## 4 Experimental Results
### Comparative Evaluation  
The comparative investigation between the proposed IDOA-DBN model and the existing image texture analysis model [23] is presented in Table 4. [PERSON], et al. [23] used circular Hough transform technique, canny edge detector, and standard Hough transform technique for segmenting the cutting edges of acquired images. Furthermore, the texture feature extraction was accomplished using conventional, completed, and adaptive LBP descriptors. The extracted feature vectors were fed into the SVM classifier for classifying the cutting edges as serviceable or worn. The experimental evaluation showed that the proposed IDOA-DBN model attained efficient performance in tool wear batch classification related to the existing image texture analysis model [23] on five different patch configurations. The feature optimization utilizing IDOA is an integral part of this manuscript that significantly reduces the computational complexity and running time of the DBN by selecting the discriminative feature vectors. The IDOA-DBN model took 43 s to process the whole dataset, which is better than other deep learning models, and the complexity of the proposed model is linear when selecting the discriminative feature vectors. Table 4 shows the comparison results of various patch configurations. Figure 6 shows the graphical presentation of the various patch configurations. The researchers tuned the network model's design and hyperparameters for the signal matrix data, leading to a high recognition accuracy. Furthermore, the suggested tool wear state technique's optimized neural network with double neurons is incompatible with all of these datasets. Based on the design structure provided in the literature, calculations were performed to discover a suitable system model, and the obtained accuracy results are presented in Table 5. The suggested IDOA-DBN approach delivers superior identification accuracy (98.83%) and a more compact network structure than existing DWF-CNN [28] techniques, which achieved 98.7%. Figure 7 shows the graphical illustration of accuracy with existing DWF-CNN [28] methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Patch Configuration** & **Classifier** & **F-Score (\%)** & **Recall (\%)** & **Precision (\%)** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{FED} & AlexNet & 83.95 & 80.96 & 85.85 & 84.58 \\  & Autoencoder & 86.77 & 87.60 & 87.67 & 87.40 \\  & ResNet-14 & 88.90 & 89.96 & 89.70 & 88.68 \\  & ResNet-18 & 93.86 & 90.80 & 94.96 & 95.58 \\  & DBN & 95.30 & 94.53 & 96.70 & 98.83 \\ \hline \multirow{8}{*}{HGD} & AlexNet & 78.50 & 77.60 & 76.70 & 83.90 \\  & Autoencoder & 79.78 & 78.90 & 77.57 & 84.20 \\  & ResNet-14 & 84.40 & 79.93 & 79.98 & 85.50 \\  & ResNet-18 & 85.48 & 84.78 & 86.70 & 87.87 \\  & DBN & 86.20 & 87.90 & 88.88 & 90.87 \\ \hline \multirow{8}{*}{SED} & AlexNet & 76.70 & 74.90 & 76.98 & 88.30 \\  & Autoencoder & 78.90 & 76.78 & 82.80 & 89.40 \\  & ResNet-14 & 86.06 & 85.40 & 88.50 & 89.69 \\  & ResNet-18 & 88.80 & 88.70 & 93.84 & 92.22 \\  & DBN & 93.20 & 92.03 & 94.50 & 96.78 \\ \hline \multirow{8}{*}{HED} & AlexNet & 79.40 & 77.78 & 80.90 & 85.50 \\  & Autoencoder & 81.20 & 78.98 & 85.49 & 86.75 \\ \cline{1-1}  & ResNet-14 & 88.78 & 84.86 & 86.26 & 89.29 \\ \cline{1-1}  & ResNet-18 & 88.76 & 89.30 & 90.88 & 93.20 \\ \cline{1-1}  & DBN & 90.45 & 93.20 & 94.56 & 95.60 \\ \hline \multirow{8}{*}{TBD} & AlexNet & 80.65 & 79.40 & 79.67 & 80.49 \\ \cline{1-1}  & Autoencoder & 82.70 & 84.40 & 84.75 & 80.99 \\ \cline{1-1}  & ResNet-14 & 86.60 & 86.78 & 87.68 & 88.90 \\ \cline{1-1}  & ResNet-18 & 91.70 & 88.89 & 92.34 & 94.11 \\ \cline{1-1}  & DBN & 92.03 & 94.57 & 95.55 & 94.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of various classification techniques with DBN. Figure 6: Graphical presentation of the various patch configurations with existing method.