Fatigue reliability prediction of shape memory alloy parts based on multi-scale high cycle fatigue criterion

This manuscript presents the development of a probabilistic approach for shape memory alloy (SMA) parts to predict the reliability of a high cycle fatigue (HCF) behaviour. The used method will take into account the recent multiaxial criterion of Auricchio F et al. (2016) which has been developed for the SMAs by the extension of the Dang Van criterion commonly used for elastoplastic metals. The proposed approach will take into account the dispersions of (i) the material parameters and (ii) the applied loading path for a fixed stress-induced martensite volume fraction. The Monte Carlo Simulation (MCS) techniques and the “strength-load†methods combined with probability boxes concepts are used in the suggested model to compute the fatigue reliability. Interesting isoprobabilistic Dang Van diagrams (PDDs) are obtained for different coefficients of variation (CVs) of the loading path and the material parameters leading to a more reliable fatigue prediction. The proposed approach leads to a more accurate HCF reliability prediction (e.g. PDDs relative to 1%,50%, and 99%) compared to the deterministic approach. It has been observed that the HCF reliability prediction of SMAs and the obtained PDDs are in good agreement with experimental fatigue failure results (e.g. Run-out∼R = 99,56% and Failure∼22,95%). The proposed method can be adopted as an interesting tool in specific engineering applications using SMAs in the fully martensitic region.