Performance characteristic prediction of WEDM process using response surface methodology and artificial neural network

In the present study, empirical relations have been reported for estimation of performance characteristics when EN-31 steel is machined by wire electrical discharge machining (WEDM) process using response surface methodology (RSM). The experimental plan was based on the face centred central composite design (FCCCD). In order to study the effects of the WEDM parameters on performance characteristics, second order polynomial models are developed. Cutting parameters such as pulse-on-time, pulse-off-time, wire tension, spark gap set voltage and servo feed are considered as inputs to the model variables whereas cutting rate, surface roughness and dimensional deviation as outputs. Further, analysis of variance (ANOVA) is used to analyse the influence of process parameters and their interaction on responses. Artificial neural network (ANN) model based on Levenberg-Marquardt (L-M) algorithm is employed to predict the performance characteristics.

[1]  Harun Akku,et al.  Predicting Surface Roughness of AISI 4140 Steel in Hard Turning Process through Artificial Neural Network, Fuzzy Logic and Regression Models , 2011 .

[2]  Sami Ekici,et al.  An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM , 2009, Expert Syst. Appl..

[3]  Kamlakar P Rajurkar,et al.  Analysis and optimization of parameter combinations in wire electrical discharge machining , 1991 .

[4]  Bijoy Bhattacharyya,et al.  Modeling and optimization of wire electrical discharge machining of γ-TiAl in trim cutting operation , 2008 .

[5]  Nihat Tosun,et al.  A study on kerf and material removal rate in wire electrical discharge machining based on Taguchi method , 2004 .

[6]  S. Gowri,et al.  Study on parametric influence, optimisation and modelling in micro-WEDM of Al alloy , 2010 .

[7]  B. Bhattacharyya,et al.  Parametric analysis and optimization of wire electrical discharge machining of γ-titanium aluminide alloy , 2005 .

[8]  S. S. Pande,et al.  Development of an intelligent process model for EDM , 2009 .

[9]  Mohan Kumar Pradhan,et al.  Comparisons of neural network models on surface roughness in electrical discharge machining , 2009 .

[10]  Jatinder Kumar,et al.  Prediction of Surface Roughness in Wire Electric Discharge Machining (WEDM) Process based on Response Surface Methodology , 2012 .

[11]  Y. S. Tarng,et al.  Determination of optimal cutting parameters in wire electrical discharge machining , 1995 .

[12]  P. K. Jain,et al.  Prediction of surface roughness during wire electrical discharge machining of SiC p /6061 Al metal matrix composite , 2012 .

[13]  Mohan Kumar Pradhan,et al.  Neuro-fuzzy and neural network-based prediction of various responses in electrical discharge machining of AISI D2 steel , 2010 .

[14]  L. Dabrowski,et al.  Electrical discharge machining characteristics of metal matrix composites , 2001 .

[15]  Jose Mathew,et al.  Optimization of Material Removal Rate in Micro-EDM Using Artificial Neural Network and Genetic Algorithms , 2010 .

[16]  Kamlakar P Rajurkar,et al.  Thermal modeling and on-line monitoring of wire-EDM , 1993 .

[17]  Mohan Kumar Pradhan,et al.  Recurrent neural network estimation of material removal rate in electrical discharge machining of AISI D2 tool steel , 2011 .

[18]  S. Zaborski,et al.  Semi-empirical model of efficiency of wire electrical discharge machining of hard-to-machine materials , 2009 .

[19]  Trevor A Spedding,et al.  Parametric optimization and surface characterization of wire electrical discharge machining process , 1997 .

[20]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[21]  K. Chandrasekaran,et al.  EXPERIMENTAL STUDY ON STAINLESS STEEL FOR OPTIMAL SETTING OF MACHINING PARAMETERS USING TAGUCHI AND NEURAL NETWORK , 2011 .

[22]  M. Ghoreishi,et al.  Neural-network-based modeling and optimization of the electro-discharge machining process , 2008 .

[23]  Sachin Maheshwari,et al.  Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II , 2012 .

[24]  P. Saha,et al.  Artificial neural network model in surface modification by EDM using tungsten–copper powder metallurgy sintered electrodes , 2010 .

[25]  R. Ramakrishnan,et al.  Modeling and multi-response optimization of Inconel 718 on machining of CNC WEDM process , 2008 .

[26]  G. Krishna Mohana Rao,et al.  Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm , 2009 .

[27]  T. A. El-Taweel,et al.  Modelling the machining parameters of wire electrical discharge machining of Inconel 601 using RSM , 2005 .