Identification of Milling Status Using Vibration Feature Extraction Techniques and Support Vector Machine Classifier

摘要

The objective of this study is to use the vibration signal features of spindles during the cutting processing to identify the different milling statuses in cases of diverse tooling parameter combinations. Accelerometers were placed on a spindle to measure vibration behaviors, and the milling status could be divided into idle cutting, initial feeding, and stable cutting. Vibration signal processing and analysis were conducted in the time domain, as well as in the frequency domain. The original vibration measurements were separated using empirical mode decomposition (EMD) in the time domain, so that the signal features could be extracted in certain frequency bands and the useless signal components and trends could be removed. Multi-scale entropy (MSE) and root mean square (RMS) were computed to extract the time domain features. In the frequency domain, the specific intrinsic mode functions (IMFs) that were decomposed using the EMD method were analyzed by fast fourier transform (FFT) and a frequency normalization technique to extract the features of apparent physical representations. The Fisher scores (FS) of the extracted features are calculated to select the high-priority signal features. The selected high-priority signal features are utilized to identify the different milling statuses through a support vector machine (SVM). The results show that an identification accuracy of 98.21% could be obtained at the Z axis, and the average accuracy would be 95.91% for the three axes combination.