基于灰色关联和主成分分析的车削加工多目标优化  

Multi-objective Optimization of Turning Based on Grey Relational and Principal Component Analysis

作　　者：刘春景[1,2] 唐敦兵[1] 何华[2] 陈兴强[2] 

机构地区：[1]南京航空航天大学机电学院,南京210016 [2]蚌埠学院机电系,蚌埠233030

出　　处：《农业机械学报》2013年第4期293-298,292,共7页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金资助项目(51175262);教育部新世纪优秀人才支持计划资助项目(NCET-08);安徽省高等学校优秀青年基金资助项目(2010SQRL117);安徽省自然科学基金资助项目(1308085ME65)

摘　　要：采用田口方法构建以切削速度、进给速度和切削深度为设计变量,以表面粗糙度、切削力和刀具磨损为输出特性指标的车削试验模型,基于灰色关联和主成分分析对车削加工进行多目标优化,灰色关联度计算中的权重系数由输出特性指标的主成分分析获取。钛合金车削加工参数最优的水平组合为A3B1C1,即切削速度为240 m/min、进给速度为0.10 mm/r、切深为0.15 mm,此时表面粗糙度为0.168μm,切削力为163.636 N,刀具磨损为0.129 mm。The turning experimental model was presented with the cutting speed, feed rate and depth of cut as design variables based on Taguchi method. The multi-objective optimization of turning was performed with the surface roughness, cutting force and tool wear as performance characteristics by using combined grey relational analysis and principal component analysis. In order to objectively reveal the relative importance for each performance characteristic in grey relational analysis, principal component analysis was specially introduced here to determine the corresponding weighting values for each performance characteristic. The result analysis showed that cutting speed of 240 m/min, feed rate of 0. 10 mm/r, depth of cut of 0. 15 mm were the optimal cutting parameters. Meanwhile, the optimal performance characteristics were surface roughness of 0. 168 μm, cutting force of 163. 636 N and tool wear of 0. 129 mm.

关 键 词：车削加工 田口方法 灰色关联 主成分分析 多目标优化 

分 类 号：TG506[金属学及工艺—金属切削加工及机床]