554 RWTH Publication No: 811907        2020       
TITLE Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship
AUTHORS Christian Gebhardt, Torsten Trimborn, Felix Weber, Alexander Bezold, Christoph Broeckmann, Michael Herty
ABSTRACT Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship Christian Gebhardta,∗, Torsten Trimbornb, Felix Webera, Alexander Bezolda, Christoph Broeckmanna, Michael Hertyb aInstitute for Materials Applications in Mechanical Engineering, RWTH Aachen University bInstitute f¨ur Geometrie und Praktische Mathematik, RWTH Aachen University Abstract 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 inclu- sions (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 in [16] 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 mi- crographs 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.
KEYWORDS Deep Neural Network, High Cycle Fatigue, Micromechanics, Shakedown Theorem, Data driven
DOI 10.1016/j.mechmat.2020.103625
PUBLICATION Mechanics of Materials, Volume 151, December 2020, 103625
CORRESPONDING AUTHOR c.gebhardt@iwm.rwth-aachen.de (Christian Gebhardt)