# On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode Decomposition

## TL;DR

This study compares Wavelet Packet Transform and Ensemble Empirical Mode Decomposition for chatter detection in turning, demonstrating that EEMD offers superior transfer learning performance with high accuracy across different machine configurations.

## Contribution

It introduces a comparative analysis of WPT and EEMD for chatter detection and highlights EEMD's better transfer learning capabilities in metal cutting applications.

## Key findings

- EEMD outperforms WPT in transfer learning scenarios.
- Both methods achieve over 94% accuracy when trained and tested on the same configuration.
- WPT's feature selection based on energy ratios may miss chatter frequencies, reducing accuracy.

## Abstract

The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting. Two prominent and advanced methods for feature extraction via signal decomposition are Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). We apply these two methods to time series acquired from an acceleration sensor at the tool holder of a lathe. Different turning experiments with varying dynamic behavior of the machine tool structure were performed. We compare the performance of these two methods with Support Vector Machine (SVM), Logistic Regression, Random Forest Classification and Gradient Boosting combined with Recursive Feature Elimination (RFE). We also show that the common WPT-based approach of choosing wavelet packets with the highest energy ratios as representative features for chatter does not always result in packets that enclose the chatter frequency, thus reducing the classification accuracy. Further, we test the transfer learning capability of each of these methods by training the classifier on one of the cutting configurations and then testing it on the other cases. It is found that when training and testing on data from the same cutting configuration both methods yield high accuracies reaching in one of the cases as high as 94% and 95%, respectively, for WPT and EEMD. However, our experimental results show that EEMD can outperform WPT in transfer learning applications with accuracy of up to 95%.