TC4钛合金材料铣削加工分析及参数优化  

Milling analysis and parameter optimization for TC4 titanium alloy material

作　　者：巩亚东[1] 丁明祥 李响 田近民 GONG Ya-dong;DING Ming-xiang;LI Xiang;TIAN Jin-min(School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819)

机构地区：[1]东北大学机械工程与自动化学院,沈阳110819

出　　处：《吉林大学学报（工学版）》2024年第4期917-925,共9页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(U1908230)。

摘　　要：为探究加工TC4钛合金材料时铣削加工参数对铣削力及材料去除率的影响规律并寻求最佳参数组合,基于正交实验方法设计实验,采用四刃球头铣刀进行侧铣实验。以铣削速度、每齿进给量、铣削深度和铣削宽度为变量,以铣削力和材料去除率为评价指标,基于极差分析法,揭示加工参数对铣削力和材料去除率的影响规律。分别采用灰色关联分析法(GRA)和粒子群优化(PSO)算法进行参数优化,基于回归分析方法建立铣削力预测模型,为PSO做准备。最后,经过试验验证,对两种优化方法进行对比分析,并对预测模型进行验证。结果表明,本文所建立的预测模型可准确高效地对铣削力进行预测,PSO优化效果更好。In order to investigate the influence of milling parameters on the milling force and material removal rate when machining TC4 titanium alloy material and to find the best combination of parameters,side milling experiments designed based on the orthogonal experimental method were carried out with a four-edged ball-ended milling cutter.Milling speed,feed per tooth,milling depth and milling width were chosen as variables,while milling force and material removal rate were selected as evaluation indicators.The influence laws of machining parameters on milling force and material removal rate,based on the range analysis method,were found out.The grey relational analysis(GRA)and particle swarm optimization(PSO)algorithms were adopted to optimize the milling parameters respectively,meanwhile a milling force prediction model was established based on the regression analysis method to prepare for the PSO.Finally,the two optimization methods were compared and analyzed and the prediction model was verified through the verification experiments.The results show that the prediction model established in this paper can accurately and efficiently predict the milling force,and the optimization effect of PSO is much better.

关 键 词：机械制造 钛合金 铣削力 材料去除率 灰色关联分析 粒子群优化 

分 类 号：TH161[机械工程—机械制造及自动化]