基于CT图像深度学习的三维编织C/C复合材料微观组分与缺陷智能识别
Intelligent identification of micro components and defects of 3D braided C/C composites based on deep learning of X-ray CT images
钱奇伟 1张昕 2杨贞军 1沈镇 3校金友4
作者信息
- 1. 武汉大学 土木建筑工程学院,武汉 430072
- 2. 浙江大学 建筑工程学院,杭州 310058
- 3. 西安航天复合材料研究所,西安 710025
- 4. 西北工业大学 航天学院,西安 710072
- 折叠
摘要
首先采用微观X射线计算机断层扫描(Micro X-ray computed tomography,XCT)对 4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 18.27 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.本文对三维编织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 18.27 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.
关键词
C/C复合材料/微观X射线计算机断层扫描/微观组分/缺陷/深度学习/语义分割Key words
C/C composite/micro X-ray computed tomography/micro components/defects/deep learning/semantic segmentation引用本文复制引用
基金项目
国家自然科学基金(52173300)
湖北省重点研发计划(2020BAB052)
出版年
2024
NETL
NSTL
万方数据