Modeling the wire-EDM process parameters for EN-8 carbon steel using artificial neural networks

Abstract

Accompanying the evolution of mechanical industry, the need for alloy materials and ceramics as well, having high toughness, hardness & impact resistance are also increasing. Wire-EDM process is a quick fix to this box. As this WEDM process is proficient in machining these materials meticulously, regardless to their toughness & hardness. The present work discusses the experimental study on wire-cut machining of EN8 Carbon Steel. This paper presents an Artificial Neural Network approach in a multi objective optimization problem, for predicting the performance measure of a machining process. The input neurons were designed by Taguchi Orthogonal Array with 5 levels each with the help of software Minitab 17. In this research work, a comparative study for predicting multiple response parameters have been carried out using Feed forward and Cascade forward back propagation algorithms of ANN. This study was conducted by varying the network designing in each case. Two different back-propagation training algorithms were used in neural network designing such as Powell-Beale Conjugate Gradient (traincgb) and Levenberg-Marquardt (trainlm). Predictive accuracy of both the models was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE) and coefficient determination (R²). The outcomes proved that both the algorithms were able to produce feasible models. Cascade model outperformed with all statistical tests between both.

Keywords: Artificial Neural Network, Feed forward back propagation, Cascade forward back propagation