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On the estimation and optimization capabilities of the fatigue life prediction models in composite laminates
In this study, the estimation and optimization capabilities of the multiaxial fatigue life prediction models, namely, Failure Tensor Polynomial in Fatigue, Fawaz-Ellyin, Sims-Brogdon and Shokrieh-Taheri are investigated comparatively. Fatigue life predictions are obtained for multidirectional graphite/epoxy, glass/epoxy, carbon/epoxy and carbon/PEEK composite laminate data taken from the literature. The prediction study shows that the models can predict the fatigue behavior of the multidirectional laminates at different degrees of proximity. In the optimization, a hybrid algorithm combining particle swarm algorithm and generalized pattern search algorithm is used to search the optimum stacking sequence designs of the laminated composites for maximum fatigue life. The hybrid algorithm shows superior performance in terms of computational time and finding improved global optima compared to the best results presented in the literature. After the capability of the models and the reliability of the algorithm are revealed, several lay-up design problems involving different cyclic loading scenarios are solved. The results indicate that the reliability of the optimization may considerably change according to the used model even if the model may yield reasonable prediction results.