Application of inverse analysis with metamodelling for identification of metal flow stress

Authors: Sztangret, Ł; Szeliga, D; Kusiak, J; Pietrzyk, M

Source: Canadian Metallurgical Quarterly, Volume 51, Number 4, October 2012 , pp. 440-446(7)

Publisher: Maney Publishing

Abstract:

The problem of effectiveness of the inverse algorithms used for identification of material model is investigated in the paper. Identification of flow stress models in metal forming processes is considered. This identification is usually performed by coupling the Finite element (FE) model with optimisation techniques which leads to long computing times. A proposition of application of the metamodel in the inverse analysis is presented in the paper. Metamodel is an alternative for the FE model. Artificial neural network was used as a metamodel of the axisymmetrical compression test. Experiments were performed on the Gleeble 3800 simulator for various materials and inverse calculations with the metamodel were performed. Validation of the results confirmed with higher degree of accuracy of the proposed approach.

Dans cet article, on examine le problème d’efficacité des algorithmes inverses utilisés dans l’identification de modè le de maté riau. On considè re l’identification de modè les de contrainte d’écoulement dans les procédés de traitement du mé tal. Cette identification est habituellement effectuée en couplant le modè le d’EF à des techniques d’optimisation, ce qui mène à de longues durées de calculs. Dans cet article, on propose l’application du métamodè le dans l’analyse inverse. Le métamodè le est une substitution du modè le d’EF. On a utilisé le réseau neuronal artificiel comme métamodè le de l’essai de compression axisymé trique. On a effectué des expériences avec le simulateur Gleeble 3800 pour des maté riaux variés et l’on a effectué des calculs inverses à l’aide du métamodè le. La validation des ré sultats a confirmé le très bon degré d’exactitude de cette approche.

Keywords: Flow stress; Plastometric tests; Inverse analysis; Metamodel; Hot forming

Document Type: Original Article

DOI: http://dx.doi.org/10.1179/1879139512Y.0000000035

Affiliations: AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland

Publication date: 2012-10-01

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