基于NSGA-Ⅱ的高空作业车伸缩臂截面优化分析  

Optimization analysis of telescopic arm section of aerial work vehicle based on NSGA-Ⅱ

作　　者：金弘睿 王国贤[1] 朱春东[1,2] 康永旺 Jin Hongrui;Wang Guoxian;Zhu Chundong;Kang Yongwang

机构地区：[1]武汉理工大学,武汉430070 [2]随州武汉理工大学工业研究院,随州441300

出　　处：《起重运输机械》2024年第15期33-39,共7页Hoisting and Conveying Machinery

摘　　要：文中对伸缩式高空作业车伸缩臂的截面进行多目标优化,以期达到伸缩臂质量最小、保证安全的目的。首先建立高空作业车上装三维模型并对其进行刚强度分析,然后对其作业工况进行分析,确定了伸缩臂变幅角度为53°时为最危险工况,并在该工况下通过径向基神经网络得到伸缩臂截面优化模型的近似模型,再以质量以及变形最小为目标,利用NSGA-Ⅱ遗传算法进行优化求解,得到伸缩臂截面的最优方案,完成了对伸缩臂的轻量化优化。In this paper,the multi-objective optimization of the cross-section of telescopic boom of a telescopic aerial work vehicle was performed to minimize the mass of the telescopic boom and ensure safety.Firstly,a three-dimensional model of aerial work vehicle was established and its rigidity and strength were analyzed.Then,working condition analysis was conducted,and a luffing angle of 53 degrees of the telescopic boom was determined as the most dangerous working condition.Under this working condition,the approximate model of the telescopic boom section optimization model was constructed by radial basis function neural network.In order to minimize the mass and deformation,NSGA-Ⅱgenetic algorithm was used to optimize the solution,and the optimal proposal of telescopic boom section was obtained,and the lightweight optimization of telescopic boom was completed.

关 键 词：高空作业车 伸缩臂 有限元分析 神经网络 NSGA-Ⅱ 

分 类 号：TH211.6[机械工程—机械制造及自动化]