Friction-stir (FS) welded aluminum matrix composite (AMC) joints are used in the automotive, naval, and aeronautical industries because of their appealing properties such as altitude strength, depress density, superb corrosion resistance, preferable thermal conductivity, depressed expansion of thermal, and preferable dimensional stability by perfect strength-to-mass ratio [1]. The interaction of matrix and encouragement particles in the pool of molten weld causes subaltern brittle phases in the molten pool of the weld or reinforcement collapse in the pool of molten metal, making AMC a quixotic material for application in modern obstetrics [2]. FSW is a less costly and more effective solid-state welding procedure for AMCs to generate efficient defect-free welds (reduced cracking, minimal deformation, lower porosity, etc.) [1]. Many researchers have begun and continue their studies in the sectors of aviation, aerospace, automotive, and shipbuilding because of the appealing properties of FSW. To improve the performance of the FS-welded joints, they are developing enhanced procedures and newer materials for the base matrix and reinforcement. For the optimization of maximal mechanical characteristics, advanced approaches such as UTS, WHN, and others are required. RSM is one of the most precise methods for selecting the best welding settings with the least amount of time, material, and labor [3].
El-Kassas et al. [4] documented that given an optimum set of welding conditions, characterized by a rotation speed of 1,800 rpm, a welding speed of 1 mm/min, a penetration depth of 2 mm, and a tool with a cylindrical-conical pin profile, FS-welded joints showed an excellent strength, in comparison with the strengths achieved resultant to using other combinations of welding conditions. Recent research has revealed that T-joints, which are commonly seen in mechanical constructions, may be fused utilizing FS welding [5]. However, in certain regions, UFSW of complicated components should still be investigated. As a result, if UFSW proves to be more useful than standard FSW, it will have a significant influence on the welding industry, helping to improve technology. As a result, given the current situation, the application of UWFSW on AA 6063 pipes, as well as examination, is critical. The friction-stir welding procedure is linked to several problems. Tunneling, worm-holes, voids, flash, kissing bonds, surface galling, porosity, lack of fill, lack of penetration, and other factors reduce the strength of a structure. Mechanical property degradation frequently leads to premature failure with little warning. However, as compared to FSW, UWFSW is less prone to these flaws [6]. Sabry et al. [7] documented that multi-objective optimization of the UWFSW process was a desirable feature because UWFSW had a high UTS of 218 MPa and a nugget zone hardness of 83 VHN compared to 201 MPa and 65 VHN for the traditional process. Researchers have successfully welded and studied plates and pipes constructed of aluminum alloys such as Al AA 6063-T6 [8], Al 6063 [9], Al 6061 [10], Al 1050 [11], and Al 1050 [12]. In comparison to traditional FSW, a high rotating speed does not influence the heat generated in UWFSW, according to the main findings. In the case of UWFSW, a balanced thermal cycle provides an extra benefit. High travel and tool rotating speeds are now suggested for UWFSW [13]. There is a dearth of literature on welding pipes with the UWFSW technique. Resistance welding, inert metal gas welding [14], inert tungsten gas welding, and other welding techniques are used in an industrial setting. Jandaghi et al.'s [15] study dealt with improvement of the mechanical strength of aluminum joints formed by friction-stir welding (FSW), and a post-weld heat treatment comprising solution treatment and subsequent aging (STA) is extensively used in their research. Aerospace aluminum alloys AA2198 and AA7475 were FSW-ed in such a way that the results were physically indistinguishable, and the resultant welded products in their different states were used in this investigation. The precipitation strengthening with the aging of welded specimens was studied using differential scanning calorimetry (DSC). The DSC results were used to construct the post-weld aging methods. As a result, welded sheets were solution treated for 10–90 min at 480°C and 540°C, and then air-cooled and aged for 2–40 h at 155°C and 170°C, respectively. Optical micrographs demonstrated that greater homogenizing temperature led to nucleation of finer grains from high stress localized spots in the stir zone (SZ) and TMAZ by a quicker growth rate due to faster recrystallization kinetics.
At SZ and TMAZ of AA7475, increased solution-treatment duration and temperature resulted in the buildup of Cu-enriched intermetallic phases in the grain boundaries, weakening of grain adhesion, and sample failure. In the as-welded state, the hardness of the AA7475 alloy rose, while the hardness of the AA2198 alloy decreased. In AA2198, post-weld heat treatment increased hardness, but in AA7475, it decreased. The grain size, on the other hand, was unaffected. In the research of Jandaghi et al. [16], dissimilar FSW sheets AA2198-AA7475 and AA2198-AA6013 were solution treated for 1 h at 460–580°C. Both dissimilar weldments on the AA2198 side were completely degraded after annealing at 580°C. Solution treatment caused abnormal grain growth in the stir zone (SZ), and higher treatment temperatures increased the fraction of transformed grains, according to microstructure inspection. The pre-melting of grain boundaries (GBs) at 540°C aided the diffusion of solute atoms to the GBs, according to SEM analysis. Massive Cu diffusion to the GBs resulted in Cu-rich eutectic phases in AA7475 and AA2198, as well as dense Cu-rich particles in AA6013. In the research of Jandaghi et al. [17], post-weld heat treatment (PWHT) of dissimilar AA7475-AA2198 FSW was performed at 560°C, followed by water-quenching. Microstructural analyses demonstrated that the composition difference at high-temperature solution treatment was the driving force for Cu diffusion from advancing AA2198 via grain boundary liquid-metal thin-films, resulting in adequate intergranular segregation of the Cu-Zn phase. Intergranular failure has been linked to weld residual stresses, poor interfacial strength, grain boundary transition, and wetting, as well as thermal expansion coefficient disparities. Numerous studies are researches that aim to improve the responses (UTS, WNH, etc.) of FSW joints made of various aluminum alloys reinforced with various ceramic particles by selecting appropriate process parameters and employing newer methods on newer and advanced materials. The goal of this research is to produce a novel hybrid composite comprised of AA6061 as a base material and reinforced with SiC to improve the mechanical and metallurgical features of UWFS-welded joints. The RSM and desirability techniques were used to establish empirical correlations between UWFSW parameters (N, S, and SiC) and the two responses (UTS and WNH), as well as to select the best welding circumstances for UWFSW joints with optimal UTS and WNH.
Aluminum alloy was employed as a matrix material. Because of its strong corrosion resistance, great machining qualities, low weight, and ductility, the aluminum alloy was chosen. The alloy Al 6061 is mostly utilized in the aerospace sector. 6061/SiC AMC was employed in this study, as well as UWFSW 6061 alloy enhancement together with SiC particles. The matrix is a standard 6061 aluminum alloy with the specified chemical composition, as provided in Table 1. The stir-casting approach was used to generate SiC enhancement 6061 matrix composites in a top-loading electric resistance muffle furnace, as indicated in the literature [1]. The average size of the SiC particles was 400 mesh (40 m). Castings were reinforced with 5 SiC wt.%, 10 SiC wt.%, and 18 SiC wt.% scale (150 mm × 750 mm × 10 mm) composite specimens that have already been slashed, cleaned, and butt-welded along the joint line for UWFSW.
The chemical make-up of 6061 Al-alloy
Wt.% | 1.1 | 0.55 | 0.4 | 0.10 | 0.9 | 0.25 | 0.04 | 0.12 | Remainder |
The geometrical dimensions are shown in Figure 1. The conical pin was designed to facilitate piercing the tool to the specimens as straightforwardly as possible during the welding process. The welding experiments were performed on an automated vertical milling machine with a custom-made fixture to clamp the welding plates, as shown in Figures 1B and 1C.
Following the standard metallographic procedure, mirror polishing and etching with Keller's reagent (HCL + HF + HNO3 + distilled water) were performed. The metallographic specimens were made from 6061 AMC and 6061 AMC that had been welded together. According to ASTM E8-04, the standard tensile specimens were sliced perpendicular to the joint line and created from all specimens [18]. TS has been prepared by the length of a gauge of 57.2 mm, the width of a gauge of 12.2 mm, and a thickness of 10 mm. The tensile specimens were done using a Blue Star universal tensile testing machine (SE UTE 200) with an extreme capacity of 200kN, as indicated in Figure 2. A Chinese-made optical scanning microscope was used to examine the microstructure of UWFS-welded specimens (XPZ-830 T) [16]. In the same way, the Al-matrix stir cast as a composite cast aluminum matrix and the welded junction were microstructurally examined. The UWFS-welded and other specimens were MHV using the FIE model VM50 VH tester. VH measurements were taken at various locations on both sides of the weld zone under a constant load of 0.8 kgf for a dwell period of 17 s. The hardness data were then categorized as average microhardness [17, 19].
Researchers and engineers are continually trying to figure out what input process parameters or characteristics will result in the best output response. The optimal values are either the extreme or minimal of a function, depending on the input process parameters. RSM is an essential mathematical and statistical approach for constructing an empirical model. Independent variables can be quantitatively stated in both industrial and experimental situations, as demonstrated in Eq. (1). These independent parameters can thus have the following functional relationship:
The response surface or function is the relationship between the response,
The response surface is represented by the second-order regression equation:
The
During the UWFSW of AA6061/SiC, three process parameters with three levels have been considered in this study: N, S, and SiC. Before actual welding, the correlation of process parameters with UTS and WNH of welded joints is usually developed in the design. The Box–Behnken design technique was utilized in this study to develop the correlation and determine the best design parameters for improving the UTS and WNH of UWFS-welded connections.
As a three-factorial for determining the correlation between the response parameters (UTS and WNH) and the input parameters, a Box–Behnken experimental design was chosen (N, S, and SiC). Table 2 shows the most influential UWFSW process factors on output responses, along with their levels, as determined using the Box–Behnken model design. −1 (low), 0 (middle), and 1 (high) are the levels of the process parameters (high). Table 3 shows that the design matrix for a total of 27 tests is required, according to the Box–Behnken experimental design.
UWFSW process parameters and their limits
Silicon carbide (SiC) (%) | 5 | 10 | 18 |
Rotation speed ( |
1,000 | 1,400 | 1,800 |
Travel speed ( |
10 | 20 | 30 |
Full factorial design matrix
1 | 5 | 1,000 | 10 | 205.00 | 64.544 |
2 | 10 | 1,000 | 10 | 196.00 | 55.243 |
3 | 18 | 1,000 | 10 | 190.00 | 52.942 |
4 | 5 | 1,000 | 20 | 200.00 | 55.980 |
5 | 10 | 1,000 | 20 | 190.00 | 53.679 |
6 | 18 | 1,000 | 20 | 180.00 | 51.378 |
7 | 5 | 1,000 | 30 | 190.00 | 55.198 |
8 | 10 | 1,000 | 30 | 185.00 | 52.897 |
9 | 18 | 1,000 | 30 | 174.00 | 50.596 |
10 | 5 | 1,400 | 10 | 202.00 | 56.113 |
11 | 10 | 1,400 | 10 | 186.40 | 53.812 |
12 | 18 | 1,400 | 10 | 174.70 | 51.511 |
13 | 5 | 1,400 | 20 | 195.30 | 54.548 |
14 | 10 | 1,400 | 20 | 183.70 | 52.247 |
15 | 18 | 1,400 | 20 | 172.30 | 49.946 |
16 | 5 | 1,400 | 30 | 193.10 | 53.766 |
17 | 10 | 1,400 | 30 | 180.03 | 51.465 |
18 | 18 | 1,400 | 30 | 170.32 | 49.164 |
19 | 5 | 1,800 | 10 | 196.00 | 56.113 |
20 | 10 | 1,800 | 10 | 185.10 | 52.380 |
21 | 18 | 1,800 | 10 | 176.20 | 50.079 |
22 | 5 | 1,800 | 20 | 191.30 | 53.117 |
23 | 10 | 1,800 | 20 | 184.20 | 50.816 |
24 | 18 | 1,800 | 20 | 171.20 | 48.515 |
25 | 5 | 1,800 | 30 | 187.10 | 52.335 |
26 | 10 | 1,800 | 30 | 181.10 | 50.034 |
27 | 18 | 1,800 | 30 | 166.30 | 47.732 |
One of the most often used strategies for multiple response optimization procedures is the desirability approach. The fundamental benefit of the desirability method is that it assigns value to every response, such as UTS or WNH. It turns these single replies into the desirability function, a dimensionless parameter that gives integers between 0 and 1. The overall desirability function converts numerous answers such as UTS and WNH into a dimensionless measure of performance. Multiple responses, such as UTS and WVH, are turned into a dimensionless measure in this study. Eq. (5) gives the total desirability function:
Design-Expert software is utilized to optimize the UWFSW process parameters. By establishing the intended goals for each input parameter and response, both numerical and graphical optimization approaches are used. All of the goals are incorporated into the overall desirability function throughout the numerical optimization process. This procedure optimizes the objective function by determining the maximum and lowest limits for each element and determining the response that will maximize the objective function. This graphical approach is utilized to identify the interaction effects of process factors on replies in graphical optimization with multiple responses, and it is represented using 2D and 3D contour plots. When dealing with a large number of replies, numerical optimization should be used initially to define a feasible region, while graphical optimization may be used to depict a viable response area, and the shaded zone reflects the unsuitable optimization criterion. The flow chart for the Design-Expert software's optimization process is shown in Figure 3.
The surface finishing for each UWFSW specimen of Al 6061/18% SiC gave a smooth weld surface without lateral flash; meanwhile Al 6061/5% SiC shows an unsmooth weld surface condition. Based on observation, we can infer that this is the relation between rotation speed, travel speed, the ratio of SiC, and flash occurrence. The lower values of rotation, travel speed, and SiC 18% resulted in an absence of lateral flash. In lieu, the decrease in SiC may direct to the presence of lateral flash. Hence, it explains that the welding particulars may influence the character of the external surface. Tensile specimens have been prepared in standard dimensions according to ASTM E8 as shown in Figure 2 and tested using the universal testing machine. A Santam STM-50 machine has been used to evaluate tensile properties. The testing machine had a crosshead speed of 5 mm/min. During the test, stress and strain have been recorded simultaneously, as shown in Figure 4 and Figure 5. The maximum UTS and YS have been obtained for specimens produced by Al 6061/18% SiC. The corresponding combination of tool rotational speed and travel speed has been 1,800 rpm and SiC 18%, respectively. The UTS and the YS have been 228 MPa and 140.3 MPa, respectively, which have been 91.2% and 92.3% of the base material.
Table 2 shows how the Box–Behnken model is used to create an experimental design matrix. FSW on hybrid composites was carried out by varying process parameters such as N, S, and SiC following the experimental design matrix provided in Table 3. According to ASTM standards, test specimens for the tensile and VH tests were made using manufactured welded joints. To create the empirical mathematical relationships, UTS and WNH were measured from each test specimen. The duties of N, S, and SiC were the UTS and WNH of the UWFSW of AA6061/SiC's UWFSW. Eqs (2) and (3) can be used to describe the response surface, as can Eq. (4), which is a second-order polynomial (regression) [20].
The quadratic model is created first. The ANOVA method is used to identify significant responses and interaction terms. The
From the interaction plot shown in Table 4, we find that the interaction of the tool rotating speed and the welding speed of the silicon carbide was not significant (
ANOVA results for UTS
Model | 2,637.03 | 9 | 293.00 | 52.07 | <0.0001 | Significant |
A-SiC | 1,896.87 | 1 | 1,896.87 | 337.08 | <0.0001 | |
B-N | 73.62 | 1 | 273.62 | 48.62 | <0.0001 | |
C-S | 397.11 | 1 | 397.11 | 70.57 | <0.0001 | |
AB | 6.56 | 1 | 6.56 | 1.17 | 0.2952 | |
AC | 1.37 | 1 | 1.37 | 0.24 | 0.6285 | |
BC | 30.72 | 1 | 30.72 | 5.46 | 0.0320 | |
A2 | 47.11 | 1 | 47.11 | 8.37 | 0.0101 | |
B2 | 19.92 | 1 | 19.92 | 3.54 | 0.0771 | |
C2 | 0.10 | 1 | 0.10 | 0.018 | 0.8943 | |
Residual | 95.66 | 17 | 5.63 | |||
Cor Total | 2,732.69 | 26 |
ANOVA results for microhardness
Model | 260.47 | 28.94 | 21.80 | <0.0001 | Significant | |
A-SiC | 138.06 | 1 | 138.06 | 104.02 | <0.0001 | |
B-N | 56.45 | 1 | 56.45 | 42.53 | <0.0001 | |
C-S | 51.50 | 1 | 51.50 | 38.80 | <0.0001 | |
AB | 2.94 | 1 | 2.94 | 2.22 | 0.1548 | |
AC | 6.75 | 1 | 6.75 | 5.09 | 0.0376 | |
BC | 2.58 | 1 | 2.58 | 1.95 | 0.1810 | |
A2 | 0.17 | 1 | 0.17 | 0.13 | 0.7234 | |
B2 | 1.32 | 1 | 1.32 | 0.99 | 0.3331 | |
C2 | 4.43 | 1 | 4.43 | 3.34 | 0.0852 | |
Residual | 22.56 | 17 | 1.33 | |||
283.03 | 26 |
The optimization criteria that were applied in this study
A:SiC | Is in range | 6.03417 | 16.9658 | 3 |
B:N | Is in range | 10,063 | 1,736.36 | 3 |
C:S | Is in range | 10 | 28.409 | 3 |
UTS | Maximize | 186.3 | 202.9 | 3 |
WNH | Maximize | 47.732 | 64.544 | 3 |
The effect of UWFSW parameters on UTS is represented in the perturbation plot (Figure 6) for an optimized design. From the plot, it is clear that the response changes when each factor moves away from the common or reference points keeping all other factors constant at that reference point. It is also clear from the plot that the tool rotating speed (A) is the most dominating factor on UTS followed by welding speed (B) and SiC (C).
The interaction influences of N, S, and SiC on the UTS are depicted in Figures 7–9. The 2D and 3D contour plots demonstrate the influence of N and S on UTS arising from retaining AA6061/18 SiC (Figures 2A and 2B).
The ideal value of the UTS (MPa) is reached in the center of the contour plot, which is shown as a concentric circle (Figures 2A and 2B). The optimal UTS of 210 MPa is attained at an N of 1,800 rpm and an S of 10 mm/min, as shown in the plot. When the N and S diverge from the specified values, the UTS will tend to drop or grow.
Figure 9 depicts the interaction impact of SiC and S at a constant N of 1,800 rpm. The approximate value of the optimal UTS of 210 MPa is attained at an S of 10 mm/min and a SiC of 18%, as illustrated in the 2D and 3D contour plots.
The perturbation plot (Figure 10) for an optimized design depicts the effect of UWFSW process parameters on WNH. At any operating level of N, WNH should be higher than that of base metal. The microhardness of base metal AA6061/18% SiC is 60.2 HV and it is usually lower compared to the stir zone (60.2 HV). The interaction impact of N, S, and AL on the WNH is depicted in Figures 11–13. Figures 13A and 14B show the effect of N and S on WNH while keeping the SiC constant at 18%.
WNH (HV) is depicted as a concentric ellipse, with the ideal WNH value located in the contour plot's center (Figures 12A and 12B). The ideal WNH of 60.2 HV is attained at an N of 1,800 rpm and an S of 105 mm/min, as illustrated in the plot. Figure 15 depicts the interaction impact of SiC and N at a constant S of 10 mm/min. The optimal WNH is attained around 60.2 HV at an N of 1,800 rpm, as seen in the 2D and 3D graphs. Figure 13 depicts the interaction impact of SiC and S at a constant N of 1,800 rpm. We observe from the 2D and 3D plots that the ideal WNH of 60.2 HV is attained at an S of 10 mm/min and a SiC of 6 kN.
The desirability function approach is a widely utilized technique in multiple response optimization. Harrington first presented the desirability function technique in 1965 [14, 20, 21]. This approach determines the optimal operating conditions for obtaining the desired response values. All outputs are translated to individual DFs with a scale factor of between 0 and 1. These functions are organized by initializing the values to be the target, minimum, or maximum output obtained during the experiments. The optimal conditions of UWFSW parameters to maximize hardness and ultimate tensile strength of UWFS-welded Al 6061/5%wt. SiC, Al 6061/10%wt. SiC, and Al 6061/18% SiC were determined using response surface methodology. The optimized UWFSW parameters to maximize hardness and ultimate tensile strength of the weld joint were obtained using a ramp chart and bar chart, as shown in Figure 14. The optimized parameters were N, a rotation speed of 1,736.36 rpm, a travel speed of 11.58 mm/min, and a SiC of 16.67%, with a desirability value of 0.922.
The experimental data are used to validate the model created using the desirability technique, and the errors are estimated for all 27 runs, as shown in Table 2. For UTS and WNH, Table 7 shows the actual value, anticipated value, and percentage error. The real values are discovered through trials, and the anticipated values are derived using the Design-Expert software's empirical equations [22,23,24]. For UTS, the proportion of error ranges from −6.133 to +5.19. WNH's percentage of inaccuracy is similar, ranging from −1.550 to +5.372. As a consequence, the newly created model has predicted UTS and WNH values that are quite similar to the experimental data. The findings of the validation trials are presented in Table 7. The model is also tested for the ideal welding circumstances that are anticipated, with an N of 1,736.36 rpm, an S of 11.59 mm/min, and a SiC of 16.96%.
The outcomes of the experiments as well as the values anticipated by the Design-Expert software for all numbers of runs
1 | 205.000 | 204.909 | 0.091 | 64.544 | 59.172 | 5.372 |
2 | 196.000 | 192.538 | 3.462 | 55.243 | 55.779 | −0.536 |
3 | 190.000 | 184.806 | 5.194 | 52.942 | 53.658 | −0.716 |
4 | 200.000 | 200.218 | −0.218 | 55.980 | 57.530 | −1.550 |
5 | 190.000 | 187.846 | 2.154 | 53.679 | 54.137 | −0.458 |
6 | 180.000 | 180.114 | −0.114 | 51.378 | 52.017 | −0.639 |
7 | 190.000 | 195.526 | −5.526 | 55.198 | 55.888 | −0.690 |
8 | 185.000 | 183.154 | 1.846 | 52.897 | 52.496 | 0.401 |
9 | 174.000 | 175.422 | −1.422 | 50.596 | 50.375 | 0.221 |
10 | 202.000 | 200.937 | 1.063 | 56.113 | 57.431 | −1.318 |
11 | 186.400 | 188.566 | −2.166 | 53.812 | 54.038 | −0.226 |
12 | 174.700 | 180.833 | −6.133 | 51.511 | 51.917 | −0.406 |
13 | 195.300 | 196.245 | −0.945 | 54.548 | 55.789 | −1.241 |
14 | 183.700 | 183.874 | −0.174 | 52.247 | 52.396 | −0.149 |
15 | 172.300 | 176.142 | −3.842 | 49.946 | 50.276 | −0.330 |
16 | 193.100 | 191.554 | 1.546 | 53.766 | 54.147 | −0.381 |
17 | 180.030 | 179.182 | 0.848 | 51.465 | 50.755 | 0.710 |
18 | 170.320 | 171.450 | −1.130 | 49.164 | 48.634 | 0.530 |
19 | 196.000 | 196.965 | −0.965 | 56.113 | 55.690 | 0.423 |
20 | 185.100 | 184.593 | 0.507 | 52.380 | 52.297 | 0.083 |
21 | 176.200 | 176.861 | −0.661 | 50.079 | 50.177 | −0.098 |
22 | 191.300 | 192.273 | −0.973 | 53.117 | 54.048 | −0.931 |
23 | 184.200 | 179.902 | 4.298 | 50.816 | 50.655 | 0.161 |
24 | 171.200 | 172.169 | −0.969 | 48.515 | 48.535 | −0.020 |
25 | 187.100 | 187.582 | −0.482 | 52.335 | 52.406 | −0.071 |
26 | 181.100 | 175.210 | 5.890 | 50.034 | 49.014 | 1.020 |
27 | 166.300 | 167.478 | −1.178 | 47.732 | 46.893 | 0.839 |
Figure 15A depicts the presence of coarse grains in the base composite material (AA6061/18% SiC), as well as the dendritic structure caused by the stir-casting process and SiC particle dispersion in the metal matrix. The macrostructural investigation of the UWFS-welded connection is shown in Figure 17B. In the cross weld microstructure of UWFS-welded MMC joints, the four separate zones of UWFSW, WNZ, TMAZ, HAZ, and unaffected zone (base material), can be seen in Figures 16A–16F.
Due to variable heating and cooling circumstances in UWFSW, it also indicates the presence of diverse microstructure and grain sizes in WNZ, TMAZ, and HAZ. In comparison to the TMAZ, HAZ, and unaffected zones, the weld nugget zone features finer granules. The presence of tiny recrystallized structures in the WNZ is also visible in this photomicrograph. Due to the mechanical stirring action of the UWFSW tool, the coarse grain structure visible in the base composite material (Figure 16A) transforms into a fine grain structure, as seen in Figure 17A–17F. The optical microscopic of the base composite material and the UWFS-welded composite is shown in Figures 16A–16F. The grain size and distribution of SiC in the base composite material (AA6061/18%wt. SiC) are shown in Figure 16A. Figure 16 depicts the existence of the four distinct zones (TMAZ, WNZ, HAZ, and unaffected zone). When compared to the TMAZ, HAZ, and unaffected zone, the WN zone possesses a smooth grain structure (Figure 16C–16F). It demonstrates the existence of reinforcing particles (SiC) in the grain borders, which function as a barrier to dendritic development. MMCs’ UTS and microhardness improve when dendritic development is reduced. It also illustrates how the degree of cluster aggregation and its expansion with the addition of SiC particles gives MMCs a lot of strength. Following the confirmation of the results, an optical microscope investigation of a cracked tensile specimen of FS-welded (AA6061/18%wt. SiC) MMC with optimal settings was performed. The optical microscope report of the microstructure of the broken tensile specimen is shown in Figures 16A–16F. It clearly distinguishes between a non-fractured and a fractured surface area.
The high-resolution optical micrographs and the XRD patterns of the prepared AMCs are shown in Figures 16 and 17, respectively. The diffraction peaks of SiC, which represent the major elements of the SiC particles, are distinctly identified. The intensities of the peaks rise as SiC content within the matrix increases. It is noticed in Figure 17 that the diffraction peaks of aluminum in the composites are slightly shifted to lower 2
In the present study, the effect of UWFSW parameters such as tool rotating speed, welding speed, and silicon carbide on ultimate tensile strength and microhardness of Al 6061/5%wt., Al 6061/10%wt., and Al 6061/18%wt. welded joint was investigated in detail. Response surface methodology was used to design the experiment, optimize, and predict outputs. The parameters’ effect (linear, interaction, and square) and their significance were determined using the response surface methodology.
The input parameters are significant for ultimate tensile strength and microhardness. Tool rotating speed is the most significant parameter, followed by welding speed and then silicon carbide. The interactions of tool rotating speed to welding speed and silicon carbide are significant, but the interaction between welding speed and silicon carbide is not significant.
The association amidst the responses (UTS and WNH) and input parameters (N, S, and SiC) has been effectively developed using the Box–Behnken experimental design. Three values have been chosen for each parameter, and tests have been conducted to optimize the UTS and WNH.
The interaction effects of welding parameters are investigated using perturbation plots, 2D and 3D contour plots, and perturbation graphs. The most influential parameter is N. At welding circumstances of N 1,736.36 rpm, S 11.58 mm/min, and SiC 16.67%, the apt UTS and WNH are determined to be 202 MPa and 59.5339 HV, respectively.
The results of all 27 tests designed using the Box–Behnken method are effectively applied to the desirability approach. At the matching welding circumstances of 1,736.36 rpm N, 11.58 mm/min S, and 16.67% SiC, the optimal values achieved are 202.59 MPa for UTS and 59.5339 HV for WNH.
By comparing the outcomes of all 27 trials with projected results for the same welding setting, the newly constructed model is verified for UTS and WNH. UTS has a maximum percentage error of −6.133 and WNH has a maximum percentage error of −1.550. The newly created model correctly predicted UTS and WNH values that were highly similar to the experimental data.
When compared to TMAZ, HAZ, unconstrained zone, and base AMC, microstructural characterization of UWFSW joints indicated that the WN zone had a smooth grain structure.