Sensor Fusion for Tool State Classification in Nickel Superalloy High Performance Cutting

摘要

A multiple sensor monitoring system, endowed with cutting force, acoustic emission and vibration sensing units, was employed for tool state classification in turning of Inconel 718. A sensor fusion signal processing paradigm based on the Principal Component Analysis was applied to the sensor signals generated during cutting in order to reduce the high dimensionality of the sensory data by extracting significant signal features. The principal components, obtained through Principal Component Analysis of sensor fusion data matrices and strongly related to sensor signals, were used as input features to a neural network based pattern recognition procedure for decision making on tool wear condition.