Commenced in January 2007
Paper Count: 30063
Process Modeling of Electric Discharge Machining of Inconel 825 Using Artificial Neural Network
Abstract:Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129213Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 H. Aytekin, Y. Akcin, “Characterization of borided Incoloy 825 alloy”, Materials and Design, Vol. 50, p.p 515–521, 2013.
 O. E. Ezugwu, “Key improvements in the machining of difficult-to-cut aerospace super alloys”, International Journal of Machine Tools & Manufacture, Vol.45, 1353–1367, 2005.
 Y. Chen, S. M Mahdivian, “Analysis of electro-discharge machining process and its comparison with experiments”, Journal of Materials Processing Technology, 104, pp. 150-157. 2000.
 J. C. Rebelo, A. M. Dias, R. Mesquita, P. Vassalo, & M. Santos, “An experimental study on electro-discharge machining and polishing of high strength copper-beryllium alloys”, Journal of Material processing Technology, 103, pp. 389-397. 2000.
 K. M. Tsai, P. J. Wang, “Predictions on surface finish in electric discharge machining based upon neural network models”, International Journal of Machine tools and Manufacture, 41, pp. 1385-1403. 2001.
 A. H Gandhi, P. P Gohil, H. K. Raval, “Simulation of three roller bending process using ANN: a parametric study”, International Journal of Manufacturing Research, 4(3), pp. 265-280, 2009.
 C. Lucignano, R. Montanari, V. Tagliaferri, N. Ucciardello, “Artificial neural network to optimize the extrusion of an aluminium alloy”, Journal of Intelligent Manufacturing, 21, pp. 569-574. 2009.
 V. S. Sharma, S. Dhiman, R. Sehgal, S. K. Sharma, “Estimation of cutting forces and surface roughness for hard turning using neural network”, Journal of intelligent Manufacturing, 19, pp. 473-483, 2008.
 J. Anithaa, Raja Dasa, Mohan Kumar Pradhanb, “Multi-Objective Optimization of Electrical Discharge Machining Processes Using Artificial Neural Network”, Jordan Journal of Mechanical and Industrial Engineering, Volume 10 No 1, pp.11-18, 2016.
 Pushpendra Singh Bharti, Sachin Maheshwari, C. Sharma, “Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II”, Journal of Mechanical Science and Technology 26(6), pp. 1875-1883. 2012.
 B. Pandey, P. K. Brahmankar, “A method to predict possibility of arcing in EDM of TiB2p reinforced ferrous matrix composite”, International Journal of Advance Manufacturing Technology, DOI 10.1007/s00170-016-8414-x. 2016.
 Chandramouli. S and Eswaraiah. K, “Modeling and Optimization of Electrical Discharge Machining Process Parameters using Artificial Neural Networks”, Journal of Material Science and Mechanical Engineering, Volume 2, No 5, pp. 466-470, 2015.
 Angelos P. Markopoulos, Dimitrios E. Manolakos, Nikolaos M. Vaxevanidis, “Artificial neural network models for the prediction of surface roughness in electrical discharge machining”, Journal of Intelligent Manufacturing, Vol.19, pp.283–292, 2008.
 Assarzadeh S, Ghoreishi M, “Neural-network-based modelling and optimization of the electro-discharge machining process”, International Journal of Advance Manufacturing Technology, Vol.39, pp.488–500, 2008.
 D. Mandal, S. K. Pal, P. Saha, “Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II”, Journal of Material Processing Technology 186, pp.154–162,2007.
 Y. S. Tarng, S. C. Ma, L. K. Chung, “Determination of optimal cutting parameters in wire electrical discharge machining”, International Journal of Machining Tools Manufacturing 35(12), pp.1693–1701, 1995.
 T. A. Spedding, Z. Q. Wang, “Parametric optimization and surface characterization of wire electrical discharge machining process”, Precision Engineering 20(1): pp.5–15, 1997.
 S. Huang, Y. Huang, “Bound on the number of hidden neurons in multi-layer perceptrons”, IEEE transactions on neural network, 2(1), pp. 47-55, 1991.
 S. N. Joshi, S. S. Pandey, “Development of an intelligent process model for EDM”, International Journal of Advanced Manufacturing Technology, Vol. 45, Issue 3-4, pp.300-317, 2009.