Tool wear prediction based on kernel principal component analysis and least square support vector machine

Abstract

To accurately predict the amount of tool wear in the machining process, a monitoring model of tool wear based on multi-sensor information feature fusion is proposed. First, by collecting the cutting force, vibration, and acoustic emission signals of the tool during the whole life cycle, the multi-domain characteristics of the signal are extracted; then, kernel principal component analysis is used to reduce the dimensionality of the extracted data, and the principal components whose cumulative contribution ratio exceeds 85% are obtained. The redundant features with little correlation with tool wear were removed from the feature vectors to generate the fusion features. Finally, the fusion features are input into the least squares support vector machine model optimized by particle swarm algorithm for regression prediction of tool wear. The non-linear mapping relationship between the physical signal and the tool wear is discovered, which effectively realizes the prediction of the tool wear. Compared with the existing tool wear prediction methods, the method proposed has higher prediction accuracy.