Advanced constitutive model calibration using genetic algorithms

2014 ◽  
pp. 485-490 ◽  
Author(s):  
C Pereira ◽  
J Maranha ◽  
A Brito
Keyword(s):  
Genetic Algorithms ◽  
Constitutive Model ◽  
Model Calibration

Calibration of a constitutive model using genetic algorithms

1996 ◽  
Vol 19 (4) ◽  
pp. 325-348 ◽  
Author(s):  
Surajit Pal ◽  
G. Wije Wathugala ◽  
Sukhamay Kundu
Keyword(s):  
Genetic Algorithms ◽  
Constitutive Model

Genetic algorithms and hybrid neural network modelling for aluminium stress—strain prediction

2003 ◽  
Vol 217 (1) ◽  
pp. 7-21 ◽  
Author(s):  
Y Y Yang ◽  
D A Linkens ◽  
M Mahfouf
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Constitutive Model ◽  
Hybrid Model ◽  
Network Model ◽  
Neural Network Model ◽  
Constitutive Equations ◽  
Model Performance ◽  
Hybrid Neural Network ◽  
Real Value

This paper addresses the design of genetic algorithms in developing a hybrid neural network model for aluminium alloy flow stress prediction. The hybrid neural network model consists of a parallel grey-box model structure, with the resulting predictions combining the outputs from the constitutive equations and a neural network. Previous work shows that the hybrid neural network model can deliver better model performance than a neural network model or the constitutive equations. However, the level of performance improvement of the hybrid model depends on the quality of the constitutive model used. This motivates the search for a better constitutive model, with genetic algorithms being employed to optimize its parameters. The advantage of genetic algorithms is that they do not require any gradient information nor continuity assumption in searching for the best parameters. A number of genetic optimization schemes, with different coding schemes (such as binary coding and real-value chromosomes) and different genetic operators for selection, crossover and mutation, have been investigated. The real-value coded genetic algorithms converge much more rapidly and are more efficient since there is no need for chromosome encoding and decoding. Compared with previous work, the resulting hybrid model performance has been improved, mainly in the generalization capability and with a simpler neural network structure. Also, the model response surfaces are much smoother and more metallurgically convincing.

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