Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool

In additive manufacturing processes, the metal melt pool is decisive for processing quality. A single sensor is incapable of fully capturing its physical characteristics and is prone to data inaccuracies. This study proposes a multi-sensor monitoring solution integrating an off-axis infrared thermal camera with an on-axis high-speed camera to address this issue; a multi-source data pre-processing procedure has been designed, a multi-source data fusion method based on video frame interpolation has been developed, and a self-supervised training strategy based on transfer learning has been introduced. Experimental results indicate that the proposed data fusion method can eliminate temperature anomalies caused by single emissivity and droplet splashing, generating highly credible fused data and significantly enhancing the stability of metal additive manufacturing and the quality of parts.