Empirical and Neural Network Modelling of Tool Wear Development in Ni-Base Alloy Machining

Tool wear measurement data from turning of Inconel 718 aircraft engine components were processed by conventional interpolation and neural network based methods, aiming at the on-line prediction of tool wear development. A four-constant empirical model was derived to predict flank wear as a function of cutting time and cutting speed. These results were compared with those obtained from the application of a supervised neural network paradigm for flank wear forecasting