基于Kriging模型的圆锥动静压轴承优化设计  

Kriging Model Based Optimization of Conical Hybrid Bearing

作　　者：张鹏飞[1] 张泽斌[1] 郭红[1] ZHANG Peng-fei;ZHANG Ze-bin;GUO Hong(School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001,China)

机构地区：[1]郑州大学机械与动力工程学院,郑州450001

出　　处：《组合机床与自动化加工技术》2021年第2期62-65,68,共5页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金资助项目(51575498);河南省高等学校重点科研项目(20A460004)。

摘　　要：为提高圆锥动静压轴承的综合性能,以单位承载力下功耗最小和平均温升最低为优化目标,考虑几何结构约束条件,采用最优拉丁超立方进行设计空间的布点,进行有限元数值计算。基于计算结果,采用Kriging方法建立目标函数的近似代理模型。在此模型基础上,使用非劣分层遗传算法(NSGA-II)获得Pareto最优解集;最后通过权重系数法求得最优非劣解。结果表明:优化后方案1的两个目标函数值分别较优化前减小了18.8%和10%,优化方案2分别降低了10.9%、32%;轴承无量纲功耗有所降低、无量纲承载力得到提升,温升降低明显,轴承整体性能较优化前有较大提升。To improve the global performance of conical hybrid bearing,the“friction-to-load”ratio and the average temperature rise are taken as optimization objectives,which subjects to several constraints including geometric structure and operating parameters.Kriging surrogate model was established based on a design of experiments method,and Non-dominated Sorting Genetic Algorithm(NSGA-II)was used to obtain the Pareto front.Finally,two optimal solutions are obtained by using a weight coefficient method.The results show that two objective function values of scheme 1 are reduced by 18.8%and 10%respectively compared with before optimization,and optimization scheme 2 is reduced by 10.9%and 32%,respectively.In addition,the non-dimensional power consumption of the bearing and temperature rise have been reduced,the non-dimensional bearing capacity has been improved,and the global performance of the conical hybrid bearing has been successfully improved.

关 键 词：圆锥动静压轴承 KRIGING模型 非劣分层遗传算法 优化设计 

分 类 号：TH133.3[机械工程—机械制造及自动化] TG506[金属学及工艺—金属切削加工及机床]