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J. Compos. Sci.
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Machine Learning Applications in the Design and Analysis of Composite...
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Machine Learning Applications in the Design and Analysis of Composite Materials

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: closed (10 March 2026) | Viewed by 5395

Special Issue Editors

Dr. Gyula Varga

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering and Informatics, University of Miskolc, H-3515 Miskolc, Hungary
Interests: manufacturing process mechanics; material characterization; mechanical properties; mechanical testing
Dr. Saiaf Bin Rayhan

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Aviation and Aerospace University, Dhaka, Bangladesh
Interests: composite structure and failure; aircraft structure; micromechanics; impact dynamics; buckling; strength of Materials; solid mechanics

Special Issue Information

Dear Colleagues,

You are invited to contribute to this upcoming Special Issue of the Journal of Composites Science, titled “Machine Learning Applications in the Design and Analysis of Composite Materials”. This Special Issue aims to gather original research and review articles focused on the integration of machine learning techniques with theoretical, numerical, and experimental approaches in the field of composite materials. Topics may include, but are not limited to, property prediction, damage detection, optimization, and performance enhancement of fiber-reinforced and novel composite structures.

As composite materials continue to play a pivotal role in aerospace, automotive, civil, and biomedical engineering, there is a growing need for intelligent, data-driven approaches to accurately characterize, predict, and optimize their behavior. The integration of machine learning with computational mechanics, material modeling, and experimental data offers a powerful framework to revolutionize composite analysis and design. This Special Issue aims to achieve the following: (1) develop machine learning models for accurate prediction of mechanical properties of composite materials; (2) investigate and classify failure mechanisms through data-driven techniques; (3) apply intelligent algorithms for damage detection and health monitoring; and (4) explore optimization strategies for material architecture and performance enhancement. Contributions that demonstrate practical, interpretable, and reliable machine learning solutions for these aims are especially encouraged. Through this Special Issue, we seek to advance the role of artificial intelligence in the modeling, failure analysis, and performance optimization of composite materials, paving the way for more efficient and innovative engineering solutions.

We look forward to receiving your valuable contributions to this Special Issue.

Dr. Gyula Varga
Dr. Saiaf Bin Rayhan
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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

  • composite materials
  • machine learning
  • mechanical properties prediction
  • structural damage detection
  • optimization
  • fatigue life prediction and analysis
  • data-driven design

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

Jump to: Review

23 pages, 4530 KB  
Article
Machine Learning Approach for Mechanical Property Prediction of a Bio-Epoxy and Glass Fiber Composite Reinforced with Titanium Dioxide Nanoparticles
by Wilson Navas-Pinto, Pablo Díaz-Leime, Germán Omar Barrionuevo, Jhon Luna-Jaén, Xavier Sánchez-Sánchez, Carlos Navas-Cárdenas and Duncan E. Cree
J. Compos. Sci. 2026, 10(3), 123; https://doi.org/10.3390/jcs10030123 - 25 Feb 2026
Viewed by 900
Abstract
Glass fiber reinforced polymers (GFRPs) have drawn significant attention given their lightweight, mechanical resistance and tunable properties through constituent selection. Due to environmental concerns, research efforts have focused on incorporating sustainable materials, such as bio-epoxy resins, to reduce the ecological impact of GFRPs. [...] Read more.
Glass fiber reinforced polymers (GFRPs) have drawn significant attention given their lightweight, mechanical resistance and tunable properties through constituent selection. Due to environmental concerns, research efforts have focused on incorporating sustainable materials, such as bio-epoxy resins, to reduce the ecological impact of GFRPs. This study characterizes a GFRP containing a bio-epoxy resin matrix, various loadings of titanium dioxide (TiO2) nanoparticles, and a stabilized arrangement of glass fiber. The unreinforced composite exhibited a tensile strength and modulus of 214 MPa, and 13 GPa, respectively, and a flexural strength and modulus of 375 MPa and 14.5 GPa, respectively. The addition of TiO2 produced an improvement in mechanical response for all the composites. The formulation with 1 wt.% TiO2 showed the best tensile response with an improvement of 13% and 14% for its tensile strength, and modulus, respectively; meanwhile, the composites with 2 wt.% TiO2 attained an improvement of 19% and 40% for the flexural strength and modulus, respectively. Scanning electron microscopy (SEM) revealed significant changes in the fracture mechanism of the composites, while energy-dispersive spectroscopy (EDS) confirmed an even nanoparticle distribution. Additionally, machine learning (ML) models were developed to predict the mechanical response as a function of the TiO2 content. Full article
(This article belongs to the Special Issue Machine Learning Applications in the Design and Analysis of Composite Materials)
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24 pages, 4872 KB  
Article
Leveraging Machine Learning (ML) to Enhance the Structural Properties of a Novel Alkali Activated Bio-Composite
by Assia Aboubakar Mahamat, Moussa Mahamat Boukar, Ifeyinwa Ijeoma Obianyo, Philbert Nshimiyimana, Blasius Ngayakamo, Nordine Leklou and Numfor Linda Bih
J. Compos. Sci. 2025, 9(9), 464; https://doi.org/10.3390/jcs9090464 - 1 Sep 2025
Viewed by 1004
Abstract
This study explored the use of Borassus fruit fiber as reinforcement for earthen matrices (BFRC). The experimental results of the testing carried out on the structural properties were used to generate a primary dataset for training and testing machine learning (ML) models. Linear [...] Read more.
This study explored the use of Borassus fruit fiber as reinforcement for earthen matrices (BFRC). The experimental results of the testing carried out on the structural properties were used to generate a primary dataset for training and testing machine learning (ML) models. Linear regression (LR), Decision tree regressor (DTR), and gradient boosting regression (GBR) were used to build an ensemble learning (EL) model during the prediction of the hygroscopic properties, Young’s modulus, and compressive strength of the BFRC. Fiber content, activation concentration, curing days, dry weight, saturated weight, mass, flexural vibration, longitudinal vibration, correction factor, maximum load, and cross-sectional area were the various inputs considered in the structural properties prediction. The performance of both EL and single models (SMs) was appraised via three performance metrics—mean square error (MSE), root mean square (RMSE), and the coefficient of determination (R2)—to comparatively ascertain the model’s efficiency. Results showed that all models exhibited high accuracy in predicting Young’s modulus and compressive strength. Ensemble learning outperformed single models in predicting these properties, with MSE, RMSE, and R2 of 0.01 MPa, 0.1 MPa, and 99% and 3,923,262.5 MPa, 1980.7 Pa, and 99% for compressive strength and Young’s modulus, respectively. However, for hygroscopic behavior, linear regression (LR) demonstrated superior performance compared to other models, with MSE, RMSE, and R2 values of 0.13%, 0.36%, and 99%. Full article
(This article belongs to the Special Issue Machine Learning Applications in the Design and Analysis of Composite Materials)
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21 pages, 4368 KB  
Article
Damage Mechanism Characterization of Glass Fiber-Reinforced Polymer Composites: A Study Using Acoustic Emission Technique and Unsupervised Machine Learning Algorithms
by Jorge Palacios Moreno, Hadi Nazaripoor and Pierre Mertiny
J. Compos. Sci. 2025, 9(8), 426; https://doi.org/10.3390/jcs9080426 - 7 Aug 2025
Cited by 6 | Viewed by 1961
Abstract
Recent advancements in composite materials design have made glass fiber-reinforced polymer composites (GFRPC) a viable choice for a wide range of engineering and industrial applications. Although GFRPCs boast attractive characteristics such as low specific mass and high specific mechanical strength, identifying and characterizing [...] Read more.
Recent advancements in composite materials design have made glass fiber-reinforced polymer composites (GFRPC) a viable choice for a wide range of engineering and industrial applications. Although GFRPCs boast attractive characteristics such as low specific mass and high specific mechanical strength, identifying and characterizing damage mechanisms in these materials is challenging. Several scientific studies have examined the root causes of GFRPC failure using various methods, including non-destructive techniques and learning algorithms. Despite this, ongoing investigations aim to accurately detect mechanical defects in GFRPCs. This study explores the use of non-destructive testing (NDT) combined with unsupervised learning algorithms to identify and classify damage mechanisms in GFRPCs. The NDT method employed in this study is acoustic emission (AE), which identifies waveforms associated with various failure mechanisms during testing. These waveforms are categorized using unsupervised learning methods such as principal component analysis (PCA) and self-organizing maps. PCA selects the most appropriate AE descriptors for distinguishing between different damage mechanisms, while the self-organizing maps algorithm performs clustering analysis and classifies failure mechanisms. Scanning electron microscope images of the observed failures are provided to sup-port the findings derived from AE data. Full article
(This article belongs to the Special Issue Machine Learning Applications in the Design and Analysis of Composite Materials)
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Review

Jump to: Research

17 pages, 763 KB  
Review
Review of Predictions of Tensile and Flexural Properties of Fiber-Reinforced Composites Using Machine Learning Models
by Md. Mominur Rahman, Al Emran Ismail, Muhammad Faiz Ramli, Azrin Hani Abdul Rashid, Tabrej Khan, Omar Shabbir Ahmed and Tamer A. Sebaey
J. Compos. Sci. 2026, 10(4), 212; https://doi.org/10.3390/jcs10040212 - 15 Apr 2026
Viewed by 911
Abstract
The Fiber-Reinforced Composites (FRCs) are instrumental in contemporary engineering as they offer a high weight-to-strength ratio as well as durability. They are, however, anisotropic and heterogeneous; as a result it is a major challenge to predict their mechanical properties when subjected to tensile [...] Read more.
The Fiber-Reinforced Composites (FRCs) are instrumental in contemporary engineering as they offer a high weight-to-strength ratio as well as durability. They are, however, anisotropic and heterogeneous; as a result it is a major challenge to predict their mechanical properties when subjected to tensile and flexural loading. Conventional techniques such as experimental testing and finite element analysis are usually resource intensive, time consuming or simplistically constrained. In this review, we explored in detail how the data-driven machine learning (ML) models could overcome these constraints and thus constitute the paradigm shift. It is a synthesis of studies in the use of a broad range of ML techniques such as regression models, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs) and ensemble models for predicting the tensile and flexural properties of FRCs. The analysis shows that although models such as Gaussian Process Regression (GPR), Random Forest (RF) and state-of-the-art neural networks (NNs) have a very high predictive accuracy (often R2 > 0.90), there are issues related to model generalization, data quality and modeling of physical principles. The paper ends with critical research gaps which include over-reliance on single-fiber systems and simulated data, while future directions include hybrid ML–physics models, multiscale modeling and exploration of a wider range of material and environmental variables to facilitate the development of safer and more efficient next-generation composites. Full article
(This article belongs to the Special Issue Machine Learning Applications in the Design and Analysis of Composite Materials)
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