Papers by Author: D.A. Linkens

Abstract: This paper presents a modelling strategy that combines neuro-fuzzy methods to dene the material model with cellular automata representations of the microstructure, all embedded within a nite element solver that can deal with the large deformations of metal processing technology. We use the acronym nf-CAFE as a label for the method. The need for such an approach arises from the twin demands of computational speed for quick solutions for ecient material characterisation by incorporating metallurgical knowledge for material design models and subsequent process control. In this strategy, the cellular automata hold the microstructural features in terms of sub-grain size and dislocation density which are modelled by a neuro-fuzzy system that predicts the ow stress. The proposed methodology is validated on a two dimensional (2D) plane strain compression nite element simulation with Al1%Mg alloy. Results from the simulations show the potential of the model for incorporating the eects of the underlying microstructure on the evolving ow stress elds. In doing this, the paper highlights the importance of understanding the local transition rules that aect the global behaviour during deformation.

Abstract: Dynamic recrystallisation (DRX) is an important aspect for industrial applications in hot metal working. Although DRX has been known for more than thirty years, its mechanisms have never been precisely investigated, in part because it was not readily possible to make local texture measurements. In the present work, the material behaviour during DRX is investigated and modelled based on the microstructure of 316L stainless steel. The developed model is based on a constitutive equation Modelling technique which incorporates the strain, strain rate and instantaneous temperature for predicting the flow stress of material being deformed under hot conditions.

Abstract: During the last decade Genetic Programming (GP) has emerged as an efficient methodology for teaching computers how to program themselves. This paper presents research work which utilizes GP for developing mathematical equations for the response surfaces that have been generated using hybrid modelling techniques for predicting the properties of materials under hot deformation. Collected data from the literature and experimental work on aluminium are utilized as the initial training data for the GP to develop the mathematical models under different deformation conditions and compositions.