Experimental cutting tests on C45 carbon steel turning were performed for sensor fusion based monitoring of chip form through cutting force components and radial displacement measurement. A Principal Component Analysis algorithm was implemented to extract characteristic features from acquired sensor signals. A pattern recognition decision making support system was performed by inputting the extracted features into feed-forward back-propagation neural networks aimed at single chip form classification and favourable/unfavourable chip type identification. Different neural network training algorithms were adopted and a comparison was proposed

Principal component analysis for feature extraction and NN pattern recognition in sensor monitoring of chip form during turning / Segreto, Tiziana; Simeone, Alessandro; Teti, Roberto. - In: CIRP - JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY. - ISSN 1755-5817. - 7:3(2014), pp. 202-209. [10.1016/j.cirpj.2014.04.005]

Principal component analysis for feature extraction and NN pattern recognition in sensor monitoring of chip form during turning

SEGRETO, Tiziana;SIMEONE, ALESSANDRO;TETI, ROBERTO
2014

Abstract

Experimental cutting tests on C45 carbon steel turning were performed for sensor fusion based monitoring of chip form through cutting force components and radial displacement measurement. A Principal Component Analysis algorithm was implemented to extract characteristic features from acquired sensor signals. A pattern recognition decision making support system was performed by inputting the extracted features into feed-forward back-propagation neural networks aimed at single chip form classification and favourable/unfavourable chip type identification. Different neural network training algorithms were adopted and a comparison was proposed
2014
Principal component analysis for feature extraction and NN pattern recognition in sensor monitoring of chip form during turning / Segreto, Tiziana; Simeone, Alessandro; Teti, Roberto. - In: CIRP - JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY. - ISSN 1755-5817. - 7:3(2014), pp. 202-209. [10.1016/j.cirpj.2014.04.005]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/617978
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