# Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM
## 4 Results and Discussion  
The classification accuracy of classifiers using a single feature was first tested by 10-fold cross validation. If the accuracy was satisfactory, using more features and more complex methods should be unnecessary. The experiment results are shown in Table 3. As can be seen from the table, when a single feature is used, the classification accuracy of SVM and BP is very close. The highest accuracy of both is about 88% using Feature 12. As the best accuracy is lower than 90%, further research was indispensable. The classification accuracy of the features selected by SFFS are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No. of Feature**} & \multicolumn{3}{c}{**Accuracy (SVM) (\%)**} & \multicolumn{3}{c}{**Accuracy (BP) (\%)**} \\ \cline{2-7}  & **Overall** & **Class R** & **Class E** & **Overall** & **Class R** & **Class E** \\ \hline
1 & 66.09 & 95.43 & 14.75 & 66.09 & 94.14  
The SVM classifier improves significantly by adding features, until five features are applied and an accuracy of 97.36% is reached. After that, additional features cannot increase the accuracy by much, although the improvement continues. The best performance is reached when ten features are used, with a maximum classification accuracy of 98.11%. Then, adding features can only decrease the accuracy. When all fifteen features are used, the accuracy is 94.40%. One possible explanation for this phenomenon is that after ten features are used, the additional features do not provide more useful information for classification, and the irrelevant information, or even noise, complicate the problem. For the BP classifier, adding features cannot increase the accuracy as much as SVM. The accuracy is 94.25% when five features are used. After that, the accuracy increases slowly, with a maximum accuracy of 95.44% using eleven features. More details are shown in Table 4.  
As can be seen from the first four rows of the table, when one feature or all fifteen features are employed, the accuracy of SVM and BP classifiers is close. The accuracy using all features is higher than using only one feature, which means that the process of high-power disk laser welding is complicated and cannot be monitoring through a single feature. From the fifth and sixth row of the table, it can be deduced that using the feature selection method, both SVM and BP reach a higher classification accuracy, but the improvement for the SVM classifier is much greater than BP. This may be caused by the instability of the BP method. Because the lack of effective initialization method, the BP training algorithm usually initializes randomly, and the initial values influence the classification ability of the BP classifier. The result is that even if the same feature subset is used, the classification accuracy of the BP classifier can be different. As the accuracy is chosen to evaluate the effectiveness of feature subsets in the SFFS algorithm, this feature selection method cannot effectively detect the optimal feature subset for the BP classifier. Consequently, compared to the other five methods, the SVM with feature selection method is more suitable for automatically monitoring the welding quality during high-power disk laser welding. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(d\)} & \multicolumn{3}{c}{**Accuracy (\%)**} & \multirow{2}{*}{**Features**} \\ \cline{3-3} \cline{5-6}  & & & **Overall** & & **Class R** & **Class E** \\ \hline
1 & BP & 1 & 88.38 & 95.51 & 75.90 & 12 \\
2 & SVM & 1 & 88.35 & 95.63 & 75.60 & 12 \\
3 & BP & 15 & 94.89 & 98.51 & 88.55 & All \\
4 & SVM & 15 & 94.40 & 99.57 & 85.35 & All \\
5 & BP & 11 & 95.40 & 98.66 & 89.70 & 2,4,6,8,9,10,11,12,13,14,15 \\
6 & SVM & 10 & 98.11 & 99.43 & 95.80 & 3,4,5,8,9,10,11,12,14,15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of different methods.