Högskolan i Skövde

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.

Laser welding of busbars to battery tabs in electric vehicles (EVs) is crucial due to the rapid advancements in electric mobility technology. Ensuring weld quality is paramount, as it depends on factors such as porosity generation, fluid flow in the molten pool during welding, applied laser power, and welding speed. However, conventional laser welding techniques, which primarily focus on adjusting laser parameters along the weld direction, struggle to effectively mitigate porosity formation. While the effect of laser angles along the weld direction has been extensively studied, the effects of off-axis laser angles, i.e., angled in the plane perpendicular to the weld direction, have not yet been explored. This study introduces an innovative approach to laser welding by varying the laser off-axis angle at different laser energy densities to optimize the process specifically for porosity reduction. By implementing a three-dimensional computational fluid dynamics (CFD) model of laser welding of aluminum AA1050, we provide a detailed analysis of the fluid flow and melt pool dimensions while employing different off-axis angles. Our model incorporates multiple reflections, upward vapor pressure, and recoil pressure to explain porosity formation at different laser off-axis angles. The results show that increasing the laser off-axis angle at optimized laser power and welding speed significantly reduces porosity. The numerical analysis indicates a maximum deviation from the experimental melt pool width of 11% at a laser off-axis angle of 4.92° and a minimum error of 2.6% at an off-axis angle of 2.74°. For melt pool depth, the maximum deviation is 7.2% at an off-axis angle of 4.92°, and the minimum difference is 0.5% at an off-axis angle of 7.42°. This study presents a novel methodology for improving laser welding processes by addressing the specific challenge of porosity formation.

For battery pack assemblies, it is crucial that the laser welded cell-to-busbar joints demonstrate both high mechanical strength and minimal electrical resistance. The present study investigates the effect of different laser welding parameters, on the mechanical strength, electrical resistance, porosity formation and joint microstructure, for dissimilar material cell-to-busbar joints. Laser welding experiments are performed, on thin nickel-plated copper and steel plates. The plates are joined in an overlap configuration, using laser beam wobbling and power modulation. Both circular and sinusoidal laser beam wobbling are used as selected strategies to increase the interface width of the joints, where also a comparison is made between the two methods. The joint quality is evaluated using joint geometry analysis, shear strength tests, computed tomography scanning and electrical resistance measurements. The results show that circular laser beam wobbling gives a larger joint shear strength compared with sinusoidal laser beam wobbling. In addition, it is observed that both the total pore volume and material mixing are significantly increased with increasing laser power and wobbling frequency for circular laser beam wobbling. However, for the sinusoidal laser beam wobbling the wobbling frequency does not show a significant impact on the total pore volume.

In the battery pack assembly, it is essential to ensure that the cell-to-busbar joints can be produced with high quality and with minimal impact on the individual battery cells. This study examines the influence of process parameters on the joint quality for nickel-plated copper and steel plates, laser welded in an overlap configuration. Artificial neural network-based meta models, trained on numerical results from computational fluid dynamics simulations of the laser welding process, are used to predict and evaluate the joint quality. A set of optimized process parameters is identified, in order to simultaneously maximize the interface width for the joints, and minimize the formation of undercuts and in-process temperatures. In an meta model-based multi-objective optimization approach, the non-dominated sorting genetic algorithm II (NSGA-II) is used to efficiently search for trade-off solutions and the meta models are used for objective approximation. As a result, the objective evaluation time is decreased from around 9 h, when evaluated directly from numerical simulations, to only tenths of a second. From the Pareto-optimal front of trade-off solutions, three optimal solutions are selected for validation. The selected solutions are validated through laser welding experiments and numerical simulations, resulting in joints with large interface widths and low in-process temperatures without a full penetration.

To advance quality assurance in the welding process, this study presents a deep learning (DL) model that enables the prediction of two critical welds’ key performance characteristics (KPCs): welding depth and average pore volume. In the proposed approach, a wide range of laser welding key input characteristics (KICs) is utilized, including welding beam geometries, welding feed rates, path repetitions for weld beam geometries, and bright light weld ratios for all paths, all of which were obtained from hairpin welding experiments. Two DL networks are employed with multiple hidden dense layers and linear activation functions to investigate the capabilities of deep neural networks in capturing the complex nonlinear relationships between the welding input and output variables (KPCs and KICs). Applying DL networks to the small numerical experimental hairpin welding dataset has shown promising results, achieving mean absolute error values of 0.1079 for predicting welding depth and 0.0641 for average pore volume. This, in turn, promises significant advantages in controlling welding outcomes, moving beyond the current trend of relying only on defect classification in weld monitoring to capture the correlation between the weld parameters and weld geometries.

The keyhole depth is a key measurement characteristic in the laser welding of busbar to battery tabs in battery packs for electric vehicles (EV), as it directly affects the quality of the weld. In this work, experiments are carried out with controlled and adjusted laser power and feed rate parameters to investigate the influence on the keyhole width, keyhole depth and porosities. A 3D numerical model of laser keyhole welding of an aluminum alloy (A1050) has been developed to describe the porosity formation and the keyhole depth variation. A new integration model of the recoil pressure and the rate of evaporation model is implemented which is closer to the natural phenomena as compared to the conventional methods. Additionally, major physical forces are employed including plume formation, upward vapor pressure and multiple reflection in the keyhole. The results show that keyhole depth is lower at higher feed rate, while lower feed rates result in increased keyhole depth. This study reveals that low energy densities result in an unstable keyhole with high spattering, exacerbated by increased laser power. Mitigating incomplete fusion is achieved by elevating laser energy density. The findings emphasize the critical role of keyhole depth in optimizing laser welding processes for applications like busbar-to-battery tab welding.