Express analysis of fatigue crack growth life under constant and variable amplitude loading

Савкин, А. Н.; Sunder, R.; Sedov, Alexander; Бадиков, К. А.

doi:10.1063/1.5084420

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…Due to this property, rubber materials find extensive applications in the automotive, machinery, and aviation industries [1]. They are utilized in the manufacturing of various products including tires [2], conveyor belts [3], isolators [4] and more. In these application contexts, rubber structural components commonly endure cyclic loads, resulting in fatigue failure as a primary form of material deterioration in rubber [5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of rubber fatigue life using an assimilation-based learning approach and incremental crack propagation model

Fang,

Chen,

Yang

et al. 2024

Preprint

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Accurately and efficiently predicting the fatigue life of rubber materials has beena long-standing challenge due to limited understanding of the fatigue mechanism.In this study, a variational assimilation-based machine learning method assistedwith incremental crack propagation model is proposed to predict the fatigue lifeof rubber materials. Firstly, according to the fracture mechanics theory, a newrubber fatigue life prediction model based on incremental crack propagation andsparse experimental data is established, which owns higher accuracy than theclassical crack energy density model. Further, a rubber fatigue life solver coupled incremental crack propagation model and nonlinear finite element method isintroduced to generate a dense fatigue life dataset of rubber materials with highaccuracy. Finally, the artificial neural network model is trained, cross-validatedand tested using the dense dataset, and the three-dimensional variational assimilation model is employed to merge the predicted values of artificial neural networkwith experimental data. By comparing against the experimental data, the effectiveness of the proposed method was verified, thereby we offer an accurate andefficient approach to predict the rubber fatigue life.

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