Modeling of Surface Roughness in Vibration Cutting by Artificial Neural Network

Authors: H. Soleimanimehr, M. J. Nategh , S. Amini

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).

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330745

References:

[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.