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<p>It is well-known that materials behavior changes with strain rate and temperature. The changes are described by an equation known as material model. The model involves a number of constants which are normally determined by experiment. In this study, the constants of Zerilli-Armstrong model are identified using dynamic indentation test combined with numerical simulation and an optimization technique. The specimens made of a steel alloy are subjected to indentation tests at four different strain rates and four temperatures and the experimental load-depth curve is recorded. The dynamic indentation test is simulated using Ls-dyna hydro code and the numerical load-depth is obtained. Attempts are made to optimize the error between the experimental and the numerical load-depth curves. This is accomplished using Surrogate model. The results are validated using the stress–strain curves obtained from Hopkinson bar tests. The study shows that the method yields acceptable results. The results obtained using artificial neural network and optimization technique, based on a quadratic polynomial, which have been reported in the previous works for Johnson–Cook model are reproduced here for Zerilli-Armstrong model for comparison purposes.</p>

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Determination of the parameters of a material constitutive relation using the surrogate model along with dynamic indentation test

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月16日

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  • G. H. Majzoobi,
  • S. Pourolajal

摘要

It is well-known that materials behavior changes with strain rate and temperature. The changes are described by an equation known as material model. The model involves a number of constants which are normally determined by experiment. In this study, the constants of Zerilli-Armstrong model are identified using dynamic indentation test combined with numerical simulation and an optimization technique. The specimens made of a steel alloy are subjected to indentation tests at four different strain rates and four temperatures and the experimental load-depth curve is recorded. The dynamic indentation test is simulated using Ls-dyna hydro code and the numerical load-depth is obtained. Attempts are made to optimize the error between the experimental and the numerical load-depth curves. This is accomplished using Surrogate model. The results are validated using the stress–strain curves obtained from Hopkinson bar tests. The study shows that the method yields acceptable results. The results obtained using artificial neural network and optimization technique, based on a quadratic polynomial, which have been reported in the previous works for Johnson–Cook model are reproduced here for Zerilli-Armstrong model for comparison purposes.