A Hybrid Method of NSGA-II and TOPSIS to Optimize the Performance of Friction Stir Extrusion

Abstract

This study investigates the effect of friction stir back extrusion (FSBE) input parameters such as traverse speed, rotational speed, and wire diameter on the mechanical and microstructural properties of the produced wire. Numerous experiments were performed with different input parameters, and the grain size, hardness, and ultimate pressure strength (UPS) of each of the produced wires were investigated. In addition, to better understand the effect of input parameters, the process was simulated using the finite element method (FEM) model, and the temperature, material flow, and strain distributions in the wires were investigated. Then, using the artificial neural network (ANN), a relationship was obtained between the input parameters of the process, such as traverse speed, rotational speed, and wire diameter, with the mechanical and microstructural properties of the produced wires. This relationship was then used in a hybrid multi-objective optimization to find the optimal process parameters. Due to the higher importance of UPS in comparison to the grain size and microhardness, the weighting of 0.6, 0.2, and 0.2 were used in the TOPSIS model, and the optimum input parameters were achieved as 6 mm, 36.35 mm/min, and 456 rpm, for the traverse speed, rotational speed, and wire diameter, respectively.