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

Abstract

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%.

Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning

“…The ATR-FTIR data processed by MSC-SG had the best performance, with an RMSEP of 0.462, R C 2 of 0.995, and R P 2 of 0.988. The support vector machine (SVM) was employed for the identification of various species in biological samples [33][34][35]. Here, we used fourteen samples with the same spectral range to demonstrate the feasibility of SVM.…”