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<p>Understanding how alloy materials behave under high temperatures and high strain-rates is essential for optimizing manufacturing processes. To capture the fundamental material behaviors, empirical viscoplastic constitutive models, such as the Johnson–Cook (J-C) model, are often adopted. However, determining accurate constitutive model parameters remains challenging, due to the unknown nature of the parametric space as well as the heavy computational cost associated with first principle-based identification with respect to experimental measurement. To address these challenges, we propose an integrated adaptive sampling framework for constitutive model coefficients exploration. Built upon stochastic optimization to minimize the difference between experimental measurement through Gleeble testing and finite element-based prediction, this framework combines a multi-response Gaussian process (MRGP) surrogate model with an adaptive sampling strategy to enhance the computational efficiency. Specifically, a multi-response Gaussian process surrogate model is trained on a small dataset of finite element results, effectively emulating the analysis while significantly reducing computational cost. The adaptive sampling strategy further refines the surrogate model by iteratively exploring the parametric space informed by response surface characteristics. This approach avoids the inefficiency of static ‘one-shot’ sampling and improves parametric identification by focusing sampling efforts on underexplored yet promising regions of the parametric space. Using AISI 9310 material and the J–C model as a case study, we validate the proposed framework by comparing it with conventional parametric identification methods. Our results demonstrate that the integrated adaptive sampling framework achieves excellent prediction accuracy and computational efficiency, providing a practical solution for constitutive model coefficient calibration in high-temperature, high strain-rate manufacturing processes such as metal forming and machining.</p>

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Ascertaining constitutive relation of AISI 9310 in underexplored parametric space through adaptive searching

  • 期刊论文

  • 发表日期：

    2025年10月10日

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  • Dong Xu,
  • Jeongho Kim,
  • Lesley Frame,
  • Jiong Tang

摘要

Understanding how alloy materials behave under high temperatures and high strain-rates is essential for optimizing manufacturing processes. To capture the fundamental material behaviors, empirical viscoplastic constitutive models, such as the Johnson–Cook (J-C) model, are often adopted. However, determining accurate constitutive model parameters remains challenging, due to the unknown nature of the parametric space as well as the heavy computational cost associated with first principle-based identification with respect to experimental measurement. To address these challenges, we propose an integrated adaptive sampling framework for constitutive model coefficients exploration. Built upon stochastic optimization to minimize the difference between experimental measurement through Gleeble testing and finite element-based prediction, this framework combines a multi-response Gaussian process (MRGP) surrogate model with an adaptive sampling strategy to enhance the computational efficiency. Specifically, a multi-response Gaussian process surrogate model is trained on a small dataset of finite element results, effectively emulating the analysis while significantly reducing computational cost. The adaptive sampling strategy further refines the surrogate model by iteratively exploring the parametric space informed by response surface characteristics. This approach avoids the inefficiency of static ‘one-shot’ sampling and improves parametric identification by focusing sampling efforts on underexplored yet promising regions of the parametric space. Using AISI 9310 material and the J–C model as a case study, we validate the proposed framework by comparing it with conventional parametric identification methods. Our results demonstrate that the integrated adaptive sampling framework achieves excellent prediction accuracy and computational efficiency, providing a practical solution for constitutive model coefficient calibration in high-temperature, high strain-rate manufacturing processes such as metal forming and machining.