## Monitoring Model Results
### Establishment of Monitoring Model  
The PSO-BP neural network was used to establish a monitoring model for the woodworking tool wear conditions in MATLAB (MathWorks Inc., Natick, MA, USA). The evolutionary algebra of the particle swarm algorithm was 30, the minimum error was 0.001, the particle swarm size was 40, the maximum flying speed of the particle was 0.5, the maximum inertia factor was 0.9, and the minimum inertia factor was 0.3. The learning rate of the BP neural network was 0.01, the number of training times was 20, the training accuracy was 0.0001, and the number of hidden layers was 5 layers.  
Figure 5 shows the fitness curve of the PSO algorithm of the monitoring model. When the evolutionary algebra was 8, the fitness remained the same. The PSO algorithm reached the state of convergence; the optimization effect of the PSO algorithm was the best, and the prediction accuracy of the BP neural network was the highest.  
Figure 6 shows the prediction accuracy of the established woodworking tool wear monitoring model. The predicted output of all test samples in the figure is basically the same as the actual expected output. Combined with the prediction error analysis of the PSO-BP neural network algorithm in Table 5, it can be concluded from the table that the maximum error rate of the test sample is 7.54%, the minimum error rate is 3.55%, and the average error rate is 5.07%. Therefore, the woodworking tool wear monitoring model established by using PSO-BP neural network algorithm can monitor the woodworking tool wear conditions under variable milling parameters, and can achieve the effect of identifying the woodworking tool wear conditions under variable milling parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & **Spindle Speed** & **Depth of Milling** & **Flank Wear** & \multirow{2}{*}{**Feature**} \\  & **(r/min)** & **(mm)** & **(mm)** & **(mm)** & \\ \hline
1 & 6000 & 0.5 & 0.1 & 565.72 \\
2 & 6000 & 0.5 & 0.2 & 570.42 \\
3 & 6000 & 0.5 & 0.3 & 5  
In order to verify the reliability of the PSO-BP neural network algorithm, the established model was run 10 times. Table 6 shows the average error rate of 10 runs; the maximum average error rate is 15.93%, the minimum average error rate is 3.14%, the average error rate (10 times) is 9.48%, and the model running time is 8.59 s. It can be seen that the PSO-BP neural network algorithm has good reliability and fast calculation speed. Therefore, the proposed PSO-BP neural network algorithm can effectively establish a monitoring model for the woodworking tool wear conditions which can realize accurate monitoring of the woodworking tool wear conditions.  
\begin{table}
\begin{tabular}{c c} \hline
**Run** & **Average Error Rate** \\ \hline
1 & 6.46\% \\
2 & 10.36\% \\
3 & 3.14\% \\
4 & 7.57\% \\
5 & 11.61\% \\
6 & 8.42\% \\
7 & 15.93\% \\
8 & 4.27\% \\
9 & 14.86\% \\
10 & 12.21\% \\ Average & 9.48\% \\ \hline \end{tabular}
\end{table}
Table 6: The analysis of error.  
Figure 5: Fitness curve of the PSO algorithm of the monitoring model.  
Figure 6: Prediction accuracy of the monitoring model.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Text ID** & **Actual Value** & **Predictive Value** & **Error Value** & **Error Rate** \\ \hline
1 & 0.1 & 0.0927 & \(-\)0.0073 & 7.32\% \\
2 & 0.2 & 0.1993 & \(-\)0.0007 & 3.55\% \\
3 & 0.2 & 0.1849 & \(-\)0.0151 & 7.54\% \\ Average & & & & 5.07\% \\ \hline \end{tabular}
\end{table}
Table 5: Error values of the test sample.