# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 1 Introduction  
Additive manufacturing technology is widely applied in the manufacturing industry due to its advantages, such as high manufacturing flexibility, short product manufacturing cycles, and high material utilization rates [1]. Metal additive manufacturing technology heats and melts materials through an electron beam or laser, achieving rapid melting and solidification of materials and forming complete parts through layer-by-layer processing [2]. In this process, the liquid metal region formed by the melting of metal materials is referred to as the melt pool. The formation, flow, and rapid solidification of the melt pool may lead to processing instability, resulting in defects such as porosity and cracks in the formed parts, which can severely impact the performance of the parts. Therefore, to ensure the quality and reliability of metal additive manufacturing parts, it is crucial to monitor the formation and solidification changes of the melt pool during the processing [3, 4]. In situ monitoring technologies based on high-speed cameras, infrared thermal cameras, Laser Cladding Profilers, Acoustic Emission sensors, and other tools have been proven to be effective in additive manufacturing process monitoring [5, 6]. However, since additive manufacturing is a complex process and a single sensor cannot fully capture its physical characteristics, multi-sensor joint monitoring has become a hot topic of research [6, 7, 8, 9]. The advancement of deep learning technology has propelled the widespread application of multi-sensor joint monitoring techniques across multiple domains [10, 11, 12, 13]. High-speedcameras and infrared thermal cameras have advantages in monitoring the dynamics of the melt pool and temperature distribution, and many studies combine these two types of sensors and integrate multi-source data through machine learning to monitor the behavior of the melt pool. [PERSON] et al. [8] designed an in-process monitoring setup consisting of high-speed melt pool imaging and melt pool temperature field imaging sensors to extract LPBF melt pool characteristics, including the morphology of the melt pool, the temperature distribution of the melt pool, and characteristics of the spatter, and used deep learning models for feature fusion to detect laser spot size deviation and predict the type and severity of porosity. [PERSON] et al. [7] extracted temperature field, melt pool geometry, and acoustic features via an off-axis short-wavelength infrared thermal camera, a coaxial melt pool vision camera, and microphone sensor data, using machine learning models to integrate multi-sensor features with real-time robot tool-center-point positions to predict local quality in the L-DED process. [PERSON] et al. [14] used CCD cameras and infrared thermal cameras to obtain the geometrical properties of and temperature field distribution features of the melt pool, designing a deep learning-based contour extraction algorithm to extract the area, length, and width of the melt pool, as well as an HOG algorithm based on trilinear interpolation to extract the temperature distribution characteristics of the side walls, and established a neural network-based model for predicting robotic trajectory deviation in wire arc additive manufacturing processes. The aforementioned research on multi-source data for laser processing of the melt pool mainly focuses on feature-level fusion, which involves extracting and analyzing feature information from various data sources for subsequent monitoring and prediction tasks. However, the temperature distribution monitored by the infrared thermal camera is susceptible to the influence of factors such as the material's emissivity and transmittance, which vary with temperature and material state, leading to monitoring errors [15]. These data errors affect the accuracy of feature extraction, reduce the precision of feature fusion, and may lead to misjudgment in process monitoring, ultimately resulting in a decline in processing quality.