Title: Neural network modelling of forces and indirect prediction of tool wear in turning of grey cast iron with ceramic tool
Authors: D.K. Sarma, U.S. Dixit
Addresses: Department of Mechanical Engineering, Indian Institute of Technology Guwahati – 781 039, India. ' Department of Mechanical Engineering, Indian Institute of Technology Guwahati – 781 039, India
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.
Keywords: dry turning; air-cooled turning; mixed oxide ceramic tooling; cutting force; tool wear estimation; RBF neural networks; modelling; grey cast iron; feed force; force measurements; wear prediction.
International Journal of Machining and Machinability of Materials, 2010 Vol.8 No.1/2, pp.55 - 75
Published online: 05 Aug 2010 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article