关键词: 刀具状态识别, 核主成分分析, 切削力特征, 支持向量机, 灰狼优化算法

Abstract: To improve the accuracy and generalization ability of real-time monitoring of tool wear state,a method based on cutting force characteristics to indirectly identify tool wear state was proposed,which established a non-linear mapping relationship between cutting force signal and tool wear.Tool performance test in tool life cycle was operated to collect cutting force signals,and the time domain,frequency domain and wavelet domain features of signals were extracted.The dimensionality reduction features of the data with kernel principal component analysis were evaluated,and the classification grade of tool wear was obtained by using the support vector machine classifier optimized of grey wolf optimization algorithm.Compared with other methods in the literatures,the experimental results showed that the proposed model could reflect the tool wear state more accurately and had higher generalization ability.

Key words: tool wear state recognition, kernel principal component analysis, cutting force characteristics, support vector machine, grey wolf optimizer algorithm