Comparative Neural Network Models on Material Removal Rate and surface Roughness in Electrical Discharge Machining

  • Morteza Sadegh Amalnik Assist. Prof.of mech. Eng. and Director of Environment Research Center of University of Qom
  • M. Mirzaei Assist. Prof.of mechanical engineering department of Qom University, Qom,I.R
  • Farzad Momeni Lecturer at Mechanical engineering Dept. University of Chamran, Ahvaz,I.R.
Keywords: EDM, ANN, BP, RBF


Electro-discharge machining (EDM) is increasingly being used in many industries for producing molds and dies, and machining complex shapes with material such as steel, cemented carbide, and engineering ceramics. The stochastic nature of EDM process has frustrated number of attempts to model it physically. Artificial neural networks (ANNs), as one of the most attractive branches in Artificial Intelligence (AI), has the potentiality to handle problems such as prediction of design and manufacturing cost, material removal rate (MRR), diagnosis, modeling, and adaptive control in a complex design and manufacturing systems. This paper uses Back Propagation Neural Network (BP) and Radial Basis Function (RBF) approach for prediction of material removal rate and surface roughness and presents the results of the experimental investigation. Charmilles Technology (EDM-ROBOFORM200) in he mechanical engineering department is used for machining parts. The networks have four inputs of current (I), voltage (V), Period of pulse on (Ton) and period of pulse off (Toff) as the input processes variables. Two outputs results of material removal rate (MRR) and surface roughness (Ra) as performance characteristics. In order to train the network, and capabilities of the models in predicting material removal rate and surface roughness, experimental data are employed. Then the output of MRR and Ra obtained from neural net compare with experimental results, and amount of relative error is calculated.


[1] R.E. Williams and K.P. Rajurkar, Study of wire electrical discharge machined surface characteristics, J. Mater.Pro.Tech, 28(1991), PP.127-138.
[2] N.K. Jain and V.K. Jain, Modeling of material removal in mechanical type advanced machining processes: a state-of-art review, IntJ.Mach.Tools Manufact. , 41(2001), PP. 1573-1635.
[3] S.M. Pandit and K.P. Rajurkar, A stochastic approach to thermal modeling applied to electro-discharge machining, Trans.ASME J. Heat Transfer,105(1983), PP.555-562.
[4] J.A. McGeough, Advanced Methods of Machining, Chapman and Hall, London and New York, 1988.
[5] F. Van Djick, Physico-mathematical analysis of the electro-discharge machining process, Ph.D.Thesis, Catholic University of Leuven, Belgium, 1973.
[6] A. Erden and B. kaftanoglu. Heat transfer modeling of electric discharge machining, 21" MTDR Conference, Swansea, UK, Macmillan Press Ltd., 1980, PP.351-358.
[7] Jilani S.T. and Pandey, P.C. 1982.Analysis and modeling of EDM parameters. Precision Engineering, 4(4) PP.215-221.
[8] Singh A. and Ghosh, A. 1999. A thermoelectric model of material removal during electric discharge machining, IntJ.Mach.Tools Manufact, 39 (1999), PP.669-682.
[9] Ghoreishi. M. and Atkinson, J. 2001. Vibro-rotary electrode: a new technique in EDM drilling, performance evaluation by statistical modeling and optimization, ISEM XIII, Spain, May 2001, PP.633-648.
[10] Ghoreishi M. and Atkinson, J. 2002. A comparative experimental study of machining characteristics in vibratory, rotary and vibro- rotary electro-discharge machining, J.Mater. Proc.Tech.120, PP.374-384.
[11] Wang P.J. and Tsai, K.M. 2001.Semi-empirical model on •work removal and tool wear in electrical discharge machining, J.Mater.Proc.Tech., 114, PP.1-17.
[12] Tsai K.M. and Wang, P.J. 2001.Semi-empirical model of surface finish on electrical discharge machining, IntJ.Mach.Tools Manufact., 41(2001), PP. 1455-1477.
[13] Freeman J.A. and Skapura, D.M. 1992. Neural Networks:algorithms, applications, and programming techniques, Addision-Wesley, Reading.MA..
[14] Kao J.Y. and Tamg, Y.S. 1997. A neural-network approach for the on-line monitoring of the electrical discharge machining process, J.Mater.Proc.Tech, 69(1997), PP.112-119.
[15] Liu H.S.and Tamg, Y.S.1997. Monitoring of the electrical discharge machining process by abductive networks, Int.J.Adv.ManufactTech.,13(1997), PP.264-270.
[16] Indurkhya G. and Rajurkar, K.P. 1992. Artificial neural network approach in modeling of EDM process, in: Proc.ArtifIcial neural networks in engineering (ANNIE92) conf., St. Louis, Missouri, USA, 15-18 November 1992, PP.845-850.
[17] Spedding T.A. and Wang, Z.Q. 1997. Study on modeling of wire EDM process, J.Mater.Proc.Tech., 69(1997), PP. 18-28.
[18] Spedding T.A. and Wang, Z.Q. 1997. Parametric optimization and surface characterization of wire electrical discharge machining process. Precision Engineering, 20(1997), PP.5-15.
[19] Tamg, Y.S., Ma S.C. and Chung, L.K. 1995. Determination of optimal cutting parametersin wire electrical discharge machining,IntJ.Mach.Tools Manufact,35(1995), PP.1693-1701. [20].K.M.Tsai,P.J.Wang, Prediction on surface finish in electrical discharge machining based upon neural network models, IntJ.Mach.Tools Manufact, 41(2001),pp.1385-1403. [21].K.M.Tsai,P.J.Wang, Comparisons of neural network models on material removal rate in electrical discharge machining, J.Mater. Proc.Tech 117(2001), pp.111-124.
[22].A.R.Barron, Neural net approximation, in: proceeding of the seventh Yale Workshop on Adaptive and Learning System,1992,pp.68-72.
How to Cite
Amalnik, M. S., Mirzaei, M., & Momeni, F. (2015). Comparative Neural Network Models on Material Removal Rate and surface Roughness in Electrical Discharge Machining. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 14(5), 5731-5741.