汽车零部件点云虚拟装配顺序优化分析  

Virtual Assembly Sequence Optimization Analysis of Auto Parts Point Cloud

作　　者：陈志 邢彦锋 周建鹏 曾胜 

机构地区：[1]上海工程技术大学机械与汽车工程学院,上海 [2]蔚来汽车(安徽)有限公司,安徽 合肥

出　　处：《建模与仿真》2023年第3期2243-2251,共9页Modeling and Simulation

摘　　要：虚拟匹配技术对汽车装配过程中车身质量控制及生产效率提高有重要作用。目前,对于多个零件不同点云获得最优匹配顺序的研究尚未开展,本文基于混沌遗传算法进行汽车零件点云匹配顺序优化。首先对PolyWorks二次开发实现匹配区域干涉间隙的自动化测量,然后设置点云匹配准则,建立虚拟匹配优化目标函数,最后应用混沌遗传算法优化点云匹配顺序。结果表明,提出的虚拟匹配优化模型相对于传统装配模型,装配偏差降低了35%,提高了装配质量,降低了制造成本。Virtual matching technology plays an important role in body quality control and production effi-ciency improvement in the automotive assembly process. At present, the research on obtaining the optimal matching order of different point clouds of multiple parts has not yet been carried out, and this paper proposes a chaotic genetic algorithm to optimize the matching order of point clouds of automotive parts. Firstly, the PolyWorks secondary development realizes the automatic measure-ment of the interference gap of the matching region, then sets the point cloud matching criterion, establishes the virtual matching optimization objective function, and finally applies the chaotic ge-netic algorithm to optimize the point cloud matching order. The results show that compared with the traditional assembly model, the virtual matching optimization model proposed in this paper reduces the assembly deviation by 35%, which improves the assembly quality and reduces the production cost.

关 键 词：点云匹配 自动化测量 混沌遗传算法 装配模型 汽车装配 汽车零部件 顺序优化 汽车零件 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]