Modeling of Surface Roughness in Vibration Cutting by Artificial Neural Network
Development of artificial neural network (ANN) for
prediction of aluminum workpieces' surface roughness in ultrasonicvibration
assisted turning (UAT) has been the subject of the present
study. Tool wear as the main cause of surface roughness was also
investigated. ANN was trained through experimental data obtained
on the basis of full factorial design of experiments. Various
influential machining parameters were taken into consideration. It
was illustrated that a multilayer perceptron neural network could
efficiently model the surface roughness as the response of the
network, with an error less than ten percent. The performance of the
trained network was verified by further experiments. The results of
UAT were compared with the results of conventional turning
experiments carried out with similar machining parameters except for
the vibration amplitude whence considerable reduction was observed
in the built-up edge and the surface roughness.
[1] S. Amini, H. Soleimanimehr, M.J. Nategh, A. Abudollah and M.H.
Sadeghi, "FEM Analysis of Ultrasonic-Vibration-Assisted Turning and
the Vibratory Tool," Journal of Materials Processing Technology, vol.
201, pp. 43-47, 2008.
[2] Chandra Nath, and M. Rahman, "Effect of machining parameters in
ultrasonic vibration cutting," International Journal of Machine Tools &
Manufacture, vol. 313, pp. 395-417, 2008.
[3] D.E. Brehl, and T.A. Dow, "Review of vibration-assisted machining,"
Precision Engineering, vol. 32, pp. 153-172, 2008.
[4] J. Pujana, A. Rivero, A. Celaya, and L.N. L├│pez de Lacalle, "Analysis of
ultrasonic-assisted drilling of Ti6Al4V," International Journal of
Machine Tools & Manufacture, doi:10.1016/j.ijmachtools.2008.12.014.
[5] U. Zuperl, F. Cus, B. Mursec and T. Ploj, "A hybrid analytical-neural
network approach to the determination of optimal cutting conditions,"
Journal of Materials Processing Technology, vol. 157-158, pp. 82-90,
2004.
[6] E. O. Ezugwua, D. A. Fadarea, J. Bonney, R. B. Da Silva and W. F.
Sales, "Modelling the correlation between cutting and process
parameters in high-speed machining of Inconel 718 alloy using an
artificial neural network," International Journal of Machine Tools &
Manufacture, vol. 45, pp. 1375-1385, 2005.
[7] Wangshen Hao, Xunsheng Zhu, Xifeng Li , and Gelvis Turyagyenda,
"Prediction of cutting force for self-propelled rotary tool using artificial
neural networks," Journal of Materials Processing Technology, vol 180,
pp. 23-29, 2006.
[8] C.C. Tsao, and H. Hocheng, "Evaluation of thrust force and surface
roughness in drilling composite material using Taguchi analysis and
neuralnetwork," journal of materials processing technology, vol. 203,
pp. 342-348, 2008.
[9] K.A. Risbood, U.S. Dixit, and A.D. Sahasrabudhe, "Prediction of
surface roughness and dimensional deviation by measuring cutting
forces and vibrations in turning process," Journal of Materials
Processing Technology, vol. 132, pp. 203-214, 2003.
[10] U. Çaydaş and A. Hasçalik, "A study on surface roughness in abrasive
waterjet machining process using artificial neural networks and
regression analysis method," Journal of Materials Processing
Technology, vol. 202, pp. 574-582, 2008.
[11] G. Krishna Mohana Rao, G. Rangajanardhaa, D. Hanumantha Rao and
M. Sreenivasa Rao, "Development of hybrid model and optimization of
surface roughness in electric discharge machining using artificial neural
networks and genetic algorithm," Journal of Materials Processing
Technology, doi.org/10.1016/j.jmatprotec.2008.04.003.
[12] Durmus Karayel, "Prediction and control of surface roughness in CNC
lathe using artificial neural," journal of materials processing technology,
In Press, Corrected Proof 2008.
[13] Bernie P. Huanga, Joseph C. Chen, and Ye Li, "Artificial-neuralnetworks-
based surface roughness Pokayoke system for end-milling
operations," Neurocomputing, vol. 71, pp 544-549, 2008.
[14] J. Paulo Davim, V.N. Gaitondeb, and S.R. Karnik , "Investigations into
the effect of cutting conditions on surface roughness in turning of free
machining steel by ANN models," journal of materials processing
technology, vol. 2 0 5, pp. 16-23, 2008.
[15] S.S. Panda, D. Chakraborty, and S.K. Pal, "Flank wear prediction in
drilling using back propagation neural network and radial basis function
network," Applied Soft Computing, vol 8. pp. 858-871, 2008.
[16] Zuperl Uros, Cus Franc, and Kiker Edi, "Adaptive network based
inference system for estimation of flank wear in end-milling," journal of
materials processing technology,vol 2 0 9, pp. 1504-1511, 2009.
[17] Abdullah Kurt, "Modelling of the cutting tool stresses in machining of
Inconel 718 using artificial neural networks," Expert Systems with
Applications , 2009.
[18] John M. Finesa, and Arvin Agah, "Machine tool positioning error
compensation using artificial neural networks," Engineering
Applications of Artificial Intelligence, vol 21, pp. 1013-1026, 2008
[19] I.A. El-Sonbaty, U.A. Khashaba, A.I. Selmy,and A.I. Ali, "Machine tool
positioning error compensation using artificial neural networks," journal
of materials processing technology, vol 2 0 0, pp. 271-278, 2008.
[20] Wu Hao, Zhang Hongtao, Guo Qianjian, Wang Xiushan,and Yang
Jianguo, "Thermal error optimization modeling and real-time
compensation on a CNC turning center," journal of materials processing
technology. Vol 2 0 7, pp. 172-179, 2008.
[21] E. Kuljanic, G. Totis, and M. Sortino, "development of an intelligent
multisensor chatter detection system in milling," Mechanical Systems
and Signal, doi:10.1016/j.ymssp.2009.01.003.
[22] Adam A. Cardi, Hiram A. Firpi, Matthew T. Bement, and Steven Y.
Liang, "Workpiece dynamic analysis and prediction during chatter of
turning process," Mechanical Systems and Signal Processing, vol 22, pp.
1481-1494, 2008.
[23] A. Jamali, N.Nariman-zadeh, A.Darvizeh, A.Masoumi, and S.Hamrang ,
"Multi-objective evolutionary optimization of polynomial neural
networks for modelling and prediction of explosive cutting process,"
Engineering Applications of Artificial Intelligence,
doi:10.1016/j.engappai.2008.11.005.
[24] Muammer Nalbant, HasanGökkaya, ─░hsan Tokta┼ƒ, and Gökhan Sur,
"Multi-objective evolutionary optimization of polynomial neural
networks for modelling and prediction of explosive cutting process,"
The experimental investigation of the effects of uncoated, PVD- and
CVD-coated cemented carbide inserts and cutting parameters on surface
roughness in CNC turning and its prediction using artificial neural
networks," Robotics and Computer-Integrated Manufacturing, vol 25,
pp. 211-223, 2009.
[25] Hong-Gyoo Kim, Jae-Hyung Sim, and Hyeog-Jun Kweon,
"Performance evaluation of chip breaker utilizing neural network,"
journal of materials processing technology, vol,2 0 9, pp. 647-656,
2009.
[26] H. Soleimanimehr, M. J. Nategh and S. Amini, "prediction of machining
force and surface roughness in ultrasonic vibration-assisted turning
using neural networks," Proc. The Int. Conf. on Advances in Materials
& Processing Technologies, 2-5 Nov., Bahrain, pp. 1-8, 2008.
[1] S. Amini, H. Soleimanimehr, M.J. Nategh, A. Abudollah and M.H.
Sadeghi, "FEM Analysis of Ultrasonic-Vibration-Assisted Turning and
the Vibratory Tool," Journal of Materials Processing Technology, vol.
201, pp. 43-47, 2008.
[2] Chandra Nath, and M. Rahman, "Effect of machining parameters in
ultrasonic vibration cutting," International Journal of Machine Tools &
Manufacture, vol. 313, pp. 395-417, 2008.
[3] D.E. Brehl, and T.A. Dow, "Review of vibration-assisted machining,"
Precision Engineering, vol. 32, pp. 153-172, 2008.
[4] J. Pujana, A. Rivero, A. Celaya, and L.N. L├│pez de Lacalle, "Analysis of
ultrasonic-assisted drilling of Ti6Al4V," International Journal of
Machine Tools & Manufacture, doi:10.1016/j.ijmachtools.2008.12.014.
[5] U. Zuperl, F. Cus, B. Mursec and T. Ploj, "A hybrid analytical-neural
network approach to the determination of optimal cutting conditions,"
Journal of Materials Processing Technology, vol. 157-158, pp. 82-90,
2004.
[6] E. O. Ezugwua, D. A. Fadarea, J. Bonney, R. B. Da Silva and W. F.
Sales, "Modelling the correlation between cutting and process
parameters in high-speed machining of Inconel 718 alloy using an
artificial neural network," International Journal of Machine Tools &
Manufacture, vol. 45, pp. 1375-1385, 2005.
[7] Wangshen Hao, Xunsheng Zhu, Xifeng Li , and Gelvis Turyagyenda,
"Prediction of cutting force for self-propelled rotary tool using artificial
neural networks," Journal of Materials Processing Technology, vol 180,
pp. 23-29, 2006.
[8] C.C. Tsao, and H. Hocheng, "Evaluation of thrust force and surface
roughness in drilling composite material using Taguchi analysis and
neuralnetwork," journal of materials processing technology, vol. 203,
pp. 342-348, 2008.
[9] K.A. Risbood, U.S. Dixit, and A.D. Sahasrabudhe, "Prediction of
surface roughness and dimensional deviation by measuring cutting
forces and vibrations in turning process," Journal of Materials
Processing Technology, vol. 132, pp. 203-214, 2003.
[10] U. Çaydaş and A. Hasçalik, "A study on surface roughness in abrasive
waterjet machining process using artificial neural networks and
regression analysis method," Journal of Materials Processing
Technology, vol. 202, pp. 574-582, 2008.
[11] G. Krishna Mohana Rao, G. Rangajanardhaa, D. Hanumantha Rao and
M. Sreenivasa Rao, "Development of hybrid model and optimization of
surface roughness in electric discharge machining using artificial neural
networks and genetic algorithm," Journal of Materials Processing
Technology, doi.org/10.1016/j.jmatprotec.2008.04.003.
[12] Durmus Karayel, "Prediction and control of surface roughness in CNC
lathe using artificial neural," journal of materials processing technology,
In Press, Corrected Proof 2008.
[13] Bernie P. Huanga, Joseph C. Chen, and Ye Li, "Artificial-neuralnetworks-
based surface roughness Pokayoke system for end-milling
operations," Neurocomputing, vol. 71, pp 544-549, 2008.
[14] J. Paulo Davim, V.N. Gaitondeb, and S.R. Karnik , "Investigations into
the effect of cutting conditions on surface roughness in turning of free
machining steel by ANN models," journal of materials processing
technology, vol. 2 0 5, pp. 16-23, 2008.
[15] S.S. Panda, D. Chakraborty, and S.K. Pal, "Flank wear prediction in
drilling using back propagation neural network and radial basis function
network," Applied Soft Computing, vol 8. pp. 858-871, 2008.
[16] Zuperl Uros, Cus Franc, and Kiker Edi, "Adaptive network based
inference system for estimation of flank wear in end-milling," journal of
materials processing technology,vol 2 0 9, pp. 1504-1511, 2009.
[17] Abdullah Kurt, "Modelling of the cutting tool stresses in machining of
Inconel 718 using artificial neural networks," Expert Systems with
Applications , 2009.
[18] John M. Finesa, and Arvin Agah, "Machine tool positioning error
compensation using artificial neural networks," Engineering
Applications of Artificial Intelligence, vol 21, pp. 1013-1026, 2008
[19] I.A. El-Sonbaty, U.A. Khashaba, A.I. Selmy,and A.I. Ali, "Machine tool
positioning error compensation using artificial neural networks," journal
of materials processing technology, vol 2 0 0, pp. 271-278, 2008.
[20] Wu Hao, Zhang Hongtao, Guo Qianjian, Wang Xiushan,and Yang
Jianguo, "Thermal error optimization modeling and real-time
compensation on a CNC turning center," journal of materials processing
technology. Vol 2 0 7, pp. 172-179, 2008.
[21] E. Kuljanic, G. Totis, and M. Sortino, "development of an intelligent
multisensor chatter detection system in milling," Mechanical Systems
and Signal, doi:10.1016/j.ymssp.2009.01.003.
[22] Adam A. Cardi, Hiram A. Firpi, Matthew T. Bement, and Steven Y.
Liang, "Workpiece dynamic analysis and prediction during chatter of
turning process," Mechanical Systems and Signal Processing, vol 22, pp.
1481-1494, 2008.
[23] A. Jamali, N.Nariman-zadeh, A.Darvizeh, A.Masoumi, and S.Hamrang ,
"Multi-objective evolutionary optimization of polynomial neural
networks for modelling and prediction of explosive cutting process,"
Engineering Applications of Artificial Intelligence,
doi:10.1016/j.engappai.2008.11.005.
[24] Muammer Nalbant, HasanGökkaya, ─░hsan Tokta┼ƒ, and Gökhan Sur,
"Multi-objective evolutionary optimization of polynomial neural
networks for modelling and prediction of explosive cutting process,"
The experimental investigation of the effects of uncoated, PVD- and
CVD-coated cemented carbide inserts and cutting parameters on surface
roughness in CNC turning and its prediction using artificial neural
networks," Robotics and Computer-Integrated Manufacturing, vol 25,
pp. 211-223, 2009.
[25] Hong-Gyoo Kim, Jae-Hyung Sim, and Hyeog-Jun Kweon,
"Performance evaluation of chip breaker utilizing neural network,"
journal of materials processing technology, vol,2 0 9, pp. 647-656,
2009.
[26] H. Soleimanimehr, M. J. Nategh and S. Amini, "prediction of machining
force and surface roughness in ultrasonic vibration-assisted turning
using neural networks," Proc. The Int. Conf. on Advances in Materials
& Processing Technologies, 2-5 Nov., Bahrain, pp. 1-8, 2008.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:52786", author = "H. Soleimanimehr and M. J. Nategh and S. Amini", title = "Modeling of Surface Roughness in Vibration Cutting by Artificial Neural Network", abstract = "Development of artificial neural network (ANN) for
prediction of aluminum workpieces' surface roughness in ultrasonicvibration
assisted turning (UAT) has been the subject of the present
study. Tool wear as the main cause of surface roughness was also
investigated. ANN was trained through experimental data obtained
on the basis of full factorial design of experiments. Various
influential machining parameters were taken into consideration. It
was illustrated that a multilayer perceptron neural network could
efficiently model the surface roughness as the response of the
network, with an error less than ten percent. The performance of the
trained network was verified by further experiments. The results of
UAT were compared with the results of conventional turning
experiments carried out with similar machining parameters except for
the vibration amplitude whence considerable reduction was observed
in the built-up edge and the surface roughness.", keywords = "Aluminum, Artificial Neural Network (ANN), BuiltupEdge, Surface Roughness, Tool Wear, Ultrasonic VibrationAssisted Turning (UAT).", volume = "3", number = "4", pages = "353-6", }
