Open AccessInvestigation and Analysis of MRR in Spark Erosion Machining Through ANN
Author(s) -
Tushar Bhakte,
Venkatesh Nawre
Publication year - 2018
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/ijsrst1184112
Subject(s) - machining , electrical discharge machining , process (computing) , spark gap , aerospace , mechanical engineering , spark (programming language) , artificial neural network , voltage , engineering , computer science , artificial intelligence , electrical engineering , aerospace engineering , programming language , operating system
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.