On Transfer Learning of Traditional Frequency and Time Domain Features in Turning

There has been an increasing interest in leveraging machine learning tools for chatter prediction and diagnosis in discrete manufacturing processes . Traditional features for studying chatter include traditional signal processing tools such as Fast Fourier Transform (FFT), Power Spectral Density (PSD), and the Auto-correlation Function (ACF)… In this study, we use these tools in a supervised learning setting to identify chatter in accelerometer signals obtained from a turning experiment . We then examine the resulting signals and tag them as either chatter or chatter-free . The tagged signals are then used to train a classifier . The classification methods include the most common algorithms: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Gradient Boost (GB) Our results show that features extracted from the Fourier spectrum are the most informative when training and testing on data from the same cutting configuration yield accuracy as high as %96. However, we conclude that while these traditional features can be highly tuned to a certain process, their transfer learning ability is limited. We also compare our results against two other methods with rising popularity in the literature: Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEM

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Keywords : chatter - features - traditional - learning - tools -

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