## 3 Discussion of Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region** & **Wire Infeed (\(\upmu\)m)** & \begin{tabular}{c} **Mean Value of** \\ **Prediction (\(\upmu\)m)** \\ \end{tabular} & \
\begin{tabular}{c} **Average Deviation** \\ **(\(\upmu\)m)** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Deviation (\(\upmu\)m)** \\ \end{tabular} \\ \hline Region 1 & \(-16\) & \(-14.92\) & \(1.08\) & \(2.14\) \\ Region 2 & \(-8\) & \(-6.36\) & \(1.64\) & \(4.38\) \\ Region 3 & \(0\) & \(3.78\) & \(3.78\) & \(4.76\) \\ Region 4 & \(8\) & \(7.4\) & \(-0.60\) & \(4.96\) \\ Region 5 & \(16\) & \(10.22\) & \(-5.78\) & \(3.11\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of the prediction of the deep neural network as a function of wire infeed. Figure 4: Results after measuring the fir-tree slot profile on the Mitutoyo 9106 Coordinate Measuring Machine. Finally, the red color means excess of material, as explained above. Comparison with the results provided by actual measurement of the profile using the CMM has been carried out in 10 regions as represented in Figure 5.  
Of course, inside each region some points exhibit a different color, but most of them show a clear trend. This observation is sound with the actual measurements by the CMM presented in Figure 4. Therefore, it has been decided to assign the dominant color to each region for the sake of clarity of results. Comparison between predictions and actual measurements is presented in Table 5.  
The quality of the predictions is obvious from the results. From the 10 regions selected, 8 of them perfectly match the results obtained by actual measuring of the component on the CMM, and only 2 of them show some deviation. As explained above, it must be taken into account that a tolerance band more demanding than that stated by the manufacturer has been established in order to check the accuracy of the predictions. This allows to check not only part geometrical requirements, but also to check the accuracy of the network to detect excess and/or defect of material removed by the process, even inside the tolerance band of the component. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region** & \
\begin{tabular}{c} **Prediction** \\ **(Dominant Color)** \\ \end{tabular} & **Results by CMM** &
\begin{tabular}{c} **Comparison with Proposed** \\ **Deep Neural Network** \\ \end{tabular} \\ \hline Region 1 & Blue & Defect of material & Matched \\ Region 2 & Yellow & Ideal part & Matched \\ Region 3 & Blue & Excess material & Unmatched \\ Region 4 & Blue & Defect of material & Matched \\ Region 5 & Red & Defect of material & Unmatched \\ Region 6 & Yellow & Ideal part & Matched \\ Region 7 & Red & Excess material & Matched \\ Region 8 & Blue & Defect of material & Matched \\ Region 9 & Red & Excess material & Matched \\ Region 10 & Blue & Defect of material & Matched \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results between the current method (CMM) and the proposed deep neural network for defect monitoring. Figure 5: Defect prediction with respect to the ideal profile as produced by the network. If the tolerance band of the network is increased to \(\pm 15\)\(\upmu\)m, the agreement with CMM results is even better. In that case the prediction by the network is that all the profile is within the tolerance band. In other words, there is only one region of mismatch, which is the region where the most important defect (see Figure 4, excess material in red) occurs.