# Feature Point Identification in Filtel Weld Joints Using an Improved CPDA Method
## 4 Numerical Verification and Experimental Validation Results
However, the closest feature corner is also removed in this process, resulting in inaccurate feature corner identification. In contrast, by integrating PCA into center line extraction in our proposed method in Figure 9b,c, smoother results can be obtained. Furthermore, with the improved CPDA algorithm, more accurate feature corner points are detected. The integration of angle monitoring criteria, as outlined in the flowchart in Figure 5, further enables effective corner point identification in fillet welding. The accuracy and smoothness of laser center line extraction can be further improved by implementing the sub-pixel interpolation technique. In the experiment of robot welding, a total of 120 captured frame images are collected, both before and after the corner identification. The corner identification result in each frame image is then checked manually. The experimental identification accuracy is then defined as the number of frame (\(n\)) with correct and unique identified corners divided by the total number (\(N\)) of frames in Table 3. Several experimental findings can be drawn from Figure 10 and Table 3:  
* As shown in the typical captured frame from the CCD camera mounted on the robot in Figure 10a, there is arc lighting interference during welding, resulting in a noisy sensing environment for image processing;  
Figure 8: Experimental setup. (**a**) Welding robot; (**b**) sensing unit. With the binarization pre-processing, the laser center line can be easily detected in the left column of Figure 10b. However, when calculating the feature corner points with only the CPDA algorithm, a multitude of pseudo-corners are generated. Moreover, the application of normalization to filter the corners obtained through CPDA results in the omission of some corners; Through the utilization of the proposed method outlined in the flow-chart in Figure 5, missing corners can be effectively restored. And, as shown in Figure 10c, the feature corner points can be correctly extracted in fillet welding for automated robot welding tracking; With acceptable increase in computation time, the proposed method yields significantly higher accuracy compared to the implementation of the CPDA algorithm in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **No. of Frames with** & **Degree of** & **Computation Time** \\  & **Correct Identification** & **Accuracy** & **per Frame (s)** \\ \hline Gravity center [33] with & & 68 & 56.6\% & 0.18 \\ origin CPDA [29] & & & \\ Our proposed method & 116 & 96.6\% & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results from identification of 120 frame images. Figure 9: Experimental result analysis (R1–R4 are four selected zoomed-in regions). **(a)** Center line extraction using grayscale gravity; **(b)** corner feature point identification using the original CPDA, the identified corners are denoted as cyan dots; **(c)** center extraction using the PCA-based method; **(d)** feature corner identification result with the proposed improved CPDA method, the identified corners are denoted as blue dots. An effective method for identifying welding feature points should demonstrate the capability to extract corner features in fillet welding applications with different thicknesses. As shown in Figure 11 and Table 4, the proposed improved CPDA method was validated in thick and thin fillet weld joints. It is worth mentioning that the selection of length-of-chord value \(L\) has a great influence on the identification accuracy. Our proposed method has higher than 95% identification accuracy in both these two fillet welding situations that are shown in Figure 11b,c. When implementing the proposed method into a welding robot, the overall welding result on the workpiece is shown in Figure 11d,e. For better demonstration of the welding quality in the fillet joint, the workpiece is rotated by 90\({}^{\circ}\) in Figure 11e.