A Method to Accurately Predict Fatigue Life of Carbon Black‐Filled Natural Rubber Components Incorporating Self‐Heating Temperature

ABSTRACT Predicting the fatigue life of rubber components accurately has proven challenging, as existing methods often produce significant discrepancies compared to experimental results. This study presents a novel approach based on the combination of Abaqus and Endurica software, which can more accurately predict the fatigue life of rubber components, focusing on the impact of self‐heating. To address this, we took a Carbon Black‐Filled Natural lateral stop as an example, developed a thermo‐mechanical coupling approach that incorporates temperature‐dependent material parameters, derived by combining dynamic mechanical analysis tests and planar tension tests. This allowed us to capture the influence of temperature on the rubber's mechanical properties. Simulations incorporating self‐heating effects demonstrated remarkable accuracy, with a maximum deviation of 15.6% from experimental data. This is a significant improvement compared to simulations neglecting self‐heating, which exhibited a minimum deviation of 96.9%. This finding highlights the importance of considering self‐heating effects in fatigue life prediction of rubber components. This approach has the potential to significantly enhance the durability and performance of rubber components across various applications by enabling more accurate predictions, leading to improved component design and longer service life.

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Publication Details

Journal
Macromolecular Theory and Simulations
Published
2025-08-14
DOI
https://doi.org/10.1002/mats.202500067
Primary Topic
Elasticity and Material Modeling
Type
article
Field-Weighted Citation Impact
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article

A Method to Accurately Predict Fatigue Life of Carbon Black‐Filled Natural Rubber Components Incorporating Self‐Heating Temperature

Zhiheng Song, Xiangxin Li, Jinpeng Liu, Dandan Zheng et al.
Macromolecular Theory and Simulations
Elasticity and Material Modeling
article

A Method to Accurately Predict Fatigue Life of Carbon Black‐Filled Natural Rubber Components Incorporating Self‐Heating Temperature

Zhiheng Song, Xiangxin Li, Jinpeng Liu, Dandan Zheng, Lin Mu
article en

Abstract

ABSTRACT Predicting the fatigue life of rubber components accurately has proven challenging, as existing methods often produce significant discrepancies compared to experimental results. This study presents a novel approach based on the combination of Abaqus and Endurica software, which can more accurately predict the fatigue life of rubber components, focusing on the impact of self‐heating. To address this, we took a Carbon Black‐Filled Natural lateral stop as an example, developed a thermo‐mechanical coupling approach that incorporates temperature‐dependent material parameters, derived by combining dynamic mechanical analysis tests and planar tension tests. This allowed us to capture the influence of temperature on the rubber's mechanical properties. Simulations incorporating self‐heating effects demonstrated remarkable accuracy, with a maximum deviation of 15.6% from experimental data. This is a significant improvement compared to simulations neglecting self‐heating, which exhibited a minimum deviation of 96.9%. This finding highlights the importance of considering self‐heating effects in fatigue life prediction of rubber components. This approach has the potential to significantly enhance the durability and performance of rubber components across various applications by enabling more accurate predictions, leading to improved component design and longer service life.

Macromolecular Theory and SimulationsVol. 34(6)
Sailun Jinyu Group (China) (CN), Tianjin Rubber Industry Institute (China) (CN), Qingdao Center of Resource Chemistry and New Materials (CN)
Openalex Percentile: Top 66%
Elasticity and Material Modeling
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A Method to Accurately Predict Fatigue Life of Carbon Black‐Filled Natural Rubber Components Incorporating Self‐Heating Temperature — Zhiheng Song, Xiangxin Li, et al. · Macromolecular Theory and Simulations (2025) | TGRS Research Map | TGRS