ANN constitutive material model in the shakedown analysis of an aluminum structure - Potrzeszcz-Sut, Beata; Pabisek, Ewa (Computer Assisted Methods in Engineering and Science, 2017)
The paper presents the application of artificial neural networks (ANN) for description of the Ramberg- Osgood (RO) material model, representing the non linear strain-stress relationship of ε ( σ ). A neural model of material (NMM) is a feed-forward layered neural network (FLNN) whose parameters were determined using the penalized least squares (PLS) method. A FLNN performing the inverse problem: σ ( ε ), using pseudo empirical patterns, was developed. Two models of NMM were developed, i.e. a standard model (SNN) and a model based on Bayesian inference (BNN). The properties of the models were compared on the example of a reference truss structure. The computations were performed by means of the hybrid FEM/NMM program, in which NMM developed previously described the current model of the material, and made it possible to explicitly build a tangent operator E t  = d σ /d ε . The neural model of material was applied to the analysis of the shakedown of load carrying capacity of an aluminum truss.