Prediction of Stiffness Degradation Degree of Spindle-tool Holder Interfaces Based on Two-signal Fusion
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 re-sponse vibration signals near the spindle-tool holder interfaces were collected,and a database reflec-ting 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 con-volution 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 ac-curacy is as 96％.
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