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Abstract
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 friction stir welding (FSW) behaviours relating to AA 5083 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 AA 5083, 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.