基于SVR和NSGA-II的钛合金铣削参数多目标优化

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。

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

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.