Multi-objective optimization of laser-welded steel sandwich panels for static loads using a genetic algorithm

We present a methodology for the multi-objective optimization of steel sandwich panels for prescribed quasi-static loads. The steel sandwich panels consist of prismatic V-cores that are bonded to the facings using laser stake welds. Candidate sandwich panel designs are analyzed using geometrically nonlinear finite element analysis. The finite element model is validated by comparing the deflection and stresses for a representative sandwich panel with published experimental and numerical results. Sandwich panels are optimized for multiple, conflicting objectives using an integer-coded non-dominated sorting genetic algorithm. The methodology is illustrated through two optimization case studies. In the first study, we consider a rectangular steel sandwich panel configuration in which the facing segments are bonded to the core segments using double welds and optimize the panel geometry to minimize its deflection and mass. The second optimization study concerns a square steel sandwich panel in which the facings are bonded to the core segments using a single weld. The results demonstrate that the proposed methodology can be used to design lightweight laser-welded steel sandwich panels with superior structural performance.