Authors: K. Rama Kotaiah, J. Srinivas, K.J. Babu
Addresses: Department of Industrial and Production Engineering, K.L. University, Vaddeswaram, Guntur (Dist), Andhra Pradesh, 522502, India. ' Department of Mechanical Engineering, Chaitanya Engineering College, Visakhapatnam, Andhra Pradesh, India. ' K.L. University, Vaddeswaram, Guntur (Dist), Andhra Pradesh, 522502, India
Abstract: This paper proposes a neural network-based optimisation scheme for predicting localised stable cutting states in inward turning operation. A set of cutting experiments are performed in inward orthogonal turning operation. The cutting forces and critical chatter locations are predicted as a function of operating variables including tool overhang length. Radial basis function neural network are employed to develop the generalisation models. Optimum cutting parameters are predicted from the model using binary-coded genetic algorithms. Results are illustrated with the data corresponding to four work materials operated over a HSS tool.
Keywords: critical chatter length; tool overhang; RBF neural networks; optimum parameters; orthogonal turning; optimisation; stable cutting states; cutting forces; genetic algorithms; HSS tooling; high speed steel.
International Journal of Machining and Machinability of Materials, 2010 Vol.7 No.3/4, pp.193 - 207
Published online: 07 May 2010 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article