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Research on Fatigue and Fracture Behavior of Polymers and Composites

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Polymeric Materials".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 538

Special Issue Editor


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Guest Editor
Department of Mechanical Engineering, University of Tulsa, Tulsa, OK, USA
Interests: design under uncertainty; composite materials; renewable energy; fatigue and fracture; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue aims to showcase the latest advancements in polymer composites, with a particular focus on fatigue and fracture behavior. All polymeric composites are welcome; however, the topics on thermoplastic composites and the integration of machine learning techniques to address sustainability challenges are highly encouraged.

Polymer composites are key in various industries due to their lightweight, high strength, and excellent mechanical properties. Understanding the fatigue and fracture behavior of these composites is crucial for ensuring their reliability and durability in practical applications. This Special Issue aims to present cutting-edge research on fatigue and fracture mechanisms, characterization techniques, and predictive modeling approaches, shedding light on the intricate nature of polymer composites under dynamic loading conditions.

In recent years, thermoplastic composites have emerged as promising alternatives to traditional thermoset composites due to their recyclability, reprocessability, and superior mechanical performance. This Special Issue features studies on fatigue and fracture of polymer composites considering novel processing techniques, property enhancements, and multifunctional applications of thermoplastic composites, providing insights into their potential for sustainable manufacturing and end-of-life options.

Furthermore, this Special Issue explores the integration of machine learning methodologies in predicting fatigue and fracture of polymer composites. By leveraging data-driven approaches, researchers can accelerate material development, optimize processing parameters, and enhance structural performance pertaining to fatigue and fracture. Machine learning algorithms offer valuable tools for predictive modeling, design optimization, and real-time monitoring of polymer composites, contributing to more sustainable and efficient material utilization.

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

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • polymer composites
  • fatigue and fracture
  • thermoplastic composites
  • machine learning
  • sustainability

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Published Papers (1 paper)

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Research

14 pages, 2298 KB  
Article
Seawater and Sunlight as Critical Ageing Factors Affecting the Mechanical Performance of Knitted Swimwear Fabrics
by Gabriela Vanja, Vesna Marija Potočić Matković and Ivana Salopek Čubrić
Materials 2025, 18(23), 5346; https://doi.org/10.3390/ma18235346 - 27 Nov 2025
Viewed by 304
Abstract
This study investigates the effects of seawater and sunlight ageing on the structural and mechanical properties of knitted fabrics designed for swimwear. Nine fabrics with varying polyamide, polyester, and elastane ratios in yarn were subjected to 200 h seawater exposure, with and without [...] Read more.
This study investigates the effects of seawater and sunlight ageing on the structural and mechanical properties of knitted fabrics designed for swimwear. Nine fabrics with varying polyamide, polyester, and elastane ratios in yarn were subjected to 200 h seawater exposure, with and without sunlight, followed by washing and drying cycles to simulate real training and use conditions. The evaluated properties included mass per unit area, thickness, horizontal and vertical density, bursting strength, breaking force, and breaking elongation. Results showed a slight increase in mass and thickness after ageing, reflecting fabric shrinkage in the course direction. Breaking force decreased on average after ageing, with statistically significant reductions in the wale direction under combined seawater and sunlight exposure, whereas shrinkage occasionally produced apparent strengthening effects. Breaking elongation increased in the wale direction and decreased in the course direction, though without statistical significance. Correlation analysis revealed that ageing alters the dependence of mechanical properties on fabric mass per unit area and thickness, with seawater enhancing strength in the wale direction, while sunlight shifted the effects toward the strength in the course direction. These findings demonstrate that seawater and sunlight are critical ageing factors that affect swimwear performance, emphasising the need for their inclusion in durability assessments. Full article
(This article belongs to the Special Issue Research on Fatigue and Fracture Behavior of Polymers and Composites)
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