多因素影响下基于Bagging-NSGAⅡ的数控铣削稳定性预测与优化研究  

Research on the Stability Prediction and Optimization of CNC Milling Based on Bagging-NSGAⅡ Under the Influence of Multiple Factors

作　　者：邓聪颖 游倩 赵洋 林丽君 殷国富[3] DENG Congying;YOU Qian;ZHAO Yang;LIN Lijun;YIN Guofu(School of Advanced Manufacturing Eng.,Chongqing Univ.of Posts and Telecommunications,Chongqing 400065,China;School of Mechanical Eng.,Chengdu Univ.,Chengdu 610106,China;School of Mechanical Eng.,Sichuan Univ.,Chengdu 610065,China)

机构地区：[1]重庆邮电大学先进制造工程学院,重庆400065 [2]成都大学机械工程学院,四川成都610106 [3]四川大学机械工程学院,四川成都610065

出　　处：《工程科学与技术》2024年第4期238-249,共12页Advanced Engineering Sciences

基　　金：国家自然科学基金项目(51705058);四川省科技计划资助项目(2023YFQ0035);重庆市教委科学技术研究项目(KJQN202300640,KJZD-K202300611)。

摘　　要：数控机床铣削过程中出现的颤振失稳是影响数控机床加工效率和加工质量的关键因素。铣削稳定性与工艺参数、工艺系统动力学特性密切相关,而工艺系统动力学特性又随加工位置、刀具悬伸量的变化或刀具的更换而变化。因此,针对多因素影响下的铣削稳定性预测和无颤振工艺参数选择问题,本文以数控机床各向移动部件位置、刀具直径、刀具悬伸量和切削参数为变量,提出一种基于引导聚集算法(Bagging)与带精英策略的快速非支配排序遗传算法(NSGA-Ⅱ)的切削稳定性预测与工艺参数优化方法。该方法首先采用正交实验设计离散数控机床的工作空间,在每个加工位置对不同悬伸量下的刀具进行锤击实验,由此得到各把铣刀对应的刀尖点频率响应函数;然后,在不同工艺参数方案下进行铣削稳定性理论预测,进而引入Bagging算法建立以各向运动部件位置(x,y,z)、刀具直径d、刀具悬伸量h、主轴转速n、切削宽度a_(e)、每齿进给量f_(z)为输入的极限切削深度a_(plim)预测模型;在此基础上,采用该Bagging模型作为铣削稳定性约束,以加工位置和工艺参数(x,y,z,d,h,n,a_(p),ae,f_(z))为优化变量,建立最大材料切除率和刀具寿命的多目标优化模型,采用NSGA-Ⅱ算法求解该模型得到Pareto最优解集,并结合熵权法和优劣解距离法(TOPSIS)选出Pareto解集中的最佳解。以一台三轴立式加工中心展开实例分析,所建极限切削深度Bagging模型的预测误差为2.99%,且铣削加工实验表明获取的(x,y,z,d,h,n,a_(p),ae,f_(z))最优配置可实现稳定铣削,验证所提方法的可行性和有效性。The occurrence of chatter in the milling process is a key factor limiting the efficiency and quality of machining.The stability of milling depends mainly on the process parameters and the dynamic characteristics of the tool-workpiece system;however,the system dynamics vary with the machining position and tool properties.Considering these multiple influencing factors,herein,a method is proposed to predict the milling stability and determine optimal machining parameters based on a bootstrap aggregating(bagging)procedure and the non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ).First,an orthogonal experimental design is used to divide the working space of the machine tool into different machining positions.Under each position,impact testing is then carried out at the tool tip for different tool-overhang lengths to obtain the corresponding frequency response functions(FRFs).Then,limiting axial cutting depth a_(plim) values are theoretically predicted using the tool-tip FRFs and machining parameters.Using sample information,the bagging algorithm is applied to establish a model for predicting a_(plim),in which the inputs are the displacements of the moving parts(x,y,z),tool diameter(d),tool-overhang length(h),spindle speed(n),cutting width(a_(e)),and feed rate per tooth(f_(z)).Taking these process parameters(x,y,z,d,h,n,a_(p),a_(e),f_(z))as design variables,a multi-objective optimization model is constructed to balance machining efficiency and tool life.Additionally,the pre-established aplim prediction model is used to express the milling-stability constraint.The multi-objective optimization model is then solved using NSGA-Ⅱ,and the Pareto-optimal set is obtained.Finally,the entropy weight method and the technique for order preference by similarity to an ideal solution(TOPSIS)are combined to select a unique optimal solution from the Pareto-optimal set.A three-axis vertical machining center was used to carry out a case study.The prediction accuracy of the established bagging model for a_(plim) was 2.99%,and no cha

关 键 词：铣削稳定性 工艺参数优化 多目标优化模型 刀具悬伸量 引导聚集算法 NSGA-Ⅱ遗传算法 

分 类 号：TG54[金属学及工艺—金属切削加工及机床]