On Real Time Prediction of Cutting Forces Using ANN

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Due to the increased calls for environmentally benign machining processes, there has been more focus and interest in making processes more lean and agile to enhance efficiency, reduce emissions and increase profitability. One approach to achieving lean machining is to develop a virtual simulation environment that enables fast and reasonably accurate predictions of machining scenarios, process output, and provide access to needed information. This paper investigates the utilization of artificial neural networks (ANNs) to predict the cutting forces resulting from various combinations of cutting parameters and can also provide values for the cutting coefficients usually predicted to calibrate the force models. Predictions are compared to measured experimental results obtained and are shown to be in good agreement
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Cutting Forces, Cutting Coefficients, Artificial Neural Networks, Mechanistic Model, Face Milling

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C.A. Luttervelt, T.H.C. Childs, I.S. Jawahir, F. Klocke, P.K. Venuvinod, Keynote papers: Present situation and future trends in modeling of machining operations, CIRP PCals - Manufacturing Technology, Vol. 47, pp. 587-626, 1998.

Mahesh, G., Muthu, S., Devadasan, S.R., Experimentation and prediction of vibration amplitude in end milling with reference to radial rake angle, (2012) International Review of Mechanical Engineering (IREME), 6 (6), pp. 1164-1174.

Yanda, H., Ghani, J.A., Haron, C.H.C., Modeling and simulation of temperature generated on work piece and chip formation in orthogonal machining, (2011) International Review of Mechanical Engineering (IREME), 5 (2), pp. 340-348.

Arun Premnath, A., Alwarsamy, T., Rajmohan, T., Experimental investigation on hardness, cutting force and roughness in milling of hybrid composites, (2012) International Review of Mechanical Engineering (IREME), 6 (1), pp. 44-49.

K.F. Ehmann, S.G. Kapoor, R.E. Devor, I. Lazoglu, Machining process modeling: A review, Journal of Manufacturing Science & Technology, Vol. 119, pp. 655-663, 1997.

F. Koenigsberger, J.P. Sabberwal, An Investigation into the cutting force pulsation during milling operations, International Journal of Machine Tool Design and Research, Vol. 1, pp. 15-33, 1961.

B.V. Coelho, Experimental Evaluation of Cutting force parapeters applying mechanistic model in orthogonal milling, Journal of the Brazilian Society of Mechanical Science and Engineering, Vol. XXV, pp. 247-253, 2003.

K.D. Jayaram, Estimation of the specific cutting pressures for mechanistic cutting force models, International Journal of Machine Tool & Manufacture, Vol. 41, pp. 265-281, 2001.

M. Wan, A novel cutting force modelling method for cylindrical end mil, Applied Mathematical Modeling, Vol. 34, pp. 823-836, 2010.

R.J. Saffar, Simulation of three dimension cutting force and tool deflection in the end milling operation based on finite element method, Simulation Model and Practise Theory, Vol. 16, pp. 1677-1688, 2008.

V. Tandon, H. El-Mounayri, A novel artificial neural networks force model for end milling, International Journal of Advanced Manufacturing Technology, Vol. 18, pp. 693-700, 2001.

S. Aykut, M. Gölcüa, S. Semiz, H.S. Ergur, Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network, Journal of Materials Processing Technology, Vol. 190, pp. 199–203, 2007.

F. Cus, U. Zuperl, M. Milfelner, Dynamic neural network approach for tool cutting force modeling of end milling operations, International Journal of General Systems, Vol. 35, pp. 603-618, 2006.

U. Zuperl, F. Cus, B. Mursec, T. Ploj, A hybrid analytical-neural network approach to the determination of optimal cutting conditions, Journal of Materials Processing Technology, Vol. 175, pp. 82–90, 2004.

A.M.A. Al-Ahmari, Predictive Machinability models for a selected hard material in turning operations, Journal of Materials Processing Technology, Vol. 190, pp. 305-311, 2007.

I.M. Deiab, Effect of fixture dynamics on the face milling process, Ph.D. dissertation, Dept. Mech. Eng., McMaster University, Hamilton, Ontario, Canada, 2003.

F. Gu, Prediction of cutting forces and surface errors in face milling with generalized cutter and workpice geometry, Ph.D. dissertation, University of Illinois at Urbana-Champaign, IL, 1994.

H.S. Kim, K.F. Ehmann, A cutting force model for face milling operations, International Journal of Machine Tool & Manufacture, Vol. 33, pp. 651-673, 1993.

A.M. Zain, Prediction of surface roughness in the end milling machining using artificial neural network, Expert Systems with Applications, Vol. 37, pp. 1755-1768, 2010.

D. Skapura, Building neural networks (ACM Press, Addison-Wesley, 1996).

MATLAB, www.mathworks.com

M. Nalbant, H. Gokkaya, I. Toktas, G. Sur, 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.


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