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Abstract

The current study of weld pool fluid dynamics in arc welding focuses on the numerical model establishment and simulation, and the x-ray combined with particle trace imaging observations, there is no real-time monitor and quantitatively characterize the weld pool flow behavior in welding process for controlling the weld quality. This study develops an innovative structured laser vision-based sensing system for three-dimensional (3D) reconstruction and quantitative analysis of weld pool surface topographies in gas tungsten arc welding (GTAW). Through characterization of dynamic weld pool morphologies, two novel parameters are proposed: the surface convexity variation rate (Rh) and fluid flow velocity (Rs), which enable quantitative assessment of weld pool fluid flow characteristics. Complementary two-dimensional(2D) geometrical parameters including weld pool width and length are simultaneously monitored for comprehensive process evaluation. Experimental results demonstrate that the reconstructed 3D pool surface topography combined with the proposed parameters effectively represents the weld pool flow patterns with acceptable accuracy (deviation< 3 %). The convexity gradient Rh exhibits strong correlation with flow intensity, where stable variations correspond to optimal bead formation. Quantitative analysis reveals an maximums fluid flow velocity of 70 mm/s, showing strong agreement with computational fluid dynamics simulations. This methodology establishes a new paradigm for in-process monitoring of weld pool dynamics, providing a scientific foundation for intelligent welding quality control.

Document Type

Article

Publication Date

2026

Notes/Citation Information

1000-9345/© 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Digital Object Identifier (DOI)

https://doi.org/10.1016/j.cjme.2025.100039

Funding Information

Supported by National Natural Science Foundation of China (Grant No. 52265050), Lanzhou Youth Science and Technology Innovation Talent Project (Grant No. 2023-QN-90), Wenzhou Science and Technology Planning Project (Grant No. 2023G0157), Gansu Provincial Key Research and Development Program (Grant No. 25YFGA023), and Gansu Provincial Innovation Fund for Higher Education Institutions (Grant No. 2025A-022).

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