# Investigating the Welding Parameters in Friction Stir Welding of Yellow Brass 405-20
## 1 Introduction
Many researchers have worked on mathematical modeling that involves an artificial neural network (ANN) model. This is a non-FEA numerical study, focusing mainly on the optimization of the FSW factor for metal alloys without considering the thermal distribution at the welded joint. [PERSON] et al. [11] established a relationship between FSW factors and the mechanical properties of aluminum alloy using an ANN model. The ANN model was employed to find the optimal FSW factors towards attaining a quality weld with both differential evolution for multi-objective (DEMO) and non-dominated sorting genetic algorithm-II (NSGA-II). The ANN DEMO was finally found to better deliver the optimal FSW factors more efficiently and effectively. Another ANN modeling approach was accomplished by [PERSON] et al. [12] for FSW of dissimilar AA5083-O and AA6063-T6 aluminum alloys. Design of experiments (DOE) based on four FSW factors, i.e., tool rotation speed, welding speed, shoulder diameter, and pin diameter, was executed using [PERSON]'s L27 OA. Joint efficiency was determined with three response parameters, which were tensile strength, micro-hardness, and grain size. A hybrid approach of the ANN mathematical model and GA was utilized to find the optimal FSW factors. A validation experiment promised a great improvement in the FSW process. [PERSON] et al. [13] also determined the optimal FSW factors using a hybridized approach of NSGA-II and TOPSIS. Weld quality was then determined with the corresponding mathematical model based on the ANN model. The fitness function for the NSGA-II technique was found employing the same ANN mathematical model. FSW optimizations were further conducted using techniques other than ANN. For instance, [PERSON] and [PERSON] [14] conducted an optimization study of using FSW to weld AA5083-H321 aluminum alloy. A Taguchi-based GRA technique was established to determine the solution of the corresponding multi-objective optimization problem. [PERSON] al. [15] offered another way of optimizing the FSW factors for aluminum alloy by applying a multi-objective optimization technique based on the dragonfly algorithm (DFA). [PERSON] and [PERSON] [16] presented the optimization of submerged FSW of 6061-T6 aluminum alloy. The RSM technique was used to develop the mathematical model, and optimal FSW factors were finally determined using the GRA application. [PERSON] et al. [17] again used the RSM-based DF approach to provide researchers with a solution to the multi-objective optimization problem regarding FSW of AA6060-T6 aluminum alloy pipes. [PERSON] et al. [18] studied the FSW of dissimilar welded joints to characterize their mechanical properties. FSWFs including tool rotational speed, tool traverse speed, tilt angle, and tool offset were set to determine their impact on characterizing the mechanical properties of the joint. Optimal FSWFs were later achieved using a mathematical modeling approach to characterize mechanical properties and a multi-objective optimization algorithm based on the biogeography optimization technique. [PERSON] et al. [19] studied the effect of a rotational speed of 1200 rev/min and welding speed of 200 mm/min on 5 mm-thick AA6061-T6 aluminum welded joints using dual rotation FSW. The microstructure of the joints was enormously improved, and fracture occurred at the thermomechanically affected zone (TMAZ) and heat affected zone (HAZ) in a ductile fashion. The maximum tensile strength achieved was 215 MPa at 400 rev/min. [PERSON] et al. [20] studied the weldability of aluminum 8006 with the reinforcement of zirconia particles. The idea of zirconia reinforcement was to improve the weld quality with optimal levels of FSWFs. The maximum tensile strength was found to be 284 MPa with improved microhardness of 156 HV at a rotational speed of 1100 rev/min. [PERSON] and [PERSON] [21] optimized the FSWFs using ANOVA and regression analysis when welding Al-Li alloy, which is widely used in the aerospace industry.