# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 5 Conclusions  
In this study, we proposed a multi-source data fusion method for metal additive manufacturing technology. Based on the understanding of the mechanism of metal melt pools, taking laser cladding as an example, we designed a multi-source data fusion method for melt pools. We constructed an experimental platform for laser cladding, monitoring the melt pool morphology with a high-speed camera in coaxial alignment and the temperature  
Figure 13: Contour extraction results of the melt pool from infrared and fused images: (**a**) infrared image; (**b**) fused image generated using the proposed data fusion method; (**c**) visible-light image; (**d**) contours of the melt pool extracted from the infrared and fused images, as well as the real contour manually extracted from the visible-light image. distribution with a thermal camera in an off-axis alignment, thereby acquiring multi-source data of the laser cladding melt pool. Preprocessing of multi-source data was conducted, including data cleansing, image registration, ROI extraction, and data augmentation, to construct multiple datasets for various models. We designed a 3D convolution-based MTVFI model for frame interpolation of melt pool infrared videos to balance the frame-rate discrepancies among multi-source data and proposed a pre-training strategy based on high-frame-rate visible-light data. Experimental results indicate that the MTVFI model under this strategy can effectively extract high-dynamic motion features from high-frame-rate visible-light videos and generate interpolated frames with high credibility and rich information. Additionally, we designed an image fusion module based on the SwinFusion image fusion model. Experimental results show that the generated fused images have low noise and rich structural information and features from the source images. Dynamic analysis of the melt pool through the proposed multi-source data fusion method shows that, compared to dynamic analysis with single data, the proposed method has higher accuracy and can correct data errors caused by single-emissivity monitoring. Experimental results of melt pool contour monitoring show that this method can improve the accuracy of melt pool contour monitoring. In summary, the multi-source data fusion method for additive manufacturing melt pools based on video frame interpolation can effectively utilize the differential information from multi-source data to generate melt pool fusion data that are high in information content and low in error, demonstrating advantages in melt pool morphology monitoring, with potential application value for melt pool defect detection and process optimization, contributing to improved process stability and part quality in metal additive manufacturing. Conceptualization, H.R. and Y.L. ; Data curation, H.R. ; Formal analysis, H.L. ; Funding acquisition, Y.L. ; Investigation, Y.L. ; Methodology, H.R. and Y.L. ; Project administration, Y.L. ; Resources, Y.Z. and Y.L. ; Software, H.R., Y.Z. and H.L. ; Supervision, Y.Z. and H.L. ; Validation, H.R., Y.Z. and H.L. ; Visualization, H.R. ; Writing--original draft, H.R. ; Writing--review and editing, H.R., Y.Z., H.L. and Y.L. All authors have read and agreed to the published version of the manuscript. The work was funded by the National Key R&D Program of China (Grant No. 2022 YFB4601601), the Key R&D Program of Guangxi Province (Grant No. GKAB23026101), and the Guangxi Natural Science Foundation (Grant No. 2023 GXNSFBA026287). The APC was paid by the authors. Not applicable. The dataset is not available due to institutional data sharing restrictions. The data presented in this study are available on request from the corresponding author. The authors gratefully acknowledge the financial support from the National Key R&D Program of China (Grant No. 2022 YFB4601601), the Key R&D Program of Guangxi Province (Grant No. GKAB23026101), and the Guangxi Natural Science Foundation (Grant No. 2023 GXNSFBA026287). The APC was paid by the authors.