关键词: 主轴/刀柄结合面, 刚度退化, 特征融合, 朴素贝叶斯分类器支持向量机模型

Abstract:  In order to predict the stiffness degradation degree of spindle/tool holder interfaces, a method was proposed based on excitation and response signal fusion. Firstly, side milling experiments of rectangular titanium alloy workpiece were carried out, instantaneous milling force signals and response vibration signals near the spindle-tool holder interfaces were collected, and a database reflecting the stiffness degradation of the spindle-tool holder interfaces was constructed. Then, according to the time-domain, frequency-domain and time-frequency domain features of the instantaneous milling forces and vibration signals in each direction in the database, three features, namely the time-domain mean value, frequency-domain center frequency and time-frequency first-order wavelet packet energy of the instantaneous milling force signals in the X direction and the vibration signals in the Z direction, were optimized based on correlation analysis. The low frequency filter convolution kernel and the high frequency filter convolution check after the preferred eigenmatrix were used for the dual channel convolution pooling processing respectively. The eigenvector of stiffness degradation degree of the deeply fused spindle-tool holder interfaces was obtained. Finally, the probabilistic mode of support vector machine(SVM) model was transformed into the conditional probability of naive Bayes model（NBC）, and the mixed classifier model NBC-SVM was constructed to improve the classification performance of the classifier. On the basis of the stiffness degradation database of the spindle-tool holder interfaces, the two-channel convolution pooled feature fusion method (CP-FF) and NBC-SVM model were used to predict the stiffness degradation degree of the spindle-tool holder interfaces, and the prediction accuracy is as 96%.

Key words: spindle-tool holder interface, stiffness degradation, feature fusion, naive Bayes classifier-support vector machine(NBC-SVM) model