Investigation and Analysis of MRR in Spark Erosion Machining Through ANN

Authors(2) :-Tushar Bhakte, Venkatesh Nawre

EDM is an advanced machining process for machining, hard material parts which are difficult to machine by conventional machining process. There are various types of products which can be produced by using Die-sinking EDM, such as dies, mould, parts of aerospace, automobile industry and surgical components can be finished machined by EDM. The objective of the paper is to achieve maximum MRR and a good surface integrity in finish cut by optimizing process variables. This paper presents a method that can be used to automatically determine and optimize the processing parameters in the EDM sinking process with the application of artificial neural networks (ANN).In the industrial tool room survey availability of machining data is prime concern in terms of tuned process parameter for precision machining. Experimental investigations are performed to study the effect of pulse current, pulse on time, area of electrode and gap voltage on response of MRR, in case of ram EDM.

Authors and Affiliations

Tushar Bhakte
Mechanical Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India
Venkatesh Nawre
Mechanical Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India

Electric Discharge Machining (EDM), AISI D2 steel, Artificial Neural Network (ANN), Material Removal Rate.

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Publication Details

Published in : Volume 4 | Issue 11 | November-December 2018
Date of Publication : 2018-11-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 01-08
Manuscript Number : IJSRST1184112
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Tushar Bhakte, Venkatesh Nawre, " Investigation and Analysis of MRR in Spark Erosion Machining Through ANN", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 11, pp.01-08, November-December-2018. Available at doi :
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