Purpose This study aims to predict the mechanical properties such as equivalent tensile strength and micro-hardness of friction-stir-welded dissimilar aluminium alloy plates AA 6063-O and AA 2014-T6, using artificial neural network (ANN). Design/methodology/approach The ANN model used for the experiment was developed through back propagation algorithm. The input parameter of the model consisted of tool rotational speed and weld-traverse speed whereas the output of the model consisted of mechanical properties (tensile strength and hardness) of the joint formed by friction-stir welding (FSW) process. The ANN was trained for 60% of the experimental data. In addition, the impact of the process parameters (tool rotational speed and weld-traverse speed) on the mechanical properties of the joint was determined by Taguchi Grey relational analysis. Findings Subsequently, testing and validation of the ANN were done using experimental data, which were not used for training the network. From the experiment, it was inferred that the outcomes of the ANN are in good agreement with the experimental data. The result of the analyses showed that the tool rotational speed has more impact than the weld-traverse speed. Originality/value The developed neural network can be used to predict the mechanical properties of the weld. Results indicate that the network prediction is similar to the experiment results. Overall regression value computed for training, validation and testing is greater than 0.9900 for both tensile strength and microhardness. In addition, the percentage error between experimental and predicted values was found to be minimal for the mechanical properties of the weldments. Therefore, it can be concluded that ANN is a potential tool for predicting the mechanical properties of the weld formed by FSW process. Similarly, the results of Taguchi Grey relational analysis can be used to optimize the process parameters of the weld process and it can be applied extensively to ascertain the most prominent factor. The results of which indicates that rotational speed of 1,270 rpm and traverse speed of 30 mm/min are to be the optimized process parameters. The result also shows that tool rotational speed has more impact on the mechanical properties of the weld than that of traverse speed.

Purpose Friction stir welding (FSW) and underwater friction stir welding (UWFSW) of aluminium alloy 2024-T351 was carried out, with a chosen set of parameters, namely, rotational speed of 450 rpm, 560 rpm and 710 rpm, welding speed of 25 mm/min, 40 mm/min and 63 mm/min and tool tilt angle of 0º, 1° and 2º. This study aims to understand the correlation between temperatures and weld parameters, finite element simulation was carried out using Abaqus®. Design/methodology/approach Comparative analysis of the mechanical properties of the samples welded with FSW and UWFSW was carried out and correlated with that of the microstructures. Microhardness survey was also conducted across the weldments to support the findings. Findings Samples welded with higher rotational speed and low traverse speed favoured good quality, defect-free welds with enhanced material flow. Underwater welded samples resulted in improved mechanical properties than that of the samples welded with conventional FSW. Higher cooling rates resulted in finer grains in all UWFSW samples than that of conventional FSW samples, which, in turn, also reflected in the microhardness survey done across the weldments. Among the chosen window of the parameter, samples welded with 710 rpm, 25 mm/min and 2° had shown improvement in mechanical properties. Research limitations/implications This work was carried out in a milling machine, which limits the rotational speed which could be used. Optimistically, this limitation also paves way for using the commonly available milling to be used for FSW. Originality/value This original research study shall open opportunities to enable FSW and UWFSW to be done on similar/dissimilar joints of varying composition. Additionally, this research study throws enough light on the age – hardenable aluminium alloy being welded in a commonly available milling machine.