Modeling of magnetic assisted EDM of EN24 steel using artificial neural network

Authors

  • Utkarsha U. Dabade Veermata Jijabai technological institute, Mumbai, Maharashtra, India
  • Prajakta R. Nalawade Walchand College of Engineering, Sangli, Maharashtra, India
  • Amar S. Bhandare Walchand College of Engineering, Sangli, Maharashtra, India

Keywords:

Artificial Neural Network, Electro Discharge Machining, Material Removal Rate, Electrode Wear Rate, Surface Roughness

Abstract

EDM is a non-conventional method of electro-thermal machining process where electrical energy produces electrical sparks. To learn the performance of the process parameter on the response variable, the experiment was carried out on EN-24 steel with a copper electrode. For analysis, process parameters like current, pulse on time, voltage are considered. The Matlab ANN Toolbox is used for
modeling purpose. ANN Model is developed with Traingdx, Learngdx, MSE, Logsig as training, learning, performance, and transfer functions, using a Feed-forward back-propagation as an algorithm with three nodes in the input layer and one node in the output layer for material removal rate (MRR), electrode wear rate (EWR), surface roughness (SR), using various nodes in hidden layers. Eight networks are tried 3-1-3, 3-3-3, 3-6-3, 3-7-3, 3- 1-3, 3-3-3-3-3, 3-6-6-3 and 3-7-7-3 structure. To predict the value of the response variable, a 3-7-3 network structure is found as best fit for the proposed model.

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Published

01-05-2021

How to Cite

Dabade, U. U., Nalawade, P. R., & Bhandare, A. S. (2021). Modeling of magnetic assisted EDM of EN24 steel using artificial neural network. Manufacturing Technology Today, 20(5-6), 31–37. Retrieved from https://mtt.cmti.res.in/index.php/journal/article/view/15

Issue

Vol. 20 No. 5-6 (2021): May-June 2021

Section

Articles

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All the articles published in Manufacturing Technology Today (MTT) Journal are held by the Publisher. Central Manufacturing Technology Institute (CMTI) as a publisher requires its authors to transfer the copyright prior to publication. This will permit CMTI to reproduce, publish, distribute, and archive the article in print and electronic form and also to defend against any improper use of the article.