AI-Driven Design and Optimization of Bending Processes

Abstract:

Air bending is a critical operation in the metalworking industry, where dimensional accuracy and process efficiency are essential to ensure product quality and economic viability. This work proposes an AI-driven design and optimization strategy which couples artificial intelligence, specifically artificial neural networks, with a quasi-random search algorithm for the metamodeling and optimization of the air bending process. An extensive simulation database was generated by varying geometrical, material, and process parameters, and neural-network-based metamodels were trained to predict the maximum punch force, maximum thickness reduction, and final bending angle, achieving high predictive accuracy with R² values exceeding 0.96. The metamodel was subsequently used to optimize process configurations by simultaneously minimizing the maximum punch force and the maximum thickness reduction while ensuring the target bending angle, leading on average to reductions of 46.7% in maximum force and 31.5% in thickness reduction compared to non-optimized cases. The results demonstrate that artificial intelligence provides an efficient and effective tool for the design and optimization of the bending process, significantly accelerating parameter selection while improving process quality and reducing manufacturing costs.

• Periodical: Key Engineering Materials (Volume 1049)
• Pages: 65-74
• DOI: https://doi.org/10.4028/p-po3dGG
• Online since: April 2026
• Authors: Diogo Fernandes, Tomás Parreira, Daniel Cruz, Armando Marques, Pedro Prates, Marta Oliveira, Abel Santos, Diogo Neto, André Pereira*
• Keywords: Bending, Forming Process, Machine Learning, Optimization

References

[1] J. Ma, T. Welo, Analytical Springback Assessment in Flexible Stretch Bending of Complex Shapes, Int. J. Mach. Tools Manuf., 160 (2021) 103653.

DOI: 10.1016/j.ijmachtools.2020.103653

[2] A.E. Marques, P.A. Prates, A.F.G. Pereira, M.C. Oliveira, J.V. Fernandes, B.M. Ribeiro, Performance Comparison of Parametric and Non-Parametric Regression Models for Uncertainty Analysis of Sheet Metal Forming Processes, Metals (Basel), 10 (2020) 457.

DOI: 10.3390/met10040457

[3] J.H. Wiebenga, A.H. van den Boogaard, G. Klaseboer, Sequential Robust Optimization of a V-Bending Process Using Numerical Simulations, Struct. Multidiscip. Optim., 46 (2012) 137–153.

DOI: 10.1007/s00158-012-0761-0

[4] S.A. Hindman, M.J. Bussey, Custom Equations for the Unfolding of Sheet Metal, Patent US8131516 B2 (2012).

[5] DIN 6935, Cold Bending of Flat Rolled Steel (2011).

DOI: 10.31030/1813521

[6] M.R. Jamli, N.M. Farid, The Sustainability of Neural Network Applications within Finite Element Analysis in Sheet Metal Forming: A Review, Measurement, 138 (2019) 446–460.

DOI: 10.1016/j.measurement.2019.02.034

[7] S.S. Miranda, M.R. Barbosa, A.D. Santos, J.B. Pacheco, R.L. Amaral, Forming and Springback Prediction in Press Brake Air Bending Combining Finite Element Analysis and Neural Networks, J. Strain Anal. Eng. Des., 53 (2018) 584–601.

DOI: 10.1177/0309324718798222

[8] D.J. Cruz, M.R. Barbosa, A.D. Santos, R.L. Amaral, J.C. de Sa, J.V. Fernandes, Recurrent Neural Networks and Three-Point Bending Test on the Identification of Material Hardening Parameters, Metals (Basel), 14 (2024) 84.

DOI: 10.3390/met14010084

[9] D.A. Molitor, V. Arne, C. Kubik, G. Noemark, P. Groche, Inline Closed-Loop Control of Bending Angles with Machine Learning Supported Springback Compensation, Int. J. Mater. Form., 17 (2024) 8.

DOI: 10.1007/s12289-023-01802-y

[10] W. Muhammad, R. Mahmoud, F. Anis, U.R.B. Saad, Prediction of Springback Using the Machine Learning Technique in High-Tensile Strength Sheet Metal during the V-Bending Process, Jordan J. Mech. Ind. Eng., 17 (2023) 481–488.

DOI: 10.59038/jjmie/170403

[11] M.A. Dib, B. Ribeiro, P. Prates, Federated Learning as a Privacy-Providing Machine Learning for Defect Predictions in Smart Manufacturing, Smart Sustain. Manuf. Syst., 5 (2021) 1–17.

DOI: 10.1520/ssms20200029

[12] D. Wang, Y. Xu, B. Duan, Y. Wang, M. Song, H. Yu, H. Liu, Intelligent Recognition Model of Hot Rolling Strip Edge Defects Based on Deep Learning, Metals (Basel), 11 (2021) 223.

DOI: 10.3390/met11020223

[13] D. Fei, P. Hodgson, Experimental and Numerical Studies of Springback in Air V-Bending Process for Cold Rolled TRIP Steels, Nucl. Eng. Des., 236 (2006) 1847–1851.

DOI: 10.1016/j.nucengdes.2006.01.016

[14] X. Li, Effects of Air Bending Parameters on Springback Using Finite Element Analysis, Appl. Mech. Mater., 423–426 (2013) 978–983.

DOI: 10.4028/www.scientific.net/amm.423-426.978

[15] M.S. Buang, S.A. Abdullah, J. Saedon, Effect of Die and Punch Radius on Springback of Stainless Steel Sheet Metal in the Air V-Die Bending Process, J. Mech. Eng. Sci., 8 (2015) 1322–1331.

DOI: 10.15282/jmes.8.2015.7.0129

[16] H.W. Swift, Plastic Instability under Plane Stress, J. Mech. Phys. Solids, 1 (1952) 1–18.

[17] R. Hill, A Theory of the Yielding and Plastic Flow of Anisotropic Metals, Proc. R. Soc. A Math. Phys. Eng. Sci., 193 (1948) 281–297.

[18] L.F. Menezes, C. Teodosiu, Three-Dimensional Numerical Simulation of the Deep-Drawing Process Using Solid Finite Elements, J. Mater. Process. Technol., 97 (2000) 100–106.

DOI: 10.1016/s0924-0136(99)00345-3

[19] I.M. Sobol', On the Distribution of Points in a Cube and the Approximate Evaluation of Integrals, USSR Comput. Math. Math. Phys., 7 (1967) 86–112.

DOI: 10.1016/0041-5553(67)90144-9