Feature selection for predicting tool wear of machine tools

Abstract

In this study, the vibration transmitted solely from a spindle to the worktable is proposed to be a crucial feature of wear prediction models for machine tools. To validate the effectiveness of the proposed feature, a feature ranking and screening methodology was also used for developing a tool wear prediction model. First, the features extracted from vibration signals were ranked according to their contributions to tool wear prediction. The features were then filtered through a screening process based on singular value decomposition to eliminate redundant features, which exhibited collinearity with features of higher rankings. The aim of the aforementioned steps was to use a relatively small number of highly appropriate features to create an accurate real-time tool wear prediction model. The results indicated that the accuracy of the tool wear prediction model based on the proposed feature ranking and screening methodology is higher than that of models without feature ranking or screening. Moreover, the proposed feature was proven to be more important and effective than other features.