Open AccessPrediction of EDM Process Parameters for AISI 1020 Steel using RSM, GRA and ANN
Author(s) -
R. Rajesh,
Manoj J. Dev,
M. Dev Anand
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1010.0782s319
Subject(s) - electrical discharge machining , machining , surface roughness , artificial neural network , brittleness , materials science , spark gap , mechanical engineering , voltage , response surface methodology , spark (programming language) , pulse duration , metallurgy , engineering , computer science , composite material , artificial intelligence , machine learning , electrical engineering , laser , physics , optics , programming language
AISI 1020 Steel is hard while machining because of its nature of harness and brittleness. Electrical Discharge Machining (EDM) is a significant technique to machine such materials. Current research examines the pulse current effect (A), discharge voltage (B), pulse on time (C), pulse off time (D),Oil pressure (E)and spark gap(F) on Metal Removal Rate (MRR) and Surface Roughness on EDM of AISI 1020 Steel. Experiments have been carried out in a methodical type taking up nearly 54 successive trails utilizing an EDM machine and a copper electrode of 10mm diameter. Three factors, three levels, Box Bekhen through response surface methodology design was utilized to analyze the outcomes. Gray relational analysis techniques are adopted for finding parameter influencing range for MRR and SR. A multi regression mathematical model was brought up in launching the association between parameters of machining and artificial neural network techniques are used for predicting the optimized parameters.