期刊
FIFTH CIRP CONFERENCE ON HIGH PERFORMANCE CUTTING 2012
卷 1, 期 -, 页码 593-598出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procir.2012.05.005
关键词
Inconel 718; Turning; Tool wear; Multiple sensor monitoring; Sensor fusion; Principal component analysis; Neural networks
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. (C) 2012 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Professor Konrad Wegener
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