Papers by Author: C.M. Sellars

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: Plane strain compression tests have been carried out on Ti stabilised interstitial free steel at 700oC with constant and changing strain rates. Specimens were annealed in a salt bath at 750oC to determine the effects of changing strain rate on the kinetics of static recrystallisation and on the recrystallised grain size. After relatively slow changes in rate, the recrystallisation behaviour at the end of the change was the same as for tests at constant strain rate with the final value. For faster changes in rate, there were transients in recrystallisation rate and recrystallised grain size at the end of the change in strain rate at a strain of 1.0. These were removed by a further increment of 0.1 strain at constant rate. In all cases the recrystallised grain size correlated with the subgrain size present at the end of 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.

Abstract: A new model of strain induced precipitation in niobium microalloyed austenite is proposed. This is based on the experimental observation of formation of microbands during hot deformation of iron – 30% nickel, which remains austenitic to room temperature. Precipitates are preferentially nucleated on nodes in the dislocation network in the microbands. This geometry enables features of earlier models to be simply explained, and facilitates extension of the model to multipass deformation. It is shown that the model captures all the essential features of previous experimental observations on microalloyed C – Mn steels. Currently, sensitivity analysis of the model and systematic experimental work are being undertaken to quantitatively validate the model.