## 4 Milling Cutter Wear Experiment and Analysis of Recognition Results
Figure 10:0 Variation in template image difference \(d\) under different parameters. Figure 10:1 Variation in DE-DDP objective function value \(d\) with iteration number \(g\). For milling cutter wear experiments under conditions 1 to 5, after obtaining the optimal parameter combinations for DDP transformation according to the aforementioned method, DDP images of vibration signals from all three axes were drawn. The image resolution was set to 224 \(\times\) 224, and then converted into grayscale images to serve as comprehensive data for the milling cutter wear state recognition model. The data under each wear state was divided into a training set and a validation set in a 7:3 ratio. The dataset from condition 6 was used as test data to assess the generalization ability of the proposed method in recognizing milling cutter wear states under varying conditions. Since the vibration signals under condition 6 were unlabeled, appropriate DDP transformation parameters could not be obtained. Therefore, the optimal parameter combinations from conditions 1 to 5 were used for the DDP transformation of vibration signals under condition 6, with each set of three-axis vibration signals producing five DDP images, which were then recognized individually, and the wear state with the highest probability was deemed the recognition result. Due to the limited comprehensive datasets in actual milling operations, training models on small sample datasets cannot effectively extract features reflecting tool wear states. Transfer learning can introduce model weights well-trained on large datasets into new models, thereby enhancing the training efficiency of new models and reducing the demand for large sample sizes. Thus, model parameters of ResNet-50 pre-trained on the large ImageNet dataset were transferred to the recognition model based on ResNet-50 as the backbone network, speeding up the model's convergence and shortening training time, while also improving the model's robustness and generalization ability. To further enhance the feature extraction capability of the recognition model, an attention mechanism was introduced to construct the CBAM-ResNet-50 network, improving the model's perception from both channel and spatial dimensions. Since the pre-trained model used the ImageNet dataset with 1000 categories, while the milling cutter wear states were divided into three classes as data labels, it was necessary to design an adapter layer with an output dimension of three to replace the final layer of the pre-trained model. Additionally, due to the different data distribution characteristics between the two  
Figure 17 shows that with an initial learning rate of 0.001, using a fixed learning rate for training resulted in slower model convergence and relatively lower accuracy. Updating the learning rate with a decay method, which reduces the learning rate after a fixed number of training epochs, can avoid the issue of parameter oscillation near the minimum value in the later stages of training. In Figure 18, with all models using the learning rate decay strategy, when the initial learning rate was set to 0.0001, the model converged slowly and failed to reach the optimum solution. When set to 0.0005, the model converged fastest but with lower accuracy. Thus, setting the initial learning rate to 0.001 and using the decay strategy was more suitable for training the recognition model. Considering the experimental conditions and model training effects, the hyperparameters for the recognition model were chosen as follows: Adam optimizer, initial learning rate of 0.001, learning rate decay strategy, 100 epochs, and a batch size of 32. To verify the performance of the proposed model in milling cutter wear state recognition, a comparative experiment was conducted with VGG-16, ShuffleNet, and ResNet-50  
Figure 10:2 Accuracy change curves of the validation set under different training strategies.