Modeling of Power Consumption in Turning of Ferrous and Nonferrous Materials using Artificial Neural Network

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2013 by IJETT Journal
Volume-4 Issue-3                       
Year of Publication : 2013
Authors :  Mr. Mangesh R.Phate , Dr. V.H.Tatwawadi


Mr. Mangesh R.Phate , Dr. V.H.Tatwawadi. "Modeling of Power Consumption in Turning of Ferrous and Nonferrous Materials using Artificial Neural Network". International Journal of Engineering Trends and Technology (IJETT). V4(3):236-241 Mar 2013. ISSN:2231-5381. published by seventh sense research group


Development of artificial neural network (ANN) for prediction of power consumption in the turning of ferrous and nonferrous materials has been the subject of the present paper. ANN was trained through field data obtained on the basis of random plan of experimentation. Various influential machining field parameters wer e taken into consideration. The inputs were machine operator, work piece, cutting tool, cutting process parameters, machine specification and the machining field environmental parameters while the output was power consumed during the machining of ferrous and nonferrous materials. It was illustrated that a multilayer perception neural network could efficiently model the power consumption as the response of the network, with a minimum error. The performance of the trained network was verified by further obs ervations. 6 - 5 - 1 topology has been used for getting simulated result. The results of ANN were compared with the results of conventional turning (CT) observations.


[1] S. M. Metev and V. P. Veiko, Laser Assisted Micro technology , 2nd ed., R. M. Osgood, Jr., Ed. Berlin, Germany: Springe r - Verlag, 1998.
[2] J. Breckling, Ed., The Analysis of Directional Time Series: Applications to Wind Speed and Direction , ser. Lecture Not es in Statistics. Berlin, Germany: Springer, 1989, vol. 61.
[3] S. Zhang, C. Zhu, J. K. O. Sin, and P. K. T. Mok, “A novel ul trathin elevated channel low - temperature poly - Si TFT,” IEEE Electron Device Lett. , vol. 20, pp. 569 – 571, Nov. 1999.
[4] M. Wegmuller, J. P. von der Weid, P. Oberson, and N. Gisin, “High resolution fiber distributed measurements with coherent OFDR,” in Proc. EC OC’00 , 2000, paper 11.3.4, p. 109.
[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, 200 4
[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 anartificial neural network,” Inte rnational 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 artificialneural 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 andneuralnetwork,” journal of materials processing technology , vol. 203, pp. 342 – 348, 2008. “PDCA12 - 70 data sheet,” Opto Speed SA, Mezzovico, Switzerland.
[9] A. Karnik, “Performance of TCP congestion control with rate feedback: TCP/ABR and rate adaptive TCP/IP,” M. Eng. thesis, Indian Institute of Science, Bangalore, India, Jan. 1999 .
[10] J. Padhye, V. Firoiu, and D. Towsley, “A stochastic model of TCP Reno congestion avoidance and control,” Univ. of Massachusetts, Amherst, MA, CMPSCI Tech. Rep. 99 - 02, 1999.
[11] Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification , IEEE Std. 802.11, 1997.
[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] J. Paulo Davim, V.N. Gaitondeb, and S.R. Karnik , “Inves tigations 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.
[14] S.S. Panda, D. Chakraborty, and S.K. Pal, “Flank we ar prediction in drilling using back propagation neural network and radial basis function network,” Applied Soft Computing , vol 8. pp. 858 – 871, 2008.
[15] Zuperl Uros, Cus Franc, and Kiker Edi, “Adaptive network based inference system for estimation of flank we ar in end - milling,” journal of materials processing technology ,vol 2 0 9, pp. 1504 – 1511, 2009.
[16] Abdullah Kurt, “ Modeling of the cutting tool stresses in machining of Inconel 718 using artificial neural networks,” Expert Systems with Applications , 2009.
[17 ] John M. Finesa, and Arvin Agah, “Machine tool positioning error compe nsation using ANN ,” e ngineering Applications of Artificial Intelligence, vol 21, pp. 1013 – 1026, 2008
[18] 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 .
[19 ] E. Kuljanic, G. Totis, and M. Sortino, “developm ent of an intelligent multisensor chatter detection system in milling,” Mechanical Systems and Signal , doi:10.1016/j.ymssp.2009.01.003.
[20 ] Adam A. Cardi, Hiram A. Firpi, Matthew T. Bement, and Steven Y. Liang, “Work piece dynamic analysis and prediction during chatter of turning process,” Mechanical Systems and Signal Processing , vol 22, pp.1481 – 1494, 2008.

Artificial Neural Network, Mathematical Model, Turning Process , ferrous and nonferr ous material & convectional machining .