# Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine
## 4 Conclusions  
1. During the high-power laser welding process, high-speed photography was used to obtain dynamic metal vapor plume images. Six features were extracted and defined. The experimental results show that three features, including the area of plume, number of spatter, and horizontal coordinate of plume centroid, are closely related to the welding quality. A SVM model based on these three features was established and 93.58% of 10-fold cross-validation accuracy rate was achieved, which indicates high prediction and generalization ability. As such, it is feasible to predict the welding performance and be applied in the control welding process.
2. The combination of SVM and Pearson product-moment correlation coefficient was used to select the features with the best predictive capacity. The method is simple and effective, which provides a valuable technique to select features for characterizing the welding quality in the welding process.  
A real-time monitoring model of welding status was established based on the consideration of laser-induced metal vapor plume by combining modeling with experimental testing, which helped  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SVM (1)**} & \multicolumn{3}{c}{**Predicted Class**} \\ \cline{2-4}  & & **Class E** & **Class R** \\ \hline \multirow{2}{*}{**Actual Class**} & Class E & 68.65\% (357/520) & 31.35\% (163/520) \\  & Class R & 9.17\% (88/960) & 90.83\% (872/960) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrices for cross-validation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**SVM (1,4)**} & \multicolumn{3}{c}{**Predicted Class**} \\ \cline{2-5}  & & **Class E** & **Class R** \\ \hline \multirow{2}{*}{**Actual Class**} & Class E & 81.92\% (426/520) & 18.08\% (94/520) \\  & Class R & 3.23\% (31/960) & 96.77\% (929/960) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrices for cross-validation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Feature(s)**} & \multicolumn{2}{c}{**Accuracy Rate (\%)**} & \multicolumn{2}{c}{**Overall Accuracy**} \\ \cline{3-5}  & & & **Class E** & **Class R** & **Rate (\%)** \\ \hline
1 & SVM & 1 & 68.65 & 90.83 & 83.04 \\
2 & SVM & 1, 4 & 81.92 & 96.77 & 91.55 \\
3 & SVM & 1, 4, 3 & 82.69 & 99.48 & 93.58 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The classification result using the three features.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SVM (1,4)**} & \multicolumn{3}{c}{**Predicted Class**} \\ \cline{2-4}  & & **Class E** & **Class R** \\ \hline \multirow{2}{*}{**Actual Class**} & Class E & 81.92\% (426/520) & 18.08\% (94/520) \\  & Class R & 3.23\% (31/960) & 96.77\% (929/960) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confusion matrices for cross-validation.  
ensure high reliability, and then on the basis of the results, a welding quality prediction model was established based on the characteristics of the metal vapor plume, so that it can be applied to real-time monitoring in the high-power laser welding process.  
This work was financially supported by the National Natural Science Foundation of China (51505158, 11674106), and the Natural Science Foundation of Guangdong Province (2014A030310153). Many thanks are given to Katayama Laboratory of Joining and Welding Research Institute of Osaka University for the technical assistance of laser welding experiments.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data and wrote the paper.  
The authors declare that there is no conflict of interest regarding the publication of this paper.