Optimization of stiffened panels via laminate smeared stiffener method and surrogate model

Stiffened panels are widely used in engineering due to their high strength-to-weight ratio. However, parametric correlation analysis and optimization of such panels typically require extensive datasets, and even finite element analysis (FEA) suffers from high computational costs and time-consuming processes. To address these challenges, this study proposes a method based on the Laminate Smeared Stiffener Method (LSSM), which simplifies data acquisition and structural optimization. By equivalently representing multiple stiffened panels of varying dimensions as a single laminated panel, the LSSM eliminates the need for repetitive geometric modeling, meshing, and stiffness matrix generation in FEA. Parametric FEA was employed to extract the maximum deformation and buckling critical load for each dimensional configuration, and a corresponding dataset was constructed. Four regression algorithms were trained on this dataset, with Ridge regression demonstrating the highest prediction accuracy. This algorithm was selected to establish a surrogate model for stiffened panel FEA. Subsequently, the optimization design of stiffened panels was carried out using a genetic algorithm with the surrogate model as the fitness function. The results demonstrate that the LSSM efficiently generates the large datasets required for optimization. In single-objective optimization, while maintaining the original design volume, the optimized panels exhibited a 47.97% improvement in anti-deformation capability and an 87.33% increase in buckling load capacity. For multi-objective optimization, the volume was reduced by 27.60% without compromising performance. This method significantly reduces computational costs and optimization times compared to traditional FEA-genetic algorithm approaches, offering an efficient and accurate solution for stiffened panel optimization.