南京航空航天大学学报（英文版）2021，Vol.38Issue(3)：462-473,12.
基于云滴粒子群优化算法的多道次端铣削高效稳定切削参数优化方法
Efficient and Stable Optimization of Multi?pass End Milling Using a Cloud Drop?Enabled Particle Swarm Optimization Algorithm
摘要
Abstract
Optimization of machining parameters is of great importance for multi-pass end milling because machining parameters adversely or positively affect the time and quality of production. This paper develops a second-order full-discretization method(2ndFDM)-based 3-D stability prediction model for simultaneous optimization of spindle speed, axial cutting depth and radial cutting depth. The optimal machining parameters in each pass are obtained to achieve the minimum production time comprehensive considering constraints of 3-D stability,machine tool performance,tool life and machining requirements. A cloud drop-enabled particle swarm optimization(CDPSO)algorithm is proposed to solve the developed machining parameter optimization,and 13 benchmark problems are used to evaluate CDPSO algorithm. Numerical results show that CDPSO algorithm has a certain advantage in computational cost as well as comparable search quality and robustness. A demonstrative example is provided.关键词
切削参数/多道次端铣削/颤振稳定性/粒子群优化/云模型Key words
machining parameter/multi-pass end milling/chatter stability/particle swarm optimization (PSO)/cloud model分类
矿业与冶金引用本文复制引用
蔡旭林,杨文安,黄超..基于云滴粒子群优化算法的多道次端铣削高效稳定切削参数优化方法[J].南京航空航天大学学报（英文版）,2021,38(3):462-473,12.基金项目
This work is supported partially by the National Science Foundation of China(No.51775279),National Defense Basic Scientific Research Program of Chi?na(No.JCKY201605B006),Fundamental Research Funds for the Central Universities(No.NT2021019),Jiangsu In?dustry Foresight and Common Key Technology(No.BE2018127). （No.51775279）
