite C1 specimen
图 9 三维编织C/C复合材料C1试件二维缺陷率曲线
Figure 9. 2 D defectivity curves of 3D braided C/C composite C1 specimen
D2 is the two-dimensional defectivity in XY direction
图 10 三维编织C/C复合材料C1试件切片1缺陷
Figure 10. Defects of 3D braided C/C composite C1 specimen in slice 1
表 1 XCT扫描三维编织C/C复合材料试件和图像参数
Table 1. XCT scanned 3D braided C/C composite specimens and image parameters
Specimen SDim/mm SRes/μm VXY/Voxel VZ
/VoxelC1 20×20×20 18.27 1747×1743 1442 C2 20×20×20 18.27 1713×1709 1307 C3 20×20×20 18.27 1737×1698 1302 C4 20×20×20 18.27 1734×1703 1299 Notes: SDim is the dimension of specimen, SRes is the scanning resolution of specimen, VXY and VZ are the image sizes in XY directions slice and Z direction. 
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表 2 三维编织 C/C 复合材料试件深度学习预测结果
Table 2. Results of deep learning predication of 3D braided C/C composite specimens
Specimen VDef/vol% VRod/vol% VFBd/vol% VMat/vol% C1 4.72 12.02 50.14 33.12 C2 5.52 11.25 46.71 36.52 C3 5.31 11.69 49.51 33.49 C4 5.01 10.96 48.07 35.96 Average of predication 5.14 11.48 48.61 34.77 Design value - 11[6] - - Average of threshold 6.83 - - - Notes: VDef, VRod, VFBd, VMat represents volume fraction of defects, rods, fiber bundles and matrix, respectively.
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