Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30063
Mathematical Expression for Machining Performance

Authors: Md. Ashikur Rahman Khan, M. M. Rahman

Abstract:

In electrical discharge machining (EDM), a complete and clear theory has not yet been established. The developed theory (physical models) yields results far from reality due to the complexity of the physics. It is difficult to select proper parameter settings in order to achieve better EDM performance. However, modelling can solve this critical problem concerning the parameter settings. Therefore, the purpose of the present work is to develop mathematical model to predict performance characteristics of EDM on Ti-5Al-2.5Sn titanium alloy. Response surface method (RSM) and artificial neural network (ANN) are employed to develop the mathematical models. The developed models are verified through analysis of variance (ANOVA). The ANN models are trained, tested, and validated utilizing a set of data. It is found that the developed ANN and mathematical model can predict performance of EDM effectively. Thus, the model has found a precise tool that turns EDM process cost-effective and more efficient.

Keywords: Analysis of variance, artificial neural network, material removal rate, modelling, response surface method, surface finish.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2021567

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF

References:


[1] M.M. Rahman., M.A.R. Khan, K. Kadirgama, M.M. Noor, and R.A. Bakar, “Optimization of machining parameters on tool wear rate of Ti-6Al-4V through EDM using copper tungsten electrode,” A statistical approach, Adv. Mater. Res., vol. 152–153, pp. 1595–1602, 2011.
[2] M.M. Rahman, M.A.R. Khan, K. Kadirgama, M.M. Noor, and R.A. Bakar, “Modeling of material removal on machining of Ti-6Al-4V through EDM using copper tungsten electrode and positive polarity,” Int. J. Mech. Mater. Eng., vol. 1, no. 3, pp. 135–140, 2010.
[3] M.A.R. Khan, M.M. Rahman, K. Kadirgama, M.A. Maleque, and R.A. Bakar, “Artificial intelligence model to predict surface roughness of Ti-15-3 alloy in EDM process,” World Acad. Sci. Eng. Technol. vol. 74, pp. 121–125, 2011.
[4] M.A.R. Khan, M.M. Rahman, K. Kadirgama, and R.A. Bakar, Artificial neural network model for material removal rate of Ti-15-3 in electrical discharge machining. Energy Education Science and Technology― Part A: Energy Science and Research, 29(2), 2012, 1025–1038.
[5] J. Longfellow, J.D. Wood, and R.B. Palme, “The effect of electrode material properties on the wear ratio in spark machining,” J. Inst. Metals, vol 96, no 2, pp. 614–617, 1968.
[6] M.L. Jeswani, “Dimensional analysis of tool wear in electrical discharge machining,” Wear, vol. 55, pp. 153–161, 1979.
[7] 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.
[8] D.D. DiBitonto, P.T. Eubank, M.R. Patel, and M.A. Barrufet, “Theoretical models of the electrical discharge machining process–I, A simple cathode erosion model,” J. Appl. Phys., vol. 66, no. 9, pp. 4095–4103, 1989.
[9] S.N. Joshi, and S.S. Pande, “Thermo-physical modeling of die-sinking EDM process,” J. Manuf. Process., vol. 12, pp. 45–56, 2010.
[10] 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.
[11] K.M. Patel, P.M. Pandey, and P.V. Rao, “Determination of an optimum parametric combination using a surface roughness prediction model for EDM of Al2O3/SiCw/TiC ceramic composite,” Mater. Manuf. Process. Vol. 24, pp. 675–682, 2009.
[12] C.J. Luis, I. Puertas, and G. Villa, “Material removal rate and electrode wear study on the EDM of silicon carbide,” J. Mater. Process. Technol., vol. 164–165, pp. 889–896, 2005.
[13] R. Karthikeyan, P.R.L. Narayanan, and R.S. Naagarazan, “Mathematical modelling for electric discharge machining of aluminium–silicon carbide particulate composites,” J. Mater. Process. Technol., vol. 87, no. 59–63, 1999.
[14] G. Petropoulos, N.M. Vaxevanidis, and C. Pandazaras, “Modeling of surface finish in electro-discharge machining based upon statistical multi-parameter analysis,” J. Mater. Process. Technol., vol. 155–156, pp. 1247–1251, 2004.
[15] N.P. Hung, I.J. Yang, and K.W. Leong, “Electrical discharge machining of cast metal matrix composites,” J. Mater. Process. Technol., vol. 41, pp. 229–236, 1994.
[16] 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.
[17] D. Kanagarajan, R. Karthikeyan, K. Palanikumar, and J.P. Davim, “Optimization of electrical discharge machining characteristics of WC/Co composites using non-dominated sorting genetic algorithm (NSGA-II),” Int. J. Adv. Manuf. Technol., vol. 36, no. 11–12, pp. 1124–1132, 2008.
[18] M.R. Shabgard, and R.M. Shotorbani, “Mathematical modeling of machining parameters in electrical discharge machining of FW4 welded steel,” World Acad. Sci. Eng. Technol. vol. 52, pp. 403–409, 2009.
[19] R.A. Mahdavinejad, “EDM process optimisation via predicting a controller model,” Arch. Comp. Mater. Sci. Sur. Eng., vol. 1, no. 3, pp. 161–167, 2009.
[20] J.S. Donat, N. Bhat, and T.J. McAvoy, “Neural net based model predictive control,” Int. J. Contr., vol. 54, no. 6, pp. 1453–1468, 1991.
[21] R.A. Mahdavinejad, “Optimisation of electro discharge machining parameters,” J. Ach. Mater. Manuf. Eng., vol. 27, no. 2, pp. 163–166, 2008.
[22] G.K.M. Rao, G.R. Janardhana, D.H. Rao, and M.S. Rao, “Development of hybrid model and optimization of metal removal rate in electric discharge machining using artificial neural networks and genetic algorithm,” ARPN J. Eng. Appl. Sci., vol. 3, no. 1, pp. 19–30, 2008.
[23] D. Mandal, S.K. Pal, and P. Saha, “Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II,” J. Mater. Process. Technol., vol. 186, pp. 154–162, 2007.
[24] K. Wang, H.L. Gelgele, Y. Wang, Q. Yuan, and M. Fang, “A hybrid intelligent method for modelling the EDM process,” Int. J. Mach. Tools Manuf., vol. 43, pp. 995–999, 2003.
[25] M.A.R. Khan, M.M. Rahman, K. Kadirgama, and A.R. Ismail, “Mathematical model for wear rate of negative graphite electrode in electrical discharge machining on Ti-5A1-2.5Sn,” J. Teknol., vol. 59, pp. 55–59, 2012.
[26] I. Puertas, C.J. Luis, and L. Alvarez, “Analysis of the influence of EDM parameters on surface quality, MRR and EW of WC–Co,” J. Mater. Process. Technol., vol. 153–154, pp. 1026–1032, 2004.