A steps-ahead tool wear prediction method based on support vector regression and particle filtering

Abstract

This paper develops a steps-ahead tool wear prediction method based on particle filtering and support vector regression. A degradation phase classification method is presented based on clustering algorithm and support vector machine. The support vector regression models are established to achieve the mapping between degradation features and the flank tool wear values. In online prediction, the measured signals are input into the SVM model to judge the current phase of the tool, and the particle filtering algorithm is used for online steps-ahead prediction of the features. The prediction of tool wear can be obtained by inputting the feature prediction results into the SVR models. The experimental tool wear dataset is introduced as an application example to test the proposed method. The results demonstrate the effectiveness of the proposed method, and the comparison with other tool wear prediction shows the advantage of the proposed method in prediction accuracy.