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基于纤维取向分布图像处理技术的短纤维增强聚合物基复合材料力学性能预测方法

管涛 李元庆 郭方亮 付绍云

管涛, 李元庆, 郭方亮, 等. 基于纤维取向分布图像处理技术的短纤维增强聚合物基复合材料力学性能预测方法[J]. 复合材料学报, 2024, 42(0): 1-11.
引用本文: 管涛, 李元庆, 郭方亮, 等. 基于纤维取向分布图像处理技术的短纤维增强聚合物基复合材料力学性能预测方法[J]. 复合材料学报, 2024, 42(0): 1-11.
GUAN Tao, LI Yuanqing, GUO Fangliang, et al. A method for predicting the mechanical properties of short fiber reinforced polymer composites based on fiber orientation distribution image processing technique[J]. Acta Materiae Compositae Sinica.
Citation: GUAN Tao, LI Yuanqing, GUO Fangliang, et al. A method for predicting the mechanical properties of short fiber reinforced polymer composites based on fiber orientation distribution image processing technique[J]. Acta Materiae Compositae Sinica.

基于纤维取向分布图像处理技术的短纤维增强聚合物基复合材料力学性能预测方法

基金项目: 国家自然科学基金(12332008;12272067;12102070);中国博士后基金(2023M730413);重庆市自然科学基金(CSTB2023NSCQ-MSX1052;CSTB2022NSCQ-MSX0608);青年人才托举工程项目(2022QNRC001)
详细信息
    通讯作者:

    付绍云,博士,教授,博士生导师,研究方向:航空复合材料 E-mail: syfu@cqu.edu.cn

  • 中图分类号: TB332

A method for predicting the mechanical properties of short fiber reinforced polymer composites based on fiber orientation distribution image processing technique

Funds: National Natural Science Foundation of China (12332008, 12272067and 12102070); China Postdoctoral Science Foundation (2023M730413); Chongqing Natural Science Foundation (CSTB2023NSCQ-MSX1052 and CSTB2022NSCQ-MSX0608); Young Elite Scien-tists Sponsorship Program (2022QNRC001)
  • 摘要: 短纤维增强聚合物基复合材料(SFRPC)具有复杂的细观结构,掌握纤维取向分布(FOD)规律是短纤维复合材料力学建模的前提。然而,由于纤维取向统计需要收集大量的纤维信息,通过传统手动标注读取显微图像的方式人工成本高且耗时长,统计效率与精度均难以保证。本文利用图像分析算法捕获纤维截面几何特征,发展了相应的纤维取向分布图像处理技术,实现了FOD信息的快速统计。探究了图像分析算法中关键参数的合理取值范围,并针对挤出注塑成型工艺制备的短玻纤增强和短碳纤增强聚醚酰亚胺复合材料(SGF/PEI 和SCF/PEI)进行微观结构表征,将统计的纤维状态信息传递至类层合板(LAA)与Fu-Lauke模型框架,进而预测了不同体积分数下两种复合材料的模量与强度,预测结果与有限元模拟结果、拉伸试验测试结果均吻合良好。本文将纤维取向分布图像处理技术与复合材料力学性能预测方法相结合,有助于更高效准确地理解短纤维增强复合材料的构效关系,对于复合材料结构设计具有较高的指导作用。

     

  • 图  1  SFRPC制备流程

    Figure  1.  Preparation process of SFRPC.

    图  2  纤维取向分布自动化统计流程图

    Figure  2.  The semi-automated measurement flow chart of fiber orientation distribution.

    图  3  建立体素化网格RVE模型流程图

    Figure  3.  Flowchart of establishing a voxelized mesh RVE model.

    图  4  SFRPC拉伸应力-应变曲线

    Figure  4.  Typical tensile stress-strain curves of SFRPC.

    图  5  (a)4%,(b)8%和(c)12%纤维体积分数的SGF/PEI复合材料和(d)4%,(e)8%和(f)12%纤维体积分数的SCF(T300)/PEI复合材料FLD结果

    Figure  5.  The FLD of SGF/PEI composites with fiber volume fractions of (a) 4%, (b) 8%, (c) 12%, as well as SCF(T300)/PEI composites with fiber volume fractions of (a) 4%, (b) 8%, (c) 12%.

    图  6  不同结构元素半径$R$的顶帽变换及二值化效果对比

    Figure  6.  Comparison of top-hat transformation and binarization effects of different structural element radius R

    图  7  不同阈值$h$的接触纤维分割效果

    Figure  7.  Contact fiber segmentation effects with different thresholds (h)

    图  8  (a)4%,(b)8%和(c)12%纤维体积分数的SGF/PEI复合材料和(d)4%,(e)8%和(f)12%纤维体积分数的T300/PEI复合材料FOD结果

    Figure  8.  FOD of SGF/PEI composites with volume fractions of (a) 4%, (b) 8%, (c) 12%, as well as T300/PEI composites with volume fractions of (a) 4%,(b) 8%, (c) 12%.

    图  9  SFRPC拉伸模量预测结果与实验对比

    Figure  9.  Comparison of predicted tensile modulus of SFRPC with experimental results.

    图  10  SFRPC拉伸强度预测结果与实验对比

    Figure  10.  Comparison of predicted tensile strength of SFRPC with experimental results.

    表  1  纤维和基体材料参数

    Table  1.   Material properties of fibers and matrix

    Material ${E_{11}}$/GPa ${E_{22}}$/GPa $ {{{v}}_{12}} $ $ {{{v}}_{23}} $ $ {{{G}}_{12}} $/GPa $ {{{G}}_{23}} $/GPa ${r_{\mathrm{f}}}$/μm
    PEI[36] 3.3 3.3 0.36 0.36 1.32 1.32
    E-GF[36] 82.2 82.2 0.22 0.22 33.61 33.61 3.25
    T300[37] 240 15 0.013 0.2 7 15 3.5
    下载: 导出CSV

    表  2  Fu -Lauke模型相关材料参数

    Table  2.   Relevant material parameters of the Fu-Lauke model

    Material ${V_f}$/(%) $\sigma ({l_c})$/MPa $\mu $ $ {\sigma _m} $/MPa
    SGF/PEI 4 4164[36] 0.47 85
    8 77
    12 67
    SCF(T300)/PEI 4 3962[36] 0.65 78
    8 69
    12 65
    下载: 导出CSV
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  • 收稿日期:  2023-12-14
  • 修回日期:  2024-03-18
  • 录用日期:  2024-04-12
  • 网络出版日期:  2024-05-14

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