Structural optimization models often feature many uncertain factors, which can be handled by robust optimization. This work presents a complete robust optimization program for composite blade based on the kriging approximation model. Two case studies were given and performed using a genetic algorithm. The first being typical optimization, where the first natural frequency of the blade is selected as the optimized objective and the optimal sizing distribution for the entire blade shell is sought to ignore the uncertain factors. The other case determines the standard deviation of the optimized objective in the first case as another optimization goal. Moreover, a 6σ robustness for the optimization results of the two cases was evaluated. The result shows that typical optimization increases the first natural frequency of the blade by 19%, while its robustness level has a reduction of 61% compared with the first blade. Nevertheless, the robust optimization not only results in an increment of 15.4% in the first natural frequency of the blade but also increases its robustness level by up to 90%. Therefore, the proposed approach can effectively improve optimization objectives, especially reduce the impacts of uncertainties on the objective functions.

结构优化模型通常具有许多不确定因素，可以通过鲁棒优化来处理。这项工作提出了一个基于克里金近似模型的复合材料叶片的完整鲁棒优化程序。给出了两个案例研究，并使用遗传算法进行了研究。第一个是典型的优化，其中选择叶片的第一固有频率作为优化目标，并寻求整个叶片壳体的最佳尺寸分布，以忽略不确定因素。另一种情况将第一种情况下的优化目标的标准偏差确定为另一个优化目标。而且，一个6σ评估了两种情况下优化结果的鲁棒性。结果表明，典型的优化将叶片的第一自然频率提高了19％，而其健壮性水平则比第一叶片降低了61％。然而，鲁棒性优化不仅会导致叶片的第一自然频率增加15.4％，而且还会将其鲁棒性水平提高多达90％。因此，所提出的方法可以有效地改善优化目标，尤其是减少不确定性对目标函数的影响。