Parametric study of die sinking EDM process on AISI H13 tool steel using statistical techniques

被引:8
作者
Bose, G. K. [1 ]
Mahapatra, K. K. [2 ]
机构
[1] Haldia Inst Technol, Dept Mech Engn, Haldia, India
[2] Cent Inst Plast Engn Technol, Tech Serv, Bhubaneswar, Orissa, India
来源
ADVANCES IN PRODUCTION ENGINEERING & MANAGEMENT | 2014年 / 9卷 / 04期
关键词
Die sinking EDM; Multi response optimization; Analysis of variance; Response surface methodology;
D O I
10.14743/apem2014.4.185
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The correct optimization of process parameters is one of the more important aspects when taking into consideration the majority of manufacturing processes and particularly for processes relating to electrical discharge machining (EDM). It is capable of machining geometrically complex or hard material components that are precise and difficult-to-machine, such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. The presented study focused on the electric discharge machining (EDM) of AISI H 13, W.-Nr. 1.2344 Grade: Orvar Supreme for finding out the effect of machining parameters such as discharge gap current (GI), pulse on time (POT), pulse off time (POF) and spark gap (SG) on performance responses such as material removal rate (MRR), surface roughness (R-a) and overcut (OC) using a square-shaped Cu tool with lateral flushing. A well-designed experimental scheme was used to reduce the total number of experiments. Parts of the experiment were conducted within the L27 orthogonal array based on the Taguchi method and significant process parameters were identified using analysis of variance (ANOVA). It was found that MRR is affected by gap current and Ra is affected by pulse on time. Moreover, the signal-to-noise ratios associated with the observed values in the experiments were determined by which factor was most affected by the responses of MRR, Ra and OC. These experimental data are investigated using response surface methodology (RSM) for the effects of four EDM parameters GI, POT, POF and SG on MRR, Ra and OC. Response surfaces and contour plots were considered for exploring the importance of the variables and their levels, so as to optimize the responses. Finally multi-response optimization was carried out by means of overlaid contour plots and desirability functions. (C) 2014 PEI, University of Maribor. All rights reserved.
引用
收藏
页码:168 / 180
页数:13
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