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Sensor fusion for tool state classification in nickel superalloy high performance cutting

: Segreto, T.


Wegener, K. ; TH Zürich -ETH-, Institut für Werkzeugmaschinen und Fertigung -IWF-; International Academy for Production Engineering -CIRP-, Paris:
5th CIRP Conference on High Performance Cutting 2012 : 4.-7. June 2012, Zürich; HPC 2012
Amsterdam: Elsevier, 2012 (Procedia CIRP 1.2012)
Conference on High Performance Cutting (HPC) <5, 2012, Zürich>
Konferenzbeitrag, Zeitschriftenaufsatz
Fraunhofer IWU ()

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.