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为了控制装配过程中的关键装配特性,以大尺寸测量技术为辅助,实现大型零部件最优位姿装配,提出基于关键装配特性的大型零部件最佳装配位姿多目标优化算法。该方法将测量辅助装配(MAA)中的关键环节——最佳装配位姿拟合问题分为两步:第1步利用基于奇异值分解的解析方法将测量坐标系与装配现场的全局坐标系进行精确的空间配准,减小了坐标系对齐的误差,并以参考点拟合的偏差为优化目标,求解移动装配体当前位姿;第2步根据装配关键特性相关公差的重要程度,计算装配综合精度要求,并以最小综合偏差为优化目标求解移动装配体间的最佳装配位姿。随后给出了上述两个步骤的粒子群优化算法模型,将每步的待求解位姿作为一个拥有3个旋转自由度与3个平移自由度的粒子进行求解。最后对卫星舱段位姿最优装配问题进行仿真计算,结果证明了该优化算法在控制各项关键特性、提高综合装配质量等方面的有效性。
In order to control the key assembly features in the assembly process, the large size measurement technology is used to achieve the optimal position and attitude assembly of large components. The multi-objective optimization algorithm for the optimal assembly position and attitude of large components based on the key assembly features is proposed. The method divides the key part of the MAA into two parts. The first step is to use the analytical method based on the singular value decomposition to compare the measured coordinate system with the global coordinate system of the assembly site Accurate registration of the space, reducing the error of the alignment of the coordinate system, and taking the deviation of the reference point fitting as the optimization target to solve the current pose of the moving assembly. Step 2: Calculate Assembly accuracy requirements, and to minimize the overall deviation of the optimization objectives for solving the optimal assembly posture between the moving assembly. Then, the particle swarm optimization algorithm model of the above two steps is given, and the pose to be solved of each step is solved as a particle with 3 rotational degrees of freedom and 3 translational degrees of freedom. Finally, the optimal pose of satellite cabin is simulated and the results show that the optimization algorithm is effective in controlling the key features and improving the quality of the integrated assembly.