Application of Continuous Wavelet Features and Multi-Class Sphere SVM to Chatter Prediction

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A cutting chatter forecast method based on continuous wavelet feature and multi-class spherical Support Vector Machines is studied in this paper. The method based on continuous wavelet transform extracts the cutting vibration signal feature and uses multi-class spherical Support Vector Machines to discern the chatter. In order to simplify computational complexity when binary classification SVM turn to multi-class classification, the algorithm makes every kind of samples have a spherical SVM. In the feature space identified the test sample and spherical SVM centre distance as a decision-making function. Experiments show that using combine spherical SVM with continuous wavelet feature Vector has good recognition effect in the milling chatter recognition system. Chatter inoculation forecast accuracy reaches 95%, and chatter outbreak forecast accuracy reaches 97.5%.

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Periodical:

Main Theme:

Edited by:

Chengyong Wang, Ning He, Ming Chen and Chuanzhen Huang

Pages:

675-680

DOI:

10.4028/www.scientific.net/AMR.188.675

Citation:

S. Wu et al., "Application of Continuous Wavelet Features and Multi-Class Sphere SVM to Chatter Prediction", Advanced Materials Research, Vol. 188, pp. 675-680, 2011

Online since:

March 2011

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$35.00

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