Abstract
This study presents the application of the finite element method and intelligent systems techniques to the prediction of microstructural mapping for aluminium alloys. Here, the material within each finite element is defined using a hybrid model. The hybrid model is based on neuro-fuzzy and physically based components and it has been combined with the finite element technique. The model simulates the evolution of the internal state variables (i.e. dislocation density, subgrain size and subgrain boundary misorientation) and their effect on the recrystallisation behaviour of the stock. This paper presents the theory behind the model development, the integration between the numerical techniques, and the application of the technique to a hot rolling operation using aluminium, 1 wt% magnesium alloy. Furthermore, experimental data from plane strain compression (PSC) tests and rolling are used to validate the modelling outcome. The results show that the recrystallisation kinetics agree well with the experimental results for different annealing times. This hybrid approach has proved to be more accurate than conventional methods using empirical equations.
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