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18.27μm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。
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 18.27μm.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.
作者
钱奇伟
张昕
杨贞军
沈镇
校金友
QIAN Qiwei;ZHANG Xin;YANG Zhenjun;SHEN Zhen;XIAO Jinyou(School of Civil Engineering,Wuhan University,Wuhan 430072,China;College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,China;Xi'an Aerospace Composite Research Institute,Xi'an 710025,China;School of Astronautics,Northwestern Polytechnical University,Xi'an 710072,China)
出处
《复合材料学报》
EI
CAS
CSCD
北大核心
2024年第7期3536-3543,共8页
Acta Materiae Compositae Sinica
基金
国家自然科学基金(52173300)
湖北省重点研发计划(2020BAB052)。
