Abstract

Advanced manufacturing systems often caters to rapidly changing product specification determination by the continuously increasing productivity, flexibility and quality demands. The estimation of cutting forces is mandatory to select tools and accessories for machining. Complex interrelationships exist between process parameters and these forces. In the present work, the applicability and relative effectiveness of artificial neural network based model has been investigated for rapid estimation of cutting forces. The results obtained are found to correlate well with the actual experimental readings of cutting forces. Experiments were conducted at different process parameters of cutting in Drilling operation. The proposed work has wide application in selection of tools and online tool wear monitoring.

References

• Kronenberg, M. , 1966. Machining Science and Applications. Oxford: Pergamon Press.
• Kasilova, A. G. and Mescheryakov, R. K. , 1985. Technological Machining Handbook. Englewood Cliffs, NJ: Prentice Hall.
• Rao, P. N. , 2000. Manufacturing Technology. New Delhi, India: Tata McGraw-Hill.
• Groover, M. P. , 2007. Fundamentals of Modern Manufacturing. USA: John Wiley and Sons.
• Fausett, L. , 1994. Fundamentals of Neural Networks. Eaglewood Cliffs, NJ: Prentice Hall.
• More, J. J. , 1977. The Levenberg - Maquardt Algorithm. G. A. Watson ed. Lecture Notes in Mathematics, Springer-Verlag, 630, 105-116.
• Aggarwal A. and Singh H. , 2005. Optimization of machining techniques – A retrospective and literature review. Sadhana, 30 (6), 699–711.
• Merchant, M. E. , 1998. Interpretive look at 20th century research on modeling of machining. Machining Science and Technology, 2 (2), 157–163.
• Osman, M. O. M. , Xistris, G. D. and Chahil G. S. , 1979. The measurement and stochastic modelling of torque and thrust in twist drilling. Interntional Journal of Production Research, 17 (4), 359-376.
• Koplev, A. , Lystrup, Aa. and Vorm, T. , 1983. The cutting process, chips and cutting forces in machining CFRP. Composites, 14 (4), 371–376.
• Veniali, F. , Di Llio, A. and Tagliaferri, V. , 1995. An experimental study of the drilling of aramid composites. Journal of Energy Resources Technology, 117 (4), 271–278.
• Fuh, K. -H. and Wang, S. -B. , 1997. Force modeling and forecasting in creep feed grinding using improved BP neural network. International Journal of Machine Tools and Manufacture, 37 (8), 1167–1178.
• Lee, B. Y. , Liu, H. S. and Tarng, Y. S. , 1998. Modeling and optimization of drilling process. Journal of Materials Processing Technology, 74 (1-3), 149–157.
• Szecsi, T. , 1999. Cutting force modeling using artificial neural networks. Journal of Materials Processing Technology, 92-93, 344–349.
• Elhachimi, M. , Torbaty, S. and Joyot, P. , 1999. Mechanical modelling of high speed drilling I: predicting torque and thrust. International Journal of Machine Tools and Manufacture, 39 (4), 553-568.
• Lachaud, F. , Piquet, R. , Collombet, F. and Surcin, L. , 2001. Drilling of composite structures. Composite Structures, 52 (3-4), 511–516.
• Ramulu, M. , Branson, T. and Kim, D. , 2001. A study on the drilling of composite and titanium stacks. Composite Structures, 54 (1), 67-77.
• Zuperl, U. and Cus, F. , 2004. Tool cutting force modeling in ball-end milling using multilevel perceptron. Journal of Materials Processing Technology, 153-154, 268–275.
• Sheng, Y. and Tomizuka, M. , 2006. Intelligent modeling of thrust force in drilling process. Journal of Dynamic Systems, Measurement, and Control, 128 (4), 846–855.
• Abrão, A. M. , Faria, P. E. , Campos Rubio, J. C. , Reis, P. and Paulo Davim, J. , 2007. Drilling of fiber reinforced plastics: A review. Journal of Materials Processing Technology, 186 (1-3), 1–7.
• Aykut, ?. , Gölcü, M. , Semiz, S. and Ergür, H. S. , 2007. Modelling of cutting forces as function of cutting parameters for face milling of stellite 6 using artificial neural network. Journal of Materials Processing Technology, 190 (1-3), 199–203.
• Tsao, C. C. and Hocheng, H. , 2008. Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. Journal of Materials Processing Technology, 203 (1-3), 342–348.
• Gilbert, W. W. , 1950. Economics of machining. In Machining – Theory and practice. Transactions of American Society for Metals, 476–480.
• Armarego, E. J. A. and Brown, R. H. , 1969. The Machining of Metals. Englewood Cliffs, NJ: Prentice Hall.
• Singh, R. , Das, G. and Setia, R. , 2007. Parametric modeling of a rotary furnace for agile production of castings with artificial neural networks. International Journal of Agile Manufacturing, 10 (2), 137–147.
• Singh, R. , Setia, R. and Das, G. , 2007. Modeling and optimization of rotary furnace parameters using artificial neural networks and genetic evolutionary algorithms. In Proceedings of the 31st National Systems Conference, Manipal, India, P. No. 75.
• Brewer, R. C. and Rueda, R. , 1963. A simplified approach to the optimum selection of machining parameters. Eng. Dig. , 24(9), 133–150.
• Ai, X. and Xiao, S. , 1985. Metal Cutting Condition Handbook. China: Mechanics Industry Press.