## 4 Experimental Results  
In the present research, the cutting edges were divided into a number of subregions by the proposed method (wear patches, or WP). Each WP was defined using textural descriptors, and were categorized as worn or serviceable using a Deep Belief Network. Lastly, the number of WPs designated as worn determines whether a cutting edge is serviceable or worn. This study separated each training set image into patches that were later identified as being in a worn or serviceable zone. The objective of this research was to develop a classification model that evaluates individual patches and, based on its predictions, renders judgement on the degree of tool wear. Therefore, manual division prevents potentially flawed patch extractions that can result in the creation of suboptimal classifiers. After manually extracting the patches, the images were left with 896 patches, 466 of which were serviceable and 430 of which were worn. The proposed IDOA-DBN model's effectiveness was validated using the MATLAB 2020a software tool on a system configuration with 16 GB random access memory, Linux operating system, and a 4 TB hard disk. The IDOA-DBN model's efficacy was analyzed by implementing many classification techniques and feature optimization algorithms, and tested using performance metrics such as F-score, recall, precision, and accuracy. In this manuscript, the confusion matrix was used to calculate the performance metrics. Here, the serviceable class was a negative class and the worn class was a positive class. The confusion matrix is clearly depicted in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Classes**} & \multicolumn{2}{c}{**Prediction Class**} \\ \cline{2-3}  & **Serviceable** & **Worn** \\ \hline Serviceable & _TN_ & _FP_ \\ Worn & _FN_ & _TP_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix.  
Figure 4: Architecture of the IDOA-DBN process.  
Precision, recall, accuracy, and F-score were the metrics used in this research to evaluate the methodology. The confusion matrix depicted in Figure 4 was created by allocating the worn class as the positive class, and the serviceable class as the negative class. The performance metric, the F-score, was determined as the harmonic mean of recall and precision, and it was estimated using Equation (23). Similarly, the recall was defined as the fraction of worn inserts, and it was computed utilizing Equation (24). In the tool wear batch classification, the performance metric, recall, played a vital role, since the cost of misclassifying a serviceable cutting edge was lower than the cost of misclassifying a worn cutting edge:  
\[F-score=\frac{2 TP}{FP+2 TP+FN}\times 100 \tag{23}\]  
\[Recall=\frac{TP}{TP+FN}\times 100 \tag{24}\]  
Similarly, the performance metric, precision, was defined as the fraction of inserts classified as worn that were actually worn. The accuracy was determined by the successful prediction for the total number of samples, where _TP, TN, FP_, and _FN_ were defined as true positive, true negative, false positive, and false negative. Figure 5 shows the confusion matrix. The mathematical expressions of precision and accuracy are defined in Equations (25) and (26):  
\[Precision=\frac{TP}{TP+FP}\times 100 \tag{25}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\times 100 \tag{26}\]