Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping

TL;DR

This paper introduces a machine learning approach using Dynamic Time Warping and k-Nearest Neighbor for chatter detection in turning processes, achieving high accuracy without manual feature extraction and with potential for real-time application.

Contribution

The paper presents a novel DTW-based method for chatter detection that outperforms traditional feature-based approaches and does not require manual feature extraction or large datasets.

Findings

Achieves up to 99% accuracy in chatter detection

Outperforms traditional methods like WPT and EEMD in most configurations

Capable of reusing classifiers across different cutting conditions

Abstract

Chatter detection from sensor signals has been an active field of research. While some success has been reported using several featurization tools and machine learning algorithms, existing methods have several drawbacks such as manual preprocessing and requiring a large data set. In this paper, we present an alternative approach for chatter detection based on K-Nearest Neighbor (kNN) algorithm for classification and the Dynamic Time Warping (DTW) as a time series similarity measure. The used time series are the acceleration signals acquired from the tool holder in a series of turning experiments. Our results, show that this approach achieves detection accuracies that in most cases outperform existing methods. We compare our results to the traditional methods based on Wavelet Packet Transform (WPT) and the Ensemble Empirical Mode Decomposition (EEMD), as well as to the more recent Topological Data Analysis (TDA) based approach. We show that in three out of four cutting configurations our DTW-based approach attains the highest average classification rate reaching in one case as high as 99% accuracy. Our approach does not require feature extraction, is capable of reusing a classifier across different cutting configurations, and it uses reasonably sized training sets. Although the resulting high accuracy in our approach is associated with high computational cost, this is specific to the DTW implementation that we used. Specifically, we highlight available, very fast DTW implementations that can even be implemented on small consumer electronics. Therefore, further code optimization and the significantly reduced computational effort during the implementation phase make our approach a viable option for in-process chatter detection.