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Application of Support Vector Machine and Vibration Feature Extraction for Identifying Milling Status under Different Rotating Speed
cutting status identification
empirical mode decomposition
support vector machine
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This study focuses on the identification of cutting status of machine tool under different preset cutting parameters through using the vibration signal analysis. The vibration signals were collected by the accelerometer on the spindle. The identified tooling status includes the idle cutting, initial feeding and stable cutting. The signal analysis is performed in the time domain and frequency domain. In the time domain analysis, the original signal is separated by using the empirical mode decomposition (EMD) method to abandon the trend term, and then the multi-scale entropy (MSE) and root mean square (RMS) of the signal are determined as the time domain features. For the feature extraction in frequency domain, the intrinsic mode functions (IMFs) that contain the physical features in certain frequency band are analyzed through the fast Fourier transform (FFT) process. The rotation speed normalization approach is employed to remove the influence of spindle rotating speed. The factors of natural frequencies of the structural vibration in machine tool are also neglected. The features of higher priorities are selected by evaluating their fisher scores. The results show that the support vector machine (SVM) can identify the different cutting status with high accuracy.
|Appears in Collections:||機械工程學系所|
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