## 4 Experiment and Discussion
### Experimental Results  
#### 4.4.1 Performance Validation of the ADCNN Model  
The fault diagnosis experiment was carried out on the dental milling machine using ADCNN, and the weights and deviations were optimized by the training of the model. Figure 12 shows the accuracy and loss of the experiment results. Figure 12a shows that the model training's accuracy is close to 100%, and the epoch quickly converges after \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type Label** & **Health Condition** & **Description** & **Processing Speed (rpm)** \\ \hline
0 & Normal & Normal processing & 20,000 \\
1 & Breaking & Overheated or too dull and resulted in breaking & 20,000 \\
2 & Wear & Poor quality of dentures due to wear that is visually undetectable & 20,000 \\
3 & Tipping & Overheating or too dull and resulted in tipping & 20,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Processing status and labeling table. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Parameter Name** & **Parameter Size** & **Output Size** \\ \hline Input & / & / & \(127\times 127\times 3\) \\ Conv1 & Convolutional kernel & 5 \(\times\) 5 & \(123\times 123\times 32\) \\ Dropout & Dropout neuron ratio & 30\% & / \\ Max-p1 & Max pooling kernel & 2 \(\times\) 2 & \(61\times 61\times 32\) \\ Conv2 & Convolutional kernel & 5 \(\times\) 5 & \(57\times 57\times 64\) \\ Dropout & Dropout neuron ratio & 30\% & / \\ Max-p2 & Max pooling kernel & 2 \(\times\) 2 & \(28\times 28\times 64\) \\ Conv3 & Convolutional kernel & 5 \(\times\) 5 & \(24\times 24\times 64\) \\ Dropout & Dropout neuron ratio & 20\% & \\ Max-p3 & Max pooling kernel & 2 \(\times\) 2 & \(12\times 12\times 64\) \\ FC1 & Fully connected neuron & 1000 & \(9216\times 1000+1000\) \\ FC2 & Fully connected neuron & 4 & \(1000\times 4+4\) \\ Output & Weight matrix & 4004 \(\times\) 4 & \(4\times 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DCNN table of model structures. 40 training cycles. The loss curve indicates a rapid decline and steady convergence after 45 training epochs. Simultaneous observation of Figure 12a,b show that the model has almost no overfitting, indicating that the learning and prediction of the model are robust. To verify the superiority of the proposed method, it was compared with four popular models, namely 1 DCNN, LSTM, CNN-LSTM, and ConvLSTM. The same six data sets were tested with 1600 training samples and 400 test samples. The parameters were set with the same epoch and batch size. A dropout layer was also added to reduce overfitting. For this experiment, the epoch was set to 200 times, and the batch size was set to 128. The results of the accuracy of the four models are shown in Figure 13. The 1D-CNN's verification accuracy (the orange line) was the highest, but it may not converge. Although the results of LSTM and CNN-LSTM show convergence, there was also some overfitting. ConvLSTM results were the worst; not only was the accuracy of the verification fairly low, but it also had issues with vibration and the inability to converge. Figure 12: ADCNN experiment results: (**a**) accuracy; (**b**) loss. Figure 13: Experiment results of other models: (**a**) accuracy of 1 DCNN; (**b**) accuracy of LSTM; (**c**) accuracy of CNN-LSTM; (**d**) accuracy of ConvLSTM. Figure 14 shows the experimental results of the loss of the four models. The results show that the verification loss (orange line) of 1 DCNN is the lowest and close to 0, but there was some vibration. Overfitting occurs for both LSTM and CNN-LSTM, and the ConvLSTM results were the worst, with obvious vibrations and an inability to converge. In order to observe the performance of all comparison methods in classifying dental milling machine tool failures, the confusion matrix is used. As shown in Figure 15, the ADCNN did not classify incorrectly during testing. Figure 16a is the confusion matrix of 1 DCNN, which shows that there were two prediction errors that the tool was normal, but it was actually worn.