Optimum Surface Roughness Prediction in Face Milling of High Silicon Stainless Steel
This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
[1] Sag˘lam, H., and ¨ Un¨ uvar, A. Tool condition monitoring in milling
based on cutting forces by a neural network. Int J Prod Res, 2003, 41,
1519-1532.
[2] Bouzid, W., Zghal, A., and Sai, L. Taguchi method for design
optimization of milled surface roughness, J. Mater. processing Technol,
2004, 19 (3), 159- 162.
[3] Topal, ES., Sinanoglu, C., Gercekcioglu, E., and Yildizli,K. Neural
Network Prediction of Surface Roughness in Milling of AISI 1040
Steel, J Balkan Trib Assoc, 2007, 13, 18-23.
[4] Dhokia, V G., Kumar, S., Vichare, P., Newman S T., and Allen, R D.
Surface roughness prediction model for CNC machining of
polypropylene. Proc. IMechE Part B: J. Engineering Manufacture, 2008,
222, 137-157.
[5] Onwubolu, G. C. Modelling and predicting surface roughness in turning
operations using hybrid differential evolution and the group method of
data handling networks, Proc IMechE, Part B: J. Engineering
Manufacture, 2008, 222(B7), 785-795.
[6] Benardos, PG., and Vosniakos, GC. Prediction of surface roughness in
CNC face milling using neural networks and Taguchi-s design of
experiments. Robot Comput Integr Manuf, 2002, 18, 343-354.
[7] Oktema, H., Erzurumlu, T., and Kurtaran, H. Application of response
surface methodology in the optimization of cutting conditions for
surface roughness, J. Mater. processing Technol, 2005, 170, 11-16.
[8] Krimpenis, A., and Fousekis, A. Assessment of sculptured surface
milling strategies using design of experiments, Int J Adv Manuf
Technol, 2005, 25, 444-453.
[9] Tansela, I.N., Ozcelikb, B., Baoa, W.Y., Chena, P., Rincona, D.,
Yanga, S.Y., and Yenilmezc, A. Selection of optimal cutting
conditions by using GONNS, Machine Tools Mf. J., 2006, 46, 26-35.
[10] Razfar, M. R., and Zanjani Zadeh, M. R. Optimum damage and surface
roughness prediction in end milling Glass fiber-reinforced plastics, using
neural network and genetic algorithm, proc. IMechE, Part B: J.Engineering
Manufacture, 2009, 223, 653-664.
[11] Oktem, Hasan., Erzurumlu, Tuncay., and Erzincanli, Fehmi. Prediction
of minimum surface roughness in end milling mold parts using neural
network and genetic algorithm, Mater Design, 2006, 27, 735-744.
[12] Birbil, S. I., and Fang, S.C., An Electromagnetism-like Mechanismfor
Global Optimization. J Global Optim, 2003, 25,263-282.
[13] Debels, D., Reyck, B. D., Leus, R., & Vanhoucke, M., A hybrid scatter
search/electromagnetism meta-heuristic for project scheduling. Eur J
Oper Res, 2006, 169, 638-653.
[14] Durcomet 5 data sheet, Flowserve Corporation, P.O. Box 8820, Dayton,
Ohio 45401-8820, (937) 226-4000
[15] Sandvik Milling. Catalogue & technical guide, Sandvik Coromant,
Sweden, 2007.
[16] Agapiou, J. S. "The optimization of machining operations based on a
combined criteria, part: 1: the use of combined objectives in single pass
operations", Trans. ASME J. Eng. Ind., 114 , (1992).
[17] Chang, P., Chen., S., and Fan, C., A hybrid electromagnetism-like
algorithm for single machine scheduling problem, Expert Sys Appl,
2009, 36, 1259-1267
[18] Naderi, B., Tavakkoli-Moghaddam, R., and Khalili, M.,
Electromagnetism-like mechanism and simulated annealing algorithms
for flowshop scheduling problems minimizing the total weighted
tardiness and makespan, Knowledge-Based Systems 23 (2010) 77-85
[19] Yurtkuran, A., and Emel, E., A new Hybrid Electromagnetism-like
Algorithm for capacitated vehicle routing problems, Expert Sys Appl,
2010, 37, 3427-3433
[1] Sag˘lam, H., and ¨ Un¨ uvar, A. Tool condition monitoring in milling
based on cutting forces by a neural network. Int J Prod Res, 2003, 41,
1519-1532.
[2] Bouzid, W., Zghal, A., and Sai, L. Taguchi method for design
optimization of milled surface roughness, J. Mater. processing Technol,
2004, 19 (3), 159- 162.
[3] Topal, ES., Sinanoglu, C., Gercekcioglu, E., and Yildizli,K. Neural
Network Prediction of Surface Roughness in Milling of AISI 1040
Steel, J Balkan Trib Assoc, 2007, 13, 18-23.
[4] Dhokia, V G., Kumar, S., Vichare, P., Newman S T., and Allen, R D.
Surface roughness prediction model for CNC machining of
polypropylene. Proc. IMechE Part B: J. Engineering Manufacture, 2008,
222, 137-157.
[5] Onwubolu, G. C. Modelling and predicting surface roughness in turning
operations using hybrid differential evolution and the group method of
data handling networks, Proc IMechE, Part B: J. Engineering
Manufacture, 2008, 222(B7), 785-795.
[6] Benardos, PG., and Vosniakos, GC. Prediction of surface roughness in
CNC face milling using neural networks and Taguchi-s design of
experiments. Robot Comput Integr Manuf, 2002, 18, 343-354.
[7] Oktema, H., Erzurumlu, T., and Kurtaran, H. Application of response
surface methodology in the optimization of cutting conditions for
surface roughness, J. Mater. processing Technol, 2005, 170, 11-16.
[8] Krimpenis, A., and Fousekis, A. Assessment of sculptured surface
milling strategies using design of experiments, Int J Adv Manuf
Technol, 2005, 25, 444-453.
[9] Tansela, I.N., Ozcelikb, B., Baoa, W.Y., Chena, P., Rincona, D.,
Yanga, S.Y., and Yenilmezc, A. Selection of optimal cutting
conditions by using GONNS, Machine Tools Mf. J., 2006, 46, 26-35.
[10] Razfar, M. R., and Zanjani Zadeh, M. R. Optimum damage and surface
roughness prediction in end milling Glass fiber-reinforced plastics, using
neural network and genetic algorithm, proc. IMechE, Part B: J.Engineering
Manufacture, 2009, 223, 653-664.
[11] Oktem, Hasan., Erzurumlu, Tuncay., and Erzincanli, Fehmi. Prediction
of minimum surface roughness in end milling mold parts using neural
network and genetic algorithm, Mater Design, 2006, 27, 735-744.
[12] Birbil, S. I., and Fang, S.C., An Electromagnetism-like Mechanismfor
Global Optimization. J Global Optim, 2003, 25,263-282.
[13] Debels, D., Reyck, B. D., Leus, R., & Vanhoucke, M., A hybrid scatter
search/electromagnetism meta-heuristic for project scheduling. Eur J
Oper Res, 2006, 169, 638-653.
[14] Durcomet 5 data sheet, Flowserve Corporation, P.O. Box 8820, Dayton,
Ohio 45401-8820, (937) 226-4000
[15] Sandvik Milling. Catalogue & technical guide, Sandvik Coromant,
Sweden, 2007.
[16] Agapiou, J. S. "The optimization of machining operations based on a
combined criteria, part: 1: the use of combined objectives in single pass
operations", Trans. ASME J. Eng. Ind., 114 , (1992).
[17] Chang, P., Chen., S., and Fan, C., A hybrid electromagnetism-like
algorithm for single machine scheduling problem, Expert Sys Appl,
2009, 36, 1259-1267
[18] Naderi, B., Tavakkoli-Moghaddam, R., and Khalili, M.,
Electromagnetism-like mechanism and simulated annealing algorithms
for flowshop scheduling problems minimizing the total weighted
tardiness and makespan, Knowledge-Based Systems 23 (2010) 77-85
[19] Yurtkuran, A., and Emel, E., A new Hybrid Electromagnetism-like
Algorithm for capacitated vehicle routing problems, Expert Sys Appl,
2010, 37, 3427-3433
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:59843", author = "M. Farahnakian and M.R. Razfar and S. Elhami-Joosheghan", title = "Optimum Surface Roughness Prediction in Face Milling of High Silicon Stainless Steel", abstract = "This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
", keywords = "cutting parameters, face milling, surface roughness,
artificial neural network, Electromagnetism-like algorithm,", volume = "6", number = "8", pages = "1695-5", }
