Optimization of friction stir welding processes using multi-attributive border approximation area comparison (MABAC) method in neutrosophic fuzzy environment

The ability to produce high quality joints, with improved mechanical and metallurgical properties, without any harmful emissions, has caused friction stir welding (FSW) to emerge as an efficient and eco-friendly solid-state welding method that has widespread use in many of the industries, especially railway, aerospace and automotive industries. However, the quality of weld produced by FSW process is significantly impacted by the welding parameters involved. To obtain the optimal values of FSW parameters, multi-criteria decision making (MCDM) methods have proven to be an effective way of dealing with multiple input parameters and conflicting responses. Relative importance assigned to various weld characteristics by the concerned stakeholders (welders, process engineers and end users) also greatly influences the final decision with respect to optimal combination of different input parameters for a given welding operation. In this paper, application of a newly developed MCDM tool, in the form of multi-attributive border approximation area comparison (MABAC) method in neutrosophic fuzzy environment considering truth-membership, indeterminacy-membership and falsity-membership functions in a single decision making framework for parametric optimization of two FSW processes is demonstrated. The optimal combination of input parameters for the first FSW process is obtained as tool rotational speed = 1200 rpm, welding speed = 275 mm/min, shoulder diameter = 18 mm and taper-cylindrical tool pin profile. On the other hand, in the second FSW process, an optimal parametric intermix of tool rotational speed = 1000 rpm, traverse speed = 140 mm/min, tool offset = 1 mm and tilt angle = 1.5° would provide the most desired values of the weld characteristics under consideration. This integrated approach would thus help in effectively optimizing FSW processes in single-valued neutrosophic fuzzy environment.