摘要
Based on experiments of low cycle fatigue for 5083-H112 aluminum alloy, two energybased predictive models have been introduced to predict the fatigue crack growth behaviors of traditional Compact Tension（CT） and small-sized C-shaped Inside Edge-notched Tension（CIET）specimens with different thicknesses and load ratios. Different values of the effective stress ratio U are employed in the theoretical fatigue crack growth models to correct the effect of crack closure.Results indicate that the two predictive models show different capacities of predicting the fatigue crack growth behaviors of CIET and CT specimens with different thicknesses and load ratios.The accuracy of predicted results of the two models is strongly affected by the method for determination of the effective stress ratio U. Finally, the energy-based Shi＆Cai model with crack closure correction by means of Newman＇s method is highly recommended in prediction of fatigue crack growth of CIET specimens via low cycle fatigue properties.
Based on experiments of low cycle fatigue for 5083-H112 aluminum alloy, two energybased predictive models have been introduced to predict the fatigue crack growth behaviors of traditional Compact Tension（CT） and small-sized C-shaped Inside Edge-notched Tension（CIET）specimens with different thicknesses and load ratios. Different values of the effective stress ratio U are employed in the theoretical fatigue crack growth models to correct the effect of crack closure.Results indicate that the two predictive models show different capacities of predicting the fatigue crack growth behaviors of CIET and CT specimens with different thicknesses and load ratios.The accuracy of predicted results of the two models is strongly affected by the method for determination of the effective stress ratio U. Finally, the energy-based Shi＆Cai model with crack closure correction by means of Newman＇s method is highly recommended in prediction of fatigue crack growth of CIET specimens via low cycle fatigue properties.
基金
financially supported by the National Natural Science Foundation of China (Nos. 11202174 and 11472228)
