Abstract：This study optimized the stacking sequence of stiffeners in a composite stiffened panel to maximize the buckling load of the panel assuming a constant mass panel. The number of finite element models was reduced by using a radial basis function neural network (RBF) as the surragate model with the lamination parameters as inputs to estimate the buckling load. The lamination input parameters reduced the nonlinearities of the objective function. Due to the irregular shape of the design space, the D-optimal method was used to determine the sample points for training the RBF. The model errors were reduced by constructing a zoomed RBF to enhance the RBF accuracy near the provisional optimal laminate. A numerical example shows the accuracy and efficiency of the RBF with the lamination parameters as inputs and how the model accuracy is increased by the zoomed RBF near the optimal region.
刘哲, 金达锋, 范志瑞. 基于代理模型的复合材料带加强筋板铺层优化[J]. 清华大学学报（自然科学版）, 2015, 55(7): 782-789.
LIU Zhe, JIN Dafeng, FAN Zhirui. Laminate optimization of a composite stiffened panel based on surrogate model. Journal of Tsinghua University(Science and Technology), 2015, 55(7): 782-789.
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