Development of regression models for wire electrical discharge machining of aluminium metal matrix composites

被引:3
|
作者
Naidu, B. Vishnu Vardhana [1 ]
Varaprasad, K. C. [2 ]
Rao, K. Prahlada [3 ]
机构
[1] JNTUA Ananthapuramu, Anantapur, Andhra Pradesh, India
[2] Sree Vidyanikethan Engn Coll, Micro Machining Res Ctr, Tirupati, Andhra Pradesh, India
[3] JNTUA Ananthapuramu, Dept Mech Engn, Anantapur, Andhra Pradesh, India
关键词
WEDM; AA; 2024; alloy; MMC; Taguchi's method; Design of experiments; Response analysis; Multiple regression; Optimization;
D O I
10.1016/j.matpr.2020.06.586
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Wire Electrical Discharge Machining (WEDM) is a contemporary approach of material removal which is conceived from the concept of Electrical Discharge Machining (EDM), predominantly employed for machining of hard to machine materials and also especially used for making difficult shapes on any electrically conductive work material. AA 2024 is an aluminium alloy, generally used as important material for various engineering applications such as aircraft structural applications. The outstanding properties of AA 2024 alloys makes this material to be best suitable for various engineering applications. The parts employed for aerospace applications needs very much nearer tolerances and more accurate shape and size. In this present study an experimental investigation has been done on WEDM of Al-MMC (Aluminium 2024 alloy + Al2O3). Pulse on time 'Pon' (mu s), Pulse off time 'Poff' (mu s), and peak current (A) are considered as input process parameters in this present analysis. Material Removal Rate (MRR) and Surface Roughness (SR) are considered as preferred performance measure that are need to be improved. Taguchi's approach has been adopted for designing and analyzing the experiments. An L-27 Orthogonal Array (OA) was employed for conducting the experimental runs. The influence of process parameters on desired outputs such as MRR and SR were examined by Taguchi's analysis. Multiple regression analysis has been developed for correlating the relationship amongst the selected input process variable and preferred outputs. (C) 2020 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:236 / 239
页数:4
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