基于遗传算法工具箱的插铣切削力的预测模型  

Prediction of Cutting Force in Plunge Milling using Genetic Algorithm Toolbox

作　　者：翟元盛[1] 张吉军[2] 胡静姝[1] 李玉甫[1] 

机构地区：[1]哈尔滨理工大学"高效切削及刀具"国家地方联合工程实验室 [2]黑龙江八一农垦大学

出　　处：《工具技术》2014年第4期28-31,共4页Tool Engineering

基　　金：黑龙江教育厅科学技术研究项目:核电材料高效插铣力学建模及有限元分析(12511090)

摘　　要：根据插铣核电材料A508-3钢各向最大切削力的正交实验数据,建立插铣过程的切削参数(切削速度、每齿进给量和径向切削宽度)各向最大切削力预测模型。把切削力预测值与实验值的残差平方和为适合度函数,采用谢菲尔大学研究的遗传算法工具箱实现各向最大切削力预测模型的各项参数。结果表明切削力预测模型与实验值吻合较好。本文目的是在特定插铣切削参数条件下预测切削力。Based on the plunge milling nuclear material A508 - 3 steel experimental results of the maximum cutting force, a predictive model with cutting parameters （cutting speed, feed per tooth and radial cutting width） is established, The residual sum of squares （RSS） is takes as fitness function. Force prediction and experimental forces of the residual sum of squares （RSS） takes as the fitness function, genetic algorithm toolbox sheffield university is used to study the maximum cutting force prediction model parameters of plunge milling. The results showed that the cutting force prediction model is in good agreement with the experimental values. The purpose of this paper is to predict the cutting force in the cutting parameters under the particular conditions of the plunge milling.

关 键 词：切削力 切削参数 遗传算法 插铣 

分 类 号：TG54[金属学及工艺—金属切削加工及机床]