%0 Journal Article
%T 基于水基MQL的DD5单晶合金铣削表面粗糙度研究
%A 李强
%A 巩亚东
%A 梁彩霞
%A 刘洺君
%J 东北大学学报（自然科学版）
%D 2018
%R 10.12068/j.issn.1005-3026.2018.09.016
%X 摘要 为探究DD5单晶镍基高温合金铣削表面质量，基于响应曲面法及水基微量润滑技术，采用四刃整体立铣刀在(001)晶面上沿［110］晶向进行槽铣实验.以主轴线速度、每齿进给量、切削液流速、空气压强及水油流量比为变量，表面粗糙度R○a为评价指标，基于极差和方差分析，找出显著影响铣削表面质量的冷却和铣削参数，并对其交互效应机理进行深入分析.进而采用逐步回归方法和粒子群优化算法对铣削表面粗糙度进行预测和优化，并基于均匀化设计对预测和优化结果进行评价.</br>Abstract：In order to explore the relative problems of milled surface roughness of DD5 single crystal Ni-based superalloy， based on the response surface method(RSM)and water-based minimum quantity lubrication(MQL)technique， a series of milling experiments on(001)crystal plane along ［110］ crystal direction with the four flute whole end mill were conducted. The main spindle linear speed， tool feed per tooth， cutting fluid flow rate， air pressure and the flow rate ratio of water and oil were selected as the variables， while the surface roughness R○a was chosen as the evaluation indicator. Based on the range and variance analysis， the milling and cooling parameters that significantly affect the surface quality were found out and the interactive effects were deeply studied. Moreover， the surface roughness was predicted and optimized with stepwise regression and particle swarm optimization(PSO)method， respectively. The results were verified based on the uniform design method.
%K DD5
%K 表面质量
%K 响应曲面法
%K 交互效应
%K 逐步回归
%K 粒子群优化&lt
%K /br&gt
%K Key words： DD5 surface quality RSM(response surface method) interactive effect stepwise regression particle swarm optimization(PSO)
%U http://xuebao.neu.edu.cn/natural/CN/abstract/abstract10683.shtml