Abstract: Composite wings have been widely used in engineering, the optimization of the ply is essential to improve the carrying capacity of the structure. In this paper, a neural network is trained by samples, and then the neural network mode is used to quickly predict the design result. A genetic algorithm evolution operation is used according to the prediction result to search for the optimal solution of the composite laminate design problem and accelerate the design efficiency of the laminate. Applying deep learning technology to quickly solve the optimal layup order of laminates, and comparing with the optimization results obtained by traditional optimization algorithms, it proves a convenience and effectiveness of deep learning technology in the optimization of laminates. The trained neural network can quickly predict the layering sequence optimization index and provide guidance for the optimization process, which has certain guiding significance for further development of the potential and engineering design of the laminate.

Key words: deep learning, genetic algorithm, multi-task learning, layup optimization, skin design