Optimizing the Mechanical Properties of AS21 Magnesium Alloy/SiC-TiO2 Hybrid Composites by Taguchi Method

Magnesium (Mg) alloys are gaining prominent attention for their wider applications in the aeronautical and automobile sectors due to their remarkable properties. Furthermore, magnesium metal matrix composites are known for their excellent castability, thermal stability, machinability, and damping characteristics [1, 2]. Because of magnesium alloy strong attraction to oxygen, the synthesis and production of Mg metal matrix materials is a major issue for academics and scientists. The casting process has gained popularity for producing Mg alloy composites for structural applications [3, 4]. High grade defense and aerospace components are manufactured using permanent mold and gravity sand casting [5]. Due to its near net form components and cost-effectiveness, stir-casting of magnesium alloy composites in a vacuum aided argon atmosphere is one of the sustainable techniques [6]. The studies [7, 8] researched the Pure Mg, Mg-TiO₂, Mg-TiC and Mg-TiN composite material using DMD process. The results showed that a small addition of (TiO₂/TiC/TiN) reinforcement to the magnesium matrix led to an enhancement in mechanical properties, without influencing the Young's modulus or cytotoxicity, which remained comparable to that of human bone.

The stiffness, strength and hardness of Mg AS21 alloy amalgamated with ceramic reinforcements are improved [9]. The characteristics of the composite are dramatically changed by homogeneous reinforcement dispersion in the Mg matrix [10]. In this context, various ceramic reinforcements such as B₄C, Al₂O₃, TiB₂ and SiC have been identified to enhance the Mg alloys properties [11-13]. A few combinations of reinforcements had a substantial impact on how well composites performed. For instance, Aatthisugan et al. [14] examined the features of B₄C and graphite reinforced into AZ91D Mg hybrid composites and found that increased weight percentages of these materials enhanced both hardness and tensile properties. The authors also noted a significant improvement in wear resistance due to the addition of B₄C-graphite reinforcement. Tensile, compressive and wear behaviour of self-lubricating sintered magnesium-based composites were examined by Narayanasamy and Selvakumar [15]. Their findings revealed that adding Gr and MoS₂ enhanced the composites tensile strength and hardness. Karthick et al. [16] investigated microstructure and mechanical properties of Al₂O₃ and SiC reinforced magnesium metal matrix hybrid composites. They found that incorporating SiC improved the composte tensile strength and hardness.

In recent years, researchers have used Taguchi Design of Experiments (DOE) to pinpoint the important variables affecting the features of metal matrix composites (MMCs). The ideal powder metallurgy and stir casting manufacturing parameters for magnesium composites were investigated and optimized by Rognatha Rao and Srinivas [17] and Balak and Zakeri [18] for magnesium composites, focusing on casting duration, temperature and reinforcement volume. For AA6063/SiC composites, Babu and Jeyapaul [19] improved wear parameters, concluding that the Taguchi approach enhances friction wear behavior under ideal experimental conditions. Elumalai et al. [20] investigated the physical, microstructural and mechanical behavior of titanium dioxide nanoparticulate reinforced magnesium composite. The authors came to the conclusion that hybrid reinforcement reduces the wear rate of AZ91 composite. In order to enhance the characteristics of Mg-TiO₂ composites produced by stir-casting Meenashisundaram et al. [21] investigated and developed the high-performance Mg-TiO₂ nanocomposites targeting for biomedical/structural applications using the ideal parameters by Taguchi method and identified the significant variables such as duration of stirring, speed of stirring and reinforcing percentage for enhancing the properties of composite. Meenashisundaram et al. [22] investigated the effects of primary processing techniques and significance of hall-petch strengthening on the mechanical response of magnesium matrix composites containing TiO₂ nanoparticulates. Because of their high hardness, stiffness, good thermal stability, relatively low density, and capacity to effectively impede dislocation movement within the magnesium matrix, SiC and TiO₂ are often employed as reinforcement materials in magnesium alloys. This results in notable improvements in the wear endurance and material strength of the resulting composite material.

In the previous studies only process variables, such as stirring speed and temperature, were used, and obtained satisfactory results on materials other than the AS21/SiC-TiO₂.

The present study aims to find the optimal stir-casting process variables for AS21/SiC-TiO₂ hybrid composites such as the melt temperature, stirring rate, weight % of SiC and TiO₂ reinforcements at three distinct levels are considered. To achieve the ideal level of the process variables, the impact of these parameters on response features of strength and hardness are examined using Taguchi L27 orthogonal array.

3.1 Hardness variations

Hardness variations for AS21/SiC-TiO₂ composites in different compositions were shown in Figure 3. The hardness of AS21 alloy was measured at the fusion temperature 750℃ and stirring speed 600 rpm.

However, in AS21 base matrices, with 2% SiC added, the hardness increased by 10.67% and 4% SiC added by 18.75%. The incorporation of SiC reinforcement greatly enhances the hardness. The reduction in local deformation in plastic caused by distribution of the Silicon Carbide particles in the notch leads to improved hardness.

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Figure 3. SiC-TiO₂ reinforcement effect for hardness of AS21 Mg composites

3.2 Tensile strength variation

The tensile strength of the AS21 Mg alloy is 141.69MPa. When 1.5% TiO₂ and 2% SiC were added to the AS21 Mg alloy, the tensile strength increased to 159.31 MPa. As the SiC content was raised from 2% to 3%, the tensile strength improved to 170.51MPa. Finally, with an increase in Silicon Carbide from 2% to 4%, the tensile strength reached 199.35 MPa. Figure 4 illustrates the stress-strain curves of the Mg-SiC-TiO₂ metal matrix composite during tensile testing at different intervals.

As a result, the SiC-TiO₂ reinforcement led to a 40.69% raise in the tensile strength of the AS21 alloy. The structure with 4% SiC reinforcement, along with robust interfacial interactions between the reinforcement and the matrix, enhanced the tensile strength of AS21 alloy.

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Figure 4. Stress-strain curves of AS21 Magnesium composites

3.3 Porosity analysis

A magnesium metal matrix composite (MMC) porosity analysis report would detail the evaluation of void content (porosity) in a magnesium-based composite. This typically examining the distribution, size and volume percentage of pores, often using methods like Optical Microscopy and scanning electron microscopy (SEM) and image analysis software to evaluate the effects of manufacturing process parameters on the structural integrity and mechanical characteristics of the composite material. Key aspects to be included in the report include: sample preparation, porosity measurement methods, porosity distribution analysis, identification of potential causes of porosity, and process optimization recommendations to minimize porosity levels within the magnesium MMC.

Figure 5 illustrates the casting time effects on the relative porosity of the composites. As casting temperature raised from 300℃ to 400℃, the porocity ratio of 15% Mg-SiC-TiO₂ composites enhanced by 38.1% at a squeeze pressure of 300 MPa, but reduced by 17.7% at 600 MPa (Figure 5). As casting temperature raised from 400℃ to 500℃, the porosity ratio reduced by 38.7% at 300MPa and decreased by 10.2% at 600MPa. For 30% Mg-SiC-TiO₂ composites, increasing the casting temperature from 300℃ to 400℃ led to a 24.9% increase in porosity ratio at 300MPa, while at 600MPa, the porosity reduced by 1.4% (Figure 5). With a temperature rise from 400℃ to 500℃, the porocity ratio in the 30% composites enhanced by 3.3% at 300MPa and increased by 17.2% at 600MPa. From Figure 5, it is evident that in the 15% Mg-SiC-TiO₂ composites, when the casting time was extended from 30 to 60 minutes, the porosity ratio enhanced by 22.4%.

As the casting time was increased from 60 to 90 minutes, the porosity reduced by 21% at a sqeeze pressure of 300MPa. A 4% decrease in porosity was observed when the casting time was enhanced from 30 to 60 minutes. In the case of 15% Mg-SiC-TiO₂ composites, the porosity decreased by 3.9% at 600MPa squeese pressure when the casting time was extended from 60 to 90 minutes. For the 30% Mg-SiC-TiO₂ composites, a 2.1% reduction in porosity ratio occurred when the casting time was enhanced from 30 to 60 minutes.

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Figure 5. Relative porosity Vs casting temperature and time

3.4 Microstructure

The microscopic images of AS21/SiC-TiO₂ hybrid composites at different process parameter levels is shown in Figure 6. The AS21 Mg hybrid composite (Figure 6 (a)) exhibits homogenous microstructure when operated at 650℃, 400rpm, 2% SiC, and 0.5% TiO₂. With the following process parameters: 750℃, 600rpm, 3% SiC, and 0.5% TiO₂ (Figure 6 (b)) and 750℃, 400rpm, 3% SiC, and 1% TiO₂ (Figure 6 (c)), similar data were reported. The composite that was created using the 750℃, 400rpm, 4% SiC, and 1.5% TiO₂ (Figure 6 (d)), process conditions, however, displayed an irregular grain structure and microscopic blow holes.

The Scanning Electron Microsope image of the AS21-3SiC+1TiO₂ hybrid composites is shown in Figure 7 (a). The microstructure clearly shows a uniform dispersion of the Silicon Carbide and TiO₂ hybrid reinforcement. In contrast, the AS21+4 SiC+1.5 TiO₂ compositions show agglomerated particles of TiO₂ and SiC reinforcements separating AS21 grains in Figure 7 (b). However, particle aggregation was observed in the microstructure of AS21 samples with 4 wt% SiC and 1.5 wt% TiO₂ reinforcements, independent of the melting temperature and stirring speed. Higher stirring speeds were observed to result in bigger grain sizes and a higher volume fraction of composites during the recrystallization process. Moreover, the layered eutectic phases on the surfaces of SiC particles broke into refined particles after casting. The influence of refined precipitates on grain boundaries becomes more significant as the particle volume fraction raises and the size of the precipitated phase reduces. This promotes dynamic recrystallization, which is accompanied by an enhanced nucleation rate and the development of refined grains. The dynamic refined recrystallized grains form as a result of the grain growth of the crystalline precipitated phase. Fabricated SiC particle reinforced magnesium metal matrix composites by the stir casting and found that increasing the deformation pass of multi-directional casting allowed additional time and energy for Initiation and expansion of recrystallized grains. Following the casting process, the creation of various crystal defects, such as dislocations and vacancy clusters, provided potential nucleation sites for dynamic recrystallization. Additionally, grain boundaries can serve as sites for nucleation, contributing to further grain refinement.

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Figure 6. Optical microstructure of AS21 composites at various process parameters

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Figure 7. SEM image of (a) AS21+3 SiC+1 TiO₂ (b) AS21+4 SiC+1.5 TiO₂

3.5 Taguchi method

Taguchi method is an effective tool for developing mechanisms using orthogonal array (OA) tests. It offers a streamlined and efficient method for predicting the optimal settings of process control parameters. A typical Taguchi optimization process for parameter design includes several key phases:

Step 1: Identification of objective and quality characteristics of experiments through pilot study.

Step 2: categorize the most influencing factors, their levels and possible interactions.

Step 3: Selection of suitable orthogonal array (OA) and allocate factors with their levels to OA.

Step 4: Experimental data analysis using S/N ratio and ANOVA analysis.

Step 5: Optimal design parameters verification using confirmation experiments.

Table 3. Taguchi L₂₇ orthogonal array with input and output characteristics

Table 4. Hardness-S/N ratio analysis table

Table 5. Tensile strength-S/N ratio analysis table

In this study, the Taguchi approach is implemented using MINITAB-21 software, focusing on a "greater is better" S/N ratio for optimal parameter prediction. The response characteristics such as tensile strength and hardness based on the L27 orthogonal array are evaluated. The final hardness and tensile strength values were determined by averaging five readings from each experiment.

Table 3 presents the response properties and Signal to Noise ratios for four control factors across three levels, following the L27 orthogonal array.

The response table for hardness for the appropriate control parameters is shown in Tables 4 and 5. The impact of input parameters on composite hardness was in the following order: silicon carbide (Wt%), titanium dioxide (Wt%), melt-temperature(℃), and stirring speed(℃/min). Table 5 display the response table for AS21 composite's tensile strength. Silicon carbide (Wt.%), titanium dioxide (Wt.%), temperature of melt (℃), and speed of stirring (rpm) had greatest effects on tensile strength.

Each control factor levels are assessed using the main effects plot analysis of the Taguchi technique in order to estimate the ideal level of operating parameters for the AS21 hybrid composite's strength and hardness. The primary influence plot of the process parameters for the response characteristics of hardness and tensile strength is shown in Figures 8 and 9 respectively. The S/N ratio analysis revealed that 750℃ of melt temperature and 400rpm of stirring speed with 2 Wt% of SiC and 0.5 Wt% of TiO₂ reinforcements were the best process parameters for AS21-SiC-TiO₂ hybrid composite to achieve exceptional mechanical performance.

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Figure 8. S/N ratio of the control factors taken into consideration and the main effect plot for hardness

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Figure 9. S/N ratio of the control factors taken into consideration and the main effect plot for tensile strength

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Figure 10. Residual plot for hardness of AS21 hybrid composite

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Figure 11. Residual plot for tensile strength of AS21 hybrid composite

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Figure 12. Interaction plot for hardness of AS21 hybrid composite

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Figure 13. Interaction plot for tensile strength of AS21 hybrid composite

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Figure 14. 3D scatter plot of tensile strength VsSiC and TiO₂

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Figure 15. 3D scatter plot of hardness vs SiC and  TiO₂

The residual strength and hardness graphs of the AS21 composite are shown in Figures 10 and 11, which further explain the consistency of the experimental data. Since the results are nearer to the center line, the conclusions are trustworthy. The hybrid reinforcements of SiC and TiO₂ are seen to have the greatest influence when compared to other components in the interaction plot of Figures 12 and 13. The 3D scatter plot of mechanical characteristics in relation to hybrid reinforcement in AS21 alloy is shown in Figures 14 and 15. The combination of 0.5% titanium dioxide and 2% silicon carbide was found to produce the maximum hardness. On the other hand, the mixture of 1% titanium dioxide and 3% silicon carbide showed greater tensile strength.

3.6 Anova results

The relevance of each control factor on various quality indicators is predicted using an ANOVA analysis. The importance of input component on the response properties of hardness is displayed in Table 6. SiC reinforcement has got second place with a contribution of 38.43%, making TiO₂ reinforcement the most important factor in the measurement of hardness. On the tensile strength measurement shown Table 7, SiC reinforcement contributes the most (47.71%), followed by TiO₂ reinforcement (41.27%). SiC reinforcement and TiO₂ reinforcement are the main factors that significantly influence the AS21 hybrid composites mechanical characteristics, it should be emphasized. Additionally, the study reveals that the R² value is greater than 90%, showing that the outcomes are only satisfactory. Therefore, hybrid reinforcement has a clear impact on AS21 hybrid composites mechanical characteristics. The interaction and scatter graphs in Figures 12-15 support the same.

Table 6. Hardness analysis-ANOVA

Table 7. Tensile strength-ANOVA

Multiple linear regression equations are designed to predict the relationship between response characteristics and input factors such as melt temperature (T_m), stirring speed (rpm), SiC reinforcement (Wt.%) and TiO₂ reinforcement (Wt.%) using the MINITAB software. Regression Eqs. (1) and (2) computed for response characteristics are as follows.

$\begin{aligned} \text { Hardness }(\mathrm{HV})= & 77.1+0.0667 T_m-0.0172 S \\ & -7.39 \mathrm{Wt.} \% \text { of } \mathrm{SiC} \\ & -15.44 \mathrm{Wt.} \text { % of } \mathrm{TiO}_2\end{aligned}$            (1)

$\begin{aligned} \text { TensileStrength } & \text { (MPa) } \\ & =281.0+0.0322 T_m-0.0511 \mathrm{~S} \\ & -8.11 \mathrm{Wt} . \% \text { of } \mathrm{SiC} \\ & -25.44 \mathrm{Wt.} \text { of } \mathrm{TiO}_2\end{aligned}$            (2)

3.8 Confirmation test

After determining the best input process parameters, validations experiments were conducted to determine the accuracy of the best combination as well as other combinations which are shown in Table 8. The mechanical parameters of AS21 composite as expected and as measured are shown in Table 9. The Taguchi optimization yields a very low variation with an error of 1.64%, demonstrating strong concurrence.

Table 8. Confirmation experiments

Table 9. Confirmation experiment results