Investigation of Mechanical and Tribological Behaviors of Aluminum Based Hybrid Metal Matrix Composite and Multi-Objective Optimization

Aluminum metal matrix composites are potential materials for aerospace and automobile industrial applications due to their enhanced mechanical and tribological properties. Aluminum reinforced with silicon carbide particles has been developed with enhanced mechanical and tribological behavior, but it lacks wettability between matrix and reinforcement causing weak bonding, which reduces the degree of enhancement. The objectives of this study were to fabricate aluminum-based metal matrix composites with enhanced wettability at varying stirring speeds (350, 450, 550 rpm), stirring time (5, 10, 15 min), weight percentage of SiC (0, 5, 10 wt.%), and weight percentage of MoS2 (0, 2, 4 wt.%). Nine samples were fabricated using stir casting based on Taguchi L9 orthogonal array. Hardness, tensile strength, and wear rate of the developed composite were investigated and analyzed as a single response characteristic using Taguchi’s signal-to-noise ratio and as a multi-response characteristic using hybrid Taguchi–grey relational analysis (HTGRA). The results revealed that the addition of SiC in the composite produced better hardness, tensile strength, and wear rate. The addition of MoS2 in the composite showed better hardness and tensile strength only up to 2 wt.% of MoS2, and in the case of wear rate, the addition of MoS2 in the composite up to 4% showed better wear resistance. Al–SiC–MoS2 hybrid composite shows better enhancement in hardness, tensile strength, and wear resistance than the Al–SiC composite.


Introduction
Aerospace and automobile industries require a lightweight, harder, stronger, stiffer, and wear resistant material [1,2]. In nature, no material satisfies this requirement; a composite is the only material that fulfills this industrial requirement [2,3]. Composite material combines two or more dissimilar materials to create a material with better behavior than either of the originals alone [4,5]. The combination of materials must have two Al 6061 is used in this paper due to its corrosion resistance and moderate strength, and its composition is tabulated in Table 1. Al 6061 is extensively used because of its low weight, low cost, and good formability and weldability. Silicon carbide particle (53 µm) reinforcement was used to develop this composite, and it certainly improves the overall strength of the composite. The processing parameters to manufacture the desired samples are given in Table 2. Aluminum MMC reinforced with SiC particles has an improvement in mechanical behavior compared to unreinforced aluminum matrix alloy [12]. Molybdenum disulfide (MoS 2 ) is used as a dry or solid lubricant to improve the poor wettability that occurs between Al 6061 and SiC because of its low coefficient of friction [9]. Table 3 depicts the experimental parameters and their levels. Table 4 represents the experimental design for composite fabrication.  Table 3. Experimental parameters and their levels.

Experimental Process Parameters and Design of Experiments
Fabrication of the composite was achieved based on the basic input variables of process parameters (stirring speed, stirring time, and weight percentage of SiC and MoS 2 ) at the same pouring temperature (730 • C) for each fabrication.
Taguchi L9 orthogonal array experimental design was used for fabrication based on the given factors and levels. In the design of the experiment table for fabrication, the letters A, B, C, and D are used to represent the process parameters of stirring speed, stirring time, wt.% of SiC, and wt.% of MoS 2 , respectively, to indicate the optimization of S/N ratio plot.

Conducted Testing
Hardness testing was conducted as per ASTM using the Vickers hardness tester machine shown in Figure 1a and the sample shown in Figure 1d. The sample used for hardness testing was a cylindrical form with a size of 20 mm in length and 20 mm in diameter. Tensile strength testing was conducted based on the ASTM E8 standard using Shimadzu (Kyoto, Japan) AG-X plus TM 50 kN universal testing machine as shown  Figure 1e. The sample size has gauge length (G = 43.75 mm), R = 6.25 mm, overall length (L = 82.5 mm), parallel length (A = 50 mm), and grip width (C = 14 mm) as shown in Figure 1g. Wear rate testing was conducted based on ASTM G99-05 using pin-on-disc (DUCOM TR-20 MICRO) equipment as shown in Figure 1c and the sample shown in Figure 1f. The sample used for wear testing was a cylindrical form with a size of 12 mm in length and 6 mm in diameter. (Kyoto, Japan) AG-X plus TM 50 kN universal testing machine as shown in Figure 1b and the sample shown in Figure 1e. The sample size has gauge length (G = 43.75 mm), R = 6.25 mm, overall length (L = 82.5 mm), parallel length (A = 50 mm), and grip width (C = 14 mm) as shown in Figure 1g. Wear rate testing was conducted based on ASTM G99-05 using pin-on-disc (DUCOM TR-20 MICRO) equipment as shown in Figure 1c and the sample shown in Figure 1f. The sample used for wear testing was a cylindrical form with a size of 12 mm in length and 6 mm in diameter.

Analysis of Particle Distribution in Matrix
The microstructural image was analyzed by determining the grain particle size area from the scaled dimension of each grain particle using ImageJ software. ImageJ software considers and counts all grain particles in the Al matrix of the composite and determines the volume fraction of the particles in the Al matrix. The data recorded from this software were the total cross-sectional area of the analyzed images, the total number of grain particles, the total area of the grain particles, the average area of grain particle size, and grain particle volume fraction in the matrix. In the analysis, there are two phases, as indicated by red and white in Figure 2. The red color indicates the total area of grain particles in the matrix obtained from the total area of the analyzed image.

Analysis of Particle Distribution in Matrix
The microstructural image was analyzed by determining the grain particle size area from the scaled dimension of each grain particle using ImageJ software. ImageJ software considers and counts all grain particles in the Al matrix of the composite and determines the volume fraction of the particles in the Al matrix. The data recorded from this software were the total cross-sectional area of the analyzed images, the total number of grain particles, the total area of the grain particles, the average area of grain particle size, and grain particle volume fraction in the matrix. In the analysis, there are two phases, as indicated by red and white in Figure 2. The red color indicates the total area of grain particles in the matrix obtained from the total area of the analyzed image. From the analysis, the volume fractions of grain particles were proportional to each other's (i.e., the higher reinforcement weight of contents in the matrix, the higher the volume fraction). Sample 3, which has the higher weight percentage of SiC and MoS2, shows the higher volume fraction; Sample 1, which has no weight percentage of SiC and MoS2, shows the smaller volume fraction in the microstructural analysis as shown in Table 5. From the analysis, the volume fractions of grain particles were proportional to each other's (i.e., the higher reinforcement weight of contents in the matrix, the higher the volume fraction). Sample 3, which has the higher weight percentage of SiC and MoS 2 , shows the higher volume fraction; Sample 1, which has no weight percentage of SiC and MoS 2 , shows the smaller volume fraction in the microstructural analysis as shown in Table 5.  Table 6 shows the experimental results of hardness, tensile strength, and wear rate of the developed composite. The hardness test was performed using a Vickers hardness tester machine. From Table 6, the highest and lowest values of hardness were 208 HV and 123 HV, respectively. When the wt.% of SiC increases (at a higher level) and wt.% of MoS 2 is at 2 wt.%, the hardness of the composite increases. In contrast, the lowest hardness was recorded as the wt.% of SiC, and wt.% of MoS 2 is lower (at a lower level). The hardness of the composite increased due to increasing content of SiC particles and strong bonding between SiC particles and matrix due to enhanced wettability with the addition of MoS 2 . SiC particles increase the grain boundaries in composites and the grain size of the composites are reduced. Dislocation mobility will be restricted or challenged to move from grain to grain due to increased grain boundaries (decreased grain size) since grain boundaries act as a barrier to the motion of dislocation. Moreover, SiC particles have an extremely low coefficient of thermal expansion and how they strain the atomic lattice of the aluminum matrix, resulting in a dramatic increase in dislocation density. Dislocation density is the average distance between dislocation decreases and the dislocation starts blocking the motion of each other. The addition of MoS 2 also shows an enhancement in hardness up to 2 wt.% and shows a decrement with 4 wt.%, which means the role of MoS 2 is only facilitating the wettability, so only a small amount is enough to enhance the wettability and bonding between both matrix and reinforcement to each other, and further increment leads to decreasing the hardness of the composite. The tensile strength testing was performed using the universal testing machine (UTM).

Results
The largest values of tensile strength were indicated in sample number seven and the lowest value in sample number one with values of 194 MPa and 75 MPa, respectively. When the wt.% of SiC increases (at the higher level) and wt.% of MoS 2 is at 2 wt.%, the tensile strength of the composite increases. In contrast, the lowest tensile strength was recorded as the wt.% of SiC, and wt.% of MoS 2 is lower (at the lower level). The tensile strength of the composite is increased due to increasing contents of SiC particles and strong bonding between SiC particles and matrix due to enhanced wettability with the addition of MoS 2 . SiC particles increase the grain boundaries in composites and the grain size of the composites are reduced. Dislocation mobility will be restricted or challenged to move from grain to grain due to increased grain boundaries (decreased grain size) since grain boundaries act as a barrier to the motion of dislocation.
Moreover, SiC particles have an extremely low coefficient of thermal expansion and how they strain the atomic lattice of the aluminum matrix, resulting in a dramatic increase in dislocation density. The addition of MoS 2 also shows an enhancement in tensile strength up to 2 wt.% and shows a decrement with 4 wt.%, which means the role of MoS 2 is only facilitating the wettability, so only a small amount is enough to enhance the wettability and bonding of both matrix and reinforcement to each other and further increment leads to decrease the tensile strength of the composite.
The wear rate test was performed using pin-on-disc apparatus testing machine. The wear rate study was conducted based on the mass loss analysis. The largest of the wear rate values was indicated in sample number three and the lowest value was in sample number one, with the values of 10 × 10 −9 kg/m and 1 × 10 −9 kg/m, respectively. When the wt.% of SiC increased (at the higher level) and wt.% of MoS 2 increased (higher level), the wear rate of the composite decreased. In contrast, the highest wear rate was recorded as the wt.% of SiC, and wt.% of MoS 2 is lower (at the lower level). The wear resistance of the composite is increased due to the increasing content of SiC particles and MoS 2 reinforcement because the wear resistance of carbides is very high, and the hard-reinforcing particles work to resist wear on the surface of the cast hybrid composite samples. Another reason for the improved wear resistance of the cast AMC is the adequate interfacial bonding between the hybrid reinforcement and the aluminum matrix due to the addition of MoS 2 , which resists the pull-out of the hybrid reinforcement during the relative movement between two contacting surfaces. The addition of MoS 2 reinforcement in Al 6061/SiC composites as a hybrid reinforcement further increases the wear and friction resistance of the composite.

Analysis of Hardness
In the figure, by increasing the weight percentage of SiC, the hardness of the developed composite increased at constant wt.% MoS 2 (0%). At the higher wt.% of SiC, the higher value of hardness is shown. Silicon carbide has the most significant effect on the hardness of the developed composite's mechanical properties such as tensile strength, hardness, and impact strength, but the high amount of SiC will lead to brittleness [7]. From Figure 3, the hardness of composites increased with increasing wt.% of SiC and the hardness values for 0, 5, and 10 wt.% SiC at constant wt.% MoS 2 (0%) are 123 HV, 180 HV, and 199 HV, respectively. This is a better result than the previous studies by [1,2]. MoS 2 is used as a solid lubricant because it does not increase the hardness of composites, but it facilitates the wettability between aluminum and silicon carbide due to its low friction properties and robustness to enhance the hardness result. The hardness is better when a small amount of MoS 2 is added to Al-SiC composite. The hardness of the matrix increases as the weight percentage of MoS 2 increases, but only up to 2%, and after then it declines at 4 wt.% MoS 2 at constant weight percentage of SiC. The values of hardness for 0, 2, and 4 wt.% MoS 2 at constant weight percentage of SiC (0%) are 123 HV, 180 HV, and 163 HV, respectively, as shown in Figure 3. The highest hardness value of the developed composite was shown when the weight percentage of SiC was 10% and the weight percentage of MoS 2 was 2%, and this shows a better result than the previous studies by [2]. Al-SiC-MoS 2 hybrid composite shows better enhancement in hardness than Al-SiC composite. MoS2 is used as a solid lubricant because it does not increase the hardness of composites, but it facilitates the wettability between aluminum and silicon carbide due to its low friction properties and robustness to enhance the hardness result. The hardness is better when a small amount of MoS2 is added to Al-SiC composite. The hardness of the matrix increases as the weight percentage of MoS2 increases, but only up to 2%, and after then it declines at 4 wt.% MoS2 at constant weight percentage of SiC. The values of hardness for 0, 2, and 4 wt.% MoS2 at constant weight percentage of SiC (0%) are 123 HV, 180 HV, and 163 HV, respectively, as shown in Figure 3. The highest hardness value of the developed composite was shown when the weight percentage of SiC was 10% and the weight percentage of MoS2 was 2%, and this shows a better result than the previous studies by [2]. Al-SiC-MoS2 hybrid composite shows better enhancement in hardness than Al-SiC composite.

Optimization of Process Parameters for Hardness
In the response to hardness variation for data analysis and prediction of optimum results, Taguchi signal-to-noise ratios were used. In this study, the effects of varying control factors (stirring speed, stirring time, wt.% of SiC, and wt.% of MoS2) on the responses of hardness were analyzed. As higher values of hardness were desirable, larger, betterquality characteristic was selected to investigate the influence of factors on hardness response.
The delta statistics of the S/N ratio for the hardness tabulated in Table 7 show the ranks of the factors affecting the hardness response based on the S/N ratios. Ranks were assigned based on their delta value. The delta values were calculated from the difference between the largest and smallest value of the mean value. The analysis showed that wt.% SiC was assigned a rank of 1 with a delta value of 2.48, signifying that it is the predominant factor that affects the hardness of the composite. The wt.% MoS2, stirring time, and stirring speed were assigned second (1.26), third (1.15), and fourth (0.88) ranks, respectively. Table 7. Signal-to-noise ratio for hardness of the composite (larger is better).

Level
Stirring

Optimization of Process Parameters for Hardness
In the response to hardness variation for data analysis and prediction of optimum results, Taguchi signal-to-noise ratios were used. In this study, the effects of varying control factors (stirring speed, stirring time, wt.% of SiC, and wt.% of MoS 2 ) on the responses of hardness were analyzed. As higher values of hardness were desirable, larger, better-quality characteristic was selected to investigate the influence of factors on hardness response.
The delta statistics of the S/N ratio for the hardness tabulated in Table 7 show the ranks of the factors affecting the hardness response based on the S/N ratios. Ranks were assigned based on their delta value. The delta values were calculated from the difference between the largest and smallest value of the mean value. The analysis showed that wt.% SiC was assigned a rank of 1 with a delta value of 2.48, signifying that it is the predominant factor that affects the hardness of the composite. The wt.% MoS 2 , stirring time, and stirring speed were assigned second (1.26), third (1.15), and fourth (0.88) ranks, respectively.  Figure 4 shows the parametric effect S/N ratio plot and the optimal parameter combination for the higher hardness. The numerical value of the maximum point in each graph shows the best optimum combination of the factors at that level. Therefore, the S/N ratio plot in Figure 4 ., A2B3C3D2). The optimum prediction condition for the S/N ratio in which the higher hardness result of the composite obtained with the term setting of A2B3C3D2 is 229 HV. Wt.% of SiC has the greatest impact on the hardness of the developed composites and is followed by wt.% of MoS2 (second), stirring time (third), and stirring speed (fourth).

Analysis of Tensile Strength
In the figure, when increasing the wt.% of SiC, the tensile strength of the developed composite increased at constant wt.% MoS2 (0%). At the higher weight percentage of SiC, the higher value of tensile strength is shown. Silicon carbide has the most significant effect on the tensile strength of the fabricated composite's mechanical properties such as tensile strength, hardness, and impact strength, but the high amount of SiC will lead to brittleness [7].
From Figure 5, the tensile strength of composites increased with increasing the weight percentage of SiC and the tensile strength values for 0, 5, and 10 wt% SiC at constant wt.% MoS2 (0%) are 75 MPa, 119 MPa, and 181 MPa, respectively. This result shows a better result than the previous studies by [1,18].
MoS2 is used as a solid lubricant because it does not increase the hardness of composites, but it facilitates the wettability between aluminum and silicon carbide due to its low friction properties and robustness to enhance the tensile strength. As a result, the tensile strength is better when a small amount of MoS2 is added to Al-SiC composite.

Analysis of Tensile Strength
In the figure, when increasing the wt.% of SiC, the tensile strength of the developed composite increased at constant wt.% MoS 2 (0%). At the higher weight percentage of SiC, the higher value of tensile strength is shown. Silicon carbide has the most significant effect on the tensile strength of the fabricated composite's mechanical properties such as tensile strength, hardness, and impact strength, but the high amount of SiC will lead to brittleness [7].
From Figure 5, the tensile strength of composites increased with increasing the weight percentage of SiC and the tensile strength values for 0, 5, and 10 wt% SiC at constant wt.% MoS 2 (0%) are 75 MPa, 119 MPa, and 181 MPa, respectively. This result shows a better result than the previous studies by [1,18].  MoS 2 is used as a solid lubricant because it does not increase the hardness of composites, but it facilitates the wettability between aluminum and silicon carbide due to its low friction properties and robustness to enhance the tensile strength. As a result, the tensile strength is better when a small amount of MoS 2 is added to Al-SiC composite.
Tensile strength of the matrix increases as the weight percentage of wt.% MoS 2 increases, but only up to 2%, and after then it declines at 4 wt.% MoS 2 . The values of tensile strength for 0, 2, and 4 wt.% MoS 2 at constant weight percentage of SiC (0 wt.%) are 75 MPa, 90 MPa, and 82 MPa, respectively, as shown in Figure 5. The highest tensile strength value of the developed composite was shown when the weight percentage of SiC was 10% wt. of SiC and the weight percentage of MoS 2 is 2% and this result shows a better result of tensile strength than the previous studies by [2]. Al-SiC-MoS 2 hybrid composite shows better enhancement in tensile strength than Al-SiC composite.

Optimization of Process Parameter for Tensile Strength
In the response of hardness variation for data analysis and prediction of optimum results, Taguchi signal-to-noise ratios were used. In this study, the effects of varying control factors (stirring speed, stirring time, wt.% of SiC, and wt.% of MoS 2 ) on the responses of tensile strength were analyzed.
The delta statistics of the S/N ratio for the tensile strength tabulated in Table 8 show the ranks of the factors affecting tensile strength responses based on the S/N ratios. Ranks were then assigned based on their delta value. The delta values were calculated from the difference between the largest and smallest value of the mean values. The analysis showed that wt.% SiC was assigned a rank of 1 with a delta value of 6.90, signifying that it is the predominant factor that affects the tensile strength of the composite. Wt.% MoS 2 , stirring time, and stirring speed were assigned second (1.23), third (0.67), and fourth (0.40) ranks, respectively.  Figure 6 shows the parametric effect S/N ratio plot and the optimal parameter combination for the higher tensile strength. The numerical value of the maximum point in each graph shows the best optimum combination of the factors at that level. Therefore, the S/N ratio plot shows the maximum points in each graph are (A) stirring speed at level 3, (B) stirring time at level 2, (C) SiC at level 3, and (D) MoS 2 at level 2, with the corresponding values of 550 rpm, 10 min, 10%, and 2% (i.e., A3B2C3D2). The optimum prediction condition for the S/N ratio in which the higher tensile strength results when the composite obtained with the term setting of A3B2C3D2 is 201 MPa. Wt.% of SiC has the greatest impact on the tensile strength of the developed composites followed by wt.% of MoS 2 (second), stirring time (third), and stirring speed (fourth).

Analysis of Wear Rate
As shown in the figure, when increasing the weight percentage of SiC, the wear rate of the developed composite decreased. At the higher weight percentage of SiC, the smaller value of wear rate is shown. Silicon carbide has the most significant effect on the wear rate of the developed composites and since SiC is a wear-resistant material, the wear rate of the composite decreases due to the addition of SiC [15]. From Figure 7, the wear rate of the composite decreased with increasing wt.% of SiC and the wear rate values for 0, 5, and 10 wt.% SiC at constant wt.% MoS 2 (0%) are 10, 8, and 4 ×10 −9 kg/m, respectively. This result shows a better result than the previous studies by [17]. prediction condition for the S/N ratio in which the higher tensile strength results when the composite obtained with the term setting of A3B2C3D2 is 201 MPa. Wt.% of SiC has the greatest impact on the tensile strength of the developed composites followed by wt.% of MoS2 (second), stirring time (third), and stirring speed (fourth).

Analysis of Wear Rate
As shown in the figure, when increasing the weight percentage of SiC, the wear rate of the developed composite decreased. At the higher weight percentage of SiC, the smaller value of wear rate is shown. Silicon carbide has the most significant effect on the wear rate of the developed composites and since SiC is a wear-resistant material, the wear rate of the composite decreases due to the addition of SiC [15]. From Figure 7, the wear rate of the composite decreased with increasing wt.% of SiC and the wear rate values for 0, 5, and 10 wt.% SiC at constant wt.% MoS2 (0%) are 10, 8, and 4 ×10 −9 kg/m, respectively. This result shows a better result than the previous studies by [17].

Analysis of Wear Rate
As shown in the figure, when increasing the weight percentage of SiC, the wear rate of the developed composite decreased. At the higher weight percentage of SiC, the smaller value of wear rate is shown. Silicon carbide has the most significant effect on the wear rate of the developed composites and since SiC is a wear-resistant material, the wear rate of the composite decreases due to the addition of SiC [15]. From Figure 7, the wear rate of the composite decreased with increasing wt.% of SiC and the wear rate values for 0, 5, and 10 wt.% SiC at constant wt.% MoS2 (0%) are 10, 8, and 4 ×10 −9 kg/m, respectively. This result shows a better result than the previous studies by [17].  MoS 2 used as solid lubricant and lubrication is one way to prevent wear. It facilitates the wettability between aluminum and silicon carbide due to its low friction properties and robustness to enhance the wear resistance of composite by decreasing the wear rate. The wear rate is smaller when the weight percentage of MoS 2 is increased to Al-SiC composite.
The wear rate of Al matrix decreases as the wt.% MoS 2 increases. The values of wear rate for 0, 2, and 4 wt.% MoS 2 at a constant weight percentage of SiC (0%) are 10, 9.002, and 9 × 10 −9 kg/m, respectively, as shown in Figure 7. The smallest wear rate value of the developed composite was shown when the weight percentage of SiC was 10% wt. of SiC and the weight percentage of MoS 2 was 4%. This shows a better result of wear rate than the previous studies by [2]. Al-SiC composite shows lower enhancement in decreasing wear rate than Al-SiC-MoS 2 hybrid composite.

Optimization of Process Parameter for Wear Rate
The delta statistics of S/N ratio for the wear rate tabulated in Table 9 show the ranks of factors affecting wear rate response based on the S/N ratios. Ranks were then assigned based on their delta value. The delta values were calculated from the difference between the largest and smallest value of the mean values. The analysis showed that the wt.% SiC was assigned a rank of 1 with a delta value of 12.196, signifying that it is the predominant factor that affects the wear rate of the cast composite. The wt.% MoS 2 , stirring time, and stirring speed were assigned second (5.152), third (3.626), and fourth (3.263) ranks, respectively. Table 9. Signal-to-noise ratio for wear rate of composite (smaller is better).

Level
Stirring  Figure 8 shows the parametric effect S/N ratio plot and the optimal parameter combination for the lower wear rate. The numerical value of the maximum point in each graph shows the best optimum combination of the factors at that level. Therefore, the S/N ratio plot shows the maximum point in each graph at (A) stirring speed at level 1, (B) stirring time at level 3, (C) wt.% SiC at level 3, and (D) wt.% MoS 2 at level 3, with the corresponding values of 350 rpm, 15 min, 10% wt.% SiC, and 3% wt.% MoS 2 , respectively (i.e., A1B3C3D3). The optimum prediction condition for the S/N ratio in which the lower wear rate results in the composite is obtained when the term setting of (i.e., A1B3C3D3) is 1. Wt.% of SiC has the greatest impact on the wear resistance of the developed composites followed by wt.% of MoS 2 (second), stirring time (third), and stirring speed (fourth).

Multi-response Optimization
For multi-response optimization purposes, hybrid Taguchi with grey relational analysis (HTGRA) was used for stirring speed, stirring time, wt.% SiC, and wt.% MoS2 process parameters.
Steps taken during optimization using GRA: Step 1: Transformation of data into S/N ratios. The S/N ratio of the experimental results of hardness, tensile strength, and wear rate of the developed composite were generated with the help of Minitab 17 software and are tabulated in Table 10.

Multi-response Optimization
For multi-response optimization purposes, hybrid Taguchi with grey relational analysis (HTGRA) was used for stirring speed, stirring time, wt.% SiC, and wt.% MoS 2 process parameters.
Steps taken during optimization using GRA: Step 1: Transformation of data into S/N ratios.
The S/N ratio of the experimental results of hardness, tensile strength, and wear rate of the developed composite were generated with the help of Minitab 17 software and are tabulated in Table 10. Step 2: Normalization of S/N values. Normalization of S/N values is a generation of grey relational and normalized data sequences for the experimental results within 0 and 1. The equations used were Equation (1) for "larger is better", i.e., for hardness and tensile strength, and Equation (2) for "smaller is better", i.e., for the wear rate S/N ratio response [19].
(For smaller is better, i.e., for wear rate) Equation (1) is used for normalizing the value of S/N ratio for hardness and tensile strength, and Equation (2) (Table 11).
Step 3: Determination of deviation sequence and grey relational coefficient (GRC).
where i = 9 (number of experiments) and j = 3 (number of responses) GRCij = GRC for the ith experiment/trial and jth dependent variable/response Deviation sequence (∆)= (max of normalized values-corresponding normalized value) Yoj = optimum performance value or the ideal normalized value of the jth response Yij = the ith normalized value of the jth response/dependent variable ∆min = smallest value of ∆ and ∆max = highest value of ∆ ∂ is the distinguishing coefficient (0 ≤ ∂ ≤ 1) Equation (3) is used for determining the grey relational coefficient. Minimum (∆min) and maximum deviation sequence (∆max) are used. ∆min for hardness, tensile strength, and wear rate are 0, 0, and 0, respectively. ∆Max for hardness, tensile strength, and wear rate are 1, 1, and 1, respectively (Table 12). Step 4: Calculation of grey relational grade (GRG) and its order of sequencing where m (3 in this case) is the number of responses (hardness, tensile strength, and wear rate) (Table 13) 4.5. Optimization of Process Parameters Using GRA Analysis of S/N Ratio for GRG For the analysis of GRG, the larger is better signal-to-noise ratio has been used. The delta statistics of the S/N ratio for the GRG tabulated in Table 14 show the ranks of the factors affecting the multi-responses based on the S/N ratios. Ranks were then assigned based on their delta value. The delta values were calculated from the difference between the largest and smallest value of the mean values. The analysis showed that wt.% SiC was assigned a rank of 1 with a delta value of 2.354, signifying that it is the predominant factor that affects the GRG response. Wt.% of MoS 2 , stirring speed, and stirring time were assigned second (1.624), third (1.110), and fourth (0.142) ranks, respectively.  Figure 9 shows the parametric effect S/N ratio plot and the optimal parameter combination for the higher GRG responses. The numerical value of the maximum point in each graph shows the optimum combination of the factors at that level. Therefore, the S/N ratio plot shows the maximum point in each graph is (A) stirring speed at level 3, (B) stirring time at level 2, (C) wt.% SiC at level 3, and (D) wt.% MoS 2 at level 2, with the corresponding values of 550 rpm, 10 min, 10 wt.% SiC, and 2 wt.% MoS 2 , respectively (i.e., A3B2C3D2) was selected. The most effective parameter of GRG response is wt.% SiC when compared with other factors and stirring time has the least effect on the GRG. The optimum prediction condition for the S/N ratio in which the higher (multi-response characteristics) GRG result of the composite is obtained with the terms set at (A) stirring speed at level 3, (B) stirring time at level 2, (C) wt.% SiC at level 3, and (D) wt.% MoS 2 at level 2, with the corresponding values of 550 rpm, 10 min, 10 wt.% SiC, and 2 wt.% MoS 2 , respectively (i.e., A3B2C3D2) (i.e., A3B2C3D2) are 0.923. A3B2C3D2) was selected. The most effective parameter of GRG response is wt.% SiC when compared with other factors and stirring time has the least effect on the GRG. The optimum prediction condition for the S/N ratio in which the higher (multi-response characteristics) GRG result of the composite is obtained with the terms set at (A) stirring speed at level 3, (B) stirring time at level 2, (C) wt.% SiC at level 3, and (D) wt.% MoS2 at level 2, with the corresponding values of 550 rpm, 10 min, 10 wt.% SiC, and 2 wt.% MoS2, respectively (i.e., A3B2C3D2) (i.e., A3B2C3D2) are 0.923. Figure 9. Main effect plots for S/N ratio of GRG.

Conclusions
In this experimental study, aluminum-based MMC at varying stirring speeds (350, 450, 550 rpm), stirring time (5, 10, 15 min), weight % of silicon carbide powder (0, 5, 10 wt.%), and weight % of MoS2 powder (0, 2, 4 wt.%) were prepared using stir casting. Microstructure, hardness, tensile strength, and wear behavior of the developed composites were studied. Based on the results, the following conclusions are drawn: The analysis of hardness, tensile strength, and wear resistance were performed with the help of the Taguchi S/N ratio for single response optimization and hybrid Taguchigrey relational analysis for multi-response optimization. Optical micrographs showed homogenous dispersion of particles in the matrix. Porosities were found and it is higher for reinforcement contents are higher.
From the S/N ratio analysis, the addition of SiC in the composite showed better hardness, tensile strength, and wear resistance. Wt.% of SiC is the only and the most significant factor affecting the hardness, tensile strength of the composite, followed by wt.% of MoS2, stirring time, and stirring speed. In the case of wear resistance, only wt.% of SiC and wt.% of MoS2 are the significant factors and wt.% of SiC is the most significant factor affecting the wear rate, followed by wt.% of MoS2, stirring time, and stirring speed. Addition of MoS2 in the composite showed better hardness and tensile strength only up to 2 wt.% of MoS2 and in case of wear rate the addition of MoS2 in the composite up to 4% showed better wear resistance than unreinforced matrix.

Conclusions
In this experimental study, aluminum-based MMC at varying stirring speeds (350, 450, 550 rpm), stirring time (5, 10, 15 min), weight % of silicon carbide powder (0, 5, 10 wt.%), and weight % of MoS 2 powder (0, 2, 4 wt.%) were prepared using stir casting. Microstructure, hardness, tensile strength, and wear behavior of the developed composites were studied. Based on the results, the following conclusions are drawn: The analysis of hardness, tensile strength, and wear resistance were performed with the help of the Taguchi S/N ratio for single response optimization and hybrid Taguchigrey relational analysis for multi-response optimization. Optical micrographs showed homogenous dispersion of particles in the matrix. Porosities were found and it is higher for reinforcement contents are higher.
From the S/N ratio analysis, the addition of SiC in the composite showed better hardness, tensile strength, and wear resistance. Wt.% of SiC is the only and the most significant factor affecting the hardness, tensile strength of the composite, followed by wt.% of MoS 2 , stirring time, and stirring speed. In the case of wear resistance, only wt.% of SiC and wt.% of MoS 2 are the significant factors and wt.% of SiC is the most significant factor affecting the wear rate, followed by wt.% of MoS 2 , stirring time, and stirring speed. Addition of MoS 2 in the composite showed better hardness and tensile strength only up to 2 wt.% of MoS 2 and in case of wear rate the addition of MoS 2 in the composite up to 4% showed better wear resistance than unreinforced matrix. Therefore, the maximum hardness = 208.30 HV has been obtained at stirring speed 450 rpm, stirring time 15 min, 10% wt.% of SiC particles, and 2% wt.% of MoS 2 , maximum tensile strength = 194.43 MPa has been obtained at stirring speed 550 rpm, stirring time 10 min, 10% wt.% of SiC particles and 2% wt.% of MoS 2 and the lowest wear rate = 1 × 10 −9 kg/m has been obtained at stirring speed 350 rpm, stirring time 15 min, 10 wt.% of SiC particles and 4 wt.% of MoS 2 . The optimum prediction condition for the higher hardness, tensile strength, and lowest wear rate has been obtained at A2B3C3D2, A3B2C3D2, and A1B3C3D3, respectively. From the grey relational analysis for multi-response characteristics, the optimum prediction condition for the S/N ratio has been obtained at stirring speed 550 rpm, stirring time 10 min, 10% weight fraction of SiC particles, and 2% weight fraction of MoS 2 (i.e., A3B2C3D2). Al-SiC-MoS 2 hybrid composite shows better enhancements in hardness, ten-sile strength, and wear resistance than the Al-SiC composite. Therefore, the enhancement of wettability has been achieved due to the addition of MoS 2 in the Al-SiC composite.