Fractal Estimation of Flank Wear in Turning

Abstract

A novel fractal estimation methodology, that uses—for the first time in metal cutting literature—fractal properties of machining dynamics for online estimation of cutting tool flank wear, is presented. The fractal dimensions of the attractor of machining dynamics are extracted from a collection of sensor signals using a suite of signal processing methods comprising wavelet representation and signal separation, and are related to the instantaneous flank wear using a recurrent neural network. The performance of the resulting estimator, evaluated using actual experimental data, establishes our methodology to be viable for online flank wear estimation. This methodology is adequately generic for sensor-based prediction of gradual damage in mechanical systems, specifically manufacturing processes. [S0022-0434(00)02401-1]