Hopkinson拉杆平板挂钩试件结构智能协同优化

与霍普金森拉杆装置中常用的螺纹、胶粘等固定连接方式相比，平板挂钩试件具有连接形式简单、可实现快速组装等优势。针对平板挂钩试件在拉伸过程中因结构几何效应引起的数据测量误差问题，基于影响拉伸试件测量精度的指标:应力平衡达到时间、变形均匀程度、过渡段相对变形以及非轴向力水平，采用正交试验设计、反向传播（back propagation，BP）神经网络与遗传算法相结合的多目标智能协同优化算法对平板挂钩试件的结构参数进行优化，得到了平板挂钩试件最优的结构参数组合，有限元模拟和实验验证了最优结构参数的有效性。该研究结果可为基于平板挂钩试件的霍普金森拉伸实验的数据可靠性分析提供参考。

Intelligent collaborative optimization of structural parameters for hook-sheet specimens used in split Hopkinson tensile bar

Compared with the fixed connection methods such as thread and adhesive commonly used in the split Hopkinson tensile bar experiments, the hook-sheet specimen has the advantages of simple connection form and quick assembly process. Aiming at measurement uncertainty caused by structural geometric effect of the hook-sheet specimen during the stretching process, based on the indicators for measurement accuracy of hook-sheet specimen, such as response of stress equilibrium, deformation uniformity, relative deformation of the transition zones and non-axial stress level, this paper adopted the multi-objective intelligent collaborative optimization algorithm which comprises orthogonal experimental design, back propagation (BP) neural network and genetic algorithm to optimize the structural parameters of hook-sheet specimen. The optimal structural parameters for hook-sheet specimen is thus obtained and the validity of the optimal structural parameters is verified by finite element simulations and experiments. The results provide a reference for data reliability analysis of split Hopkinson tensile bar experiments based on hook-joint sheet specimen.