# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 4 Results and Discussion
### Data Fusion Method Evaluation
#### 4.2.3 Multi-Source Data Fusion for High-Dynamic Analysis  
This study employed the proposed method for data fusion to obtain high-frame-rate fused images. Figure 12a demonstrates the evolution behavior of the melt pool based on the fused images, particularly the high-dynamic changes of the plume. Within 0-180 ms, during the formation of the melt pool, changes in the melt pool width lead to an unstable state of the plume. After 180 ms, the melt pool width stabilizes, and the plume also tends to stabilize, consistent with the analysis results in Section 4.2.1, indicating that plume stability is related to the melt pool width. Additionally, during the formation of the melt pool, the direction of the plume is related to the flicker of the melt pool temperature distribution, \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **PSNR** & **FMI** & **Qabf** & **MS-SSIM** & **Nabf** & **AG** & **SD** & **SF** \\ \hline U2 Fusion & 73.08 & 0.950 & 0.342 & 0.963 & 0.065 & 0.651 & 17.85 & 5.150 \\ EMFusion & 73.92 & 0.948 & 0.435 & 0.952 & 0.123 & 0.659 & 17.87 & 4.997 \\ SwinFusion & 71.68 & 0.951 & 0.639 & 0.983 & 0.035 & 0.902 & 23.76 & 6.460 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative results of the three fusion models. Bold indicates the best results. Figure 11: Qualitative comparison of different image fusion algorithms in the multi-source cladding dataset designed in this study: (**a**) the visible-light image (VIS image) of the melt pool; (**b**) the infrared image (IR image) of the melt pool; (**c**) the fused image generated by the U2 Fusion model; (**d**) the fused image generated by the EMFusion model; (**e**) the fused image generated by the image fusion module. indicating that the stability of the melt pool temperature distribution directly affects the stability of the plume. Figure 12b shows that in the initial stage of laser irradiation, the width, length, and area of the melt pool increase rapidly. After 500 ms, the growth rate slows down, and after 600 ms, the melt pool changes little, reaching a quasi-steady state. Continuous integration of metal powder causes fluctuations in the melt pool, leading to an asymmetric temperature distribution in the melt pool, with the length and area of the melt pool also fluctuating. A low-temperature area formed by liquid splashes exists at the periphery of the melt pool temperature distribution. Figure 12c shows the temperature gradient formed by liquid droplet splashes around the melt pool. The fast splashing of droplets creates a trail, forming a prominent temperature area around the melt pool in the fused image. Figure 12d shows that when melt metal enters the melt pool, it is affected by surface tension and Marangoni forces, moving towards the center of the melt pool and causing fluctuations in the melt pool after integration. Due to the different emissivities of liquid metal, liquid-solid mixed metal, and solid metal, using a single emissivity to monitor the melt pool results in abnormal temperature distributions, leading to inaccurate monitoring of the melt pool morphology. Figure 13 demonstrates the results of melt pool contour extraction from infrared and fused images, using visible-light images as a reference for the true contour. It can be observed that  
Figure 12: Dynamic analysis of multi-source data fusion: (**a**) high-dynamic changes in the melt pool plume based on the fused image; (**b**) analysis of melt pool fluctuations during the quasi-steady-state process of the melt pool from 500 ms to 600 ms; (**c**) droplet splashing caused by melt pool movement, with red arrows indicating the direction of splashing motion; (**d**) changes in the motion state of the melt pool during the process of melt metal entering the melt pool. The red circle indicates the splashing droplets, and the red arrow shows the direction of the droplets’ movement. temperature anomalies caused by droplet splashing and emissivity errors are quite evident, while the proposed method can effectively balance these temperature anomaly areas.