Variable-angle filament-wound (VAFW) cylinders are herein optimized for minimum mass under manufacturing constraints, and for various design loads. A design parameterization based on a second-order polynomial variation of the tow winding angle along the axial direction of the cylinders is utilized to explore the nonlinear steering-thickness dependency in VAFW structures, whereby the thickness becomes a function of the filament steering angle. Particle swarm optimization coupled with three Kriging-based metamodels is used to find the optimum designs. A single-curvature Bogner–Fox–Schmit–Castro finite element is formulated to accurately and efficiently represent the variable stiffness properties of the shells, and verifications are performed using a general purpose plate element. Alongside the main optimization studies, a vast analysis of the design space is performed using the metamodels, showing a gap in the design space for the buckling strength that is confirmed by genetic algorithm optimizations. Extreme lightweight while buckling-resistant designs are reached, along with non-conventional optimum layouts thanks to the high degree of thickness build-up tailoring.

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A reliability-based optimization framework is introduced and used to design filament-wound cylindrical shells with variable angle tow. Seven design cases are investigated to enable a comparison between constant-stiffness and variable angle tow designs, also considering effects of thickness variation created due to overlapping tow paths, determined using the kinematics of the filament winding manufacturing process. The uncertainty in the winding angle is considered in the optimization by means of metamodels constructed using the Kriging method. Moving search windows are incorporated into the Kriging metamodel to accelerate its convergence by reducing the number of training iterations. The results prove the efficacy of the proposed framework and clearly demonstrate the advantage of variable-stiffness designs over conventional ones for achieving a maximum load carrying capacity, while keeping the robustness of the design towards manufacturing uncertainties.@en