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Artificial Neural Network-Based Springback Prediction in Sheet Metal Bending for Industry 4.0 Applications

In: Technology Management for Intelligent, Open and Responsible Organizations and Ecosystems

Author

Listed:
  • Weidher Possidonio Cardoso

    (Centro Universit’ario das Faculdades Associadas de Ensino – UNIFAE)

  • William Regone

    (Centro Universit’ario das Faculdades Associadas de Ensino – UNIFAE)

  • Rodrigo Furlan de Assis

    (Ecole de Technologie Supérieure, Department of Systems Engineering)

Abstract

Springback is a major challenge in sheet metal forming, affecting the dimensional accuracy of bent components. This study proposes an Artificial Neural Network (ANN) to predict springback angles from material properties and bending parameters, trained on an industrial dataset including sheet thickness, internal radius, yield strength, elastic modulus, and applied force. The model achieved high predictive accuracy, generalized across materials and conditions, and reduced output variability while slightly underestimating extreme cases. Validation tests confirmed its ability to capture nonlinear elastic recovery, highlighting its potential for integration into Industry 4.0 manufacturing environments.

Suggested Citation

  • Weidher Possidonio Cardoso & William Regone & Rodrigo Furlan de Assis, 2026. "Artificial Neural Network-Based Springback Prediction in Sheet Metal Bending for Industry 4.0 Applications," Springer Proceedings in Business and Economics, in: Fabiano Armellini & Syrine Njah & Elaine Mosconi & Breno Nunes (ed.), Technology Management for Intelligent, Open and Responsible Organizations and Ecosystems, pages 268-277, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-23282-3_33
    DOI: 10.1007/978-3-032-23282-3_33
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