上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 2: 232 - 241

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.322

船舶海洋与建筑工程

基于X射线CT原位试验的平纹SiCf/SiC压缩损伤演化机理

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  • 1.上海交通大学 船舶海洋与建筑工程学院,上海 200240
    2.中国航发商用航空发动机有限责任公司,上海 201180
程相伟(1998-), 硕士生, 从事陶瓷基复合材料力学研究.

收稿日期: 2022-08-24

  修回日期: 2022-10-10

  录用日期: 2022-10-18

  网络出版日期: 2023-03-13

基金资助

国家自然科学基金(12072192);上海市自然科学基金(20ZR1429500)

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In-Situ X-Ray CT Characterization of Damage Mechanism of Plain Weave SiCf/SiC Composites Under Compression

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  • 1. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2. Aero Engine Corporation of China Commercial Aircraft Engine Co., Ltd., Shanghai 201180, China

Received date: 2022-08-24

  Revised date: 2022-10-10

  Accepted date: 2022-10-18

  Online published: 2023-03-13

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摘要

为揭示陶瓷基复合材料的损伤演化及失效机理,开展了平纹SiCf/SiC复合材料X射线CT原位压缩试验,得到了材料加载过程中和破坏后的CT原位图像数据;采用数字体积相关(DVC)技术获得了材料的位移场和应变场,利用图像处理软件建立复合材料内部三维可视化模型,借助深度学习算法获得纤维束劈裂等损伤的空间分布,进行了压缩损伤演化定性分析以及定量分析.结果表明:在单向压缩过程中,材料在厚度方向出现较大鼓出变形,在宽度方向则发生较小的收缩;厚度方向鼓出变形是引起材料压缩损伤的主要原因.载荷较大时出现表层基体脱落、纤维束劈裂、分层等损伤;纤维束压缩弯折导致材料压缩失效,断口处出现明显V形剪切带.平纹 SiCf/SiC 的压缩损伤演化分析表明,DVC技术和基于深度学习的图像分割方法可以有效地揭示陶瓷基复合材料压缩损伤演化机理.

关键词： 陶瓷基复合材料; 碳化硅; 深度学习; 图像分割; 数字体积相关技术

本文引用格式

程相伟, 张大旭, 杜永龙, 郭洪宝, 洪智亮 . 基于X射线CT原位试验的平纹SiCf/SiC压缩损伤演化机理[J]. 上海交通大学学报, 2024 , 58(2) : 232 -241 . DOI: 10.16183/j.cnki.jsjtu.2022.322

Abstract

In order to reveal the damage evolution and failure mechanism of ceramic matrix composites (CMCs), in-situ X-ray CT compression tests of plain weave SiCf/SiC composites were conducted, and the CT data during loading and after failure were obtained. Displacement and strain distributions of the material were evaluated by the digital volume correlation (DVC) technology. The three-dimensional visual model of the composite was created by using image processing software. The spatial distributions of tow split and other damages were segmented by the deep learning algorithm. The qualitative and quantitative analysis of compression damage evolution were performed. The results show that there is a relatively large expansion induced by barreling in the thickness direction and a little shrinkage in the width direction during the unidirectional compression, while the barreling in the thickness direction is the main reason to trigger compressive damages of the material. Damages such as matrix falling-off at surface, tow split, delamination, will occur as the compression was approaching the ultimate load. Fiber kinking results in the final compressive failure of the material, while an obvious V-shaped shear band is observed in the fracture. The analysis of compressive damage evolution of plain weave SiCf/SiC shows that the DVC technology and deep learning-based image segmentation methods could effectively reveal the compressive damage evolution mechanism of ceramic matrix composites.

Key words： ceramic matrix composites (CMCs); silicon carbide; deep learning; image segmentation; digital volume correlation (DVC) technology

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