Investigation of optimal feature for milling chatter identification using supervised machine learning techniques

In order to overcome the complex mathematical models and tedious analytical skills and improve the machining performance, various effective methods have been developed using time domain, frequency domain and time and frequency domain-based features. However, the selection of these features can be difficult, and results can be alleviated if it is wrongly selected. This study proposes a methodology that helps identify the optimal feature from eight time-domain statistical features using supervised machine learning algorithms. In this work, 44 milling experiments have been performed and labelled as chatter, transient, and stable states by observing the tool-machining condition. Subsequently, the eight time-domain-based features, i.e. mean, variance, peak-to-peak, root mean square, crest factor, form factor, kurtosis and skewness, have been calculated. After that, four machine learning techniques, i.e., random forest, gradient boosting, support vector machine and logistic regression, were utilized, and their accuracy score was 92.86 %, 96.8 %, 94.8 % and 91 %, respectively. After that, their feature score was evaluated to investigate the effectiveness of all 8 time domain-based features. Feature scores for mean, variance, peak-to-peak, root mean square, crest factor, form factor, kurtosis and skewness are 0.28, 0.25, 0.11, 0.095, 0.085, 0.08, 0.065 and 0.035, respectively. The outcome of this research is that peak-to-peak time domain-based features, along with gradient boosting, can extract chatter features in the presence of an extraneous noisy signal.