TOOL WEAR MONITORING FOR OPTIMIZING CUTTING CONDITIONS

Tool flank wear during turning is monitored through artificial neural networks of which the input consists of the AR coefficients representi ng 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 characteristic constants of a we ar equation, by which the wear rate after the change of cutting condit ions is predicted and the optimum conditions are finally selected for a case study.