Use of machine learning in determining the parameters of viscoplastic models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F25%3A00376594" target="_blank" >RIV/68407700:21220/25:00376594 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1108/EC-02-2024-0166" target="_blank" >https://doi.org/10.1108/EC-02-2024-0166</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1108/EC-02-2024-0166" target="_blank" >10.1108/EC-02-2024-0166</a>

Result language
angličtina
Original language name
Use of machine learning in determining the parameters of viscoplastic models
Original language description
Purpose The 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/approach An 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. Findings A reasonable correlation between experimental and numerical data was achieved using the determined material parameters. Originality/value This paper fulfils a need to research alternative methods of identifying material parameters.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20302 - Applied mechanics

Project
<a href="/en/project/GA21-06645S" target="_blank" >GA21-06645S: Life assessment of mechanical components under multiaxial thermo-mechanical loading with variable amplitude</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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