Abstract
Tool flank wear during turning is monitored through artificial neural networks of the input consists of the AR coefficients representing the power spectrum of cutting force and some other parameters The order of AR model is effectively determined by AIC. The monitored and measured flank wear agree very well. The flank wear rate monitored is further used to adaptively revise the characteristics constants of a wear equation, by which the wear rate after the change of cutting conditions is predicted and the optimum conditions are finally selected for a case study.
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