基于GA-LSSVR的铣削加工变形预测  

Milling Machining Deformation Forecasting Based on GA-LSSVR

作　　者：赵清艳[1] 张超 

机构地区：[1]中山职业技术学院,广东中山528404 [2]广州番禺珠江冷暖气工程公司,广州511400

出　　处：《组合机床与自动化加工技术》2011年第12期57-60,共4页Modular Machine Tool & Automatic Manufacturing Technique

摘　　要：为了解决传统预测方法铣削加工变形预测精度低等问题,文章提出基于遗传算法优化最小二乘支持向量回归法(GA-LSSVR)的铣削加工变形预测方法,首先,提出了基于遗传算法优化最小二乘支持向量回归法的铣削加工变形预测原理,其中通过遗传算法优化最小二乘支持向量回归模型参数,以获取高性能的最小二乘支持向量回归模型。实验结果表明,基于遗传算法优化最小二乘支持向量回归法的铣削加工变形预测精度高于支持向量机。In order to solve the problem of low forecasting accuracy of traditional prediction methods for milling machining deformation,least squares support vector regression optimized by genetic algorithm（GA-LSSVR） is proposed to forecast milling machining deformation in the paper.Firstly,the principle of forecasting milling machining deformation based on LSSVR optimized by genetic algorithm is presented,w here genetic algorithm is used to optimize the parameters of least squares support vector regression, and least squares support vector regression w ith high performance is gained.The experimental results show that the prediction accuracy of GA-LSSVR is higher than that of support vector regression（SVR）.

关 键 词：铣削加工 回归法 最小二乘支持向量机 预测模型 遗传算法 

分 类 号：TH161[机械工程—机械制造及自动化]