## 4 Experimental Verification
### Experimental Result
#### 4.2.2 Results and Analysis
During the iterative runs, the KANS-ResNet-34 and ResNet-34 models required a similar amount of time, with 50 iterations taking approximately 1 h. In contrast, the ResNet-50 model took approximately 33.3 h for 50 iterations. The accuracy of KANS-ResNet-34, proposed in this paper, is about 1.07% higher than that of the original ResNet-34 model. Although it is 0.36% lower than that of the ResNet-50 model, the running time of ResNet-50 at the same number of iterations is 33.68 times longer than that of KANS-ResNet-34. Considering that both models have an accuracy of more than 99%, the economy and feasibility of the KANS-ResNet-34 model are significantly better than those of ResNet-50. Therefore,  
From Table 3, it can be seen that the model has the best performance in specificity (Spe), which is 100% for all three categories, representing the model's ability to classify consistently across all categories. The model's recall (Rec) is 100% for the initial and severe wear phases, indicating that the model is extremely sensitive to data from these two phases. The slightly lower recall for the normal wear phase may be due to the larger number of intermediate samples. The precision (Pr) of the model is also 100% for the initial and normal wear phases, indicating 0% misidentification in these two phases, while the lower precision in the severe wear phase is due to misidentifying four samples from the normal wear phase as being in the severe wear phase. The model's performance on the F1 scores for the three categories is superior, all above 95%, with the best F1 score even reaching 100%. This indicates that the model's overall classification performance is well-balanced and has the ability to handle large-scale and complex data. Similar conclusions can be seen in Tables 4 and 5, where the recall for the normal wear phase is still slightly lower at 93.90% in Table 4, compared to 97.56% in Table 5, and the F1 scores for both are 96.85% and 98.76%, respectively, and the ResNet-34 model has lower values for each of the values in the normal wear phase than the KANS-ResNet-34 model, whereas the KANS-ResNet-34 model is very close to the ResNet-50 model for all values. For the severe wear stage, the three models have the same trend as the normal wear stage. The F1 scores of the three models are ResNet-50, KANS-ResNet-34, and ResNet-34 in descending order, and the optimized KANS-ResNet-34 model is superior in terms of the F1 scores and the cost of time used by the three models in the previous section and is well suited for tool wear monitoring. high-frequency portion of the signal, which was converted into a form of a picture that accurately described the signal's local details of the time-frequency information, making it more suitable as an input to the classification network. 2. Optimizing the KAN by activating the inputs using different basis functions and then combining them linearly reduced the computational cost of the computer and significantly improved efficiency. To accommodate this optimized structure, the L1 regularization of the tensor in the original network was replaced by the L1 regularization of the weights, making the two compatible. Additionally, the parameters were initialized using the Kaiming initialization method. 3. Using the nonlinear structure of the KANS network, the include_top_kan nonlinear classifier was constructed to replace the top linear classifier in the ResNet-34 neural network. Experiments conducted on the ResNet-34 and ResNet-50 models demonstrated the feasibility of replacing the deep network model with a shallow network model, substantially saving time and guaranteeing classification accuracy. The accuracy of the KANS-ResNet-34 model reached 99.28% in the validation set and 97.79% in the test set. Conceptualization, B.C. and Y.Z. ; methodology, B.C. ; software, Y.Z. ; validation, B.C., Y.Z., and B.L. ; formal analysis, B.C. ; investigation, Y.Z. ; resources, Y.Z. ; data curation, C.P. ; writing--original draft preparation, B.C. ; writing--review and editing, Y.Z. ; visualization, B.L.