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 11
Special Issue Editor
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
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Keywords
- polymer composites
- fatigue and fracture
- thermoplastic composites
- machine learning
- sustainability
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