Experimental and parametric Investigation of Submerged Friction Stir Welding of 6063 Aluminium Alloy

Abstract

Several welding processes have been developed over time, however welding aluminium alloys has always been difficult using traditional welding techniques because of its low melting temperature. In addition to this some applications, such as underwater pipeline maintenance, automobile repair, and maritime engineering projects, demand underwater welding. Traditional welding methods, such as arc welding, could allow for this, but it is also a sophisticated and dangerous process because it allows the welder to work with electric equipment underwater. This study tried to overcome those problems by implementing the new SFSW process. SFSW is a relatively recent welding technology that is primarily used to join lightweight alloys such as aluminium alloys. The experiment was carried out on a 5 mm 6063 aluminium alloy sheet with a butt joint design. The investigation covers both experimental and numerical investigations. The influence of different welding parameters on thermal histories, tensile characteristics, and microstructural properties was investigated in the experiment. Finite element modelling was used in the numerical investigation to forecast changes in material properties and thermal profiles as the welding parameters vary. The following process parameters were used: (900, 1200, and 1400) RPM, (15, 30, and 45) mm/min tool speed, and straight cylindrical, taper cylindrical, and square tool profiles. According to the study's findings, the experimental and numerical tensile values of parent material were 163MPa and 171MPa, respectively. The maximum tensile strength of the experimental inquiry was achieved by combining 1400 RPM, 30 mm/min traverse speed, and straight cylindrical tool shape, yielding 151MPa. The most influential characteristics for successful SFSW operation and producing high mechanical attributes were discovered to be rotational speed. Under each parameter combination, the ABAQUS finite element model exactly predicted the temperature profile generated by SFSW.