摘要
电动轮矿用汽车低速大转矩的运行特性需要传动比较大的减速器,因此行星轮式轮边减速系统被广泛应用。针对二级行星轮式减速器结构特点,采用传统设计方法对行星齿轮系统进行可靠度分析,根据已计算的可靠度对齿轮系统进行参数优化。将系统可靠度最大转化为各齿轮的可靠度最大,作为多个目标函数,在实际工况的基础上建立约束条件,利用MATLAB多目标优化函数进行求解,获得最佳的齿轮参数。根据得到的行星齿轮系统的参数,对优化后整套减速系统的可靠性进行分析。分析结果可知,优化设计后,传递效率为0.946,传递比为30.54,满足使用要求;二级太阳轮齿面接触强度可靠性系数为2.52,二级内齿圈齿根弯曲疲劳强度可靠性系数为2.59,二级齿轮可靠度为0.989,可靠性明显提高;主要零件的安全系数等级高;二级减速系统各齿轮的寿命达到设计要求;为同类行星齿轮系统的优化设计提供参考。
The low speed and high torque of the electric drive wheel truck requires a relatively large transmission ratio gear reducer,so the planetary gear wheel hub reducer system is widely used in such truck. According to the structural characteristics of the two-stage planetary gear wheel hub reducer,the traditional design method is used to analyze the reliability of the planetary gear system,and the parameters of the gear system are optimized according to the calculated reliability. The system maximum reliability is changed into the gear reliability maximum as multiple objective functions. Based on the actual conditions,the constraints are established. By using the MATLAB,the multi-objective optimization function is solved,the optimal parameters of the gear are obtained. Based on the optimal parameters of the planetary gear system,the reliability of the whole system is analyzed. The analysis results show that,after the optimized design,transfer efficiency is 0. 969,and the transfer ratio is 30. 54,which meet the requirements. The sun gear tooth surface contact strength reliability coefficient is 2. 52,and the second stage ring gear tooth root bending fatigue strength reliability coefficient is2. 59,and the second stage gear reliability is 0. 989,so the reliability is obviously improved. The safety level of coefficient parts is high. The service life of the system can meet the design requirements,which provide reference for the optimization design of the similar planetary gear system.
出处
《机械传动》
CSCD
北大核心
2017年第7期54-61,共8页
Journal of Mechanical Transmission
基金
河北省自然科学基金计划项目(D2015000669)
关键词
轮边减速器
行星轮式
可靠性
模型
优化设计
Wheel hub reducer
Planetary gear
Reliability
Model
Optimization design