摘要
针对齿轮系统在初始设计阶段普遍存在的振动噪声突出问题,本文以某电驱动系统搭载的二级减速齿轮系统为研究对象,以传递误差波动量最小、啮合线方向振动加速度均方根值最小和啮合线离散点载荷密度最小为优化目标,以齿面修形参数为设计变量,采用NSGA-Ⅱ进行了多目标寻优。基于MASTA软件进行了加载齿面接触分析(LTCA),结果表明,相比于未修形阶段,经过微观修形优化后的齿轮系统啮合传递误差和系统传递误差波动量均大幅降低,齿面载荷分布更加均匀,最大接触应力和轴承座振动加速度幅值均显著降低,系统动力学性能得到整体改善。本文的研究思路和方法可为更广泛的齿轮系统优化设计提供指导。
The prominent vibration and noise problems are commonly exist in the initial design stage of gear system. To solve this problem,this paper takes the two-stage reduction gear system carried by an electric drive system as the research object. The minimum fluctuation of transmission error,minimum root mean square value of vibration acceleration in the direction of meshing line,and minimum load density of discrete points on meshing line are taken as optimization objectives. The tooth surface micro modification parameters are taken as design variable,and the algorithm NSGA-Ⅱ is used to conduct multi-objective optimization. The loaded tooth surface contact analysis(LTCA) is carried out based on the software MASTA. The results show that,compared with unmodified stage,the fluctuation of meshing transmission error and system transmission error after micro modification optimization are significantly reduced,the load distribution on the tooth surface is more uniform,the maximum contact stress and the amplitude of vibration acceleration of the bearing seat are greatly decreased,and the overall dynamic performance of gear system is improved. The research ideas and methods in this paper can provide guidance for wider optimization design of gear system.
作者
杨红波
史文库
陈志勇
郭年程
赵燕燕
YANG Hong-bo;SHI Wen-ku;CHEN Zhi-yong;GUO Nian-cheng;ZHAO Yan-yan(State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China;Automotive Research Institute,China National Heavy Duty Truck(Group Corp.),Jinan 250100,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2022年第7期1541-1551,共11页
Journal of Jilin University:Engineering and Technology Edition
基金
国家重点研发计划项目(2018YFB0106200).
关键词
车辆工程
齿轮系统
齿面修形
遗传算法
优化设计
vehicle engineering
gear system
tooth surface modification
genetic algorithm
optimization design
