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<p>To reduce vehicle weight, the automotive industry has increasingly adopted ultra-high-strength steel. However, accurately predicting springback during the roll forming of 1.7GPa-grade steel remains challenging, primarily to the material’s complex behaviors—such as anisotropy and variations in elastic modulus. This study established an anisotropic constitutive model considering the variation of elastic modulus for 1.7GPa martensitic steel. The model and roll forming process were validated and analyzed through multi-scale experiments and numerical simulations. The results show that the plastic strain is 2.75%, and the nonlinear modulus attenuation ranges from 5.49% to 7.75%. It is found that the springback is positively correlated with hardness and stress. The hardness of pass 6 decreases by 3%, and the springback decreases by 15.85%. The Hill48-E model achieved a springback prediction error of 2.89%, which is 19.58% to 34.21% lower than those of the Hill48 and von Mises models. By accounting for modulus degradation, this model resolves the associated high-prediction-error issue and significantly improves springback prediction accuracy.</p>

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A Study on Forming Characteristics of Roll Forming Process with 1.7GPa Ultra-high Strength Steel

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Ran Yao,
  • Fei Han,
  • ZiHuai Ning

摘要

To reduce vehicle weight, the automotive industry has increasingly adopted ultra-high-strength steel. However, accurately predicting springback during the roll forming of 1.7GPa-grade steel remains challenging, primarily to the material’s complex behaviors—such as anisotropy and variations in elastic modulus. This study established an anisotropic constitutive model considering the variation of elastic modulus for 1.7GPa martensitic steel. The model and roll forming process were validated and analyzed through multi-scale experiments and numerical simulations. The results show that the plastic strain is 2.75%, and the nonlinear modulus attenuation ranges from 5.49% to 7.75%. It is found that the springback is positively correlated with hardness and stress. The hardness of pass 6 decreases by 3%, and the springback decreases by 15.85%. The Hill48-E model achieved a springback prediction error of 2.89%, which is 19.58% to 34.21% lower than those of the Hill48 and von Mises models. By accounting for modulus degradation, this model resolves the associated high-prediction-error issue and significantly improves springback prediction accuracy.