Knowledge discovery for friction stir welding via data driven approaches: Part 2 – multiobjective modelling using fuzzy rule based systems

In this final part of this extensive study, a new systematic data-driven fuzzy modelling approach has been developed, taking into account both the modelling accuracy and its interpretability (transparency) as attributes. For the first time, a data-driven modelling framework has been proposed designed and implemented in order to model the intricate FSW behaviours relating to AA5083 aluminium alloy, consisting of the grain size, mechanical properties, as well as internal process properties. As a result, ‘Pareto-optimal’ predictive models have been successfully elicited which, through validations on real data for the aluminium alloy AA5083, have been shown to be accurate, transparent and generic despite the conservative number of data points used for model training and testing. Compared with analytically based methods, the proposed data-driven modelling approach provides a more effective way to construct prediction models for FSW when there is an apparent lack of fundamental process knowledge

A model of grain refinement and strengthening of Al alloys due to cold severe plastic deformation

This paper presents a model which quantitatively predicts grain refinement and strength/hardness of Al alloys after very high levels of cold deformation through processes including cold rolling, equal channel angular pressing (ECAP), multiple forging (MF), accumulative rolling bonding (ARB) and embossing. The model deals with materials in which plastic deformation is exclusively due to dislocation movement, which is in good approximation the case for aluminium alloys. In the early stages of deformation, the generated dislocations are stored in grains and contribute to overall strength. With increase in strain, excess dislocations form and/or move to new cell walls/grain boundaries and grains are refined. We examine this model using both our own data as well as the data in the literature. It is shown that grain size and strength/hardness are predicted to a good accuracy.<br/