Optimisation of multiple response characteristics on end milling of aluminium alloy using Taguchi-Grey relational approach

被引:56
|
作者
Pillai, Jayakrishnan Unnikrishna [1 ]
Sanghrajka, Ikshit [1 ]
Shunmugavel, Manikandakumar [1 ]
Muthuramalingam, T. [2 ]
Goldberg, Moshe [1 ]
Littlefair, Guy [3 ]
机构
[1] Deakin Univ, Sch Engn, Fac Sci Engn & Built Environm, Waurn Ponds, Vic 3216, Australia
[2] SRM Inst Sci & Technol, Dept Mech Engn, Kattankulathur, India
[3] Auckland Univ Technol, Fac Design & Creat Technol, Auckland, New Zealand
关键词
Milling; Grey relational analysis; Robotic machining centres; Optimisation; SURFACE-ROUGHNESS; PROCESS PARAMETERS; MACHINING PARAMETERS; CUTTING PARAMETERS; TURNING OPERATIONS; NEURAL-NETWORK; DESIGN; STEEL;
D O I
10.1016/j.measurement.2018.04.052
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Computer Aided Manufacturing improves productivity in the modern manufacturing environment, however optimisation of numerous factors involved in automated manufacturing or material removal environment is critical to produce high quality products. This present study aims to derive a set of optimal process parameter combination for end milling process of Al6005A alloy on a 6-axis robotic machining centre. In addition, the effect of process parameters such as tool path strategic, spindle speed and feed rate on the performance characteristics such as machining time and surface roughness have been studied using Taguchi-Grey relational optimisation method. From the experimental results, it has been found that the tool path strategy has the most considerable influence on the performance characteristics considered, since it can optimise the motion of the robotic machining arm to provide high productivity and product quality. The optimal combination of the process parameters has been estimated using Taguchi-Grey relational analysis and the improvement of performance characteristics has been verified in the confirmation test for optimising milling processes of robotic machining centers.
引用
收藏
页码:291 / 298
页数:8
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