面向高生产率低成本的镗削切削用量多目标优化  

Multi-objective Optimization Model of Boring Parameters for High Productivity and Low Cost

作　　者：杨宁 李峰[1] 吴瑶[1] 胡明茂[1] Yang Ning;Li Feng;Wu Yao;Hu Mingmao(School of Mechanical Engineering,Hubei University of Automotive Technology,Shiyan 442002,China)

机构地区：[1]湖北汽车工业学院机械工程学院,湖北十堰442002

出　　处：《湖北汽车工业学院学报》2021年第3期54-57,62,共5页Journal of Hubei University Of Automotive Technology

基　　金：湖北省重点研发计划项目(TC200802C,TC200A00W);湖北汽车工业学院博士基金(BK201801)。

摘　　要：切削用量的合理选择对提高镗床生产率、降低成本有很大影响,在考虑机床和切削参数的约束下,建立了面向高生产率低成本的镗削参数多目标优化模型。在模型优化部分选取改进NSGA-Ⅱ算法,获得帕累托优化解集,最后应用模糊物元法对解集进行评价分析,得到最优解。结果显示:参数优化后生产率相较于低成本优化提高12.9%,总成本相较于高生产率优化降低24.4%。The reasonable choice of cutting parameters has a great impact on improving the productivity of boring machines and reducing costs.Taking into account the limitations of the machine and cutting parameters,a multipurpose model for optimizing boring parameters was created to ensure high productivity and low cost.In the model optimization part,the improved NSGA-Ⅱalgorithm was selected to obtain the Pareto optimization solution set.Finally,the fuzzy matter element method was used to evaluate and analyze the solution set,and the optimal cutting amount was obtained.The results show that the productivity after parameter optimization is 12.9%higher than that of the low-cost optimization,and the total cost is 24.4%lower than that of the high productivity optimization.

关 键 词：镗削 高生产率 低成本 改进NSGA-Ⅱ 模糊物元 

分 类 号：TH162[机械工程—机械制造及自动化]