Application of Data-driven Optimized Evolutionary Learning in the Design of Filament-wound Conical Pressure Vessels

This study presents an innovative approach to designing filament-wound conical pressure vessels loaded by internal pressure, combining analytical computations based on classical lamination theory with data-driven evolutionary algorithms. The most critical area of the whole cone is the balanced layer at the lower base, in which the safety level is evaluated using the failure index based on Hoffman’s strength criterion. The designed mathematical model related the geometrical parameters of the cone to the magnitude of internal pressure, representing the performance of the structure and safety expressed by the failure index. The optimization loop runs on two modules –Evolutionary Deep Neural Networks EvoDN2 module for machine learning and creating a lighter surrogate model, together with Constrained Reference Vectors Evolutionary Algorithms (cRVEA) for multicriteria optimization. This was applied to three different material configurations: E-glass/DA4518U (glass-epoxy), T300/N5208 (carbon-epoxy) and AS4/3501-6 (carbon-epoxy). The results show that the critical area is invariant to the material configuration. In addition, it was shown that the material configurations with higher anisotropy achieve a more uniform distribution of the load in the layers of the conical three-layered wall.