Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model.

ABSTRACT: Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however,

SUBMITTER: Feng S 

PROVIDER: S-EPMC12300121 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature