Intelligent Prediction of Fatigue Life for Rubber Vibration Isolators
Under constant amplitude fatigue loads with different strain ratios,uniaxial fatigue tests are conducted on dumbbell-shaped specimens made from a commonly used filled natural rubber material for rubber vibration isolators.Subsequently,a support vector regression(SVR)model is constructed to predict the fatigue life of the rubber.To improve the convergence speed,accuracy,and stability of the prediction model,an improved sparrow search algorithm(ISSA)is introduced to optimize the hyperparameters of the SVR model.Comparison results with SVR models optimized using the genetic algorithm(GA),particle swarm optimization algorithm(PSO),standard sparrow search algorithm(SSA),and hybrid grey wolf optimization(HGWO)show that the ISSA-SVR model performs best in terms of prediction accuracy,speed,and stability.To further demonstrate the predictive capability of the ISSA-SVR model,a fatigue life analytical model considering the impact of strain ratio was established.Comparative analysis of the ISSA-SVR model,the analytical model,and two published models indicates that the ISSA-SVR model has the most accurate life predictions,with results concentrated within a 1.5 times dispersion line.
万方数据
维普
