Publication | Closed Access
Rubber fatigue life prediction using a random forest method and nonlinear cumulative fatigue damage model
46
Citations
18
References
2019
Year
Random Forest MethodFatigue LifeEngineeringService Life PredictionLife PredictionMechanicsMechanical EngineeringPredictive MaintenanceCivil EngineeringStructural Health MonitoringBiostatisticsDamage EvolutionDeterioration ModelingLow-cycle FatigueMechanics Of MaterialsRubber Fatigue Life
ABSTRACT In this study, a random forest machine‐learning method is introduced on the basis of the analysis of measured constant amplitude stress fatigue data. This method aims to predict rubber fatigue life under constant amplitude stress. Strain mean value, strain amplitude, and strain ratio are used as independent variables, and the prediction model of rubber fatigue life under constant amplitude stress is established. A nonlinear cumulative fatigue damage model is proposed to calculate rubber fatigue life under the variable amplitude stress. Results show that the random forest method has high precision and generalization capability for rubber fatigue life prediction under constant amplitude stress and the nonlinear cumulative fatigue damage model could be employed to calculate the fatigue life of rubber under variable amplitude stress with enough accuracy according to the constant amplitude stress fatigue life data. This research can provide a reference for rubber fatigue life prediction. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137 , 48519.
| Year | Citations | |
|---|---|---|
Page 1
Page 1