Journal of Materials and Engineering Structures
Volume 13, Numéro 2, Pages 321-331
2026-06-30

Experimental And Numerical Characterization Of Springback In V-bending Of Aisi 304l Stainless Steel Enhanced By Machine Learning Prediction



Authors : Bencherif Mokhtar . Saidani Abdelkrim . Almansba Madjid . Ferhoum Rabah . Boudedja Amar . Belmihoub Khellaf .

Abstract

Accurate prediction of springback is a major challenge in sheet metal forming due to the material's ductility, strain hardening, and deformation-induced martensitic transformation. In this study, an integrated methodology is proposed, combining experimental measurements, finite element analysis, and machine learning to investigate springback in the V-bending of 1.5 mm thick AISI 304L austenitic stainless steel sheets. V-bending tests conducted at angles from 30° to 90° revealed springback percentages ranging between 20.30% and 37.33%. Subsequently, numerical simulations performed with ABAQUS/Explicit and calibrated using mechanical properties derived from tensile test results showed a good correlation with experimental data (MAE = 9.31%). To address the limitation of the number of physical samples, a data augmentation strategy based on values derived from literature was implemented, generating a training set of 200 synthetic data points. Seven regression models were evaluated via bootstrap validation, identifying degree-2 polynomial regression as the optimal model, with a mean absolute error of 2.98° ± 0.06° and a coefficient of determination (R²) of 0.985. The statistical validity of the model was confirmed by residual analysis demonstrating normality via the Shapiro-Wilk test (p = 0.644). A validated compensation table was generated for the direct calibration of industrial tools, demonstrating that even with a limited number of experimental data points, data-driven models can offer a reliable methodology for optimizing predictive manufacturing processes in sheet metal forming.

Keywords

Springback ; V-bending ; ABAQUS ; Stainless Steel ; Metamodeling

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