基于SVR和NSGA-Ⅱ的钛合金铣削参数多目标优化  

Cutting Parameters Multi-object Optimization of Titanium Alloy Milling Process Based on Support Vector Regression and NSGA-Ⅱ

作　　者：向国齐[1] 

机构地区：[1]攀枝花学院资源与环境工程学院,四川攀枝花617000

出　　处：《航空精密制造技术》2016年第5期36-40,共5页Aviation Precision Manufacturing Technology

基　　金：四川省科技支撑计划项目(12YZJ009);四川省教育厅项目(13za0310)资助项目

摘　　要：对钛合金材料Ti6Al4V铣削加工进行有限元数值计算,结合试验设计方法构建了基于支持向量回归机(SVR)的铣削力预测模型,以材料去除率和刀具寿命为优化目标,提出一种基于支持向量回归机和带精英策略的非支配排序遗传算法(NSGA-Ⅱ)的优化方法。结果表明,该方法能够获得满意的Pareto解集,为钛合金铣削参数优化提供一种新的方法,具有良好的推广价值。In this paper, the Titanium Alloy Ti6Al4V milling process is analysized by ifnite element method, a milling force prediction model was established based on Support Vector Regression （SVR）, The optimization design methodology based on SVR and NSGA-II is proposed for Titanium Alloy milling process cutting parameters. The results show that this methodology has a good performance in ifnding satisfying Pareto solutions, and thus can be used in the machining process parameters optimum and other material processing ifelds.

关 键 词：钛合金 支持向量机 NSGA-II算法 多目标优化 

分 类 号：TG506.1[金属学及工艺—金属切削加工及机床]