Research Projects

The proposed research aims to develop bioinspired functionally graded composite structures (FGCS) using Polyaryletherketone (PAEK)-a high-performance thermoplastic polymer recognized for its exceptional mechanical strength, thermal stability, and resistance to harsh chemical environments [1]. These properties make PAEK an ideal matrix for advanced structural applications. The research will integrate multi-scale reinforcements to emulate the intricate hierarchical architectures found in natural materials known for their superior impact tolerance, such as bone, nacre, and arthropod exoskeletons. To engineer a multifunctional composite, PAEK will be reinforced with: • Short Carbon Fibers (SCF): for enhanced stiffness and load-bearing capacity, • Graphene Nanoplatelets (GNP): to improve toughness and interfacial bonding, • Nano Silica (SiO₂): for superior surface hardness and energy dissipation capabilities. These reinforcements will be spatially distributed in a functionally graded manner, enabling local tuning of mechanical properties across the composite volume. This gradation mirrors natural structural strategies where variation in composition leads to optimized stress distribution, controlled crack propagation, and efficient energy absorption under dynamic loading conditions. The project will leverage Additive Manufacturing (AM)-specifically Fused Deposition Modeling (FDM)-as the primary fabrication route, allowing for layer-wise control over reinforcement concentration, orientation, and architecture. Inspired by Bouligand and helicoidal microstructures, which are known for their resistance to impact and delamination, the printed composites will exhibit engineered anisotropy and gradient-based toughness, ensuring resistance against catastrophic failure in high-strain-rate environments. A significant innovation of this project lies in the development of customized, AM-compatible composite filaments. These will be synthesized through melt-blending or solution-mixing techniques, incorporating reinforcements in strategically designed concentrations. The resulting filaments will be tailored to allow precise spatial deposition during FDM, enabling seamless transition between stiff and tough zones within a single build. The research will establish processing-structure-property (PSP) relationships by systematically investigating how the type, size, morphology, and distribution of reinforcements influence the mechanical behavior under both quasi-static and dynamic loading conditions, including low-velocity and high-velocity impacts. To complement the experimental work, a computational-experimental framework will be developed. This includes: • Finite Element Analysis (FEA): to simulate stress wave propagation, energy absorption, and failure patterns. • Machine Learning (ML) models: to create predictive links between AM process parameters, microstructural features, and mechanical performance. • Digital twin integration: combining real-time experimental data, microstructural imaging (SEM, XCT), and computational models to establish a closed-loop feedback system for continuous optimization of design and manufacturability. This interdisciplinary approach not only enables the design of next-generation impact-resistant structures, but also sets a precedent for data-driven materials engineering in additive manufacturing. The outcomes of this research are expected to advance applications in aerospace, defense, and transportation sectors, where lightweight, damage-tolerant, and high-performance materials are critical.