Abstract
This study presents the application of FE modelling and intelligent systems techniques to the prediction of microstructural mapping for aluminium alloys. Here, the material within each finite element is defined using a set of non-linear physically-based equations. These are then solved in real-time using a semi-physical grey-box model. This model is based on a neuro-fuzzy approach and it has been embedded within the FE technique. The model maps 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 model development, the integration between the numerical techniques, and the application of the technique to a hot rolling operation using an Aluminium, 1wt% Magnesium alloy. In addition, the developed model includes a CA (Cellular Automata) level to represent inhomogeneous nature of microstructural beheviour.
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