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

Author

Listed:
  • 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))

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.

Suggested Citation

  • Amaia Abanda & Amaia Arroyo & Fernando Boto & Miguel Esteras, 2025. "Combining physics-based and data-driven methods in metal stamping," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2583-2599, April.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02374-7
    DOI: 10.1007/s10845-024-02374-7
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    References listed on IDEAS

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    1. Darren Wei Wen Low & Akshay Chaudhari & Dharmesh Kumar & A. Senthil Kumar, 2023. "Convolutional neural networks for prediction of geometrical errors in incremental sheet metal forming," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2373-2386, June.
    2. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.
    3. Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, January.
    Full references (including those not matched with items on IDEAS)

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