Multiple characterisation modelling of friction stir welding using a genetic multi-objective data-driven fuzzy modelling approach

Friction Stir Welding (FSW) is a relatively new solid state joining technique, which is versatile, environment friendly, and energy and time efficient. For a comprehensive understanding of the effects of process conditions, such as tool rotation speed and traverse speed, on characterisations of welded materials, it is essential to establish prediction models for different aspects of the materials' behaviours. Because of the high complexity of the FSW process, it is often difficult to derive accurate and yet transparent enough mathematical models. In such a situation, a systematic data-driven fuzzy modelling approach is developed and implemented in this paper to model FSW behaviour relating to AA5083 aluminium alloy, consisting of microstructural features, mechanical properties, as well as overall weld quality. This methodology allows constructing transparent fuzzy models considering both accuracy and interpretability attributes of fuzzy systems. The elicited models proved to be accurate, interpretable and robust and can be further applied to facilitate the optimal design of process parameters, with the aim of finding the optimal combinations of process parameters to achieve desired welding properties.