Abstract
Supervision or condition monitoring of the cutting tool wear is a complex phenomenon in any machining process. This paper describes an in-depth study on the development of a cutting tool condition monitoring (TCM) system for high speed turning operations. An attempt has been made to create artificial flank wear using electrical discharge machining (EDM) process. Using this (artificial worn out) cutting tool, experiments were carried out for various machining conditions. Vibration and strain data from the cutting process are recorded using two accelerometers and a strain gauge bridge. Time domain and power spectral analyses are carried out and an artificial neural networks (ANN) back propagation algorithm is used to classify and estimate the wear parameters.
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