**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**30077

##### Artificial Intelligent Approach for Machining Titanium Alloy in a Nonconventional Process

**Authors:**
Md. Ashikur Rahman Khan,
M. M. Rahman,
K. Kadirgama

**Abstract:**

Artificial neural networks (ANN) are used in distinct researching fields and professions, and are prepared by cooperation of scientists in different fields such as computer engineering, electronic, structure, biology and so many different branches of science. Many models are built correlating the parameters and the outputs in electrical discharge machining (EDM) concern for different types of materials. Up till now model for Ti-5Al-2.5Sn alloy in the case of electrical discharge machining performance characteristics has not been developed. Therefore, in the present work, it is attempted to generate a model of material removal rate (MRR) for Ti-5Al-2.5Sn material by means of Artificial Neural Network. The experimentation is performed according to the design of experiment (DOE) of response surface methodology (RSM). To generate the DOE four parameters such as peak current, pulse on time, pulse off time and servo voltage and one output as MRR are considered. Ti-5Al-2.5Sn alloy is machined with positive polarity of copper electrode. Finally the developed model is tested with confirmation test. The confirmation test yields an error as within the agreeable limit. To investigate the effect of the parameters on performance sensitivity analysis is also carried out which reveals that the peak current having more effect on EDM performance.

**Keywords:**
Ti-5Al-2.5Sn,
material removal rate,
copper tungsten,
positive polarity,
artificial neural network,
multi-layer perceptron.

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1336905

**References:**

[1] R.K. Garg, and K. Ojha, "A review of tool electrode designs for sinking EDM process,” Recent researches in multimedia systems, signal processing, robotics, control and manufacturing technology, WSEAS, 2011, pp. 25-30.

[2] D.D. Kopeliovich. Titanium alpha and near alpha alloys. Substances and Technologies. Available: http://www.substech.com/dokuwiki/ doku.php?id=titanium alpha and near alpha alloys, 2009.

[3] A. Hascalik, U. Caydas, and H. Gurun, "Effect of traverse speed on abrasive water jet machining of Ti–6Al–4V alloy,” Mater. Des., vol. 28, 2007, pp. 1953–1957.

[4] 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, 2009, pp. 1512–1520.

[5] K.M. Tsai, and P.J. Wang, "Comparisons of neural network models on material removal rate in electrical discharge machining,” J. Mater. Process. Technol., vol. 117, 2001, pp. 111-124.

[6] K.M. Tsai, and P.J. Wang, "Predictions on surface finish in electrical discharge machining based upon neural network models,” Int. J. Mach. Tools Manuf., vol. 41, 2001, pp. 1385–1403.

[7] 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, 2003, pp. 995–999.

[8] 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, 2007, pp. 154-162.

[9] M.H.K. Dave, D.K.P. Desai, and D.H.K. Raval, "Investigations on prediction of MRR and surface roughness on electro discharge machine using regression analysis and artificial neural network programming,” Proc. World Congress on Eng. Computer Sci., October 2008.

[10] 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, 2008, pp. 19-30.

[11] M.K. Pradhan, R. Das, and C.K. Biswas, "Comparisons of neural network models on surface roughness in electrical discharge machining,” J. Eng. Manuf, vol. 223, no. 7, 2009, pp. 801-808.

[12] U. Caydas, and A. Hascalik, "Modeling and analysis of electrode wear and white layer thickness in die-sinking EDM process through response surface methodology,” Int. J. Adv. Manuf. Technol., vol. 38, 2008, pp.1148-1156.

[13] I. Puertas, C.J. Luis, and G. Villa, "Spacing roughness parameters study on the EDM of silicon carbide,” J. Mater. Process. Technol., vol. 164–165, 2005, pp. 1590–1596.

[14] M. Kunieda, B. Lauwers, K.P. Rajurkar, and B.M. Schumacher, "Advancing EDM through fundamental insight into the process,” CIRP Annals-Manuf. Technol., vol. 54, 2005, no. 2, pp. 64-87.

[15] S.S. Habib, "Study of the parameters in electrical discharge machining through response surface methodology approach,” Appl. Math. Model., vol. 33, 2009, pp. 4397-4407.

[16] H. Ouarda, "A neural network based navigation for intelligent autonomous mobile robots,” Int. J. Math. Models Methods Appl. Sci., vol. 4, 2010, pp. 177-86.

[17] A. Zak, "Neural model of underwater vehicle dynamics,” Int. J. Math. Com. Sim., vol. 1, 2007, pp. 189-195.

[18] M.I. Rajab, "Cooperative neural network and low-level feature extraction scheme,” Int. J. Bio. Biomed. Eng., vol. 2, 2007, pp. 37-40.

[19] A. Ziaie, I. Mahmoudi, and A. Kyioumarsi, "Using neural network in plate frequency calculation,” Int. J. Math. Com. Sim., vol. 2, 2008, pp. 179-186.