Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks

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.