Investigation of the effects of different chip breaker forms on the cutting forces using artificial neural networks

Year 2012, Volume: 25 Issue: 3, 803 - 814, 06.01.2012

Hüseyin Gurbuz Abdullah Kurt Ulvi Seker

Abstract

This paper presents a new approach based on artificial neural networks (ANNs) to determine the effects of different chip breaker forms on cutting forces such as principal cutting force, feed force and passive force, in the machining of AISI 1050. The backpropagation learning algorithm and fermi transfer function were used in the network. The best fitting training data set was obtained with nine neurons in the hidden layer, which made it possible to predict cutting forces with an accuracy which is at least as good as that of the experimental error, over the whole experimental range. After training, it was found that the R² values are 0.9829, 0.9667 and 0.9492 for F_C, F_f and F_p, respectively. The average error is %0.145. As seen from the results of mathematical modeling, the calculated cutting forces are obviously within acceptable uncertainties.

 

                Keywords: Cutting forces, Chip breaker form, Artificial neural

               networks

Keywords

Cutting forces, Chip breaker form, Artificial neural networks

References

• Cook, N.H., Jehaveri, P., “The mechanism of chip curl and its importance in metal cutting”, Trans., 85(B): 374- 380 (1963).
• Spaans, C., Geel, P.F.H.J., “Breaking mechanisms in cutting with a chip breaker”, Ann. CIRP,18: 87-92 (1966).
• Karahasan, Z.O. The Influences of tool geometry and chip breaker form of cutting on tool performance, MSc. Thesis (in Turkish), Yıldız Technical University, Science and Technology, Istanbul, Turkey, pp.153-161 (1995).
• Mesquita, R.M.D., Barata Marques, M.J.M., “Effect of chip-breaker geometries on cutting forces”, J. Mater. Pro. Tech., 31: 317-325 (1992).
• Fang, N., “Influence of the geometrical parameters of the chip groove on chip breaking performance using new-style chip formers”, J. Mater. Pro. Tech., 74: 268-275 (1998).
• Kim, J.D., Kweun, O.B., “A chip-breaking system for mild steel in turning”, Int. J. Mach. Tools and Manuf., 37: 607-617 (1997).
• Das, N.S., Chawla B.S., Biswas C.K., “An analysis of strain in chip breaking using slip-line field theory with adhesion friction at chip/tool interface”, Journal of Materials Processing Technology, 170: 509–515 (2005).
• Mahashar, A. J. Murugan M., “Influence of chip breaker location and angle on chip form in turning low carbon steel”, International journal of machining and machinability of materials A., 5(4): 452-475 (2009).
• Zuperl, U., Cus, F., Mursec, B., and Ploj, T., “A hybrid analytical-neural network approach to the determination of optimal cutting conditions”, Journal of Materials Processing Technology, 157– 158: 82-90 (2004).
• Ezugwua, E. O., Fadarea, D. A., Bonney, J., Da Silva, R. B. and Sales, W. F., “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,;45: 1375-1385 (2005).
• Karayel, D., “Prediction and control of surface roughness in CNC lathe using artificial neural network”, Journal of Materials Processing Technology, 209: 3125-3137 (2009).
• Paulo Davim, J., Gaitondeb, V.N., and Karnik, S.R., “Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models”, Journal of Materials Processing Technology, 2 0 5: 16–23 (2008).
• Kurt, A., “Modelling of the cutting tool stresses in machining of Inconel 718 using artificial neural networks”, Expert Systems with Applications, 36: 9645-9657 (2009).
• Adam, A. Cardi, Hiram A. Firpi, Matthew T. Bement, and Steven Y. L., “Workpiece dynamic analysis and prediction during chatter of turning process”, Mechanical Systems and Signal Processing, 22: 1481–1494 (2008).
• Jamali, A., Nariman-zadeh, N., Darvizeh, A., Masoumi, A., and Hamrang, S., “Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process”, Engineering Applications of Artificial Intelligence, 22: 676-687 (2009).
• Nalbant, M., Gökkaya, H., Toktaş, İ., Sur, G., “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, 25: 211–223 (2009).
• Soleimanimehr, H., Nategh, M. J. and Amini, S., “Prediction of machining force and surface roughness in ultrasonic vibration-assisted turning using neural networks”, Advanced Materials Research, 83-86: 326-334 (2010).
• Szecsi, T., “Cutting force modeling using artificial neural networks”, Journal of Materials Processing Technology, 92-93: 344-349 (1999).
• Kim H.G., Sim J.H., Kweon H.J., “Performance evaluation of chip breaker utilizing neural network”, Journal of Materials Processing Technology A. 209(2): 647-656 (2009). [20] Jiang, C.Y., Zhang, Y.Z. and Chi, Z.J., “Experimental research of the chip flow direction and its application to the chip control”, Ann. CIRP, 33: 81-84 (1984).
• Komanduri, R. and Brown, R.H., “On the mechanics of chip segmentation in machining”, Transactions of American Society of Mechanical Engineers Journal of Engineering for Industry, 103: 33–51 (1981).
• Jawahir, I.S. and Luttervelt, C.A.V, “Recent Developments in Chip Control Research and applications”, Annals of the CIRP, 42(2): 659–693 (1993).
• Jawahir, I.S., Qureshi, N. and Arsecularatne, J.A., “On the interrelationships of some machinability parameters in finish turning with cermet chip forming tool inserts”, Int. J. Mach. Tools Manuf., 32: 709–723 (1992).
• Fang, X.D., Fei, J. and Jawahir, I.S., “A Hybrid Algorithm Breakability in Machining”, Int. J. Mach. Tools Manuf., 36(10): 1093–1107 (1996). Form/Chip
• Gürbüz, H., The Effect on The Cutting Tool Stresses of The Different Chip Breaker Form in Turning, MSc. Thesis (in Turkish), Gazi University, Institute of Science and Technology, Ankara, Turkey, (2006).
• Gürbüz, H., Kurt, A., Korkut, İ., Şeker, U., "The experimental investigation of the effects of different chip breaker forms on the cutting forces", Advanced Materials Research, 23: 191-194 (2007).
• Kalogirou, S.A., “Artificial intelligence for the modeling and control of combustion processes: a review”, Progress in Energy and Combustion Science, 29: 515-566 (2003).
• Sozen, A., “Future projection of the energy dependency of Turkey using artificial neural network”, Energy Policy, 37(11): 4827-4833 (2009).
• Karataş, C., Sozen, A., Dulek, E., “Modelling of residual stresses in the shot peened material C-1020 by artificial neural network”, Expert Systems with Applications, 36(2): 3514-3521 (2009).
• Sözen, A., Arcaklioğlu, E., Menlik, T., Ozalp, M., “Determination of thermodynamic properties of an alternative refrigerant (R407c) using artificial neural network”, Expert Systems with Applications, 36(3): 4346-4356 (2009).
• Mitsubishi Carbide, Turning Tools, Rotating Tools, Tooling Solutions, General Catalogue, (2005).
• Chen, W., “Cutting Forces and Surface Finish When Machining Medium Hardness Steel Using CBN Tools”, International Journal of Machine Tools & Manufacture, 40: 455–466 (2000).
• Trent, E.M., Metal Cutting, Butterworths Press, 1- 171, London, (1989).
• Zhao, J., Ai X. and Li, Z., “Finite element analysis of cutting forces in high speed machining”, Materials Science Forum, 532-533: 753-756 (2006).
• Seker, U., Lecture notes in Tool design (in Turkish), Gazi University, Technical Education Faculty, Ankara, Turkey, pp. 33-44, pp.47-72 (1997).