## 3 Results and Discussion
### Application Validation  
In order to verify whether the image contour of the solidified region obtained by the method in this article can be used as a means of LPBF forming quality detection, the contour recognition effect is gained by comparing it with CT scanning results. The 3D model after scanning is obtained by CT detection of the lattice structure sample, and the slice maps of the 3D model are acquired according to the layer thickness set during forming. The solidified region images were matched with the CT scan slice images one by one, and the correlation between the two sets of images were analyzed. The last 250 layers of the forming layers were taken out, and the contour recognition results of the solidified region images and CT images extracted from each layer were obtained. The pixel area and perimeter of each contour were calculated using python language, which mapped the entity interior and contour recognition effects, respectively. [PERSON]'s correlation coefficient was used to evaluate the correlation between the two regarding area data and perimeter data, and the calculation results are shown in Table 4. At the same time, line graphs were plotted to observe the overall trend, then CT images and solidified region images of the corresponding layers were extracted according to the turning points of the line graphs to analyze the reasons for their turning points. Due to the non-uniform size of the acquired images, the data were fitted to the same scale size, but since their benchmarks were not identical, the comparison of individual values was not informative. Therefore, the main focus was to analyze their overall value distribution. The data processing results of this section are shown in Figure 6. SR represents the solidified region image and CT represents the CT scan image in the figure. The Pearson's correlation coefficient calculated in Table 4 shows a strong correlation between the two sets of data. From Figure 6a, it can be further observed that the shape of the lattice structure changes continuously during the forming process; the values of its forming area and perimeter also change accordingly. The overall trend of changes in the calculation results of the solidified region images and CT images is basically consistent; however, due to the incomplete elimination of camera transverse distortion during the acquisition process of solidified region images, there are still significant differences in some recognition results of image geometric features. Combined with Figure 6b, it can be concluded that the larger size of the solidified region image in this part of layer A to B has a more serious effect of aberrations on the calculation results compared to the other regions, especially in the calculation of the area, where the transverse aberrations bring about a difference that is multiple times that of the perimeter. Between layer B and layer C, as the image size decreases, the distortion effect gradually decreases, and the degree of overlap between the contour calculation results of the solidified region image and CT image gradually increases. The line chart shows that \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Area** & **Perimeter** \\ \hline [PERSON]’s correlation coefficient & 0.99 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: [PERSON]’s correlation coefficient of 250 layer images. before reaching layer B, the area of the solidified region image has begun to decrease. At the position of layer C, the area and perimeter values of the two datasets overlap highly. This trend indicates that the portion of the sample rod shown in the solidified region images is longer than the actual rod. There are two reasons for the analysis. On the one hand, when the laser scans the powder bed in the actual forming process, the powder size is reduced in the process of powder melting and solidifying under laser irradiation, resulting in a decrease in the powder layer thickness.