Advanced Polymer Matrix Composites: Design, Manufacturing and Analysis

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Polymer Physics and Theory".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3478

Special Issue Editors


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Guest Editor
School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, China
Interests: damage and fracture; multiscale modelling; advanced manufacturing of composites

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Guest Editor
School of Materials, Sun Yat-Sen University, Guangzhou, China
Interests: bio-inspired composites; novel composite design and manufacturing; impact mechanics

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Guest Editor
Department of Aeronautics and Astronautics, The University of Tokyo, Tokyo, Japan
Interests: composite; fracture; modelling

Special Issue Information

Dear Colleagues,

Continuous fiber-reinforced thermosetting resin matrix composites have been demonstrated to be successful in aerospace applications. In the past decade, emerging ideas, materials and technologies have brought revolutionary changes to the conventional design, manufacturing and life-cycle evaluation of composite structures. In addition to high-performance behavior, ongoing efforts have been devoted to the production of high-volume, low-cost composites. New developments have even surpassed our original understanding of these materials. This Special Issue aims to collect recent advances in polymer matrix composites (PMCs) with a focus on structural applications, including biomimetic design, additive manufacturing and integrated design and manufacture. Moreover, we are calling for papers related to advanced testing and analysis approaches for PMCs, such as novel experimental characterization, model-/data-driven artificial intelligence and high-fidelity physics modeling.

Dr. Jie Zhi
Dr. Jialong Liu
Dr. Xin Lu
Guest Editors

Manuscript Submission Information

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Keywords

  • fiber-reinforced composites
  • bio-inspired composites
  • additive manufacturing
  • artificial intelligence
  • integrated design and manufacture
  • damage tolerance

Published Papers (3 papers)

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Research

10 pages, 2734 KiB  
Article
Study of Hygrothermal Aging for Basalt Fiber/Epoxy Resin Composites Modified with CeCl3
by Chong Li, Longwang Zhang, Haoyu Wang, Yiguo Song and Jiayou Wang
Polymers 2024, 16(6), 819; https://doi.org/10.3390/polym16060819 - 15 Mar 2024
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Abstract
With increasing attention being paid to environmental issues, the application of natural fibers in fiber-reinforced composites has attracted more and more attention. Composite materials with basalt fibers (BFs) as reinforcement have excellent properties and are widely used in many fields. Hydrothermal aging crucially [...] Read more.
With increasing attention being paid to environmental issues, the application of natural fibers in fiber-reinforced composites has attracted more and more attention. Composite materials with basalt fibers (BFs) as reinforcement have excellent properties and are widely used in many fields. Hydrothermal aging crucially influences the durability of basalt fiber/epoxy resin composites (BF/ERCs). In this study, BFs were used as reinforcing materials, whose surfaces were modified with a rare earth modification solution (CeCl3). The density, mechanical performance, and chemical properties of BF/ERCs subjected to hygrothermal aging were analyzed by the weight method, static mechanical performance testing, scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FT-IR). The effects of the modification solution with different Ce concentrations on the water absorption, tensile, bending and interlaminar shear strength (ILSS) of BF/ERCs were investigated. The test results showed that the water absorption of BF/ERCs treated with a modification solution that contained Ce 0.5 wt % as the minimum value and the retention rate of the mechanical properties of BF/ERCs reached maximum values after hygrothermal aging. Full article
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14 pages, 4955 KiB  
Article
Prediction Models of Mechanical Properties of Jute/PLA Composite Based on X-ray Computed Tomography
by Xintao Zhao, Junteng Li, Shangbin Su and Ning Jiang
Polymers 2024, 16(1), 160; https://doi.org/10.3390/polym16010160 - 4 Jan 2024
Viewed by 968
Abstract
The tensile strength and modulus of elasticity of a jute/polylactic acid (PLA) composite were found to vary nonlinearly with the loading angle of the specimen through the tensile test. The variation in these properties was related to the fiber orientation distribution (FOD) and [...] Read more.
The tensile strength and modulus of elasticity of a jute/polylactic acid (PLA) composite were found to vary nonlinearly with the loading angle of the specimen through the tensile test. The variation in these properties was related to the fiber orientation distribution (FOD) and fiber length distribution (FLD). In order to study the effects of the FOD and FLD of short fibers on the mechanical properties and to better predict the mechanical properties of short-fiber composites, the true distribution of short fibers in the composite was accurately obtained using X-ray computed tomography (XCT), in which about 70% of the jute fibers were less than 300 μm in length and the fibers were mainly distributed along the direction of mold flow. The probability density functions of the FOD and FLD were obtained by further analyzing the XCT data. Strength and elastic modulus prediction models applicable to short-fiber-reinforced polymer (SFRP) composites were created by modifying the laminate theory and the rule of mixtures using the probability density functions of the FOD and FLD. The experimental measurements were in good agreement with the model predictions. Full article
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27 pages, 5681 KiB  
Article
A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites
by Yunmei Zhao, Zhenyue Chen and Xiaobin Jian
Polymers 2023, 15(19), 3962; https://doi.org/10.3390/polym15193962 - 30 Sep 2023
Viewed by 1172
Abstract
This study aims to develop a high-generalizability machine learning framework for predicting the homogenized mechanical properties of short fiber-reinforced polymer composites. The ensemble machine learning model (EML) employs a stacking algorithm using three base models of Extra Trees (ET), eXtreme Gradient Boosting machine [...] Read more.
This study aims to develop a high-generalizability machine learning framework for predicting the homogenized mechanical properties of short fiber-reinforced polymer composites. The ensemble machine learning model (EML) employs a stacking algorithm using three base models of Extra Trees (ET), eXtreme Gradient Boosting machine (XGBoost), and Light Gradient Boosting machine (LGBM). A micromechanical model of a two-step homogenization algorithm is adopted and verified as an effective approach to composite modeling with randomly distributed fibers, which is integrated with finite element simulations for providing a high-quality ground-truth dataset. The model performance is thoroughly assessed for its accuracy, efficiency, interpretability, and generalizability. The results suggest that: (1) the EML model outperforms the base members on prediction accuracy, achieving R2 values of 0.988 and 0.952 on the train and test datasets, respectively; (2) the SHapley Additive exPlanations (SHAP) analysis identifies the Young’s modulus of matrix, fiber, and fiber content as the top three factors influencing the homogenized properties, whereas the anisotropy is predominantly determined by the fiber orientations; (3) the EML model showcases good generalization capability on experimental data, and it has been shown to be more effective than high-fidelity computational models by significantly lowering computational costs while maintaining high accuracy. Full article
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