Artificial Intelligence Model to Predict Surface Roughness of Ti-15-3 Alloy in EDM Process
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.
[1] 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.
[2] R.R. Boyer, "An overview on the use of titanium in the aerospace
industry," Mater. Sci. Eng.,vol. A213, pp. 103-114, 1996.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11]. 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.
[12] 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.
[13] 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.
[14] 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
[1] 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.
[2] R.R. Boyer, "An overview on the use of titanium in the aerospace
industry," Mater. Sci. Eng.,vol. A213, pp. 103-114, 1996.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11]. 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.
[12] 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.
[13] 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.
[14] 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
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:54819", author = "Md. Ashikur Rahman Khan and M. M. Rahman and K. Kadirgama and M.A. Maleque and Rosli A. Bakar", title = "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.", keywords = "Ti-15l-3, surface roughness, copper, positive polarity, multi-layered perceptron.", volume = "5", number = "2", pages = "357-5", }