  The heterogeneous microstructure in metallic components results in locally
varying fatigue strength. Metal fatigue strongly depends on size and shape of
non-metallic inclusions and pores, commonly referred to as "defects". Nodular
cast iron (NCI) contains graphite inclusions (nodules) whose shape and
frequency influence the fatigue strength. Fatigue strength can be simulated by
micromechanical finite element models. The drawback of these models are the
large computational costs. Therefore, we employ a data-driven machine learning
methodology. More precisely, we utilize the simplified residual neural network
(SimResNet) which was recently introduced (Herty et al., Kinetic Theory for
Residual Neural Networks, 2020) to predict fatigue strength from metallographic
data. For the training, we use fatigue data which is simulated with a
micromechanical model and the shakedown theorem. The micromechanical models are
derived directly from micrographs of nodular cast iron, respectively. The
application of SimResNet shows a good performance to predict fatigue strength
by local microstructures of nodular cast iron. We show several test cases. The
simplified character of SimResNet enables fast predictions of fatigue by
microstructures, even in comparision to classical residual neural networks.
