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

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.