Online prediction of cutting tool life in turning via cognitive decision making

Multiple sensor monitoring of machining was investigated for online cutting tool life assessment through cognitive decision making based on signal processing for feature extraction and pattern recognition. Sensor signals obtained from sensor monitoring of turning operations were processed and analysed. The outcome was a set of extracted signal features correlated with the consumed tool life percentage. The aim of the work is to build an online cognitive system, based on artificial neural networks, able to predict the consumed tool life during turning operations. A preliminary experimental campaign was carried out for the construction of the sensorial knowledge database; the neural network type, architecture and training algorithm.