## 3 Results and Discussion
### Application Validation
In addition, the depth of laser action during scanning is difficult to control precisely, so that there is a partial re-melting phenomenon between layers, resulting in a certain overlap rate between layers. On the other hand, during the layer-by-layer forming process, the material surface cools rapidly after the end of the laser action, which leads to a significant temperature gradient along the longitudinal direction. According to the thermal strain formula [30],  
\[\varepsilon=\alpha\Delta T \tag{6}\]  
where \(\varepsilon\) is the thermal strain, \(\alpha\) is the coefficient of thermal expansion, and \(\Delta T\) represents the temperature change. For TC4 material, its coefficient of thermal expansion \(\alpha\) value is a positive value. When the temperature decreases, \(\Delta T\) is a negative value, so the material undergoes shrinkage deformation, resulting in a decrease in the actual layer thickness. During this process, the shrinkage of the material is constrained by the underlying solidified layer, inducing thermal stress within the material. As cooling progresses, thermal stress that cannot be released remains in the material, further exacerbating the deformation of the formed part. Figure 6: The correlation between solidified region images and CT slice images. (**a**) Trends in geometric feature changes; (**b**) slice layers corresponding to turning points. The orange dash-dotted line is used to correspond the number of layers at the turning point of the line with the uppercase letters marked, for subsequent analysis. Due to the symmetric structure of the lattice, the geometric feature variation trend of the C-D-E layer is consistent with that of the C-B-A layer. The area and perimeter values from layer E to layer F vary significantly, as the number of recognized contours in this section has increased from one to four, resulting in a significant increase in perimeter values. Similar to layer A, layer E is the transition region between the rod part of the lattice model and the cross part. There is a serious interaction between foreground and background in the middle part of the solidified region images collected in this region, which makes it difficult for the model in this paper to accurately distinguish the blurred image contours. It can be seen from the solidified region images of layer E that the middle background part of the image was not accurately identified, so the perimeter value that should be the sum of four contour length values was calculated as an overall contour length value, resulting in a smaller perimeter calculation result and obvious anomalies. The changes from F to G layers are relatively stable, and the foreground contours of this part of the image are more distinct, resulting in better recognition performance. By comparing and analyzing the model recognition results with CT scan results, it can be concluded that the two have a highly similar trend of change. Although there are some differences in the data due to objective factors, these differences are not caused by the contour recognition method. The research results indicate that the difference in the geometric data between the recognition results and the scanned images is relatively small. Therefore, the model recognition method proposed in this paper has similar effects to CT detection in contour recognition, which confirms that the method in this paper has the ability to detect the contour of solidified region images preliminarily.