Abstract
Friction stir welding (FSW) is one of the leading joining processes used for aluminum and its alloys. This most recent joining process joins materials below the melting point which make it ahead of any other joining process. This joining technique is efficient, energy economical, environment gracious, and adaptable. It is a modern solid-state joining technique and has been employed in aerospace, rail, automotive, and marine industries. The FSW process parameters play a major role in deciding the weld quality. In this research work, tool rotational speed, tool hardness, shoulder to pin diameter ratio, and welding speed/feed were selected as the parameters to see how we can increase the quality of the weld by varying these parameters. Highlights of this research work are: (a) efficient tool design regarding material selection for the tool and design of the tool (D/d ratio), (b) effects of FSW parameters mostly tool rotational speed on mechanical properties related to quality and strength of the weld, and (c) effect of tool hardness and welding speed on strength of friction stir welding. Design of experiment software was used to design experiments and tool geometry. Another technique that was used in this research was related to regression analysis to forecast the tensile strength and hardness of friction stir-welded joint. The tensile strength and hardness of welding joints were predicted by taking the parameters like tool rotation speed, weld speed, tool hardness, and D/d ratio as a task. From the investigation of influence of the parameters, it was concluded that welding speed is the most significant parameter, while rotational speed, tool hardness, and D/d ratio are least significant parameters; however, these parameters still affect the quality of weld. The highest tensile strength of 447.16 and hardness of 138.5 HB have been achieved at a rotational speed of 1200 rpm, welding speed of 60 mm/min, D/d ratio of 3.11, and tool hardness of 50 HRC with simple cylindrical tool pin profile. An evaluation was made between measured and predicted results by developing a regression model, and the values obtained for the response tensile strengths and hardness were compared with measured values. The accuracy of the experimental results can be demonstrated by plotting the graphs of regressions' projected values against the experimental data. Scanning electron micrography (SEM) analysis was done on the low, medium, and high values samples to visualize the grain structure by fractography. Three confirmation tests were carried out in order to validate the regression models.