Transfer learning prediction technology for frequency response function prediction of rotary tool tip

Abstract:

Tool tip frequency response function (FRF) changes with the position of the machine tool spindle, spindle speed, and tool parameters. In order to quickly and accurately obtain the frequency response function of the machine tool tip, a method based on a small number of experimental samples to predict the rotating tool tip FRFs under different tool parameters is proposed by introducing transfer learning. Firstly, an orthogonal programming table for the position and speed of the machine tool spindle is generated, and the prediction model of the rotating tool tip FRFs related to the machining space and the spindle speed is established based on the combination of the self-excitation method and the convolutional neural network （CNN） algorithm; Then, considering the influence of parameters such as tool elongation, diameter and type, transfer learning is adopted to train the prediction model of tool tip FRFs under different tool conditions by a small amount of data. Finally, the prediction model is trained using experimental data from hammer impact experiments and self-excitation experiments at the machining center VMC80IV. The modal parameters of each order output by the model are reconstructed to obtain the tool tip FRFs using the modal superposition method, and the predicted values of the model are compared with the actual measured values. The experimental results show that for the prediction model of the FRFs of the rotating tool tip under different tool working conditions, the prediction error of each order of natural frequency did not exceed 2%, and the prediction error of damping ratio did not exceed 5%, which verifies the effectiveness and accuracy of the prediction model.