Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8048678 | Manufacturing Letters | 2018 | 7 Pages |
Abstract
Additively 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.
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Arnd Koeppe, Carlos Alberto Hernandez Padilla, Maximilian Voshage, Johannes Henrich Schleifenbaum, Bernd Markert,