## 5 Results and Discussion  
The training of the base model was carried out using 223 images, and 105 images were split from the original dataset for validation; the validation data set consists of approximately 33 percent from each of the damaged, deformation, and abrasive wear categories. Figure 6a gives the accuracy for base model training runs. The validation accuracy stabilized around the 200 th epoch, and the validation accuracy is 83.75 percent. Figure 6b presents the loss of over 250 epochs, and the loss is the indication of the magnitude of deviation between prediction and the actual value. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Layer Type** & **Input Shape** & **Output Shape** & **Filter Size** & **Trainable Parameters** \\ \hline
0 & Input layer & 200,200,3 & 200,200,3 & 0 & \\
1 & Convolution layer & 200,200,3 & 198,198,32 & 3,3,32 & 0 \\
2 & Max pooling layer & 198,198,32 & 99,99,32 & 2,2,32 & 0 \\
3 & Convolution layer & 99,99,32 & 97,97,32 & 3,3,32 & 0 \\
4 & Max pooling layer & 97,97,32 & 48,48,32 & 2,2,32 & 0 \\
5 & Convolution layer & 48,48,32 & 46,46,64 & 3,3,64 & 0 \\
6 & Max pooling layer & 46,46,64 & 23,23,64 & 2,2,64 & 0 \\
7 & Convolution layer & 23,23,64 & 21,21,64 & 3,3,64 & 0 \\
8 & Max pooling layer & 21,21,64 & 10,10,64 & 2,2,64 & 0 \\
9 & flatten (Flatten) & 10,10,64 & 6400,1 & 0 & 0 \\
10 & Dense layer & 6400,1 & 50,1 & 0 & 0 \\
11 & Dense layer & 50,1 & 35,1 & 0 & 0 \\
12 & Dense layer & 35,1 & 10,1 & 0 & 360 \\
13 & Dense layer & 10,1 & 2,1 & 0 & 22 \\ \hline \end{tabular}
\end{table} 
Table 3: Target model architecture. For the second part, the objective is to demonstrate the capability of the system to adapt to the new task of TCP deployment using fewer images and shorter training time. For this, the data are partitioned into three sections: training, validation, and test dataset; various training runs are carried out using a different number of images. The summary of the number of images used for each run is shown in Table 4. All the images in the three sections are different and are not repeated. The images in the test data set can be seen in Figure 7; the GO category images have no wear or have typical wear pattern; these tools produce conforming parts, and the NO GO category have visible wear on the edges; these tools produce non-conforming parts. Figure 8 shows the accuracy and loss values for different runs. It can be seen from Figure 8b that the loss value plateaued around the 5 th epoch in most of the runs, signifying that the optimization of the parameters requires fewer iterations, which enables the system to accommodate a variety of \begin{table}
\begin{tabular}{c c c c} \hline
**RUN** & **Training Pictures** & **Validation Pictures** & **Test Pictures** \\ \hline
1 & 13 & 10 & 20 \\
2 & 18 & 12 & 20 \\
3 & 23 & 16 & 20 \\
4 & 27 & 18 & 20 \\
5 & 37 & 26 & 20 \\ \hline \end{tabular}
\end{table} 
Table 4: Training, validation, and test data split for different runs. Figure 6: (**a**) Accuracy of the base model. (**b**) Loss convergence graph of the base model. Figure 7: Test image data: (**a**) GO images; (**b**) NO GO images. TCPs and with fewer training requirements. Figure 8c and d show the accuracy and loss of the trained models on the test dataset. It can be seen that the accuracy of the models increases with the number of images used for training the model. Run 4 had a smaller loss value when compared to run 5 but had lower accuracy; this can be attributed to overfitting of data which led to misclassification of the images in the NO GO category test data as GO category images. Run 5 had the best results in terms of accuracy on test data, where 37 images were used in training the model. The accuracy of the test data for run 5 was 85 percent; the confusion matrix for run 5 can be seen in Table 5. The model predicted all NO GO label images correctly and predicted 3 images of GO labels incorrectly. The final part of the study is the deployment of the system using a graphical user interface (GUI).