【摘要】 复合材料在航空工程中有着广泛的应用,其中铺层优化对提高结构承载能力至关重要。本文中首先运用基于神经网络的深度学习方法快速预测设计结果,然后依据预测结果,通过遗传算法搜索复合材料层合板设计问题的最优解,提高铺层设计效率,最后对比应用深度学习技术与传统优化算法得到的优化结果,证明了深度学习技术在层合板铺层优化中的便捷性和有效性。更多还原

【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.更多还原