Review on fiber composites for sustainable high strain rate applications

Abstract

Over the past two decades, the growing demand for sustainable, high-performance materials has driven significant advancements in fiber reinforced polymer composites (FRPCs), particularly for dynamic and ballistic applications. This review provides an integrated overview of recent developments, highlighting sustainable reinforcements, novel stacking configurations, and advanced machine learning (ML) predictive approaches. Particular emphasis is placed on bio-inspired helicoidal laminates and hybrid architectures, which offer superior energy absorption, damage tolerance, and impact mitigation. Hybrid laminates incorporating satin weaves, high-modulus fibers, and compliant matrices enhance post-impact toughness and better structural integrity. Additionally, embedding a high-hardness (70–90 Shore A) rubber core with a compliant matrix mitigates conical crack propagation, improves strain rate sensitivity, and reduces delamination under both low- and high-velocity impacts. The dynamic response of FRPCs is examined using experimental techniques such as Split Hopkinson Pressure Bar (SHPB) testing and impact assessments, revealing the influence of design variables on strain-rate-dependent behavior. To support material selection and design optimization in fiber composites, ML techniques, Ashby charts, and Multi-Criteria Decision-Making (MCDM) frameworks are explored, balancing performance, sustainability, and manufacturability. Failure mechanisms such as delamination, fiber pull-out, and matrix cracking are analyzed with respect to hybridization strategies and environmental effects. Finite element analysis (FEA) tools, including ABAQUS, ANSYS AUTODYN, and LS-DYNA, are reviewed for their predictive accuracy, validated against experimental results. Standardized testing protocols (ASTM D7136, D7137, F1342; NIJ-0101.06) ensure the consistent evaluation of both flexible and rigid armor systems. The review also discusses manufacturing advancements such as resin transfer molding (RTM) and filament winding, which improve scalability and reduce waste. Non-destructive testing (NDT) methods, including acoustic emission, ultrasonic C-scanning, and X-ray CT, are highlighted for real-time damage assessment. Finally, integrating ML algorithms such as MLP, SVM, RF, and CNN with experimental and simulation data enhances predictive modeling, damage classification, and tailored composite design. This convergence of bio-inspired design, computational tools, and intelligent systems is accelerating the development of next-generation FRPCs for aerospace, defense, automotive, and civil engineering applications. © 2025 The Authors