A Combined Cycle Fatigue (CCF) life prediction model considering the effect of load sequence was proposed. To account for the interaction of high and low cycle fatigue, the CCF load was divided into two different loading paths of variable stress amplitude and stress ratio. Based on the iso-damage curves, a CCF life prediction model independent of fitting parameters was proposed, agreeing well with the experimental results. Finally, the effect of load sequence on CCF was discussed according to the fracture morphology of designed blade-like specimen. The results showed that the predicted CCF life was almost located in three-fold dispersion band for the LCF-HCF (LH) and HCF-LCF (HL) loading paths, especially for the average results of both. Compared with other models, the proposed model had better predictive and generalization abilities for multiple materials and variable experimental conditions.

This paper aims to propose a creep life evaluation method considering the effect of crystallographic orientation. First, the maximum Schmid factor of {111}〈112〉 and the corresponding lattice rotation angle were introduced to form an “orientation factor”. Then the equivalent stress was calculated by multiplying this factor and the nominal stress. Latterly, the LarsonMiller Parameter (LMP) method was adopted as the rupture life evaluation criteria, in which the input variable was the equivalent stress instead of the nominal stress. All the predictions showed high accuracy when the proposed method was applied to Mar-M247, SC7-14, CMSX-2, Alloy454, CMSX-4 and DD6. Finally, the applicable temperature ranges of the orientation-dependent method (using equivalent stress) and the traditional LMP method (using nominal stress) were discussed. The results show that only the Orientation-Dependent (OD) method is reliable at intermediate temperatures (760–850 °C) because the orientation has significant effect on the stress rupture life, while the influence of orientation is considerably reduced at high temperatures. Both methods provide precise predictions in this situation, and the LMP method should be favored since it is much easier to implement.