A Data-driven Framework Combining Ahp-topsis and Rsm-based Desirability for Optimal Hybrid Composite Laminate Selection

This study advances the design of high-performance hybrid composites by integrating experimental characterization, multi-criteria decision-making, and statistical optimization. Epoxy-based composites reinforced with jute (J), glass (G), and carbon (C) fibers were fabricated in six distinct stacking sequences and evaluated for tensile strength, flexural strength, tensile modulus, flexural modulus, and break strain. The C5 configuration, featuring carbon fibers on the outer layers and glass fibers internally, demonstrated exceptional flexural strength (227 MPa) and tensile modulus (7.79 GPa), underscoring the critical role of fiber placement in optimizing mechanical performance. While C5 exhibited a marginal reduction in tensile strength (3.7% lower than C1), its balanced properties validated the efficacy of hybrid architectures. AHP-TOPSIS analysis ranked configurations using weighted mechanical criteria, identifying CG4C as optimal for balanced performance. To validate and refine this selection, Response Surface Methodology (RSM) was employed to model nonlinear relationships between stacking parameters and mechanical responses. High predictive accuracy (R² > 0.90 for modulus and break strain) and desirability-based optimization confirmed C5’s superiority, achieving a composite desirability score of 0.57. This work establishes a novel framework bridging decision-theoretic ranking (AHP-TOPSIS) and statistical modeling (RSM), demonstrating their synergistic utility in composite design. The methodology not only identifies optimal configurations but also quantifies trade-offs between strength, stiffness, and ductility, offering a scalable pathway for developing sustainable, application-specific hybrid composites. By validating rankings against RSM-predicted performance regions, this approach enhances confidence in material selection processes for structural and high-stiffness applications in aerospace, automotive, and construction industries.