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Friction stir welding (FSW) method becomes an effective technique for welding dissimilar alloys such as AA2024 and AA5083 as the conventional fusion welding methods are not very suitable for welding the dissimilar alloysand cause many defects during solidification process. Since the strength of FSWedjoints depends on the process parameters, a new method was used in this study to predict the tensile behavior of friction stir welded AA5083 and AA2024 aluminum alloy joints in terms of tensile elongation (TE) and ultimate tensile strength (UTS). A new metaheuristic algorithm called Marine Predators Algorithm (MPA) has been integrated with Random Vector Functional Link (RVFL) network to improve the prediction accuracy. Rotational speed, welding speed, tool axial force, and pin profile were used as input parameters while UTS and TE works as the corresponding outputs. The MPA-RVFL model was tested and validated,a great agreement was demonstrated between the experimental results and the predicted results indicating that the developed technique is accurate and reliable to predict the tensile behavior of welded aluminum joints.

Friction Stir Welding (FSW) has been paid more attention in recent years due to its efficiency in welding materials that are difficult to weld by conventional fusion welding methods. There are several parameters that affect FSW process, so it is important to understand the relationship between different process parameters to maximize the quality and strength of the joint. This paper proposed an alternative method to predict FSW parameters and make a decision using a modified version of the adaptive neuro-fuzzy inference system (ANFIS) integrated with harris hawks optimizer (HHO). HHO was used to search for optimal values of ANFIS parameters and to determine the optimal operating conditions of the FSW process. The shared effect of welding speed, tool rotational speed, and plunge force on the mechanical properties of welded aluminium plates was simulated. The proposed model, called ANFIS-HHO, was used to predict the mechanical properties of FSW Al plates in terms of ultimate tensile strength (UTS) as functions of welding speed, tool rotational speed, and plunge force. The adequacy of the model was tested; the predicted data were in good agreement with the experimental data. The tool rotational speed and the empirical force index (EFI) have a significant impact on the mechanical properties of the welded joints. ANFIS-HHO technique was found to be a powerful optimization tool for predicting FSW parameters to achieve high joint strength.

Dissimilar friction stir lap welded joints of AA6061-T6 and Ti6Al4V were successfully welded using suitable parameters, and the effect of tool rotational speed was studied to investigate the influence on mechanical properties, microstructure, and fracture mechanisms. It was observed that tool rotational speed has a great effect on the strength of the welded joint, as it controls the amount of heat input and plasticity at Al/Ti interface as well as the formation of intermetallic compounds (IMCs) and different brittle phases that were formed at the Al/Ti mixture. The maximum shear strength of 3.5 KN was achieved using low rotational speed of 600 rpm and maximum hardness of 384 HV, while minimum shear strength of 2.5 KN was attained at 1000 rpm with minimum hardness of 343.2 HV. Hybrid fracture surface was produced at lower rotation speed which had both brittle and ductile characteristics, while brittle fracture mechanisms were observed at high rotational speed due to the concentration of brittle IMCs layers, TiAl, and TiAl3phases at the interface due to the excessive heat input which caused pores and cracks and thus deteriorated the joint strength.