Enhancing GMAW simulations through a hybrid Eulerian and Lagrangian method considering an inclined welding torch

The comprehensive simulation of gas metal arc welding (GMAW) remains challenging due to the continuously evolving topologies of both the welding arc region and the weld seam formation zone. GMAW involves phenomena, such as melting, hydrodynamic flows within the weld pool, and crystallization, occurring on timescales significantly distinct from the rapid plasma physics processes in the arc. This plasma exhibits pronounced heterogeneity in voltage gradients, necessitating highly precise modeling of anode and cathode layers. In contrast, traditional simulation methods couple liquid metal and arc behaviour within a single domain, typically employing Volume of Fluid (VOF) or Level Set techniques. These methods often rely on substantial simplifications in the plasma boundary layers, thereby compromising predictive accuracy. To overcome these limitations, this paper presents a hybrid modeling approach coupling a Lagrangian Smoothed Particle Hydrodynamics (SPH) model for weld pool and droplet dynamics with an Eulerian framework tailored for the arc zone. The SPH method effectively captures large-scale phenomena in the weld pool, including heat and mass transfer, droplet detachment, and solidification. On the other hand, the Eulerian model accurately represents the complex electromagnetic and thermal behaviour within the anode and cathode layers of the arc plasma.

This study also addresses torch inclination effects on weld pool dynamics, heat flux distribution, and current density on the cathode, providing insights into their influence on weld geometry. Through the synthesis of both, Lagrangian and Eulerian approaches, the proposed method provides a robust tool for addressing the multiscale, multiphysics nature of the GMAW process. Results from 3D calculations include quasi-stationary Eulerian solutions for the plasma domain and transient SPH simulation of the heat and mass transfer for a drag/push weld.

Although still under active development, this hybrid model shows significant potential for enhancing the predictive capabilities of GMAW simulations, thus offering additional possibilities in process optimization and quality improvement in industrial welding.