Open AccessReview on MRR in Spark Erosion Machining (SEM) Through ANN
Author(s) -
Rahul Shrinivasan,
Tushar Bhakte
Publication year - 2022
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/ijsrst229138
Subject(s) - machining , electrical discharge machining , spark gap , artificial neural network , spark (programming language) , process (computing) , surface integrity , mechanical engineering , computer science , voltage , manufacturing engineering , engineering , artificial intelligence , electrical engineering , programming language , operating system
EDM is a modern machining technology used to machine hard material pieces that are difficult to produce using traditional machining methods. This article aims to optimize process factors to obtain maximum MRR and high surface integrity in the end cut. Using artificial neural networks, this research proposes a technique for automatically determining and optimizing processing parameters in the EDM sinking process (ANN). The availability of machining data in the industrial tool room survey is the primary issue regarding adjusted process parameters for precision machining. Experiments are conducted to investigate the effect of pulse current, pulse on time, electrode area, and gap voltage on MRR response.