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Analyzing the Influence of Electrical Parameters on EDM Process of Ti6Al4V Alloy Using Adaptive Neuro-Fuzzy Inference System (ANFIS)


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DOI: https://doi.org/10.15866/ireme.v9i3.5247

Abstract


Electrical discharge machining (EDM) is machining process that is suitable for machining very hard materials that are electrically conductive. In this process the material is removed by series of repeated electrical discharges, produced by electric pulse generators at short intervals in dielectric fluid medium. Thus, the electrical parameters are the main process parameters. However, the complexity of this cutting process will not permit pure analytical physical investigating. Therefore, the conventional mathematical models may not be suitable for analysing the output responses. The aim of this research is to investigate and predict the influence of the electrical parameters: peak current (PC), pulse duration (PD) and duty factor (DF) on the surface roughness (SR), Material Removal Rate (MRR) and Tool Wear Rate (TWR) using Adaptive Neuro-Fuzzy Inference System (ANFIS) as one of the effective soft computing methods. In this research, a set of experimental data was obtained with different levels. The measured values have been used to train the ANFIS system to find minimum error. The results indicate that even with the complexity of the EDM process, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was found to be adequate in predicting the SR, MRR and TWR with high accuracy.
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Keywords


EDM; Ti6Al4V Alloy; PM Copper Electrode; Surface Roughness; MRR; TWR; ANFIS

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References


M. S., Popa, G., Contiu, G., Pop, & P. Dan, New technologies and applications of EDM process. International Journal of Material Forming, 2(1), 633-636. 2009.
http://dx.doi.org/10.1007/s12289-009-0648-9

Udaya Prakash, J., Milton Peter, J., Moorthy, T.V., Optimization of wire EDM process parameters of aluminium alloy/flyash/boron Carbide hybrid composites, (2012) International Review of Mechanical Engineering (IREME), 6 (3), pp. 449-455.

Dhanabalan, S., Sivakumar, K., Narayanan, C.S., Optimization of machining parameters in EDM of Inconel 718 for form tolerance using grey relational analysis, (2012) International Review of Mechanical Engineering (IREME), 6 (7), pp. 1453-1459.

Savadamuthu, L., Muthu, S., Rakhul, T., Jothimani, S., Multi characteristic optimization in wire cut EDM by using taquchi data envelopment analysis based ranking methodology, (2013) International Review of Mechanical Engineering (IREME), 7 (3), pp. 468-473.

Kumaravelu, A., Jegadheesan, C., Senthilkumar, C., Developing empirical relationships to predict MRR and overcut of ECM of EN38 steel, (2014) International Review of Mechanical Engineering (IREME), 8 (1), pp. 168-173.

K., Wang, H. L., Gelgele, Y., Wang, Q., Yuan & M Fang. A hybrid intelligent method for modeling the EDM process. International Journal of Machine Tools and Manufacture, 43, 995–999.2003.
http://dx.doi.org/10.1016/s0890-6955(03)00102-0

O., Yilmaz, O., Eyercioglu, & N. N. Z. Gindy. A user-friendly fuzzy-based system for the selection of electro discharge machining process parameters. Journal of Materials Processing Technology, 172, 363–371. 2006.
http://dx.doi.org/10.1016/j.jmatprotec.2005.09.023

M. T., Yan, & C. C. Fang. Application of genetic algorithm-based fuzzy logic control in wire transport system of wire-EDM machine. Journal of Materials Processing Technology, 205(1), 128-137. 2008.
http://dx.doi.org/10.1016/j.jmatprotec.2007.11.091

K. M., Tsai, & P. J. Wang. Predictions on surface finish in electrical discharge machining based upon neural network models. International Journal of Machine Tools and Manufacture, 41(10), 1385-1403. 2001.
http://dx.doi.org/10.1016/s0890-6955(01)00028-1

K, Mpofu NS.Tlale, Multi-level decision making in reconfigurable machining systems using fuzzy logic. Journal of Manufacturing Systems; 31: 103–12. 2012.
http://dx.doi.org/10.1016/j.jmsy.2011.08.006

M. R., Shabgard,, M. A., Badamchizadeh, G., Ranjbary, & K.Amini Fuzzy approach to select machining parameters in electrical discharge machining (EDM) and ultrasonic-assisted EDM processes. Journal of Manufacturing Systems, 32(1), 32-39. 2013.
http://dx.doi.org/10.1016/j.jmsy.2012.09.002

C. T., Lin, . F., Chung, S. Y. IHuang. Improvement of machining accuracy by fuzzy logic at corner parts for wire-EDM. Fuzzy Sets and Systems;122: 499–511. 2001.
http://dx.doi.org/10.1016/s0165-0114(00)00034-8

M. B., Ndaliman, M. H. F. Al Hazza, A. A., Khan, & M. Y Ali,. (2012). Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method. Australian Journal of Basic and Applied Sciences. 2012.

M., Buragohain, & C. Mahanta. A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8(1), 609-625.2008.
http://dx.doi.org/10.1016/j.asoc.2007.03.010

J. S. Jang,. ANFIS: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on, 23(3), 665-685. 1993.
http://dx.doi.org/10.1109/21.256541

C. P., Kurian, V. I., George, J., Bhat, & R. S. Aithal, ANFIS model for the time series prediction of interior daylight illuminance. International Journal on Artificial Intelligence and Machine Learning, 6(3), 2006.


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