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

doi:10.13801/j.cnki.fhclxb.20240417.001

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

doi: 10.13801/j.cnki.fhclxb.20240417.001

重庆大学　航空航天学院, 重庆 400044

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，研究方向为航空复合材料　E-mail: syfu@cqu.edu.cn

中图分类号: TB332
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出版历程
- 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: 2024-04-18
- 刊出日期: 2024-09-15

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

College of Aerospace Engineering, Chongqing University, Chongqing 400044, China

Funds: National Natural Science Foundation of China (12332008, 12272067, and 12102070); China Postdoctoral Science Foundation (2023M730413); Chongqing Natural Science Foundation (CSTB2023NSCQ-MSX1052 and CSTB2022NSCQ-MSX0608); Young Elite Scien-tists Sponsorship Program (2022QNRC001)

摘要

摘要: 短纤维增强聚合物基复合材料(Short fiber reinforced polymer composites，SFRPC)具有复杂的细观结构，掌握纤维取向分布(Fiber orientation distribution，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(Laminate analogy approach，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。
- 热塑性聚合物 /
- 注塑成型 /
- 短纤维增强复合材料 /
- 纤维取向 /
- 力学性能
Abstract: Short fiber reinforced polymer composites (SFRPC) possess complex microscale structures, and understanding the fiber orientation distribution (FOD) within the SFRPC is a prerequisite for mechanical modeling of short fiber composites. However, the statistical analysis of fiber orientation requires collecting a large amount of fiber orientation information, and traditional manual annotation and retrieval of micrographs are costly and time-consuming, making it challenging to ensure both statistical efficiency and accuracy. In this study, image analysis algorithms were employed to capture geometric features of fiber cross-sections, and a corresponding image processing technique for FOD was developed, enabling the rapid statistical analysis of FOD information. The reasonable range of key parameters in image analysis algorithms was explored, and microstructure characterization was conducted on short glass fiber-reinforced and short carbon fiber-reinforced polyetherimide (SGF/PEI and SCF/PEI) composites fabricated using extrusion and injection molding processes. The statistical fiber orientation information was then incorporated into the laminate analogy approach (LAA) and Fu-Lauke model frameworks to predict the modulus and strength of the two composites with different volume fractions. The predicted results exhibited good agreement with finite element simulation results and experimental tensile test data. By combining the image processing technique for FOD with the prediction methods for composite mechanical properties, this study is helpful to more efficient and accurate understanding of the structure-property relationship of short fiber reinforced composites, providing valuable guidance for composite structural design.
- thermoplastic polymer /
- injection moulding /
- short fiber reinforced composites /
- fiber orientation /
- mechanical properties

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图 1 SFRPC制备流程

Figure 1. Preparation process of short fiberreinforced polymer composites (SFRPC)

SGF/PEI—short glass fiber-reinforced polyetherimide; SCF/PEI—short carbon fiber-reinforced polyetherimide

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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

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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

RVE—Representative Volume Element

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图 4 SFRPC拉伸应力-应变曲线

Figure 4. Typical tensile stress-strain curves of SFRPC

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

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

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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

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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)

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

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

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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

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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

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表 1 纤维和基体材料参数

Table 1. Material properties of fibers and matrix

Material	${E_{11}}/{\mathrm{GPa}}$	${E_{22}}/{\mathrm{GPa}}$	$ {{{v}}_{12}} $	$ {{{v}}_{23}} $	$ {{{G}}_{12}}/{\mathrm{GPa}} $	$ {{{G}}_{23}}/{\mathrm{GPa}} $	${r_{\mathrm{f}}}/{\text{μ}}{\mathrm{m}}$
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

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表 2 SGF/PEI复合材料和T300/PEI复合材料纤维长度参数

Table 2. Fiber length parameters of SGF/PEI composites and SCF(T300)/PEI composites

Material	V_f/%	l_mean/μm	a	b
SGF/PEI	4	115	129.8	2.4
	8	98	110.6	2.2
	12	95	106.9	2.2
SCF(T300)/PEI	4	152	170.6	2.6
	8	145	163.5	2.4
	12	137	153.2	2.4

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表 3 SGF/PEI复合材料和T300/PEI复合材料纤维取向参数

Table 3. Fiber orientation parameters of SGF/PEI composites and SCF(T300)/PEI composites

Material	V_f/%	θ_mean/（°）	p	q
SGF/PEI	4	28	1.9	7
	8	28	1.6	6
	12	29	1.5	5
SCF(T300)/PEI	4	35	1.8	3.9
	8	30	1.5	4
	12	34	1.8	4.2

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表 4 Fu -Lauke模型相关材料参数

Table 4. Relevant material parameters of the Fu-Lauke model

Material	V_f/%	$\sigma ({l_{\mathrm{c}}})/{\mathrm{MPa}}$	$\mu $	$ {\sigma _{\mathrm{m}}}/{\mathrm{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

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