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Deep Learning-based Damage Identification and Evaluation of 2.5D Ceramic Matrix Composites
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Yuqi FENG1, Yi ZHANG2, Daxu ZHANG1, Weiyu GUO1, Yaoshengshu HOU1, Bin LI3
Journal of the Chinese Ceramic Society | 2021, 49(8) : 1765 - 1775
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Journal of the Chinese Ceramic Society | 2021, 49(8): 1765-1775
• Research Article •
Deep Learning-based Damage Identification and Evaluation of 2.5D Ceramic Matrix Composites
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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

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

About Author:

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

Published: 2021-08-20 doi: 10.14062/j.issn.0454-5648.20200829
Outline
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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
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Year 2021 volume 49 Issue 8
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Article Info
doi: 10.14062/j.issn.0454-5648.20200829
  • Receive Date：2020-11-04
  • Online Date：2026-10-30
  • Published：2021-08-20
Article Data
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History
  • Received：2020-11-04
  • Revised：2021-01-06
Funding
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:

ZHANG Daxu (1975-), male, Ph. D., Professor. E-mail:
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表12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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