Experimental and numerical investigation of process-induced recoil force in keyhole laser welding: Insights for validating multi-physics process simulations and modelling assumptions
Year: 2025
Type: article
Abstract: Among the various driving forces involved in the molten pool during keyhole laser welding, the vaporization-induced recoil pressure is the dominant one. This study experimentally measured the process-induced recoil force during laser welding of aluminium and copper. A customized measurement setup was used to measure the specimen displacement caused by the recoil force, which was then determined by means of a finite element (FE) analysis. Furthermore, multi-physics computational fluid dynamics (CFD) models of the laser welding process were developed. After calibration, these models were used to predict the recoil force and its dependence on various process parameters. When only the recoil pressure acting on regions where vaporization occurs was considered, excluding the gaseous phases in the model, the total recoil force was underestimated. To account for that the formed gas contributes to the total recoil force as it rises and exits the keyhole, the total recoil force was calculated based on the predicted net mass flow due to vaporization and condensation. This simplified model showed good agreement between predicted and experimentally measured recoil forces, demonstrating that the observed consistent recoil force with increasing laser power may be due to a corresponding increase in the condensation rate. This highlights the importance of understanding the behaviour of the vaporized gas phase to determine appropriate simplifications and assumptions in laser welding process modelling. The findings of this study support the development and validation of multi-physics process models, further advancing knowledge of relevant modelling approximations.
Institution: University of Skövde
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,980
Funder: VINNOVA
OpenAlex ID: W4410740765