## 4 Experimental Results
### Comparative Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Patch Configurations** & **Model** & **F-Score (\%)** & **Precision (\%)** & **Accuracy (\%)** \\ \hline \multirow{2}{*}{FED} & Texture analysis [23] & 85.50 & 93.40 & 86.40 \\  & IDOA-DBN & 95.30 & 96.70 & 98.83 \\ \hline \multirow{2}{*}{HGD} & Texture analysis [23] & 81.50 & 80 & 81.20 \\  & IDOA-DBN & 86.20 & 88.88 & 90.87 \\ \hline \multirow{2}{*}{SED} & Texture analysis [23] & 90.30 & 89.70 & 90.30 \\  & IDOA-DBN & 93.20 & 94.50 & 96.78 \\ \hline \multirow{2}{*}{HED} & Texture analysis [23] & 87.20 & 91 & 87 \\  & IDOA-DBN & 90.45 & 94.56 & 95.60 \\ \hline \multirow{2}{*}{TBD} & Texture analysis [23] & 90.90 & 93.40 & 90.90 \\  & IDOA-DBN & 92.03 & 95.55 & 94.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of various patch configurations. Figure 7: Graphical illustration of accuracy with existing method. The prediction impacts of BiLSTM methods based on SVD features and time domain features were evaluated to demonstrate the effectiveness of IDOA-DBN features in tool wear detection. SVD-BiLSTM [30] was utilized for experimental studies to further validate the functionality of the IDOA-DBN tool wear prediction system. Because all learning methods have the same architecture, only the type of network layers needed to be changed; the performance parameters were maintained, and all design variables were SVD features. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Square Error (MSE) values were computed to generate time domain features of the same scale before the SVD features. Table 6 lists the outcomes of the prediction results in terms of the training and testing sets. The proposed IDOA-DBN model was more suitable in the error assessment of the training set, and computation was easier. Therefore, IDOA-DBN produced better training outcomes in the training set in the same time frame. Despite this, existing SVD-BiLSTM [30] lacks cell state and has inadequate long-term memory capacity, which is evident in the test set error values produced by SVD-BiLSTM [30]. In this study, IDOA-DBN was proposed and considered to be the appropriate design in all data and test sets, because the IDOA-DBN can memorize all data features. Consequently, in both the training set and the test set, IDOA-DBN is superior to SVD-BiLSTM [30]. The model's input data were constructed using a time step of five, which means that the cutting edge signals for each of the previous five sampling periods were matched with the tool wear value for the 5 th sampling period. The first 265 sets of samples were used as the model training sample out of 315 total measured data, which were divided into 311 sets of sample data. The remaining 46 groupings of samples were employed as a model test sample. Figure 8 shows the graphical illustration of the prediction results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Training Set**} & \multicolumn{3}{c}{**Testing Set**} \\ \cline{2-7}  & **MAE** & **MAPE** & **MSE** & **MAE** & **MAPE** & **MSE** \\ \hline SVD-BiLSTM [30] & 2.4948 & 2.4112 & 3.6782 & 4.3044 & 1.8896 & 5.0873 \\ Proposed IDOA-DBN & 2.4625 & 2.3729 & 3.5988 & 4.2553 & 1.8309 & 4.1032 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of prediction results in training and testing set. Figure 8: Graphical illustration of prediction results with existing method.