为提升一种新型交叉簧片柔性微位移机构的动力学性能, 采用响应曲面优化分析对微位移结构参数进行多目标优化设计。首先建立机构的FEA参数化模型, 分析机构的工作原理, 利用ANSYS对微位移机构进行动力学分析; 确定微位移机构的性能指标参数; 采用内切中心复合设计法建立响应曲面模型, 确定优化设计参数; 以交叉铰链簧片厚度作为优化设计变量, 前三阶频率作为目标函数, 微位移刚度作为约束函数, 采用非线性二次规划算法建立多目标参数优化模型, 在优化推荐的三个最优解中进行修正, 得到最终的设计方案。比较优化前后微位移机构的性能指标, 在机构刚度不变的情况下, 一阶频率提升80%, 二、三阶频率提升4%。研究得出以下结论, 簧片厚度对微位移机构动力学性能影响呈正相关, 且不同节点处簧片厚度影响不同, 采用该优化设计方案有效提升了机构的动力学性能。

To achieve dynamic performance improvement of the cross-spring compliant micro-displacement magnifying mechanism(CMDMM), response surface methodology(RSM) was used to optimize the structure parameters for multi-objective analysis. Firstly the parametric analysis FEA model was built, and the working principle of the mechanism was analyzed. Having analyzed the kinematics, dynamics with the ANSYS software, the design performance parameters of the mechanism were determined. The standard response surface were established adopting the inscribed central composite designs methods, and the optimize parameters were extracted. The 8 groups of cross-spring thicknesses were selected to be the design variables. The former three steps natural characteristics were taken as optimal objective. The stiffness of the mechanism was used as the constraint function. A multi-objective optimization design was proposed by Nonlinear Programming by Quadratic Lagrangian(NLPQL) method. Three candidate points were given after thousands iterations. One best candidate point was chose to be corrected as the final design point. The whole optimization program was established from all above steps. Compared the former three steps natural frequency of vibration before and after optimization, the first order frequency raised by 80%, the second and third orders become more than 4% increase. From those discussions above, a positive correlation between thethickness of the cross-spring and the dynamic performance can be concluded. It also showed that the thicknesses from different kinematic pairs had different effects on the dynamic. The response surface methodology was effective in dynamics optimization design.