Neural network modelling of forces and indirect prediction of tool wear in turning of grey cast iron with ceramic tool Online publication date: Thu, 05-Aug-2010
by D.K. Sarma, U.S. Dixit
International Journal of Machining and Machinability of Materials (IJMMM), Vol. 8, No. 1/2, 2010
Abstract: In the present work, cutting and feed forces in the dry and air-cooled turning of grey cast iron with mixed oxide ceramic cutting tool are modelled. The radial basis function neural network is used for this purpose. The forces could be predicted with a reasonable accuracy. Indirect estimation of tool wear based on the force measurements is also attempted. It is observed that rate of change of tool wear with respect to forces can be used for the estimation of the tool wear. However, the prediction can be made only in a probabilistic sense. The replicate experiments justify it.
Online publication date: Thu, 05-Aug-2010
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