Koeppe, A.A.KoeppeHernandez Padilla, C.A.C.A.Hernandez PadillaVoshage, M.M.VoshageSchleifenbaum, J.H.J.H.SchleifenbaumMarkert, B.B.Markert2022-03-052022-03-052018https://publica.fraunhofer.de/handle/publica/25236610.1016/j.mfglet.2018.01.002Additively manufactured structures can be tailor-made to optimally distribute mechanical loads while remaining light-weight. To efficiently analyze the locally unique mechanical behavior of structures made from a large number of small lattice cells, a strategy which employs neural networks and deep learning to predict the maximum stresses in the realm of linear elasto-plasticity of a detail-level finite-element model is presented. The strategy is demonstrated on a single lattice cell specimen. Good agreements between experimental, finite element and neural network results are found at a significant reduction in computation time.en621journal article