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Article(id=1322943413322129427, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1322942707957641522, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20200829, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1604419200000, receivedDateStr=2020-11-04, revisedDate=1609862400000, revisedDateStr=2021-01-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1793343888835, onlineDateStr=2026-10-30, pubDate=1629388800000, pubDateStr=2021-08-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793343888835, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793343888835, creator=13701087609, updateTime=1793343888835, updator=13701087609, issue=Issue{id=1322942707957641522, tenantId=1146029695717560320, journalId=1263187385517883426, year='2021', volume='49', issue='8', pageStart='1519', pageEnd='1784', issueExtLink='null', onlineDate='null', pubDate='1629388800000', pubDateStr='2021-08-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793343720664, creator='13701087609', updateTime=1793345848275, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322951631859494962, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1322942707957641522, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322951631859494963, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1322942707957641522, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1765, endPage=1775, ext={EN=ArticleExt(id=1322943413523456020, articleId=1322943413322129427, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Deep Learning-based Damage Identification and Evaluation of 2.5D Ceramic Matrix Composites, columnId=1319726165087077079, journalTitle=Journal of the Chinese Ceramic Society, columnName=Research Article, runingTitle=null, highlight=null, articleAbstract=

The X-ray CT in-situ tensile test of 2.5D-SiCf/SiC composites was conducted to reveal the damage evolution and failure mechanism of the ceramic matrix composites. The CT data of the materials during the loading process and failure fracture were obtained. A deep learning-based image segmentation method was used to propose a three-dimensional visualization model of the internal damage of the composites, and the number, volume, spatial distribution and spatial angle of the microcracks generated due to the stretching were analyzed. At high loads, the tensile microcrack volumes are 0-5.3×10-4 mm3, the fracture microcrack volumes are 0-2.2×10-4 mm3, and the proportion of the microcracks with spatial angles of about 0° and 45° is relatively high. Meanwhile, the microcracks are closely correlated to the pore distribution. According to the results by fracture damage analysis, the tensile damage process of 2.5D-SiCf/SiC composites includes matrix cracking, fiber fracture, fiber pull-out and interface debonding. The fiber pull-out lengths are in the range of 16.02-250.32 μm, and the average length is 88.26 μm. It is indicated that the image segmentation method based on deep learning could offer an effective way to reveal the mechanism of material damages and evaluate the degree of the damages.

, authors=Yuqi FENG1, Yi ZHANG2, Daxu ZHANG1, Weiyu GUO1, Yaoshengshu HOU1, Bin LI3, authorsList=Yuqi FENG, Yi ZHANG, Daxu ZHANG, Weiyu GUO, Yaoshengshu HOU, Bin LI, authorCompany=null, correspAuthors=Daxu ZHANG, authorNote=

FENG Yuqi (1996-), female, Master candidate. E-mail:

, correspAuthorsNote=
ZHANG Daxu (1975-), male, Ph. D., Professor. E-mail:
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为揭示陶瓷基复合材料的损伤演化与失效机理，开展了2.5D-SiCf/SiC复合材料的X射线CT原位拉伸试验，得到了材料加载过程和失效断口的CT数据；采用基于深度学习的图像分割方法，利用图像处理软件建立了复合材料内部损伤的三维可视化模型；对拉伸产生的微裂纹进行了数量、体积、空间分布和空间角度分析。微裂纹分析表明：较高载荷状态下的拉伸微裂纹体积在0~5.3×10-4 mm3之间，断口微裂纹体积在0~2.2×10-4 mm3之间，空间角度为0°和45°左右的微裂纹占主要部分，微裂纹与孔隙分布有很强的相关性。断口损伤分析表明：2.5D-SiCf/SiC复合材料的拉伸损伤包括纤维断裂、纤维拔出、基体开裂和界面脱粘，其中纤维拔出长度范围为16.02~250.32 μm，平均长度为88.26 μm；2.5D-SiCf/SiC复合材料的损伤分析表明基于深度学习的图像分割方法为揭示材料损伤演化机理与评估损伤程度提供了有效手段。

, authors=冯宇琦1, 张毅2, 张大旭1, 郭纬愉1, 侯耀晟树1, 李斌3, authorsList=冯宇琦, 张毅, 张大旭, 郭纬愉, 侯耀晟树, 李斌, authorCompany=null, correspAuthors=张大旭, authorNote=

冯宇琦（1996—），女，硕士研究生。

, correspAuthorsNote=
张大旭（1975—），男，博士，教授，博士研究生导师。
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FENG Yuqi (1996-), female, Master candidate. E-mail:

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冯宇琦（1996—），女，硕士研究生。

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冯宇琦（1996—），女，硕士研究生。

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基于深度学习的2.5D陶瓷基复合材料损伤识别与评估
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冯宇琦 1 , 张毅 2 , 张大旭 1 , 郭纬愉 1 , 侯耀晟树 1 , 李斌 3
硅酸盐学报 | 研究论文 2021,49(8): 1765-1775
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硅酸盐学报 |研究论文 2021 , 49 (8) : 1765 -1775
基于深度学习的2.5D陶瓷基复合材料损伤识别与评估
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冯宇琦（1996—），女，硕士研究生。

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冯宇琦1 , 张毅2, 张大旭1 , 郭纬愉1, 侯耀晟树1, 李斌3
作者信息
  • 1．上海交通大学船舶海洋与建筑工程学院，上海 200240
  • 2．西北工业大学超高温结构复合材料重点实验室，西安 710072
  • 3．火箭军装备部驻西安地区第六军事代表室，西安 710072
通讯作者:
张大旭（1975—），男，博士，教授，博士研究生导师。
作者简介:

冯宇琦（1996—），女，硕士研究生。

Deep Learning-based Damage Identification and Evaluation of 2.5D Ceramic Matrix Composites
Yuqi FENG1 , Yi ZHANG2, Daxu ZHANG1 , Weiyu GUO1, Yaoshengshu HOU1, Bin LI3
Affiliations
  • 1.School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • 2.Science and Technology on Thermostructural Composite Materials Laboratory, Northwestern Polytechnical University, Xi'an 710072, China
  • 3.The Sixth Military Representative Office of the Rocket Force Equipment Department in Xi'an, Xi'an 710072, China
    • Corresponding Author:
    ZHANG Daxu (1975-), male, Ph. D., Professor. E-mail:

    FENG Yuqi (1996-), female, Master candidate. E-mail:

出版时间: 2021-08-20 doi: 10.14062/j.issn.0454-5648.20200829
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为揭示陶瓷基复合材料的损伤演化与失效机理，开展了2.5D-SiCf/SiC复合材料的X射线CT原位拉伸试验，得到了材料加载过程和失效断口的CT数据；采用基于深度学习的图像分割方法，利用图像处理软件建立了复合材料内部损伤的三维可视化模型；对拉伸产生的微裂纹进行了数量、体积、空间分布和空间角度分析。微裂纹分析表明：较高载荷状态下的拉伸微裂纹体积在0~5.3×10-4 mm3之间，断口微裂纹体积在0~2.2×10-4 mm3之间，空间角度为0°和45°左右的微裂纹占主要部分，微裂纹与孔隙分布有很强的相关性。断口损伤分析表明：2.5D-SiCf/SiC复合材料的拉伸损伤包括纤维断裂、纤维拔出、基体开裂和界面脱粘，其中纤维拔出长度范围为16.02~250.32 μm，平均长度为88.26 μm；2.5D-SiCf/SiC复合材料的损伤分析表明基于深度学习的图像分割方法为揭示材料损伤演化机理与评估损伤程度提供了有效手段。

碳化硅  /  陶瓷  /  复合材料  /  损伤识别  /  深度学习  /  图像分割  /  评估

The X-ray CT in-situ tensile test of 2.5D-SiCf/SiC composites was conducted to reveal the damage evolution and failure mechanism of the ceramic matrix composites. The CT data of the materials during the loading process and failure fracture were obtained. A deep learning-based image segmentation method was used to propose a three-dimensional visualization model of the internal damage of the composites, and the number, volume, spatial distribution and spatial angle of the microcracks generated due to the stretching were analyzed. At high loads, the tensile microcrack volumes are 0-5.3×10-4 mm3, the fracture microcrack volumes are 0-2.2×10-4 mm3, and the proportion of the microcracks with spatial angles of about 0° and 45° is relatively high. Meanwhile, the microcracks are closely correlated to the pore distribution. According to the results by fracture damage analysis, the tensile damage process of 2.5D-SiCf/SiC composites includes matrix cracking, fiber fracture, fiber pull-out and interface debonding. The fiber pull-out lengths are in the range of 16.02-250.32 μm, and the average length is 88.26 μm. It is indicated that the image segmentation method based on deep learning could offer an effective way to reveal the mechanism of material damages and evaluate the degree of the damages.

silicon carbonate  /  ceramics  /  composites  /  damage identification  /  deep learning  /  image segmentation  /  evaluation
冯宇琦, 张毅, 张大旭, 郭纬愉, 侯耀晟树, 李斌. 基于深度学习的2.5D陶瓷基复合材料损伤识别与评估. 硅酸盐学报, 2021 , 49 (8) : 1765 -1775 . DOI: 10.14062/j.issn.0454-5648.20200829
Yuqi FENG, Yi ZHANG, Daxu ZHANG, Weiyu GUO, Yaoshengshu HOU, Bin LI. Deep Learning-based Damage Identification and Evaluation of 2.5D Ceramic Matrix Composites[J]. Journal of the Chinese Ceramic Society, 2021 , 49 (8) : 1765 -1775 . DOI: 10.14062/j.issn.0454-5648.20200829
基金
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  • 国家自然科学基金项目(12072192; U1831105; 51802263)
  • 上海市自然科学基金项目(20ZR1429500)
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2021年第49卷第8期
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doi: 10.14062/j.issn.0454-5648.20200829
  • 接收时间：2020-11-04
  • 首发时间：2026-10-30
  • 出版时间：2021-08-20
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出版历史
  • 收稿日期：2020-11-04
  • 修回日期：2021-01-06
基金
国家自然科学基金项目(12072192; U1831105; 51802263)
上海市自然科学基金项目(20ZR1429500)
作者信息
    1．上海交通大学船舶海洋与建筑工程学院，上海 200240
    2．西北工业大学超高温结构复合材料重点实验室，西安 710072
    3．火箭军装备部驻西安地区第六军事代表室，西安 710072

通讯作者:

张大旭（1975—），男，博士，教授，博士研究生导师。
参考文献
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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20200829
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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