## 4 Milling Cutter Wear Experiment and Analysis of Recognition Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Initial Learning Rate** & **Learning Rate Type** & **Accuracy/\%** \\ \hline
0.001 & fixed learning rate & 94.51 \\  & decaying learning rate & 97.39 \\ \hline
0.0005 & fixed learning rate & 94.1 \\  & decaying learning rate & 95.25 \\ \hline
0.0001 & fixed learning rate & 94.81 \\  & decaying learning rate & 95.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model training results with different learning rates. Figure 18: Accuracy change curves of the validation set under different initial learning rates. neural networks, all trained under the same conditions using models pre-trained on the ImageNet dataset. After 100 epochs, the models' performances were evaluated using the accuracy and loss values of the validation set. The accuracy and loss changes of the validation set during the training process for each model are shown in Figures 19 and 20. Figure 19 shows that all models trained on the ImageNet dataset and transferred to milling cutter wear state recognition achieved high accuracies in a short time, indicating that transfer learning can effectively solve the problems of long training time and low accuracy in classification recognition algorithms. Compared to the VGG-16 and ShuffleNet neural networks, the ResNet-50 network structure incorporates residual blocks, allowing for deeper networks to extract high-dimensional feature information and avoid network degradation. The CBAM-ResNet-50 network, which introduces an attention mechanism on the basis of ResNet-50, enables the neural network to automatically learn and selectively focus on important information in the input, thus converging faster and predicting more accurately, ultimately achieving an accuracy of 97.39%. The loss change curves in Figure 20 represent the change in the gap between model predictions and true results as the number of iterations increases. The CBAM-ResNet-50 network's loss rate converges first, with a relatively smooth curve and less fluctuation than other models, showing more stability and the smallest loss value. To further test the generalization ability of the proposed method in recognizing milling cutter wear states under varying conditions, the trained model was saved, and the test set based on data from condition 6 was classified. The classification results of the test set were visualized using a confusion matrix, as shown in Figure 21, with the horizontal axis representing the predicted milling cutter wear states and the vertical axis representing  
Figure 19: Accuracy change curves of the validation set under different models. Figure 20: Loss change curves of the validation set under different models. the true wear states. The elements on the main diagonal of the matrix indicate the number of samples correctly predicted, while other parts represent the number of samples incorrectly predicted. Precision, recall, and F1 score, commonly used to evaluate the performance of multi-classification models, were obtained from the confusion matrix. Precision is the proportion of correctly predicted samples of a certain type, recall is the proportion of correctly predicted samples among the true samples of that type, and the F1 score is the harmonic mean of precision and recall, providing a comprehensive evaluation of both. The performance indicators of the recognition model's generalization ability test under varying conditions, based on the confusion matrix in Figure 21, are shown in Table 4.  
Table 4 shows that the recognition model performed well on different types of samples in the test set, with relatively few misidentifications, demonstrating strong generalization ability. The results indicate that the proposed method can effectively extract feature information from milling vibration signals and reduce the impact of condition changes on the mapping relationship of milling cutter wear states.