Aiming at the problems of demagnetization effect of electromagnetic buffer (EMB) caused by high velocity under intensive impact load and the difficulty and error of machining composite thin-walled long tube, a segmented EMB is proposed. The inner tube and air-gap are divided into initial segments and the traversing segments. Through theoretical analysis, impact test and simulation, it can be found that the RRF curve has two peaks. Firstly, in order to reduce the resultant resistance force (RRF) peaks, the sensitivity analysis based on optimal Latin hypercube design (OLHD) and polynomial regression was performed. The results show that the smallest contribution ratio to the dynamic response is the seventh and ninth segments of the inner tube, which are less than 1%. Then, fully considering the uncertain factors, important parameters are selected for uncertain optimization after sensitivity analysis. The interval order and interval probability degree methods are used to establish interval uncertain optimization model of the RRF considering robustness. The model was solved using an interval nested optimization method based on radial basis function (RBF) neural network. Finally, the Pareto front is obtained and numerical simulation is performed to verify the optimal value. It indicates that the two kinds of RRF peak is obviously reduced, and the optimization object and strategy are effective.

针对高强度冲击载荷下高速产生的电磁缓冲器（EMB）的消磁效果以及复合薄壁长管加工的难点和误差，提出了一种分段式EMB。内管和气隙分为初始段和横向段。通过理论分析，冲击试验和仿真，可以发现RRF曲线有两个峰。首先，为了减少合成阻力（RRF）的峰值，进行了基于最佳拉丁超立方体设计（OLHD）和多项式回归的灵敏度分析。结果表明，对动态响应的最小贡献率是内管的第七段和第九段，小于1％。然后，充分考虑不确定因素，在进行敏感性分析后，选择重要参数进行不确定性优化。考虑到鲁棒性，采用区间序和区间概率度方法建立了RRF的区间不确定性优化模型。使用基于径向基函数（RBF）神经网络的间隔嵌套优化方法对模型进行求解。最后，获得帕累托前沿，并进行数值模拟以验证最优值。这表明两种RRF峰值明显降低，优化目标和策略是有效的。使用基于径向基函数（RBF）神经网络的间隔嵌套优化方法对模型进行求解。最后，获得帕累托前沿，并进行数值模拟以验证最优值。这表明两种RRF峰值明显降低，优化目标和策略是有效的。使用基于径向基函数（RBF）神经网络的间隔嵌套优化方法对模型进行求解。最后，获得帕累托前沿，并进行数值模拟以验证最优值。说明两种RRF峰值明显降低，优化目标和策略是有效的。