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<p>Aluminum alloy sheets are widely used in lightweight industries due to their excellent performance, but the springback defects in complex forming processes are difficult to predict and control precisely. In this paper, S600 high-strength aluminum was taken as the target. A method combining theoretical analysis, finite element simulation, and forming-process tests was adopted to develop the model. The multi-objective improved non-dominated sorting genetic algorithm was used to optimize the anisotropic yield criterion parameters of Yld2000-2D anisotropic yield criterion. Subsequently, a hybrid calibration strategy combining the multi-objective optimization algorithm and the inverse solution method was adopted to collaboratively optimize the parameters of the Chaboche hybrid strengthening model. And the springback simulation and forming test were conducted to verify the accuracy of springback prediction of the established constitutive models. For two-dimensional cylindrical surface and three-dimensional space complex surface, the maximum prediction deviations of the Yld2000-2D-Chaboche model have increased 24.3% and 12.7% respectively. For different stamping amounts (20&#xa0;mm and 30&#xa0;mm) of the C-shaped beam, the average errors were 0.2&#xa0;mm and 0.06&#xa0;mm respectively. This series of comparative verifications not only confirmed the accuracy of the optimized Yld2000-2D-Chaboche model, but also fully proved its wide applicability in complex forming processes.</p>

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Research on springback prediction of S600 aluminum alloy sheets based on in-plane anisotropy and hybrid strengthening model

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Zhuoyun Yang,
  • Jinjin Wang,
  • Yuyang Fang,
  • Hanpeng Yu,
  • Guojiang Dong

摘要

Aluminum alloy sheets are widely used in lightweight industries due to their excellent performance, but the springback defects in complex forming processes are difficult to predict and control precisely. In this paper, S600 high-strength aluminum was taken as the target. A method combining theoretical analysis, finite element simulation, and forming-process tests was adopted to develop the model. The multi-objective improved non-dominated sorting genetic algorithm was used to optimize the anisotropic yield criterion parameters of Yld2000-2D anisotropic yield criterion. Subsequently, a hybrid calibration strategy combining the multi-objective optimization algorithm and the inverse solution method was adopted to collaboratively optimize the parameters of the Chaboche hybrid strengthening model. And the springback simulation and forming test were conducted to verify the accuracy of springback prediction of the established constitutive models. For two-dimensional cylindrical surface and three-dimensional space complex surface, the maximum prediction deviations of the Yld2000-2D-Chaboche model have increased 24.3% and 12.7% respectively. For different stamping amounts (20 mm and 30 mm) of the C-shaped beam, the average errors were 0.2 mm and 0.06 mm respectively. This series of comparative verifications not only confirmed the accuracy of the optimized Yld2000-2D-Chaboche model, but also fully proved its wide applicability in complex forming processes.