A Design And Development Of An Intelligent Information System For Electric Discharge Machine

A Design And Development Of An Intelligent Information System For Electric Discharge Machine
Authors : M.Venkata Naveen, K.L.N. Murthy, T.S.S.R.Krishna
Publication Date: 30-08-2012


Author(s):  M.Venkata Naveen, K.L.N. Murthy, T.S.S.R.Krishna

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Vol.1 - Issue 6 (August- 2012)

e-ISSN:   2278-0181


The electrical discharge machining (EDM) process has been extensively used in machining hard, high- strength and temperature-resistant materials. The material is removed rapidly and repeatedly by spark dischargers across gap between the tool and the work piece. In EDM, it is important to select machining parameters for achieving optimal machining performance. Usually, the desired machining parameters are determined based on experience or on handbook values. However, this does not ensure that the selected machining parameters result in optimal or near optimal machining performance for that particular electrical discharge machine and environment. The important output parameters of the process are the material removal rate (MRR) and surface roughness (Ra). And the input parameters are peak current (I), pulse on-time (T-ON) and pulse off-time (T-OFF).In this thesis, Analysis of variance (ANOVA) technique was used to ?nd out how various parameters affecting the surface roughness and material removal rate. Results from the analysis show that peak current and pulse-off time are signi?cant variables to the surface roughness. The surface roughness of the test specimen increases when these two parameters increase. And peak current, pulse-on time and pulse-off time are signi?cant variables for material removal rate. The material removal rate of the test specimen increases when peak current increase and decrease when pulse-on time and pulse-off time increase. Finally for the prediction and optimal selection of process parameters in small deep hole drilling EDM a mathematical model was developed using regression analysis to formulate the input parameters to the output parameters. The developed model was validated with a set of experimental data, and the verified experimentally, and the amounts of relative errors where calculated. The errors are all in acceptable ranges, which, again, confirm the feasibility and effectiveness of the adopted approach.


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