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

GUAN Tao; LI Yuanqing; GUO Fangliang; FU Shaoyun

doi:10.13801/j.cnki.fhclxb.20240417.001

Volume 41 Issue 9

Sep. 2024

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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, 2024, 41(9): 4752-4762. doi: 10.13801/j.cnki.fhclxb.20240417.001

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, 2024, 41(9): 4752-4762. doi: 10.13801/j.cnki.fhclxb.20240417.001

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, 2024, 41(9): 4752-4762. doi: 10.13801/j.cnki.fhclxb.20240417.001

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

doi: 10.13801/j.cnki.fhclxb.20240417.001

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)

Received Date: 2023-12-14
Accepted Date: 2024-04-12
Rev Recd Date: 2024-03-18

Available Online: 2024-04-18

Publish Date: 2024-09-15

Abstract

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
Long Abstrsct
Innovation Points

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References(37)

References

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