Abstract: Aiming at the problems of low one-time assembly success rates of large aviation structural parts， unpredictable assembly deviations and unpredictable repair quantities， an iterative optimization and evaluation method of repair quantity was proposed based on measured data. With the goal of improving the quality of repairs， the model was reconstructed by integrating the measured data in the assembly processes， and the overall scheme of gradual optimization of repair scheme was proposed. According to the requirements of repair processes， a repair simulation optimization method was proposed based on improved particle swarm optimization algorithm to carry out repair simulation and assembly accuracy prediction， so as to ensure that the assembly accuracy met the requirements first. According to the membership degree theory of fuzzy mathematics， a comprehensive quantitative evaluation of qualitative factors such as repair cost and difficulty was carried out to realize the re-optimization of the repair plans. Finally， taking the position deviation control of the rib bar of a certain type of central wing box assembly station as an example， a software tool for generating a repair plan was developed， and the prediction and repair simulation optimization were carried out before the actual assembly operations. Field applications show that the proposed method may predict the assembly accuracy more accurately， determine an accurate repair plan in advance， and effectively improved the assembly quality and efficiency. 

Key words: aviation structural part, measured data, assembly accuracy prediction, improved particle swarm algorithm, fuzzy comprehensive evaluation

摘要： 针对航空大型结构件一次装配成功率低、装配偏差难以预测、修配量无法精准预知等问题，提出了一种融合实测数据的修配量迭代寻优及综合评价方法。以提高修配质量为目标，融合装配过程实测数据重构模型，提出了修配方案逐步优化的总体方案，并根据修配工艺要求提出了基于改进粒子群算法的修配仿真优化方法，进行修配仿真与装配精度预测，确保装配精度首先满足要求；根据模糊数学隶属度理论对修配成本、难度等定性因素进行综合量化评价，实现了多组修配方案的最终优化。以某型中央翼盒总装站位的肋缘条位置偏差控制为例，开发修配方案生成软件工具，开展实际装配作业前的精度预测与修配仿真优化工作。现场应用表明：所提方法可精确预测装配误差，事先准确确定修配方案，切实提高装配质量及效率。

关键词: 航空结构件, 实测数据, 装配精度预测, 改进粒子群算法, 模糊综合评价