High-fidelity architecture modeling and compressive strength prediction of 3D woven composite material

被引:1
|
作者
Shao, Tian [1 ]
Zhang, Sihao [1 ]
Liu, Wushuai [1 ]
Liu, Rui [1 ]
Xu, Wu [1 ]
机构
[1] Shanghai Jiao Tong Univ, Aerosp Struct Res Ctr, Sch Aeronaut & Astronaut, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金;
关键词
3D woven composite; Weaving process; Weaving architecture; Compressive strength; MECHANICAL-PROPERTIES; PROGRESSIVE DAMAGE; KINK BAND; FAILURE; BEHAVIOR; PERMEABILITY; SIMULATION; FABRICS;
D O I
10.1016/j.compstruct.2024.118775
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
The compressive strength of 3D woven composites (3DWC) is significantly lower than the tensile strength, because of the misalignment of the weaving yarn. Therefore, high-fidelity weaving architecture of 3DWC is crucial for the prediction of the compressive strength. In this paper, an optimal modeling strategy is proposed to obtain high-fidelity architecture of the 3DWC using a digital element approach and ABAQUS/Explicit. The virtual architecture obtained from the present method replicates the actual textures. Subsequently, a meso-scale finite element model based on the high-fidelity architecture is created to predict the compressive strength and damage process. To remedy the mesh dependence, the corrected crack band model for logarithmic strain is used to model the softening behaviors of the yarn and matrix. The predicted compressive strengths for 2.4 mm, 3.4 mm, and 5.4 mm thick 3D woven composites in the warp and weft directions are in good agreement with the test results.
引用
收藏
页数:18
相关论文
共 50 条
  • [41] High-Fidelity Texture Generation for 3D Avatar Based On the Diffusion Model
    Cheng, Hao
    Yu, Hui
    Jin, Haodong
    Zhang, Sunjie
    2024 16TH INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTION, HSI 2024, 2024,
  • [42] Evaluating psychiatric patients using high-fidelity animated 3D faces
    Csukly, G
    Simon, L
    Kiss, B
    Takács, B
    CYBERPSYCHOLOGY & BEHAVIOR, 2004, 7 (03): : 278 - 279
  • [43] 3D High-Fidelity Mask Face Presentation Attack Detection Challenge
    Liu, Ajian
    Zhao, Chenxu
    Yu, Zitong
    Su, Anyang
    Liu, Xing
    Kong, Zijian
    Wan, Jun
    Escalera, Sergio
    Escalante, Hugo Jair
    Lei, Zhen
    Guo, Guodong
    2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021), 2021, : 814 - 823
  • [44] High-Fidelity Printing Strategies for Printing 3D Vascular Hydrogel Structures
    Pataky, K.
    Ackermann, M.
    Braschler, T.
    Lutolf, M.
    Renaud, P.
    Brugger, J.
    NIP 25: DIGITAL FABRICATION 2009, TECHNICAL PROGRAM AND PROCEEDINGS, 2009, : 411 - +
  • [45] Real-time generation and high-fidelity visualization of 3D video
    Matsuyama, T
    Vu, X
    Takai, T
    ICCIMA 2003: FIFTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND MULTIMEDIA APPLICATIONS, PROCEEDINGS, 2003, : 373 - 378
  • [46] ScanNet plus plus : A High-Fidelity Dataset of 3D Indoor Scenes
    Yeshwanth, Chandan
    Liu, Yueh-Cheng
    Niessner, Matthias
    Dai, Angela
    2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV, 2023, : 12 - 22
  • [47] High-fidelity 3D face reconstruction with multi-scale details
    Jin, Yiwei
    Li, Qingyu
    Jiang, Diqiong
    Tong, Ruofeng
    PATTERN RECOGNITION LETTERS, 2022, 153 : 51 - 58
  • [48] SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction
    Xie, Weixing
    Yao, Junfeng
    Cao, Xianpeng
    Lin, Qiqin
    Tang, Zerui
    Dong, Xiao
    Guo, Xiaohu
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT VI, 2024, 15006 : 617 - 627
  • [49] AvatarStudio: High-Fidelity and Animatable 3D Avatar Creation from Text
    Zhang, Xuanmeng
    Zhang, Jianfeng
    Zhang, Chenxu
    Liew, Jun Hao
    Zhang, Huichao
    Yang, Yi
    Feng, Jiashi
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 2025,
  • [50] High-fidelity Image Restoration of Large 3D Electron Microscopy Volume
    Kreinin, Yuri
    Gunn, Pat
    Chklovskii, Dmitri
    Wu, Jingpeng
    MICROSCOPY AND MICROANALYSIS, 2024, 30 (05) : 889 - 902