Multi-Objective Particle Swarm Optimization of Damping and Global Torsional Stiffness in Composite Vehicle Structures

This study presents a multi-objective optimization framework to enhance both damping performance and global torsional stiffness of composite vehicle structures. Fiber-reinforced polymer matrix composites offer superior stiffness-to-weight ratios and intrinsic damping capacities, making them ideal for lightweight, acoustically efficient, and structurally safe automotive designs. The proposed methodology integrates finite element-based analyses with the modal strain energy method to evaluate damping and static simulations to assess global torsional stiffness. A Multi-Objective Particle Swarm Optimization algorithm is employed, treating the fiber orientations of laminate layers as design variables to identify optimal stacking sequences that balance vibration attenuation and structural rigidity. The optimization process, executed in a high-performance computing environment, enables the parallel evaluation of large-scale finite element models and efficient exploration of the design space. Application to a composite car roof demonstrates that damping capacity can be improved by up to 70% without compromising global torsional stiffness or safety. The optimized Kevlar/epoxy roof achieves stiffness comparable to its steel counterpart while reducing mass by nearly 80%. These findings illustrate the strong potential of multi-objective composite optimization in developing next generation vehicle structures that are lightweight, acoustically refined, and mechanically robust.