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 proposedI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.