TOOL WEAR PREDICTION FROM ACOUSTIC EMISSION AND SURFACE CHARACTERISTICS VIA AN ARTIFICIAL NEURAL NETWORK

We examine the application of an artificial neural network to classification of tool wear states in face milling. The input features were derived from measurements of acoustic emission during machining and topography of the machined surfaces. Five input features were applied to the back-propagating neural network to predict a wear state of light, medium or heavy wear. We present results from milling experiments with multi- and single-point cutting and compare the neural network predictions with observed cutting insert wear states.