摘要
为降低差速器齿轮在传动过程中的振动和噪声,采用了行星齿轮齿廓修形和偏心螺旋线修形。通过KISSSoft分析了齿廓修形量、螺旋线修形量、螺旋线修形因子Ⅰ和螺旋线修形因子Ⅱ对传动误差峰值差、齿面最大接触应力、行星齿轮齿根弯曲应力和半轴齿轮齿根弯曲应力的影响,通过Minitab建立4个响应量的回归方程,得到以传动误差峰值差最小、齿面最大接触应力最小以及行星齿轮齿根弯曲应力不大于1100 MPa为目标的修形方案,修形后传动误差峰值差降低了11.39%,齿面最大接触应力下降了3.89%,行星齿轮和半轴齿轮的齿根弯曲应力分别下降了4.40%和5.62%。最后,采用有限元仿真分析,验证了修形后的差速器齿轮的疲劳寿命满足要求,并通过台架试验进行了验证。
In order to reduce vibration and noise,the modification of the tooth profile of the planetary gear and the eccentric helix are studied.The influence of tooth profile modification,helix modification,helix modification factorⅠand helix modification factorⅡon transmission error peak difference,tooth surface maximum contact stress,tooth root bending stress of planetary gear and tooth root bending stress of half-shaft gear are analyzed respectively by KISSSoft.The regression equations of four response quantities are established by Minitab,and the optimal modification scheme is obtained under the condition of minimum transmission error peak difference,minimum contact stress on the tooth surface and the bending stress at the tooth root of the planetary gear is no more than 1100 MPa.After modification,the peak difference of transmission error is reduced by 11.39%,the maximum contact stress of tooth surface is reduced by 3.89%and the bending stress of tooth root of planetary gear and half-shaft gear is reduced by 4.40%and 5.62%respectively.Finally,finite element simulation analysis is used to verify that the fatigue life of the modified differential gear meets the requirement,and the bench experiment is carried out to verify.
作者
封旗旗
王陶
王良模
夏汉关
董义
王耀祖
Feng Qiqi;Wang Tao;Wang Liangmo;Xia Hanguan;Dong Yi;Wang Yaozu(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210018,China;Jiangsu Pacific Precision Forging Co.,Ltd.,Jiangyan 225500,China)
出处
《机械传动》
北大核心
2020年第9期153-158,共6页
Journal of Mechanical Transmission
基金
国家科技重大专项(2018ZX04024001)
中央高校基本科研业务费专项资金资助(309181B8809,30919011203)。
关键词
数值仿真
齿轮修形
回归模型
多目标优化
疲劳寿命
Numerical simulation
Gear modification
Regression model
Multi-objective optimization
Fatigue life
