Examination of machine learning method for identification of material model parameters

Pogorelko, V.V.; Mayer, A.E.; Fomin, E.V.; Fedorov, E.V.

doi:10.1016/j.ijmecsci.2023.108912

International Journal of Mechanical Sciences

2024

DOI: 10.1016/j.ijmecsci.2023.108912

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Examination of machine learning method for identification of material model parameters

V.V. Pogorelko,

A.E. Mayer,

E.V. Fomin

et al.

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Cited by 5 publications

References 90 publications

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Dynamic deformation and fracture of brass: Experiments and dislocation-based model

Rodionov,

Pogorelko,

Lupanov

et al. 2024

International Journal of Plasticity

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Dynamic deformation and fracture of brass: Experiments and dislocation-based model

Rodionov,

Pogorelko,

Lupanov

et al. 2024

International Journal of Plasticity

View full text Add to dashboard Cite

Multi-objective optimization of heat transfer performance and power consumption of Taylor-Couette flow with elliptical helical slits wall

Song,

Liu,

Sun

et al. 2025

International Journal of Thermal Sciences

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Use of machine learning in determining the parameters of viscoplastic models

Halamka,

Bartošák

2024

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PurposeThe constitutive models determine the mechanical response to the defined loading based on model parameters. In this paper, the inverse problem is researched, i.e. the identification of the model parameters based on the loading and responses of the material. The conventional methods for determining the parameters of constitutive models often demand significant computational time or extensive model knowledge for manual calibration. The aim of this paper is to introduce an alternative method, based on artificial neural networks, for determining the parameters of a viscoplastic model.Design/methodology/approachAn artificial neural network was proposed to determine nine material parameters of a viscoplastic model using data from three half-life hysteresis loops. The proposed network was used to determine the material parameters from uniaxial low-cycle fatigue experimental data of an aluminium alloy obtained at elevated temperatures and three different mechanical strain rates.FindingsA reasonable correlation between experimental and numerical data was achieved using the determined material parameters.Originality/valueThis paper fulfils a need to research alternative methods of identifying material parameters.

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Examination of machine learning method for identification of material model parameters

Cited by 5 publications

References 90 publications

Dynamic deformation and fracture of brass: Experiments and dislocation-based model

Dynamic deformation and fracture of brass: Experiments and dislocation-based model

Multi-objective optimization of heat transfer performance and power consumption of Taylor-Couette flow with elliptical helical slits wall

Use of machine learning in determining the parameters of viscoplastic models

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