Optimization and Mechanical Characteristics of AA6061/Zirconia Nanocomposites Fabricated by Ultrasonic-Aided Stir Casting Method

Gacem, Amel; Refat, Moamen S.; Ali, H. Elhosiny; Naidu, S. C. V. Ramana Murty; Beenarani, B.; Deole, Pranjali; Kumar, S. Sandeep; Rama, S.; Alsuhaibani, Amnah Mohammed; Diriba, Abdi

doi:https://doi.org/10.1155/2022/2453412

Journal of Nanomaterials

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Abstract Introduction Methods and Materials Results and Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

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Green Nanometal Oxides for Environmental and Biomedical Applications

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Research Article | Open Access

Volume 2022 | Article ID 2453412 | https://doi.org/10.1155/2022/2453412

Optimization and Mechanical Characteristics of AA6061/Zirconia Nanocomposites Fabricated by Ultrasonic-Aided Stir Casting Method

Amel Gacem,¹Moamen S. Refat,²H. Elhosiny Ali,^3,4,5S. C. V. Ramana Murty Naidu,⁶B. Beenarani,⁷Pranjali Deole,⁸S. Sandeep Kumar,⁹S. Rama,¹⁰Amnah Mohammed Alsuhaibani,¹¹and Abdi Diriba¹²

Academic Editor: Arpita Roy

Received08 May 2022

Revised16 Jul 2022

Accepted19 Jul 2022

Published20 Sept 2022

Abstract

Using the Taguchi Grey response surface approach, this study investigates the effect of novel ultrasonic-aided stir casting conditions on the production of AA6061/zirconia nanocomposites. A Taguchi L₁₆ orthogonal array was utilized to conduct the researches, which included ultrasonic power (1.75-2.5 kW), time (5-20 min), temperature (750-900°C), which can cause premature solidification, stir pressure (100-250 MPa), and reinforcement weight percentage (wt% of reinforcement). Ultrasonic-aided stir casting technique has five adjustable parameters (2-5). The ultimate tensile strength, elongation percentage, hardness, and size of the grain material were some of the metrics used to evaluate the process performance. It was decided to employ the response surface approach to model and optimize the numerous replies into one grey relational analysis. AA6061/zirconia nanocomposites were studied using statistical methods such as 3D surface plots and variance analysis. 2.2537 kW, 16.28 min ultrasonic duration, weight % of reinforcement of 1.9, stirring temperature of 700.73°C, and stirring pressure of 142.63 MPa were found to be the best parameter values.

1. Introduction

In a variety of industries, metal matrix composites play an important role due to their light weight, greater durability, and outstanding resistance to wear as well as corrosion [1]. Lightweight aluminum matrix composites (AMCs) are employed in several technical applications because they have tensile strength, less coefficient of thermal expansion [2], increased mechanical and anticorrosion qualities, and a less coefficient of thermal extension [3, 4]. In contrast, the mechanical characteristics of aluminum matrix composites are strongly reliant on the kind, quantity, and nature of the strengthening properties contained in its matrix [5]. AA6061 is a heating-resistant metal of aluminum and Cu that is a common alloying element [6]. Weight, strength, and corrosion resistance (and high machinability) make them ideal for use in aerospace or automobile engineering. A chemically inert ceramic with outstanding thermomechanical characteristics, zirconia (ZrO₂) is a versatile material [7]. For example, zirconia’s density is 3.95 grams per cubic centimeter, its melting point is 2056°C, and its thermal expansion coefficient is 7.4 μm/°C. Zirconia reinforcements have been found to greatly upgrade the mechanical characteristics of aluminum alloys in previous studies [8, 9]. Furthermore, nanosized zirconia elements have a significant impact on the mechanics and thermomechanic characteristics of nanostructured owing to their high surface-to-volume proportion [10]. MMCs with near net forms can be produced using the liquid metallurgical process known as stir casting [11]. Since it eliminates porosity and improves surface texture and mechanical qualities while also utilizing a finer microstructure than conventional casting, stir casting is a preferable method [12]. While traditional stir casting may be used to introduce nanoparticles [13] into the metal matrix, it is quite difficult to do so. Excessive churning will result in the matrix being oxidized and clumping together [14]. In these circumstances, ultrasonic therapy (UST) can be employed to efficiently distribute nanosized reinforcements [15]. Grains are refined and degassed using UST in the composite production sectors. When high-intensity ultrasonic waves are used during ultrasonic therapy, the melt is subjected to intense cavitation and acoustic streaming. Because of this, ultrasonic therapy breaks apart nanoparticulate groups and evenly disperses the particles that make up the material melt [16]. It is therefore ineffective to just employ the use of ultrasonic therapy in the production of nanocomposites. To improve nanocomposites’ microstructure and mechanical characteristics, the ultrasonic therapy stir casting process is a common production technique [17]. Many recent studies have shown that adjusting the casting process parameters utilizing various optimization tools may improve the mechanical and microstructural characteristics of composites [18]. It was found that 106 MPa, 200°C, and 30 s are the optimal values for stir casting pressure, die heating temperature, and stir duration for the stir casting method of LM₂₄ alloy to increase the UTS and stiffness. Wetting of dispersion issues was resolved by treating AA6061/1 wt% ZrO₂ nanocomposites with ultrasonic treatment, which caused in a rise in ultimate tensile strength yield of 37% and 81%, correspondingly, as equated to the base alloy [19]. It was found that ultrasonic processing with a 1400 W/cm^-2 power level and a 5% solute concentration resulted in the best grain refinement in a study by [20]. With the help of T-GRA and ANOVA, [21] investigated whether process parameters (UTS, stiffness, and % of extension) affected the microstructure and mechanical characteristics of AA6061-ZrO₂ compounds (ANOVA). The optimal values were discovered to be a stirring pressure of 128 MPa, a molten temperature of 848°C, and a weight percentage of SiC [22]. Improve multiple responses processes with the use of the RSM and examination of the desirability approach, two commonly used methodologies. It is possible to determine the best input conditions using these methods. An ANOVA, RSM, and a desirability function-based technique was utilized by [23] to identify the best combinations of manufacturing parameter A413 alloy, which is made by stir casting. It was found that the physical and microstructural characteristics of LM₁₃ alloy were affected by molten metal and die temperatures, as well as stir pressure. Higher stir pressure decreased grain size and enhanced hardness and density, according to the data [24]. However, hardness declined with increasing melt and die temperatures. Microstructure and mechanical characteristics of 6061 alloy AA were examined by [25]. Assuming 700°C and 140 MPa, the average grain size was 80 nm. Pouring temperature had no effect when the stir pressure was more than 70 MPa. To improve the wear characteristics of AA7150-hBN composites, [26] utilize Taguchi L₂₅ orthogonal array-ANOVA. Taguchi’s L₁₆ orthogonal array, stirring pressure, pressure holding duration, and die preheating temperature all had a significant impact on composite reactions including stiffness and high strengthening, according to [27, 28] employing an artificial neural network prediction model to investigate how the settings used in stir casting affected the solidification time. It was shown that both the mold and pouring temperatures had significant effects on casting quality and freeze time. For AA2024-SiC nanocomposites, [29] established remarkably low error % artificial neural network models to forecast the density and hardness. Stir casting processing parameters include pouring temperature, pressuring duration, and die temperature which were examined in depth by [30]. Nanocomposites using traditional stir-stir casting have had difficulty achieving acceptable microstructural and mechanical qualities, as has been widely documented in previous investigations [31]. These obstacles can be solved by employing a technology known as ultrasonication-stir linked stir casting, which has proven effective in the last several years. No one has employed an optimal combination of ultrasonic therapy stir casting parameters, and the literature study clearly shows that there is a considerable need for improving these parameters to fabricate aluminum nanocomposites. According to [32], L₁₆ orthogonal array 16 distinct AA6061/zirconia nanocomposites were made utilizing the UST-stir linked stir casting process. There were five variables that could be tweaked in the ultrasonic-aided stir casting process: power (1.75-2.5 kW), duration (5-20 min), pour temperature (750-900°C), stir pressure (100-250 MPa), and wt% ZrO₂ (2-5%). The results were analyzed using these parameters. We selected hardness, elongation percentage, and particle size as the replies. The TGRSM approach was utilized to invent the finest mixture of parameters [33–35]. The best condition was verified by conducting a desire analysis and a confirmation experiment.

2. Methods and Materials

2.1. Matrix and Reinforcement

It was decided to use zirconia alloy 6061 as the matrix composite in this study. The nanopowder of zirconia (ZrO₂) was used as reinforcement. Table 1 lists the characteristics of ZrO₂.

2.2. Selection of Process Parameters and Responses

For high-quality castings, it is vital to pick the right casting process parameters and operating levels. Selecting control parameters with an inadequately big or narrow operating range, you may end up with a flawed or incomplete solution. Nanoparticle aggregation and matrix material oxidation would occur if the ultrasonic therapy power and the ultrasonic therapy duration were increased [34]. Lower ultrasonic therapy power and duration, on the other hand, are insufficient for dissolving nanoparticle clusters and removing dendrites from microstructures. Due to a delayed cooling rate, dendritic structures emerge when the melt is poured at a higher temperature. Premature solidification might occur if the pouring temperature is too low and the processing parameters are shown in Table 2.

2.3. AA6061/Zirconia Nanocomposite Fabrication

Initial melting was done in mild steel employing an electric resistance furnace with highest heating system of 1200°C. A K-type thermocouple has been used to show the temperature of the furnace and the melt. We used an inert gas shield to safeguard the melting process. Zirconia nanoparticles were also dried in a muffle furnace by heating them to 300°C for an hour. The composites were manually stirred for two minutes at 500 revolutions per minute utilizing a graphene layered stainless steel stirrer when it reached the appropriate melting temperature. Zirconia nanopowder was added to the melt and agitated for another 5 minutes before being removed [35]. A titanium alloy ultrasonic probe was used to disseminate ultrasonic waves into the slurry after adequate churning. The sonotrode was submerged in the melting space to a deepness of 300 mm during the ultrasonication operation. Power was raised to 2.5 kilowatt and the frequency was set at 20 kHz. Warm steel mold dies (300 mm high by 50 mm wide) were used to bottom-pour the melt before it could harden. It was critical that all of the experiments be conducted with a 1-minute stir period. Accordingly, the process parameters for producing the 16 distinct AA6061/zirconia nanocomposites were adjusted according to the Taguchi L₁₆ orthogonal array. A high-precision weighing scale (accurate to 0.00001 g) was used to measure the porosity of the manufactured materials.

2.4. Heat Treatment of Nanocomposites

Precipitation hardening under a T-6 tempering condition hardened the nanocomposites. In solution treatment, the nanocomposites were maintained at 510°C for two hours before being immediately quenched in water. In addition, the aging process was carried out for 14 hours at 165°C. Heat treatment was done in an argon endangered atmosphere employing a high-temperature muffle boiler. In a double disc-polishing machine, abrasive polishing sheets of 400, 600, 1200, and 2000 grit were used to make microstructural study specimens. Particle size was determined using the ASTM E 112-96 linear intercept method.

2.5. Hardness Test

Nanocomposites were tested for microhardness (VHN) using a Vickers hardness tester (Bluestar Vickers hardness tester). Indentation testing was performed on the polished specimens using a 10 kg load as well as a dwell duration of 15 seconds.

2.6. Tensile Test

Tensile tests were conducted to determine how casting factors affected the nanocomposites’ ultimate tensile strength and elongation %. Figure 1 indicates the specimen schematic of tensile tests done on an Instron tensile testing equipment with a strain rate of 1 mm/min. A gauge span of 251 mm was used for the tensile test, which was completed successfully. Hardness and tensile testing were performed on samples.

3. Results and Discussion

3.1. Intermetallic Phase Analysis of Nanocomposites

The XRD patterns of AA6061 and AA6061/2 weight % Zr nanocomposite are shown in Figures 2(a) and 2(b), correspondingly. It can be shown in Figure 2(a) that the natural intermetallic phase generated by Cu and Al atoms reacting in the 6061-aluminum alloy corresponds to the peaks associated with both the phase and the intermetallic CuA_l2 phase. It may be seen in Figure 2(b).

(a)

(b)

3.2. Process Optimization Using TGRSM

Taguchi L₁₆ orthogonal array was employed to done the testing, with two replications for each trial. Results for each stage of the experiment are summarized in Tables 3 and 4. First, the response values were functional to Grey relational analysis and its GRA output was employed as an input for response surface methodology displaying and optimizing. Every set of normalized sound to noise ratio values of replies is used to determine the GRC values. Grey relational grade was created by averaging the GRC values of different GRCs into a single quality index. All responses and their accompanying grey relational grade are shown in Table 5. When grey relational grade reaches its maximum value on the sixteenth trial, it is close to the optimal parameter level. Variations in grey relational grade values, as seen in Figure 3, illustrate that parameter levels selected have a significant influence on the grey relational grade. RSM was used to analyze the grey relational grade values obtained by RSM. Minitab software was used to determine the model coefficients for the second-order polynomial equation that expresses the impact of factors on grey relational grade.

3.3. ANOVA on Grey Relational Grade

The ANOVA was chosen to investigate the most critical factors that contribute to the generation of grey relational grade values. Table 6 indicates the ANOVA outcomes for grey relational grade. While the “ value” showed the importance of the model terms, the “ value” showed the model’s significance. We identified all of the factors and interactions that were examined in the model (A/B/C/D/E) as the most important variables. Table 7 shows that the polynomial model’s -squared value was quite near to unity. Using this data, the model is able to forecast future results based on the results of actual experiments.

Figure 4(a) demonstrates the model’s suitability. Polynomial model predictions of grey relational grade agree with those obtained under experimental circumstances; it is discovered. The adjusted -squared value was quite close to the projected -squared value. To put this into perspective, the model has an adequate precision of 30.016, as indicated in the tables. Figure 4(b) displays that the studentized residuals follow the normal distribution, as may be demonstrated. This suggests that polynomial models can be used to predict GRG values in the experimental area with reasonable accuracy and precision.

(a)

(b)

3.4. Grey Relational Grade Surface Plots in 3D

3D surface plots, as illustrated in Figures 5(a)–5(c), were produced to study the effects of processing factors on grey relational grade. The grey relational grade value is strongly influenced by the ultrasonic therapy power and pouring temperature, as illustrated in Figure 5(a). It has been found that grey relational grade values increase when ultrasonic therapy power increases. Respondent values increased as ultrasonic therapy power increased owing to particle dispersal, grain enhancement, and varied nucleation. Pouring temperatures increase the grey relational grade value. Because the freezing period was prolonged and the number of secondary dendrites increased, there were more secondary dendrite forms. At lower ultrasonic therapy power levels, pouring temperature had little effect. UST time, melt pouring temperature, and other variables, including ultrasonic therapy power (2250 watts), stir pressure (200 MPa), and weight % of reinforcement, are depicted in Figure 5(b) as a function of grey relational grade. The grey relational grade value rises to 0.678 at minimum pouring temperatures and lengthier ultrasonic therapy intervals. Figure 5(b) displays a strong link among the ultrasonic therapy time as well as the pouring temperature. The grey relational grade value fell as the pouring temperature was increased to 900°C. It was found that the grey relational grade was highest when the ultrasonic therapy was longer and the pouring temperature was lower. The best strategy to ensure that the product is safe to consume is to increase the ultrasonic therapy duration and lower the pouring temperature as much as feasible. A longer ultrasonic therapy time will reduce the chance of nanoparticle agglomeration. Pouring at a lower temperature speeds up the cooling process, which improves the qualities of the finished product.

(a)

(b)

(c)

Figure 5(c) shows the interaction between stirring pressure and weight percentage. When the ultrasonic therapy power, the ultrasonic therapy duration, and the temperature are all persistent, the grey relational grade values are projected over the assortment of stir pressure and particles weight. According to this diagram, a high grey relational grade is produced when the wt% of particles is 6% and the stirring pressure is 250 MPa. Grain refining, elimination of porosity melt, and dendritic fragmentation all contribute to a rise in the grey relational grade value as the stir pressure increases. At first, an increase in nanoparticle weight % increased the multiperformance of AA6061/zirconia nanocomposites cast by ultrasonic-aided stir casting. Reinforcement with a higher wt% results in an increased grey relational grade; this indicates, therefore, an ideal 3 wt% ZrO₂ concentration for enhanced mechanical properties.

3.5. Influence of Factors on the Response

By means of main effect plots, it is possible to demonstrate the influence of numerous factors on the results. Plots of the principal effects for a range of responses are shown in Figures 6(a)–6(d). On the graphs, it appears that ultrasonic therapy power (A) is the most important factor in determining the correct responses. In addition to improving hardness, ultimate tensile strength, and % of elongation, increasing the UST power also reduced grain size. Intense ultrasonication at a greater ultrasonic therapy power helped disperse the nanoparticles evenly throughout the matrix. At this moment, the nanoparticles were strapped into the intergranular regions of the previously produced grains. Due to the grain’s inability to spread any more as a result of this technique, an even finer grain structure was created.

(a)

(b)

(c)

(d)

With regard to ultrasonic therapy time (B), it is clear that an increase in this value enhances the replies up to a certain point in time. The ultimate tensile strength, % elongation, and particle size all improved pointedly after a 15-minute UST time. The nanoparticles were evenly disseminated throughout the matrix because of the ultrasonication procedure. As the ultrasonic therapy time increased to 20 min, the hardness, ultimate tensile strength, and elongational percentage all dropped, but the grain size improved. Due to nanoparticle aggregation, the maximum UST duration had less of an impact on the reactions of the subjects. Because of this, it is possible that an optimal ultrasonic therapy duration at midvalues is all that is needed to distribute nanoparticles effectively.

Plots of the main impacts show how answers vary depending on the weighted percentage of reinforcement used (E). Up to 3% of nanoparticles in the solution increased the reactivity. This was possible because of the uniform dispersion of nanoparticles throughout the system. Amounts of nanoparticles in excess of 3 wt% reduced the responses. It is common for nanoparticles to agglomerate in the matrix at higher percentages of the total weight. In addition to acting as stress concentration locations, the agglomerated particles also reduced mechanical responses. As a result, increasing the weight % of reinforcement reduced ultimate tensile strength and % of elongation. Table 8 shows the confirmation trials results.

Stress and strain graphs of materials manufactured under different processing conditions are shown in Figure 7. Increased ultrasonic therapy power was clearly associated with an increase in strain rate. Using high-powered sonication, a uniform dispersion of nanoparticles and fine grain refinement is achieved. Increases in nanoparticle weight % diminish elongation at various ultrasonic therapy powers, according to stress-strain curves. For example, at increased ultrasonic therapy power, the grain fineness was greater and the dispersion of nanoparticles was more homogenous at 1.5 and 6 wt%. Consequently, the ductility improved greatly. In contrast, low-power sonication was unable to achieve uniform dispersion at high levels of reinforcement. To compensate for the loss of ductility, the material was coated with nanoparticles.

4. Conclusion

TGRSM was utilized to optimize ultrasonic therapy power, duration, pouring temperature, and the stir pressure. The investigation’s findings are as follows. (i)This study demonstrated that grain refinement, homogeneous dispersion, and nanoparticle clustering were all impacted by distinct processing factors. Zirconia particles were shown to be nonreactive with AA6061 according to XRD measurements(ii)Multiobjective problems were transformed into similar single-objective problems using GRA. Using a model with a coefficient of determination of 0.9886, we were able to accurately predict the experimental response values. In the sixteenth trial, A₄B₄C₁D₃E₂ produced the greatest GRG value(iii)As a result of these and other factors, the final results were significantly affected by the UST power and time as well as the temperature, stir pressure, and the weight percentage of reinforcement. It was found that AC, BC, and DE had significant ANOVA interactions(iv)Response surface plots were used to describe the impacts of factors on grey relational grade, and the role of specific parameters was explored. Grain size dropped as UST power rose, yet hardness, ultimate tensile strength, and elongation % increased. Higher pouring temperatures resulted in a decreased solidification cooling rate, which resulted in better composite manufacturing outputs. At a stir pressure of 142 MPa, better responses were obtained and additional increases in pressure had very minimal effects on responses(v)Using the TGRSM method, the following was found to be the best set of parameters: pouring temperature was 700.73°C, stir pressure was 142.63, and reinforcement was 1.9 wt%. Hardness of 151.62 VHN, ultimate tensile strength of 346.89 MPa, elongational % of 10.82, and particle size of 48.73 micrometer were found to be the optimal response parameters. Grey relational grade improved by a factor of 0.203 in the confirmation test

Data Availability

The data used to support the findings of this study are included within the article. Further data or information is available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Acknowledgments

The authors extend their appreciation to the Research Center for Advanced Materials Science, King Khalid University, Saudi Arabia, for funding this work under grant number KKU/RCAMS/G011-21. Taif University Researchers Supporting Project number (TURSP-2020/01), Taif University, Taif, Saudi Arabia.

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Copyright © 2022 Amel Gacem et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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