Intelligent tool failure monitoring for machining processes

An improved strategy is presented for intelligent tool wear monitoring under varying cutting conditions. The proposed strategy uses wear feature extraction based on procett modelling and parameter estimation. The adaptive model traces the properties of cutting processes by combining process state signals, cutting conditions, a force model and the least squares method. The tool wear feature is obtained from the estimated parameters of the model. Experimental results show that changes of the parameters in the cutting force model reliably indicate tool wear independent of variation of the cutting conditions.