ANN constitutive material model in the shakedown analysis of an aluminum structure

• Autorzy: Potrzeszcz-Sut B. , Pabisek E.
• Języki publikacji: EN

Abstrakty

EN

The paper presents the application of artificial neural networks (ANN) for description of the Ramberg-Osgood (RO) material model, representing the nonlinear 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 = dσ/dε. The neural model of material was applied to the analysis of the shakedown of load carrying capacity of an aluminum truss.

Słowa kluczowe

EN

artificial neural network; inverse problem; material modeling; finite element method; hybrid program; shakedown analysis

PL

sztuczna sieć neuronowa; problem odwrotny; modelowanie materiałów; metoda elementów skończonych; hybryda

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Methods in Engineering and Science
• Rocznik: 2014
• Tom: Vol. 21, no. 1
• Strony: 49--58
• Opis fizyczny: Bibliogr. 15 poz., rys., wykr.

Twórcy

autor

Potrzeszcz-Sut B.

• Department of Mechanics, Metal Structures and Computer Methods, Kielce University of Technology Al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland

autor

Pabisek E.

• Institute for Computational Civil Engineering Cracow University of Technology Warszawska 24, 31-155 Kraków, Poland

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