Optimisation of friction stir welding parameters using combined Taguchi L9 and genetic algorithm
by Devaki Nandan; Maneesh Tewari
International Journal of Spatio-Temporal Data Science (IJSTDS), Vol. 1, No. 2, 2021

Abstract: 'Friction stir welding' (FSW) is an eco-friendly, energy-efficient solid-state welding procedure invented for high strength alloys as well as materials that are otherwise difficult for welding through the traditional fusion welding methods. This research paper elaborates on a hybrid Taguchi-genetic algorithm that optimises the FSW process parameters to yield favourable electrical conductivity of aluminium alloy AA1350. The optimisation parameters considered are travel speed, rotational speed and tool tilt angle. First, the Taguchi method is applied to reduce the number of design experiments and find the optimal set for quality parameters of the system. Subsequently, the genetic algorithm is employed to search for the optimum set of design parameters by using Taguchi solution as the initial population. The results obtained by the proposed method were compared to the outcomes of the conventional Taguchi L9 method and the simple genetic algorithm. The best performance was obtained by the proposed method in 51 iterations, whereas simple genetic algorithm used 182 iterations for the same.

Online publication date: Tue, 10-Aug-2021

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