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Implicit constitutive modelling for viscoplasticity using neural networks
Author(s) -
Furukawa Tomonari,
Yagawa Genki
Publication year - 1998
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/(sici)1097-0207(19980930)43:2<195::aid-nme418>3.0.co;2-6
Subject(s) - viscoplasticity , constitutive equation , artificial neural network , representation (politics) , computer science , experimental data , range (aeronautics) , state space , state space representation , algorithm , mathematics , artificial intelligence , finite element method , engineering , structural engineering , statistics , politics , law , political science , aerospace engineering
Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviours of materials. The fatal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modelling, inelastic material behaviours are generalized in a state‐space representation and the state‐space form is constructed by a neural network using input–output data sets. A technique to extract the input–output data from experimental data is also described. The proposed model was first generated from pseudo‐experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy. © 1998 John Wiley & Sons, Ltd.