Design of compressed variable stiffness panels with steering-thickness coupling genetic algorithm. Knowing the current state of angle distributions proved to be important while calculating the coupled thickness build-up. The optimization problem is constrained by the critical linear buckling load, herein calculated using finite elements with MSC Nastran ® , and by manufacturing and design constraints, such as the minimum steering radius of AFP and CTS and common design guidelines for laminated composites. The results are compared with an optimized baseline design using conventional straight-fibre laminates to quantify how the design is changed in terms of overall geometry, buckling loads and structural weight.

One of the core technologies in lightweight structures is the optimal design of laminated composite stiffened panels. The increasing tailoring potential of new materials added to the simultaneous optimization of various design regions, leading to design spaces that are vast and non-convex. In order to find an optimal design using limited information, this paper proposes a workflow consisting of design of experiments, metamodeling and optimization phases. A machine learning strategy based on support vector machine (SVM) is used for data classification and interpolation. The combination of mass minimization and buckling evaluation under combined load is handled by a multi-objective formulation. The choice of a deterministic algorithm for the optimization cycle accelerates the convergence towards an optimal design. The analysis of the Pareto frontier illustrates the compromise between conflicting objectives. As a result, a balance is found between the exploration of new design regions and the optimal design refinement. Numerical experiments evaluating the design of a representative upper skin wing panel are used to show the viability of the proposed methodology.