  The analysis of standardized low cycle fatigue (LCF) experiments shows that
the failure times widely scatter. Furthermore, mechanical components often fail
before the deterministic failure time is reached. A possibility to overcome
these problems is to consider probabilistic failure times. Our approach for
probabilistic life prediction is based on the microstructure of the metal.
Since we focus on nickel-base alloys we consider a coarse grained
microstructure, with random oriented FCC grains. This leads to random
distributed Schmid factors and different anisotropic stress in each grain. To
gain crack initiation times, we use Coffin-Manson- Basquin and Ramberg-Osgood
equation on stresses corrected with probabilistic Schmid factors.
  Using these single grain crack initiation times, we have developed an
epidemiological crack growth model over multiple grains. In this mesoscopic
crack percolation model, cracked grains induce a stress increase in neighboring
grains. This stress increase is realized using a machine learning model trained
on data generated from finite element simulations. The resulting crack clusters
are evaluated with a failure criterion based on a multimodal stress intensity
factor. From the generated failure times, we calculate surface dependent hazard
rates using a Monte Carlo framework. We compare the obtained failure time
distributions to data from LCF experiments and find good coincidence of
predicted and measured scatter bands.
