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

Research Area:
Sustainable Design


CIRP Journal of Manufacturing Science and Technology

SMART Authors:
Alessandro Simeone

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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.

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