Application of sensor fusion and polynomial classifiers to tool wear monitoring

Abstract

This paper presents a novel approach to model and predict cutting tool wear using statistical signal analysis, pattern recognition and sensor fusion. The data are acquired from two sources: an acoustic emission sensor (AE) and a tool post dynamometer. The pattern recognition used here is based on two methods: artificial neural networks (ANN), and polynomial… (More)

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