Flank Wear Estimation in Face Milling Based on Radial Basis Function Neural Networks

This paper presents an estimation of flank wear in face milling operations using radial basis function (RBF) networks. Various signals such as acoustic emission (AE), surface roughness, and cutting conditions (cutting speed and feed) have been used to estimate the flank wear. The hidden layer RBF units have been fixed randomly from the input data and using batch fuzzy C means algorithm, and a comparative study has been carried out. The results obtained from a fixed RBF network have been compared with those from a resource allocation network (RAN).