## 5 Conclusions  
This paper presents an industrially relevant investigation into the springback behavior of a complex sheet metal component, combining numerical simulations with experimental validation. The use of a real manufacturing part--rather than an idealized geometry--marks a significant step forward in practical springback prediction, revealing the strengths and limitations of widely used constitutive models such as Barlat YLD2000. Key findings include the following:  
* The overall springback behavior of the part is well captured, validating the use of current simulation tools in early design stages. * Springback magnitude decreases with increasing BHF, which is primarily due to the reduction in bending moments in the sidewall zones. * Discrepancies between experimental and numerical results suggest that standard anisotropic models may fall short in accounting for work-hardening stagnation, particularly in advanced high-strength steels. These results carry important implications for industry: more accurate early-stage predictions can reduce costly tool modifications and shorten development cycles. The proposed methodology thus supports faster time-to-market and more efficient product development. For future research, efforts will focus on the following:  
* Studying large strain ranges to refine material parameter identification. * Instrumenting and monitoring the experimental force vs. time in the press. * Exploring additional forming parameters and extending simulations to a wider range of real-world parts. * Integrating more advanced constitutive models such as the Yoshida-Uemori model to capture complex material behavior. The next step is to integrate models such as the Yoshida-Uemori model, as previous studies have shown that this approach provides more accurate springback predictions compared to using a purely anisotropic yield model like YLD2000. This integration is expected to better capture the transient work-hardening behavior and its stagnation, thereby reducing the discrepancies between numerical predictions and experimental results. * Developing robust, meaningful metrics to evaluate springback prediction quality based on qualitative criteria relevant to product assessment. Standard statistical metrics, such as R\({}^{2}\), were considered but ultimately deemed inadequate at the product/industrial quality level. From the product development perspective, it was determined that quantitative metrics fail to capture the nuances involved--evaluating a corner versus a frontal view presents very different challenges, and factors such as visual attributes and subjective quality judgments necessitate a more nuanced, qualitative approach. Future work will focus on studying these qualitative aspects with the goal of developing a meaningful quantitative indicator of modeling quality in accordance with these criteria. In summary, this study contributes a validated, practical approach to springback prediction in industrial components, offering both theoretical and applied value to the forming community. Conceptualization, all authors; methodology, C.A., A.d.-J. and M.I. ; software, C.A. ; validation, P.G. and C.A. ; investigation, P.G. and C.A. ; resources, A.d.-J. and M.I. ; writing, all authors; visualization, P.G. and C.A. ; project administration, A.d.-J. and M.I. ; funding acquisition, A.d.-J. and M.I. All authors have read and agreed to the published version of the manuscript. The authors gratefully acknowledge project 08.Dl23.649 funded by Banco Santander, Government of Cantabria and University of Cantabria within the Industrial Doctoral call. Data Availability Statement: The data presented in this study are available on request from the corresponding author due to industrial secrets. Conflicts of Interest: Author [PERSON] was employed by the company BSH Home Appliances Group.