Modeling of Surface Roughness in Wire Electrical Discharge Machining Using Artificial Neural Networks

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ISSN: 2278-0149 (Online)
Short Title: Int. J. Mech. Eng. Robot. Res.
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Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.

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Home > Articles > All Issues > 2013 > Volume 2, No. 1, January 2013 >

P Vijaya Bhaskara Reddy1 , Ch R Vikram Kumar1, and K Hemachandra Reddy2

1.Department of Mechanical Engineering, N.B.K.R, I.S.T, Vidyanagar, Nellore (Dt.), Andhra Pradesh 524413, India.
2.J N T University College of Engineering, Anantapur, Andhra Pradesh, India.

Abstract—In this paper the Artificial Neural Network (ANN) model is developed to predict the surface roughness in Wire Electrical Discharge Machining (WEDM) of WP7V steel, which is used in automobile industry. The neural network model is trained with experimental results conducted using L16 orthogonal array by considering the input parameters such as pulse duration, open voltage, wire speed and dielectric flushing pressure at four different levels. The mathematical relation between the work piece surface roughness and WEDM cutting parameters is also established by multiple regression analysis method. Predicted values of surface roughness by NN and regression analysis, are compared with the experimental values and their closeness with the experimental values. The predicted values in neural network with two hidden layers are very close to the experimental results than regression values. The complete experimental and modeling results are presented and analyzed in this paper

Index Terms—Wire EDM, Multiple regressions, ANN.wp7v

Cite: P Vijaya Bhaskara Reddy, Ch R Vikram Kumar, and K Hemachandra Reddy, "Modeling of Surface Roughness in Wire Electrical Discharge Machining Using Artificial Neural Networks," International Journal of Mechanical Engineering and Robotics Research, Vol.2, No. 1, pp. 57-64, January 2013.