Commenced in January 2007
Paper Count: 30114
Artificial Intelligence Model to Predict Surface Roughness of Ti-15-3 Alloy in EDM Process
Abstract:Conventionally the selection of parameters depends intensely on the operator-s experience or conservative technological data provided by the EDM equipment manufacturers that assign inconsistent machining performance. The parameter settings given by the manufacturers are only relevant with common steel grades. A single parameter change influences the process in a complex way. Hence, the present research proposes artificial neural network (ANN) models for the prediction of surface roughness on first commenced Ti-15-3 alloy in electrical discharge machining (EDM) process. The proposed models use peak current, pulse on time, pulse off time and servo voltage as input parameters. Multilayer perceptron (MLP) with three hidden layer feedforward networks are applied. An assessment is carried out with the models of distinct hidden layer. Training of the models is performed with data from an extensive series of experiments utilizing copper electrode as positive polarity. The predictions based on the above developed models have been verified with another set of experiments and are found to be in good agreement with the experimental results. Beside this they can be exercised as precious tools for the process planning for EDM.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061591Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1453
 S. Singh, S. Maheshwari and P.C. Pandey, "Some investigations into the electric discharge machining of hardened tool steel using different electrode materials," J. Mater. Process. Technol., vol. 149, pp. 272- 277, 2004.
 R.R. Boyer, "An overview on the use of titanium in the aerospace industry," Mater. Sci. Eng.,vol. A213, pp. 103-114, 1996.
 M. Rahman, Z.G. Wang and Y.S. Wang, "A review on high-speed machining of titanium alloys," JSME Int. J., vol. 49, no. 1, pp. 11-20, 2006.
 B.H. Yan, H.C. Tsai and F.Y. Huang, "The effect in EDM of a dielectric of a urea solution in water on modifying the surface of titanium," Int. J. Machine Tools Manufac., vol.45, pp. 194-200, 2005.
 K.L. Wu, B.H. Yan, F.Y. Huang and S.C. Chen, "Improvement of surface finish on SKD steel using electro-discharge machining with aluminum and surfactant added dielectric," Int. J. Machine Tools Manufac., vol. 45, pp. 1195-1201, 2005.
 M.M. Rahman, M.A.R. Khan, K. Kadirgama, M.M. Noor, R.A. Bakar, "Optimization of machining parameters on tool wear rate of Ti-6Al-4V though EDM using copper tungsten electrode: A statistical approach," Advanced Materials Research, vol. 152-153, pp. 1595-1602, 2011.
 H.K. Kansal, S. Singh and P.Kumar, "Effect of silicon powder mixed EDM on machining rate of AISI D2 die steel," J. Manufac. Process., vol. 9, no. 1, pp. 13-22, 2007.
 P.J. Wang and K.M. Tsai, "Semi-empirical model on work removal and tool wear in electrical discharge machining," J. Mater. Process. Technol., vol. 114, pp. 1-17, 2001.
 G.K.M. Rao, G. Rangajanardhaa, D.H. Rao and M.S. Rao, "Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm," J. Mater. Process. Technol., vol. 209, pp. 1512-1520, 2009.
 A.P. Markopoulos, D.E. Manolakos and N.M. Vaxevanidis, "Artificial neural network models for the prediction of surface roughness in electrical discharge machining," J. Intelligent Manufac., vol. 19, pp. 283-292, 2008.
. S. Assarzadeh and M. Ghoreishi, "Neural-network-based modeling and optimization of the electro-discharge machining process," Int. J. Adv. Manufac. Technol., vol. 39, pp. 488-500, 2008.
 K.D. Chattopadhyay, S. Verma, P.S. Satsangi and P.C. Sharma, "Development of empirical model for different process parameters during rotary electrical discharge machining of copper-steel (EN-8) system," J. Mater. Process. Technol., vol. 209, pp. 1454-1465, 2009.
 I. Puertas and C.J. Luis, "A study on the machining parameters optimisation of electrical discharge machining," J. Mater. Process. Technol., vol. 143-144, pp. 521-526, 2003.
 M. Kunieda, B. Lauwers, K.P. Rajurkar and B. M. Schumacher, "Advancing EDM through fundamental insight into the process," CIRP Annals-Manufac. Technol., vol. 54, no. 2, pp. 64-87, 2005