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Hyper-Pseudo-Viscoelastic Model and Parameter Identification for Describing Tensile Recovery Stress–Strain Responses of Rubber Components in TBR

Tires are often in service under dynamic conditions. Realizing the high-precision prediction of the mechanical response of rubber materials under cyclic loading can provide guidance for the design of high-performance tires. In this work, the tensile recovery stress-strain responses of rubber materia...

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Autores principales: Pan, Gao, Chen, Meimei, Wang, Yao, Zhang, Jichuan, Liu, Li, Zhang, Liqun, Li, Fanzhu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824763/
https://www.ncbi.nlm.nih.gov/pubmed/36616426
http://dx.doi.org/10.3390/polym15010076
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author Pan, Gao
Chen, Meimei
Wang, Yao
Zhang, Jichuan
Liu, Li
Zhang, Liqun
Li, Fanzhu
author_facet Pan, Gao
Chen, Meimei
Wang, Yao
Zhang, Jichuan
Liu, Li
Zhang, Liqun
Li, Fanzhu
author_sort Pan, Gao
collection PubMed
description Tires are often in service under dynamic conditions. Realizing the high-precision prediction of the mechanical response of rubber materials under cyclic loading can provide guidance for the design of high-performance tires. In this work, the tensile recovery stress-strain responses of rubber materials in nine different components of a truck and bus radial (TBR) tire were obtained through experiments. Before fitting, an experimental data processing method was proposed to facilitate the parameter identification for a hyper-pseudo-viscoelastic model, that is, the raw experimental data were changed to the adjusted test data. The HyperFit software was used to fit the adjusted test data based on the Yeoh hyperelastic model and the Ogden-Roxburgh pseudoelastic model to obtain the initial material parameters for the two models. In order to describe the permanent set, the Prony series viscoelastic model was introduced. The Isight software was adopted to optimize the parameters. The results showed that the hyper-pseudo-viscoelastic model (i.e., the combination of Yeoh, Ogden-Roxburgh and Prony series models) can describe the tensile recovery mechanical responses (loading curve, unloading curve and permanent set) of nine different rubber components in TBRs. The fitting results are in good agreement with the adjusted data, and all the coefficients of determination (R(2)) exceed 0.975. Finally, the cyclic deformation simulation of a dumbbell rubber specimen was carried out based on the above constitutive model and fitted parameters. R(2) was used to describe the simulation accuracy and its value reached 0.968.
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spelling pubmed-98247632023-01-08 Hyper-Pseudo-Viscoelastic Model and Parameter Identification for Describing Tensile Recovery Stress–Strain Responses of Rubber Components in TBR Pan, Gao Chen, Meimei Wang, Yao Zhang, Jichuan Liu, Li Zhang, Liqun Li, Fanzhu Polymers (Basel) Article Tires are often in service under dynamic conditions. Realizing the high-precision prediction of the mechanical response of rubber materials under cyclic loading can provide guidance for the design of high-performance tires. In this work, the tensile recovery stress-strain responses of rubber materials in nine different components of a truck and bus radial (TBR) tire were obtained through experiments. Before fitting, an experimental data processing method was proposed to facilitate the parameter identification for a hyper-pseudo-viscoelastic model, that is, the raw experimental data were changed to the adjusted test data. The HyperFit software was used to fit the adjusted test data based on the Yeoh hyperelastic model and the Ogden-Roxburgh pseudoelastic model to obtain the initial material parameters for the two models. In order to describe the permanent set, the Prony series viscoelastic model was introduced. The Isight software was adopted to optimize the parameters. The results showed that the hyper-pseudo-viscoelastic model (i.e., the combination of Yeoh, Ogden-Roxburgh and Prony series models) can describe the tensile recovery mechanical responses (loading curve, unloading curve and permanent set) of nine different rubber components in TBRs. The fitting results are in good agreement with the adjusted data, and all the coefficients of determination (R(2)) exceed 0.975. Finally, the cyclic deformation simulation of a dumbbell rubber specimen was carried out based on the above constitutive model and fitted parameters. R(2) was used to describe the simulation accuracy and its value reached 0.968. MDPI 2022-12-25 /pmc/articles/PMC9824763/ /pubmed/36616426 http://dx.doi.org/10.3390/polym15010076 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pan, Gao
Chen, Meimei
Wang, Yao
Zhang, Jichuan
Liu, Li
Zhang, Liqun
Li, Fanzhu
Hyper-Pseudo-Viscoelastic Model and Parameter Identification for Describing Tensile Recovery Stress–Strain Responses of Rubber Components in TBR
title Hyper-Pseudo-Viscoelastic Model and Parameter Identification for Describing Tensile Recovery Stress–Strain Responses of Rubber Components in TBR
title_full Hyper-Pseudo-Viscoelastic Model and Parameter Identification for Describing Tensile Recovery Stress–Strain Responses of Rubber Components in TBR
title_fullStr Hyper-Pseudo-Viscoelastic Model and Parameter Identification for Describing Tensile Recovery Stress–Strain Responses of Rubber Components in TBR
title_full_unstemmed Hyper-Pseudo-Viscoelastic Model and Parameter Identification for Describing Tensile Recovery Stress–Strain Responses of Rubber Components in TBR
title_short Hyper-Pseudo-Viscoelastic Model and Parameter Identification for Describing Tensile Recovery Stress–Strain Responses of Rubber Components in TBR
title_sort hyper-pseudo-viscoelastic model and parameter identification for describing tensile recovery stress–strain responses of rubber components in tbr
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824763/
https://www.ncbi.nlm.nih.gov/pubmed/36616426
http://dx.doi.org/10.3390/polym15010076
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