Prediction of multiaxial high cycle fatigue at small scales based on a micro-mechanical model

• A micromechanics based prediction of high cycle fatigue (HCF) is established.
• The HCF model can be applied for deterministic or probabilistic studies on aggregates of grains.
• A parameter study and a sensitivity analysis are performed.
• Prediction and real behavior are classified with help of EBDS, SEM, and FIB investigations.
• Non-conservative false predictions of “no damage” are as low as 1%.

An approach for prediction of high cycle fatigue (HCF) at a length scale of 5–100 μm is established, which evaluates the accumulated plastic shear strain in slip bands of grains. Damage mechanisms initiated by dislocations and the grain microstructure are the key factors that influence the fatigue of metals in small dimensions. For this reason the HCF model considers the elasto-plastic behavior of metals at the grain level and microstructural parameters, specifically grain size and grain orientation. The HCF model can be applied either as a criterion for deterministic predictions of the failure in individual grains, or as a failure function in probabilistic studies on aggregates of grains, if the input parameters are given by specific distributions. This is addressed in a parameter study and a sensitivity analysis of the failure function with respect to different parameters. For model verification, the predicted results of the failure function are compared with the observed micro-damage in individual grains of nickel micro-samples. It is shown that the overall predictive power of the HCF model is fairly good. Nevertheless, some misclassifications occur as some grains are damaged, which were predicted to be safe. Those misclassifications are addressed in post-fatigue investigations on individual grains.

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