Evaluation of Optimization Parameters of Semi-Solid Metal 6063 Aluminum Alloy from Friction Stir Welding Process Using Factorial Design Analysis

Abstract

The semi-solid-metal 6063 aluminum alloy was developed for the automotive industry. The objective of this research was to optimize parameters in friction stir welding process that can provide the highest tensile strength. The ANOVA factorial design was used to analyze rotation speed, welding speed, and tool geometry at different factor levels of experimentation. The results showed that the optimized tensile strength was 120.7 MPa from the cylindrical tool, rotation speed was from 1300 to 2100 rpm, and welding speed less than 75 mm/min in the coefficient of determination R2 was 95.09%, as can be considered from the regression equation. The examination of the stir-zone and thermal mechanical affected zone using SEM and EDX showed that the new recrystallization of the microstructure causes fine grain in the stir-zone, coarse grain in advancing-side thermal mechanical affected zone, and equiaxed grain in the retracting-side thermal-mechanical affect zone. The intermetallic compounds of β-Al5FeSi phase transformation phase were formed to three types, i.e., β″-Al5Fe, Mg2Si, and Al8Fe2Si phase were observed. Moreover, in the stir-zone and thermal-mechanical-affected zone, defects were found such as flash defects, void or cavity defects, crack defects, lack of penetration defects, tunnel defects, kissing bond defects, and dendrite formation defects affecting weldability.

Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

“…We used 12 test examples collected from the literature [15,28,33,[71][72][73][74][75][76][77][78][79]. We identified the optimal parameters using the proposed methods and compared these with the results obtained using the D-optimal/RSM approaches on the aforementioned literature.…”

“…Recent studies on the optimal process parameters for aluminum welding found that rotation speed, welding speed, shoulder diameter per pin diameter ratio, tool geometry, and surface shoulder can cause increases or decreases in the strength and number of weld line defects [25][26][27]. Several approaches for predicting the optimal aluminum welding parameters (factorial design [28], Taguchi design [29], the response surface method, a combined method [30], etc.) are shown in Table 1.…”

A multi-objective optimization using response surface model coupled with particle swarm algorithm on FSW process parameters

“…As expected, no dendrite formation is detected in the stir and thermo-mechanically affected zones after the welding process. This defect type is not usually found in semi-solid materials 26 , 27 .

Figure 18 SEM images of the thermo-mechanically affected zone region of the samples: ( a ) threaded cylindrical pin, ( b ) conical pin, ( c ) b17, and ( d ) c12.

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Optimization of process parameters for friction stir welding of dissimilar aluminum alloys using different Taguchi arrays

“…The optimization of pin profile in addition to rotational and traverse speeds has been also investigated [30][31][32][33][34]. Sefene et al [30] studied multi-responses of hardness and tensile on AA6061 similar joints with a thickness of 5 mm using gray relation with Taguchi L9OA (3P:3L (1/3 of 3 3 )).…”

“…Sagheer-Abbasi et al [31] studied the tensile response of AA5052-H32 similar joints with a thickness of 1 mm using Taguchi L9OA (3P:3L (1/3 of 3 3 )). Meengam and Sillapasa [32] used a full factorial design of L64OA (3P:4L) for the optimization of AA6063 (6 mm) FSW joints. Balamurugan et al [33] studied the tensile response of dissimilar joints of AA5052-H32/AA6061-T6 with a thickness of 5 mm using Taguchi L27OA (3P:3L).…”