Intelligent prediction of fatigue life of natural rubber considering strain ratio effect

An intelligent prediction model of rubber fatigue life considering strain ratios based on the support vector regression (SVR) and an improved sparrow search algorithm (ISSA) is proposed. In order to solve the problem that traditional methods may lead to unsatisfactory prediction results, we establish a prediction model based on SVR. To further improve the prediction accuracy, we utilize the ISSA algorithm to optimize various hyper-parameters in the model. Fatigue tests of filled natural rubber on dumbbell-type specimens are performed and applied to verify the effectiveness of the proposed model. The results show that the ISSA optimized SVR (ISSA-SVR) model is superior in terms of prediction accuracy, convergence speed, and stability compared with SVR models using the grid search or some standard optimization algorithms. Furthermore, comparisons of some published related models demonstrate that the ISSA-SVR has the most accurate predictive results, which gather within 1.5 times the dispersion lines.