The present study investigates the effect of two parameters of process type and tool offset on tensile, microhardness, and microstructure properties of AA6061-T6 aluminum alloy joints. Three methods of Friction Stir Welding (FSW), Advancing Parallel-Friction Stir Welding (AP-FSW), and Retreating Parallel-Friction Stir Welding (RP-FSW) were used. In addition, four modes of 0.5, 1, 1.5, and 2 mm of tool offset were used in two welding passes in AP-FSW and RP-FSW processes. Based on the results, it was found that the mechanical properties of welded specimens with AP-FSW and RP-FSW techniques experience significant increments compared to FSW specimens. The best mechanical and microstructural properties were observed in the samples welded by RP-FSW, AP-FSW, and FSW methods, respectively. Welded specimens with the RP-FSW technique had better mechanical properties than other specimens due to the concentration of material flow in the weld nugget and proper microstructure refinement. In both AP-FSW and RP-FSW processes, by increasing the tool offset to 1.5 mm, joint efficiency increased significantly. The highest weld strength was found for welded specimens by RP-FSW and AP-FSW processes with a 1.5 mm tool offset. The peak sample of the RP-FSW process (1.5 mm offset) had the closest mechanical properties to the base metal, in which the Yield Stress (YS), ultimate tensile strength (UTS), and elongation percentage (E%) were 76.4%, 86.5%, and 70% of base metal, respectively. In the welding area, RP-FSW specimens had smaller average grain size and higher hardness values than AP-FSW specimens.

Penetration is one of the most vital parameters because of its influences on weld strength and weld quality. Choosing the input parameters in submerged arc welding (SAW) has significant effects on input heat into the weld pool, which affects the quality of weldment. The amazing advancement of nanotechnology in various industrial areas persuaded researchers to use nanoparticles in new research. Zirconium dioxide (ZrO2) nanoparticles were selected in order to identify their effects in the weld pool in combination with other welding input parameters. The five-level, five-parameter central composite rotatable design, response surface methodology, the slime mold algorithm, and Harris hawks optimization were utilized to design and develop the research and finally to predict and optimize the weld penetration affected by the arc voltage, welding current, nozzle-to-plate distance, welding speed, and the thickness of ZrO2 nanoparticles coated on ST-37 steel. The main and interaction effects of input parameters on weld penetration were drawn. The results demonstrated that weld penetration decreased initially by increasing the thickness of ZrO2 nanoparticles up to 0.25 mm, which was due to reversing Marangoni convection mode. Furthermore, weld penetration increased with increase in ZrO2 nanoparticles above thicknesses of 0.75 mm. The reason for increase in weld penetration was because of the fact the ZrO2 nanoparticles have low thermal conductivity, which leads to less heat transfer. The results confirmed that the accuracy of model obtained by slime mold algorithm was 5.2 % more than response surface methodology.