## 5 Conclusions  
This study provided an in-depth analysis of how welding path directions, sequences, and locating schemes influence the geometric precision of welded assemblies. Utilizing finite element simulations and a design of experiments (DOE) approach, the individual and combined effects of these parameters on post-welding displacement were systematically evaluated. Among the factors studied, locating schemes emerged as the most critical determinant of geometric displacement, significantly influencing the final displacements and overall stability of welded components. Optimized locating schemes resulted in a substantial reduction in geometric deformation after welding and springback, ensuring that parts remained better aligned during and after the welding process. Welding sequences also played a key role in reducing displacements, highlighting the importance of determining an optimal order in which welds are applied to further minimize deformations. The influence of weld path directions was found to be less impactful compared to locating schemes and sequences, particularly in assemblies with simpler geometries. However, for assemblies with more complex shapes, such as those with curvature, the weld path direction demonstrated a more pronounced effect on geometric distortions. This suggests that while weld path direction might be of secondary importance in simpler cases, it becomes crucial in more intricate assemblies. Overall, this study emphasizes the importance of a combined, systematic approach in optimizing welding parameters to enhance geometric accuracy and reduce distortions in welded structures, resulting in precise and efficient welding processes in industrial applications.  
Future research includes extending the current work to more complex geometries and materials and incorporating real-world physical experiments to validate the simulation results further. This study highlights the need to explore the interaction between more welding parameters, such as heat input and clamping forces, to refine the control of distortions. Additionally, integrating advanced real-time optimization algorithms, such as deep reinforcement learning, could further enhance the prediction and minimization of welding-induced distortions across a broader range of industrial applications.  
Conceptualization, R.S.T.; methodology, R.S.T., P.F., D.C., K.W. and R.S.; software, R.S.T. and L.L.; validation, R.S.T. and L.L.; formal analysis, R.S.T.; investigation, R.S.T.; resources, R.S.T.; data curation, R.S.T.; writing--original draft preparation, R.S.T., K.W. and R.S.; writing--review and editing, R.S.T., P.F., D.C., K.W. and R.S.; visualization, R.S.T.; project administration, R.S.T., D.C., K.W. and R.S.; funding acquisition, K.W. and R.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Swedish Energy Agency, grant no. P2022-00228-Energi.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
This work was carried out at the Wingquist Laboratory within the Area of Advance Production at Chalmers, Sweden, and in collaboration with Warwick Manufacturing Group (WMG) at the University of Warwick. The project is supported by the Swedish Energy Agency. The support is gratefully acknowledged.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.