{"title":"Artificial Intelligence Model to Predict Surface Roughness of Ti-15-3 Alloy in EDM Process","authors":"Md. Ashikur Rahman Khan, M. M. Rahman, K. Kadirgama, M.A. Maleque, Rosli A. Bakar","volume":50,"journal":"International Journal of Mechanical and Mechatronics Engineering","pagesStart":503,"pagesEnd":508,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/5730","abstract":"Conventionally the selection of parameters depends\r\nintensely on the operator-s experience or conservative technological\r\ndata provided by the EDM equipment manufacturers that assign\r\ninconsistent machining performance. The parameter settings given by\r\nthe manufacturers are only relevant with common steel grades. A\r\nsingle parameter change influences the process in a complex way.\r\nHence, the present research proposes artificial neural network (ANN)\r\nmodels for the prediction of surface roughness on first commenced\r\nTi-15-3 alloy in electrical discharge machining (EDM) process. The\r\nproposed models use peak current, pulse on time, pulse off time and\r\nservo voltage as input parameters. Multilayer perceptron (MLP) with\r\nthree hidden layer feedforward networks are applied. An assessment\r\nis carried out with the models of distinct hidden layer. Training of the\r\nmodels is performed with data from an extensive series of\r\nexperiments utilizing copper electrode as positive polarity. The\r\npredictions based on the above developed models have been verified\r\nwith another set of experiments and are found to be in good\r\nagreement with the experimental results. Beside this they can be\r\nexercised as precious tools for the process planning for EDM.","references":"[1] S. Singh, S. Maheshwari and P.C. Pandey, \"Some investigations into the\r\nelectric discharge machining of hardened tool steel using different\r\nelectrode materials,\" J. Mater. Process. Technol., vol. 149, pp. 272-\r\n277, 2004.\r\n[2] R.R. Boyer, \"An overview on the use of titanium in the aerospace\r\nindustry,\" Mater. Sci. Eng.,vol. A213, pp. 103-114, 1996.\r\n[3] M. Rahman, Z.G. Wang and Y.S. Wang, \"A review on high-speed\r\nmachining of titanium alloys,\" JSME Int. J., vol. 49, no. 1, pp. 11-20,\r\n2006.\r\n[4] B.H. Yan, H.C. Tsai and F.Y. Huang, \"The effect in EDM of a dielectric\r\nof a urea solution in water on modifying the surface of titanium,\" Int. J.\r\nMachine Tools Manufac., vol.45, pp. 194-200, 2005.\r\n[5] K.L. Wu, B.H. Yan, F.Y. Huang and S.C. Chen, \"Improvement of\r\nsurface finish on SKD steel using electro-discharge machining with\r\naluminum and surfactant added dielectric,\" Int. J. Machine Tools\r\nManufac., vol. 45, pp. 1195-1201, 2005.\r\n[6] M.M. Rahman, M.A.R. Khan, K. Kadirgama, M.M. Noor, R.A. Bakar,\r\n\"Optimization of machining parameters on tool wear rate of Ti-6Al-4V\r\nthough EDM using copper tungsten electrode: A statistical approach,\"\r\nAdvanced Materials Research, vol. 152-153, pp. 1595-1602, 2011.\r\n[7] H.K. Kansal, S. Singh and P.Kumar, \"Effect of silicon powder mixed\r\nEDM on machining rate of AISI D2 die steel,\" J. Manufac. Process.,\r\nvol. 9, no. 1, pp. 13-22, 2007.\r\n[8] P.J. Wang and K.M. Tsai, \"Semi-empirical model on work removal and\r\ntool wear in electrical discharge machining,\" J. Mater. Process.\r\nTechnol., vol. 114, pp. 1-17, 2001.\r\n[9] G.K.M. Rao, G. Rangajanardhaa, D.H. Rao and M.S. Rao,\r\n\"Development of hybrid model and optimization of surface roughness in\r\nelectric discharge machining using artificial neural networks and genetic\r\nalgorithm,\" J. Mater. Process. Technol., vol. 209, pp. 1512-1520, 2009.\r\n[10] A.P. Markopoulos, D.E. Manolakos and N.M. Vaxevanidis, \"Artificial\r\nneural network models for the prediction of surface roughness in\r\nelectrical discharge machining,\" J. Intelligent Manufac., vol. 19,\r\npp. 283-292, 2008.\r\n[11]. S. Assarzadeh and M. Ghoreishi, \"Neural-network-based modeling and\r\noptimization of the electro-discharge machining process,\" Int. J. Adv.\r\nManufac. Technol., vol. 39, pp. 488-500, 2008.\r\n[12] K.D. Chattopadhyay, S. Verma, P.S. Satsangi and P.C. Sharma,\r\n\"Development of empirical model for different process parameters\r\nduring rotary electrical discharge machining of copper-steel (EN-8)\r\nsystem,\" J. Mater. Process. Technol., vol. 209, pp. 1454-1465, 2009.\r\n[13] I. Puertas and C.J. Luis, \"A study on the machining parameters\r\noptimisation of electrical discharge machining,\" J. Mater. Process.\r\nTechnol., vol. 143-144, pp. 521-526, 2003.\r\n[14] M. Kunieda, B. Lauwers, K.P. Rajurkar and B. M. Schumacher,\r\n\"Advancing EDM through fundamental insight into the process,\" CIRP\r\nAnnals-Manufac. Technol., vol. 54, no. 2, pp. 64-87, 2005","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 50, 2011"}