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J. Compos. Sci.
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Characterization and Modeling of Composites, 4th Edition
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Characterization and Modeling of Composites, 4th Edition

A special issue of Journal of Composites Science (ISSN 2504-477X). This special issue belongs to the section "Composites Modelling and Characterization".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4302

Special Issue Editor

Dr. Stelios K. Georgantzinos

E-Mail Website
Guest Editor
General Department, Evripus Campus, National and Kapodistrian University of Athens, Psachna, Evoia, Greece
Interests: nanostructures; nanocomposites; composite structures; finite element method; design; modeling; computational analysis; nanotechnology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Composites have been increasingly used in various structural components in the aerospace, marine, automotive, and wind energy sectors. Composites’ material characterization is a vital part of the product development and production process. Physical, mechanical, and chemical characterization helps developers to further their understanding of products and materials, thus ensuring quality control. Achieving an in-depth understanding and consequent improvement of the general performance of these materials, however, still requires complex material modeling and simulation tools, which are often multiscale and encompass multiphysics.

This Special Issue is aimed at soliciting promising, recent developments in composite modeling, simulation, and characterization, in both design and manufacturing areas, including experimental as well as industrial-scale case studies. All submitted manuscripts will undergo a rigorous review and will only be considered for publication if they meet journal standards. 

Dr. Stelios K. Georgantzinos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Composites Science is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fiber-reinforced composites
  • unidirectional and woven reinforcements
  • noncrimp fabrics (NCFs)
  • three-dimensional composites
  • nanocomposites
  • natural fiber and biocomposites
  • hybrid composites
  • composite structures
  • modeling and characterization
  • numerical simulation
  • experimental studies
  • industrial case studies

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Published Papers (7 papers)

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Research

28 pages, 5048 KiB  
Article
Voxel-Based Finite Element Investigation of Micromechanics Models for Stiffness Prediction of Cross-Ply Laminates
by Darya Forooghi and Yunhua Luo
J. Compos. Sci. 2025, 9(6), 288; https://doi.org/10.3390/jcs9060288 - 4 Jun 2025
Viewed by 176
Abstract
Laminate plate and shell structures with symmetric cross-ply configurations are widely used due to their high stiffness-to-weight ratio. However, conventional lamination theories rely on simplifying assumptions that may introduce inaccuracies. This study evaluates the predictive capability of such theories by integrating multiple micromechanics [...] Read more.
Laminate plate and shell structures with symmetric cross-ply configurations are widely used due to their high stiffness-to-weight ratio. However, conventional lamination theories rely on simplifying assumptions that may introduce inaccuracies. This study evaluates the predictive capability of such theories by integrating multiple micromechanics models with First-Order Shear Deformation Theory (FSDT), and comparing the results against voxel-based finite element modeling (VB-FEM), which serves as a high-fidelity numerical reference. A range of models—including Voigt–Reuss, Chamis, Halpin–Tsai, Bridging, and two iterative isotropized formulations—are assessed for unidirectional laminae with fiber volume fractions from 40% to 73%. Quantitative comparison reveals that while all models predict the longitudinal modulus accurately, significant deviations arise in predicting transverse and shear properties. The Bridging Model consistently yields the closest agreement with VB-FEM across all five elastic constants, maintaining accuracy even at high volume fractions where the modified Halpin–Tsai model begins to fail. Discrepancies in micromechanics-based lamina properties propagate to laminate-level stiffness predictions, highlighting the critical role of model selection. These findings establish VB-FEM as a valuable tool for validating analytical models and guide improved modeling strategies for laminated composite design. Full article
(This article belongs to the Special Issue Characterization and Modeling of Composites, 4th Edition)
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12 pages, 756 KiB  
Article
Exploring Artificial Neural Network Techniques for Modeling Surface Roughness in Wire Electrical Discharge Machining of Aluminum/Silicon Carbide Composites
by Yogesh S. Sable, Hanumant M. Dharmadhikari, Sunil A. More and Ioannis E. Sarris
J. Compos. Sci. 2025, 9(6), 259; https://doi.org/10.3390/jcs9060259 - 25 May 2025
Viewed by 315
Abstract
Understanding wire-cut electrical discharge machining (WEDM) parameters’ impact on surface roughness (Ra) is crucial for optimizing processes. This study uses artificial neural network (ANN) techniques to estimate the surface roughness of Al/SiC composites during WEDM, examining how process parameters affect the roughness. The [...] Read more.
Understanding wire-cut electrical discharge machining (WEDM) parameters’ impact on surface roughness (Ra) is crucial for optimizing processes. This study uses artificial neural network (ANN) techniques to estimate the surface roughness of Al/SiC composites during WEDM, examining how process parameters affect the roughness. The experiment used a stir casting aluminum alloy with a 7.5% silicon carbide metal matrix composite (MMC), adjusting parameters like the wire tension (WT), servo voltage (SV), peak current (IP), pulse on time (TON), and pulse off time (TOFF). An ANN model was created to forecast the surface roughness. The study developed an ANN model to forecast surface roughness in Al/SiC composites during WEDM, demonstrating its accuracy in identifying the link between surface finish and input parameters, thereby improving the surface quality. The ANN model accurately predicted the surface roughness based on WEDM parameters, with strong correlations between predictions and actual data, demonstrating its ability to estimate surface quality accurately. Full article
(This article belongs to the Special Issue Characterization and Modeling of Composites, 4th Edition)
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27 pages, 8076 KiB  
Article
Micro-Modeling of Polymer–Masonry Wall Composites Under In-Plane Loading
by Houria Hernoune, Younes Ouldkhaoua, Benchaa Benabed, Rajab Abousnina, Vanissorn Vimonsatit, Ali Mohammed and Allan Manalo
J. Compos. Sci. 2025, 9(4), 179; https://doi.org/10.3390/jcs9040179 - 7 Apr 2025
Viewed by 578
Abstract
Fiber-reinforced polymers (FRPs) are effective for strengthening masonry walls. Debonding at the polymer–masonry interface is a major concern, requiring further investigation into interface behavior. This study utilizes detailed micro-modeling finite element (FE) analysis to predict failure mechanisms and analyze the behavior of brick [...] Read more.
Fiber-reinforced polymers (FRPs) are effective for strengthening masonry walls. Debonding at the polymer–masonry interface is a major concern, requiring further investigation into interface behavior. This study utilizes detailed micro-modeling finite element (FE) analysis to predict failure mechanisms and analyze the behavior of brick masonry walls strengthened with externally bonded carbon fiber-reinforced polymer (CFRP) under in-plane loading. The research investigates three CFRP strengthening configurations (X, I, and H). The FE model incorporates the nonlinear behavior of brick masonry components using the Concrete Damage Plasticity (CDP) model and uses a cohesive interface approach to model unit–mortar interfaces and the bond joints between masonry and CFRPs. The results demonstrate that diagonal CFRP reinforcement enhances the ductility and capacity of masonry wall systems. The FE model accurately captures the crack propagation, fracture mechanisms, and shear strength of both unreinforced and reinforced walls. The study confirms that the model can reliably predict the structural behavior of these composite systems. Furthermore, the study compares predicted shear strengths with established design equations, highlighting the ACI 440.7R-10 and CNR-DT 200/2013 models as providing the most accurate predictions when compared to experimental results. Full article
(This article belongs to the Special Issue Characterization and Modeling of Composites, 4th Edition)
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30 pages, 7078 KiB  
Article
Enhancement of Mechanical and Tribological Properties of MWCNT-Reinforced Bio-Based Epoxy Composites Through Optimization and Molecular Dynamics Simulation
by Pavan Hiremath, Y. M. Shivaprakash, Kiran Keshyagol, Suhas Kowshik, B. M. Gurumurthy, D. V. Ghewade, Shivashankarayya Hiremath and Nithesh Naik
J. Compos. Sci. 2025, 9(4), 176; https://doi.org/10.3390/jcs9040176 - 5 Apr 2025
Viewed by 421
Abstract
This study investigated the enhancement of the mechanical and tribological properties of MWCNT-reinforced bio-based epoxy composites through systematic experiments and analysis. Composites incorporating MWCNTs at varying weight percentages were evaluated for hardness, wear rate, interfacial shear strength, and friction coefficient under diverse load, [...] Read more.
This study investigated the enhancement of the mechanical and tribological properties of MWCNT-reinforced bio-based epoxy composites through systematic experiments and analysis. Composites incorporating MWCNTs at varying weight percentages were evaluated for hardness, wear rate, interfacial shear strength, and friction coefficient under diverse load, sliding speed, and distance conditions. An optimal MWCNT content of 0.3–0.4% resulted in a maximum hardness of 4 GPa and a minimum wear rate of 0.0058 mm3/N·m, demonstrating a substantial improvement over the non-reinforced system. FTIR and XRD analyses confirmed robust interfacial bonding between the MWCNTs and epoxy matrix, while molecular dynamics simulations revealed cohesive energy density and stress distribution profiles. The Taguchi optimization identified the MWCNT weight percentage as the most influential parameter, contributing over 85% to wear rate reduction. Contour plots and correlograms further illustrate the parameter interdependencies, emphasizing the role of MWCNT dispersion in enhancing the composite properties. These findings establish that MWCNT-reinforced bio-based epoxy composites are promising candidates for high-performance and sustainable tribological applications. Full article
(This article belongs to the Special Issue Characterization and Modeling of Composites, 4th Edition)
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19 pages, 49232 KiB  
Article
Tribological Study of Multi-Walled Carbon Nanotube-Reinforced Aluminum 7075 Using Response Surface Methodology and Multi-Objective Genetic Algorithm
by Endalkachew Mosisa Gutema, Mahesh Gopal and Hirpa G. Lemu
J. Compos. Sci. 2025, 9(3), 137; https://doi.org/10.3390/jcs9030137 - 14 Mar 2025
Viewed by 472
Abstract
Aluminum metal matrix composites (AlMMCs) are widely employed in the aerospace and automotive industries due to their greater qualities in comparison to the base alloy. Adding nanocomposites like multi-walled carbon nanocomposites (MWCNTs) to aluminum enhances its mechanical properties. In the current research, aluminum [...] Read more.
Aluminum metal matrix composites (AlMMCs) are widely employed in the aerospace and automotive industries due to their greater qualities in comparison to the base alloy. Adding nanocomposites like multi-walled carbon nanocomposites (MWCNTs) to aluminum enhances its mechanical properties. In the current research, aluminum 7075 with MWCNT particles was prepared and characterized to study its tribological behaviors, such as its hardness and specific wear rate. The experiment was designed with varying weight percentages of MWCNTs of 0.5, 1.0, and 1.5, and these were fabricated using powder metallurgy, employing compacting pressures of 300, 400, and 500 MPa and sintering temperatures of 400, 450, and 500 °C. Further, the experimental setup was designed using Design-Expert V13 to examine the impact of influencing parameters. A second-order mathematical model was developed via central composite design (CCD) using a response surface methodology (RSM), and the performance characteristics were analyzed using an analysis of variance (ANOVA). The hardness (HV) and specific wear rate (SWR) were measured using a hardness tester and pin-on-disk apparatus. From the results thus obtained, it was observed that an increase in compacting pressure and sintering temperature tends to increase the hardness and specific wear rate. An increasing weight percentage of MWCNTs increased their hardness, while the SWR was less between the weight percentages 0.9 and 1.3. A multi-objective genetic algorithm (MOGA) was trained and evaluated to provide the best feasible solutions. The MOGA suggested sixteen sets of non-dominated Pareto optimal solutions that had the best and lowest predicted values. The confirmatory analytical results and predicted characteristics were found to be excellent and consistent with the experiential values. Full article
(This article belongs to the Special Issue Characterization and Modeling of Composites, 4th Edition)
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28 pages, 4274 KiB  
Article
Sustainable Composites from Sugarcane Bagasse Fibers and Bio-Based Epoxy with Insights into Wear Performance, Thermal Stability, and Machine Learning Predictive Modeling
by Mahima Samanth, Pavan Hiremath, G. Divya Deepak, Nithesh Naik, Arunkumar H S, Srinivas Shenoy Heckadka and R. C. Shivamurthy
J. Compos. Sci. 2025, 9(3), 124; https://doi.org/10.3390/jcs9030124 - 6 Mar 2025
Cited by 1 | Viewed by 1385
Abstract
The global push for sustainable materials has intensified the research on natural fiber-reinforced composites. This study investigates the potential of sugarcane bagasse fibers, combined with a bio-based epoxy matrix, as a sustainable alternative for high-performance composites. A comprehensive approach was adopted, including wear [...] Read more.
The global push for sustainable materials has intensified the research on natural fiber-reinforced composites. This study investigates the potential of sugarcane bagasse fibers, combined with a bio-based epoxy matrix, as a sustainable alternative for high-performance composites. A comprehensive approach was adopted, including wear testing, thermal and structural characterization, and machine learning predictive modeling. Ethylene dichloride-treated fibers exhibited the lowest wear rate (0.245 mg/m) and the highest thermal stability (T20% = 260 °C, char yield = 1.3 mg), highlighting the role of optimized surface modifications. XRD (X-ray diffraction) analysis revealed that pre-treated fibers achieved the highest crystallinity index of 62%, underscoring the importance of structural alignment in fiber-matrix bonding. Machine learning insights using a Random Forest model identified fiber treatment as the most significant parameter influencing wear performance, with accurate predictions validated through experimental results. This work demonstrates the transformative potential of sugarcane bagasse fibers in sustainable polymer composites, offering a pathway for environmentally friendly, lightweight, and durable material solutions. These findings integrate experimental rigor with computational insights, paving the way for advancements in natural fiber-based composite technologies. Full article
(This article belongs to the Special Issue Characterization and Modeling of Composites, 4th Edition)
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22 pages, 5704 KiB  
Article
Application of DFT and Experimental Tests for the Study of Compost Formation Between Chitosan-1,3-dichloroketone with Uses for the Removal of Heavy Metals in Wastewater
by Joaquín Alejandro Hernández Fernández, Jose Alfonso Prieto Palomo and Rodrigo Ortega-Toro
J. Compos. Sci. 2025, 9(2), 91; https://doi.org/10.3390/jcs9020091 - 19 Feb 2025
Cited by 1 | Viewed by 623
Abstract
The environment presently contains greater amounts of heavy metals due to human activities, causing toxicity, mutagenicity, and carcinogenicity. This study evaluated a chitosan (CS) composite material combined with 1,3-dichlorocetone to extract heavy metals from affected waters, integrating experimental and computational analyses. The synthesis [...] Read more.
The environment presently contains greater amounts of heavy metals due to human activities, causing toxicity, mutagenicity, and carcinogenicity. This study evaluated a chitosan (CS) composite material combined with 1,3-dichlorocetone to extract heavy metals from affected waters, integrating experimental and computational analyses. The synthesis of chitosan, obtained from shrimp waste chitin, reached a yield of 85%. FTIR analysis confirmed key functional groups (NH2 and OH), and XRD showed high crystallinity with peaks at 2θ = 8° and 20°. The physicochemical properties evaluated included a moisture content of 7.3%, ash content of 2.4%, and a deacetylation degree of 73%, consistent with commercial standards. Chitosan exhibited significant solubility in 1.5% acetic acid, moderate solubility in water, and insolubility in NaOH, demonstrating its versatility for environmental applications. In adsorption tests, heavy metal concentrations were reduced by CS derivatives, with Cr and Pb dropping to 0.03 mg/L, and Cu and Zn to less than 0.05 mg/L. CS cross-linked with 1,3-dichlorocetone proved the most efficient, outperforming other derivatives such as glutaraldehyde and epichlorohydrin. Computational analysis evaluated key molecular interactions using DFT and the B3LYP/LANLD2Z method. The band gap energies (HOMO–LUMO) decreased to 0.09753 eV for Zn and 0.01485 eV for Pb, indicating high affinity, while Cd showed lower interaction (0.11076 eV). The total dipole moment increased remarkably for Zn (14.693 Debye) and Pb (7.449 Debye), in contrast to Cd (4.515 Debye). Other descriptors, such as chemical hardness (η), reflected a higher reactivity for Zn (0.04877 eV) and Pb (0.00743 eV), which favors adsorption. The correlation between experimental and computational results validates the efficiency and selectivity of CS/1,3-dichlorocetone for removing heavy metals, especially Pb and Zn. This material stands out for its adsorbent capacity, sustainability, and economic viability, positioning it as a promising solution for wastewater remediation. Full article
(This article belongs to the Special Issue Characterization and Modeling of Composites, 4th Edition)
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