Combining physics-based and data-driven methods in metal stamping

Amaia Abanda (), Amaia Arroyo (), Fernando Boto () and Miguel Esteras ()
Additional contact information
Amaia Abanda: TECNALIA Basque Research and Technology Alliance (BRTA)
Amaia Arroyo: TECNALIA Basque Research and Technology Alliance (BRTA)
Fernando Boto: TECNALIA Basque Research and Technology Alliance (BRTA)
Miguel Esteras: TECNALIA Basque Research and Technology Alliance (BRTA)

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 4, No 18, 2583-2599

Abstract: Abstract This work presents a methodology for combining physical modeling strategies (FEM), machine learning techniques, and evolutionary algorithms for a metal stamping process to ensure process quality during production. Firstly, a surrogate model or metamodel is proposed to approximate the behavior of the simulation model for different outputs in a fraction of time. Secondly, based on the surrogate model, multiple soft sensors that estimate different quality measures of the stamped part departing from the draw-ins are proposed, which enables their integration into the process. Lastly, evolutionary algorithms are used to estimate the latent blank characteristics and for the prescriptions of process parameters that maximize the quality of the stamped part. The obtained numerical results are promising, with relative errors around 2 2% in most cases and outperforming a naive method. This methodology aims to be a decision support system that moves towards zero defects in the stamping process from the process conception phase.

Keywords: Metal stamping quality; FEM; Machine learning; Metaheuristics (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02374-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02374-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02374-7

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().