Due to the functional requirements of structures, a large number of thin-walled structures with cutouts are adopted in the structural design of aviation, aerospace, shipbuilding and other fields, leading to a significant reduction in the bearing capacity of such structures. Although the curved stiffening method has great potential in improving the load-bearing performance of open structures, the sharp increase in design variables presents a challenge for structural optimization.The data-driven deep learning method is used to optimize the design of hierarchical stiffened thin-walled structures with cutouts reinforced by curvilinear stiffeners.

For structures with cutouts, the hierarchical curvilinearly stiffened method is designed, and the image representation method of structural parameters is proposed. The deep learning network model for structural response feature-learning is established to realize structural optimization design under data-driven conditions.

The results show that compared with the classical surrogate models constructed by structural numerical parameters, the prediction accuracy of the proposed structural response feature-learning model based on image recognition is improved roughly twofold. In the optimization design of structures based on the learning model, the bearing capacity of hierarchical orthogonal stiffened structures increased by 10.78%, and the bearing capacity of hierarchical curvilinearly stiffened structures increased by 18.19%.

The results show that this deep learning-based structural optimization method is more effective for hierarchical stiffened structures with large numbers of design variables and dynamic changes in the number of design variables. Compared with traditional straightly stiffened panels, the curvilinearly stiffened panel is more effective in strengthening the bearing capacity of thin-walled structures with cutouts.