Multi-objective optimization of lightweight composite sandwich panels for enhanced energy absorption and perimeter shear strength

The growing demand for lightweight, high-performance composites necessitates structural optimization to enhance mechanical performance in automotive and aerospace applications. In this study, an intelligent design framework was developed to optimize composite sandwich panels considering mass, bending energy absorption and perimeter shear ultimate load. To investigate interface damage and failure characteristics, a 3D intralaminar progressive damage model based on lamination parameters was employed, which led to an optimized high-strength, lightweight layup design. In multi-objective optimization problems, the Non-dominated Sorting Genetic Algorithm III (NSGA-III), coupled with an artificial neural network-based surrogate model, was performed to identify Pareto-optimal solutions for three-point bending energy absorption, perimeter shear ultimate load, and mass. The proposed method achieves an optimal trade-off between energy absorption and shear ultimate load, resulting in a 46.01% lower coefficient of variation between these two properties compared to the pre-optimized panel. In particular, this method has been comprehensively compared with optimization based on weight and energy absorption or weight and perimeter shear ultimate load, to evaluate the effect on the optimal solution mechanical properties, using these three different approaches. The findings offer valuable guidance for designing lightweight sandwich panels with enhanced load-carrying performance, demonstrating practical value in engineering applications.