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Elastomeric Component Fatigue Analysis: Rubber Fatigue Prediction and Correlation Comparing Crack Initiation and Crack Growth Methodologies

Journal Article
2020-01-0193
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 14, 2020 by SAE International in United States
Elastomeric Component Fatigue Analysis: Rubber Fatigue Prediction and Correlation Comparing Crack Initiation and Crack Growth Methodologies
Sector:
Citation: Zarrin-Ghalami, T., Datta, S., Bodombo Keinti, R., and Chandrashekar, R., "Elastomeric Component Fatigue Analysis: Rubber Fatigue Prediction and Correlation Comparing Crack Initiation and Crack Growth Methodologies," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(4):1754-1763, 2020, https://doi.org/10.4271/2020-01-0193.
Language: English

Abstract:

Many elastomeric components in automotive industry applications are subjected to dynamic service loads. Fatigue must be a consideration in the design of these components.
The two approaches in rubber fatigue analysis discussed in this study are the conventional crack initiation approach, based on continuum mechanics parameters versus life, and the crack growth approach, based on fracture mechanics parameters. These methodologies are demonstrated for a passenger vehicle engine mount here. Temperature effects are not considered since testing was performed at ambient room temperature and with a low frequency, typically less than 5 Hz.
The promising methodology for fatigue life prediction, discussed in this paper, is a critical plane approach based on crack energy density. Rainflow cycle counting method and Miner’s damage rule are used for load cycle characterization and damage accumulation respectively. A fracture mechanics approach is implemented based on specimen crack growth data.
This methodology is validated with component testing under constant amplitude experiments. Results are investigated through analysis of critical stress locations hot spots, life values, and strain states. Comparative results are presented. Numerical results show reasonable correlation with experimental data.