Fuzzy controlled backpropagation neural network for tool condition monitoring in face milling

Abstract

The performance of a fuzzy controlled backpropagation neural network has been studied to predict the tool wear in a face milling process based on simple process parameters and sensor signal features. The results show the potentiality of the method in comparison to the standard backpropagation neural network and one of its variants. The speed of convergence, accuracy of prediction and total time of system development make fuzzy controlled backpropagation an attractive technique amenable for online tool condition monitoring.