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Synthesis and Experimental Investigations of Tribological and Corrosion Performance of AZ61 Magnesium Alloy Hybrid Composites

Synthesis and Experimental Investigations of Tribological and Corrosion Performance of AZ61... Hindawi Journal of Nanomaterials Volume 2022, Article ID 6012518, 12 pages https://doi.org/10.1155/2022/6012518 Research Article Synthesis and Experimental Investigations of Tribological and Corrosion Performance of AZ61 Magnesium Alloy Hybrid Composites 1 2 3 4 5 R. Venkatesh, C. Ramesh Kannan, S. Manivannan, M. Vivekanandan, J. Phani Krishna, 6 7 8 Amine Mezni, Saiful Islam, and S. Rajkumar Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Chennai, 602105 Tamil Nadu, India Department of Mechanical Engineering, Dr. Navalar Nedunchezhiyan College of Engineering Tholudur, 606303, India Center for Material Science, Department of Mechanical Engineering, Karpagam Academy of Higher Education Coimbatore, 642021, India Department of Mechanical Engineering, National Engineering College, Kovilpatti, 628503 Tamil Nadu, India Design Engineering, Powder Handling Solutions, RIECO Industries Ltd., Pune 411005, India Department of Chemistry, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Civil Engineering Department, College of Engineering, King Khalid University, Abha 61413, Saudi Arabia Department of Mechanical Engineering, Faculty of Manufacturing, Institute of Technology, Hawassa University, Hawassa, Ethiopia Correspondence should be addressed to S. Rajkumar; rajkumar@hu.edu.et Received 1 March 2022; Revised 12 April 2022; Accepted 15 April 2022; Published 2 May 2022 Academic Editor: Hiwa M. Ahmed Copyright © 2022 R. Venkatesh 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. Magnesium alloy is the light weight material compared to aluminium alloy, and it possesses high strength; these alloys are used to manufacturing of vehicle parts. Magnesium alloy has extreme mechanical and thermal properties, and it is applied to aerospace applications. This study planned to improve the tribological and corrosion resistance of AZ61 magnesium alloy with reinforcement of boron carbide (B C) and zirconium dioxide (ZrO2). Magnesium alloy hybrid composites are fabricated through stir casting process. Tribological and corrosion performance are analyzed through Taguchi L27 Orthogonal Array. In the tribological analysis, four parameters are involved such as % of reinforcement (4%, 8%, and 12%), disc speed (1 m/s, 2 m/s, and 3 m/s), normal load (30 N, 40 N, and 50 N), and sliding distance (1300 m, 1500 m, and 1700 m). Similarly, in salt spry corrosion analysis, four parameters are influenced used such as % of reinforcement (4%, 8%, and 12%), pH (7, 8, and 9), ° ° ° temperature (30 C, 35 C, and 40 C), and hanging time (30 hrs, 40 hrs, and 50 hrs). From this analysis, percentage of reinforcement is highly influenced in wear test, and in corrosion test, temperature is extremely influenced. 1. Introduction [2]. Magnesium possesses low density, and particular prop- erties are lifting the consuming level of magnesium. For Normally, the composite materials are offered excellent recent trends, the magnesium and its alloys are used in the high end applications. Magnesium is having some good mechanical properties and thermal properties. In composite preparation, the matrix material and reinforced particle quality characters, namely, excellent manufacturability, easy to fusion, and higher machinability [3–5]. All these points selections are highly toughest one due to obtain of the are considered to selection of materials with omitting of alu- desired properties [1]. Naturally, the pure magnesium is the light weight material compared to aluminum; it has minium and such other materials. In machinability point of view, the magnesium has 50% higher than aluminium and 33% lighter in similar manner 75% lighter than steel material 2 Journal of Nanomaterials possesses more energy [6–8]. In automobile sectors, magne- sium is one of the substitute materials for aluminium for making of steering shaft, pistons, and brake components [9]. In major consideration in the engine design, the magne- sium reduces the weight of the engine compared to cast iron [10–12]. It is a suitable one for replacing of cast iron engine and improving the vehicle mileage as well as reducing the fuel consumption. In the magnesium material, the wear occurrence takes place in moderate range but addition of reinforced particles is influenced to reduce the wear rate of the magnesium [13–15]. Hybrid composites generally pos- sess high wear resistance as well as corrosion resistance even influencing of high performance parameters [16]. Taguchi optimization is one of the statistical tools to improve the mechanical properties, increasing the corrosion resistance, reduced the wear rate by the way of parameter optimization [17–20]. The main objective of this experimental work is focusing on to prepare the magnesium hybrid composites Figure 1: Dry sliding wear test apparatus. with influencing of boron carbide and zirconium oxide- reinforced particles. Further study of wear and corrosion rate of the fabricated composites is conducted through Taguchi tool. 2. Materials and Methods In this experimental investigation, 2 kg of AZ61 magnesium alloy is purchased from the Andavan Arul Alloys, Chennai. Reinforced particles of boron carbide and zirconium dioxide are procured from Ashoka Marketing Agencies, Chennai, by each 500 g. Stir casting methodology is considered for this investigation to make a high strength magnesium alloy hybrid composites [21]. Bottom pouring stir casting equip- ment is used for this experimental work. Agitation process is controlled by electric motor in the stir casting process. (a) 3. Experimental Work Stir casting process is one of the economical ways to produce the hybrid composites within a short period. Initially, the reinforced particles are preheated in the crucible; different weight percentages (4%, 8%, and 12%) of boron carbide and zirconium oxide are preheated at 450 C for 5 hours in the crucible [22]. The preheating process remove the impu- rities present in the reinforced particles. Pure magnesium alloy is heated by applying of 800 C in the bottom pouring furnace; further, the preheated reinforce particles are mixed to the pure magnesium alloy. Mixing of preheated reinforced (b) particles and the pure magnesium alloy are mixed well by using stirring action, the stirring process is controlled by Figure 2: (a) Image of wear test specimens. (b) Image of salt spray test specimens. electric controller in the motor [23]. Stirring speed is main- tained as 400 rpm with 1 hour. After stirring action, the mol- standard under the dimensions of 12 mm diameter and ten material is pouring into the die and allowed to cool; 30 mm length. Initially, the disc and specimens are cleaned further, the cooled material is separated from the die. The well by using, and then, each sample is weighted initially required dimensions of wear and corrosion samples are with the help of digital balance [24]. EN 32 steel disc with sliced out from the casted magnesium hybrid composites. 65 HRC is used for conducting of dry sliding wear test; dif- ferent process parameters are involved to conduct the wear 3.1. Wear Test. Wear analysis is conducted through test such as % of reinforcement, disc speed, normal load, DUCOM model dry sliding wear test apparatus as shown and siding distance. in Figure 1. Specimens are prepared as per the ASTM G99 Journal of Nanomaterials 3 Table 1: Wear test process parameters and their levels. S. no. Parameters Level 1 Level 2 Level 3 1 % of reinforcement 4 8 12 2 Disc speed (m/s) 1 2 3 3 Normal load (N) 20 30 40 4 Sliding distance (m) 1300 1500 1700 Table 2: Response table for means (wear rate). Exp. runs % of reinforcement Disc speed (m/s) Normal load (N) Sliding distance (m) Wear rate (mm /m) 1 4 1 20 1300 0.0038 2 4 1 20 1300 0.046 3 4 1 20 1300 0.0034 4 4 2 30 1500 0.0341 5 4 2 30 1500 0.0117 6 4 2 30 1500 0.034 7 4 3 40 1700 0.0062 8 4 3 40 1700 0.027 9 4 3 40 1700 0.0071 10 8 1 30 1700 0.0023 11 8 1 30 1700 0.051 12 8 1 30 1700 0.0086 13 8 2 40 1300 0.0073 14 8 2 40 1300 0.0128 15 8 2 40 1300 0.0148 16 8 3 20 1500 0.0039 17 8 3 20 1500 0.0048 18 8 3 20 1500 0.0156 19 12 1 40 1500 0.0028 20 12 1 40 1500 0.0126 21 12 1 40 1500 0.0018 22 12 2 20 1700 0.0107 23 12 2 20 1700 0.0061 24 12 2 20 1700 0.0037 25 12 3 30 1300 0.0027 26 12 3 30 1300 0.0147 27 12 3 30 1300 0.0025 The sample is placed vertically against to rotating disc; for evaluating the difference of mass loss and also find the time taken to conduct wear test per sample is 20 min. corrosion rate. Figure 2(b) shows the salt spray test speci- Finally, after conducting the wear test, the samples are mens; initially, the specimens are loaded in the salt spray weighted to calculate the mass loss. Figure 2(a) illustrates chamber such as all the specimens are hung in the chamber the 27 numbers of wear test specimens. [25]. The salt spray model is CARELAB with the dimensions of 650 mm × 450 mm × 4010 mm. After loading the samples, 3.2. Salt Spray Test. Salt spray test is one of the faster the chamber cabin door is closed; using an atomizer, the 5% methods to analyze the corrosion resistance of the materials. of NaCl solution is continually sprayed on the specimen’s surfaces [26]. The salt spray is achieved by using of pump In this work, initially, the samples are prepared as per the ASM standard ax (ASTM B117) with the dimensions of circulation with constant flow rate. After reaching the spec- 30 mm diameter and 10 mm thickness. Samples are cleaned ified time period, the specimens are taken out from the well and weighted; the initial weights are noted carefully chamber and cleaned well with using of running water; 4 Journal of Nanomaterials Table 3: Response table for signal to noise ratios (wear rate); smaller is better. Level % of reinforcement Disc speed (m/s) Normal load (N) Sliding distance (m) 1 0.019267 0.014726 0.010683 0.012052 2 0.013246 0.015028 0.018009 0.013282 3 0.006473 0.009233 0.010294 0.013652 Delta 0.012794 0.005795 0.007715 0.001601 Rank 1 3 2 4 Main effects plot for means data means Disc speed (m/s) % of reinforcement 0.020 0.015 0.010 0.005 4 812 1 2 3 Sliding distance (m) Normal load (N) 0.020 0.015 0.010 0.005 20 30 40 1300 1500 1700 Figure 3: Main effects plot for mean (wear rate). further, the specimens are dried. All specimens are weighed Table 2 and Table 3 presented the response tables of and calculate the difference of mass loss [27]. wear rate; these tables presented the higher influence param- Table 1 presented the wear test parameters and their eter in priority order. Higher priority was illustrated by the levels; in wear test, four parameters such as % of reinforce- delta and rank order; the percentage of reinforcement was ment, disc speed, normal load, and sliding distance were higher priority in the wear test. Normal load was the second selected. All four parameters have three levels ton satisfying priority, disc speed was the third priority, and sliding dis- L27 OA. The wear rate is obtained with tance was fourth priority. Optimal parameters of the wear rate was found as 12% of reinforcement, 3 m/s of disc speed, W1 − W2 40 N of normal load, and 1500 m of sliding distance. Wearrate WI = ðÞ : ð1Þ Figures 3 and 4 represented the main effects plot for Time × Density means and S/N ratios of the wear rate; increasing of rein- forcement percentage decreases the wear rate. Maximum 4. Results and Discussion reinforcement 12% offered minimum wear rate; further 4.1. Wear Analysis. Influencing of four parameters and three increasing of disc speed also reduces the wear rate. Maxi- mum level 3 m/s of disc speed offered minimum wear rate. levels was extremely influenced to estimate the wear rate of specimens. Table 2 presented the wear input parameters Influencing of normal load such as 20 N to 30 N wear rates and the response of wear rate. From this wear test analysis, was increased; further, 30 N to 40 N of normal load, the wear rate was decreased. Finally, the 40 N of normal load recorded the minimum wear rate was recorded as 0.0018 mm /m. The minimum wear rates attained by the influence of minimum wear rate. Consideration of sliding distance, 1500 m of sliding distance offered minimum wear rate; fur- parameters were 12% of reinforcement, 1 m/s of disc speed, ther increasing of 1500 m and 1700 m of sliding distance, 40 N of normal load, and 1500 m of sliding distance. On the wear rate was increased. Figure 5 highlights the residual the contrary, the maximum wear rate was occurred as plot for the wear rate. 0.051 mm /m by influencing of 8% of reinforcement, 1 m/s of disc speed, 30 N of normal load, and 1700 m of sliding Normal probability plot represents that all the points were lying on the mean line; it tell about the selected distance. Mean of means Journal of Nanomaterials 5 Main effects plot for SN ratios data means Disc speed (m/s) % of reinforcement 4 8 12 1 2 3 Sliding distance (m) Normal load (N) 20 30 40 1300 1500 1700 Signal-so-noise: smaller is better Figure 4: Main effects plot for S/N ratios (wear rate). Residual plots for wear rate (mm /m) Normal probability plot Versus fits 0.04 0.02 0.00 –0.02 –0.02 0.00 0.02 0.04 0.005 0.010 0.015 0.020 Residual Fitted value Histogram Versus order 0.04 0.02 0.00 –0.02 2 4 6 8 10 12 14 16 18 20 22 24 26 –0.012 0.000 0.012 0.024 0.036 Residual Observation order Figure 5: Residual plots for wear rate. 40 N of normal load produced minimum wear rate. parameters, and the levels are good one. In versus fits plot, all the points were distributed uniformly, and within the Figure 6(c) presents the connection between normal load limits, similar trends were observed in the versus order plot. and sliding distance. Minimum wear rate was registered All these points demonstrate that chosen model is appropri- by 40 N of normal load and 1500 m sliding distance. ate one. In histogram plot, all the rectangles were skewed in Figure 6(d) shows the minimum wear rate by 20 N of nor- mal load and 12% of reinforcement. normal position. Figure 6 demonstrates the parallel set plot for wear rate; Figure 6(a) shows the correlation between % of reinforcement and disc speed. From this parameter con- 4.2. Corrosion Analysis. Table 4 illustrates the corrosion nection, the minimum wear rate was recorded by 12% of input parameters and the response of corrosion rate. From reinforcement and 1 m/s of disc speed. this corrosion test analysis, the minimum corrosion rate Figure 6(b) illustrates the relations between disc speed was registered as 0.112 mm/year. The minimum corrosion and normal load. In this analysis, 1 m/s of disc speed and rates obtained by the influence of parameters were 8% of Frequency Percent Mean of SN ratios Residual Residual 6 Journal of Nanomaterials 0.0038 0.046 0.0034 0.0341 0.0117 0.034 0.0062 0.027 0.0071 0.0023 0.051 0.0086 0.0073 8 2 0.0128 0.0148 0.0039 0.0048 0.0156 0.0028 0.0126 0.0018 0.0107 0.0061 0.0037 0.0027 0.0147 0.0025 % of reinforcement Disc speed (m/s) Wear rate (mm /m) (a) 0.0038 0.046 0.0034 0.0341 1 20 0.0117 0.034 0.0062 0.027 0.0071 0.0023 0.051 0.0086 0.0073 2 30 0.0128 0.0148 0.0039 0.0048 0.0156 0.0028 0.0126 0.0018 0.0107 0.0061 3 40 0.0037 0.0027 0.0147 0.0025 Disc speed (m/s) Normal load (N) 3 Wear rate (mm /m) (b) Figure 6: Continued. Journal of Nanomaterials 7 0.0038 0.046 0.0034 0.0341 20 1300 0.0117 0.034 0.0062 0.027 0.0071 0.0023 0.051 0.0086 0.0073 30 1500 0.0128 0.0148 0.0039 0.0048 0.0156 0.0028 0.0126 0.0018 0.0107 0.0061 40 1700 0.0037 0.0027 0.0147 0.0025 Sliding distance (m) Normal load (N) 3 Wear rate (mm /m) (c) 0.0038 0.046 0.0034 0.0341 20 4 0.0117 0.034 0.0062 0.027 0.0071 0.0023 0.051 0.0086 0.0073 0.0128 30 8 0.0148 0.0039 0.0048 0.0156 0.0028 0.0126 0.0018 0.0107 40 12 0.0061 0.0037 0.0027 0.0147 0.0025 Normal load (N) % of reinforcement 3 Wear rate (mm /m) (d) Figure 6: Parallel set plot: (a) % of reinforcement vs. disc speed; (b) disc speed vs. normal load; (c) normal load vs. sliding distance; (d) normal load vs. % of reinforcement. reinforcement, 7 pH value, 35 C of temperature, and 50 hrs Figures 7 and 8 illustrated the main effects plot for of hanging time. means and S/N ratios of the corrosion rate; increasing of Table 5 and Table 6 presented the response tables of cor- reinforcement percentage decreases the corrosion rate. Min- rosion rate; these tables illustrated the higher influence imum corrosion rate was obtained by 8% of reinforcement; parameter in priority order. Based on the delta and rank similarly, increasing of pH value the corrosion rate was values, the higher priority was decided; from this analysis, decreased. Minimum corrosion rate was recorded by chamber temperature was a higher priority in the corrosion influencing of 8 pH value. Increasing of temperature from ° ° ° test. Further, the parameters were followed by pH value, 30 Cto35 C decreases the corrosion rate, 35 C of tempera- hanging time, and percentage of reinforcement. Optimal ture offered minimum corrosion rate. Higher hanging hours parameters of the corrosion rate was recorded as 8% of rein- such as 50 hours recoded minimum corrosion rate. forcement, 8 pH value, 35 C of temperature, and 50 hrs of Figure 9 presented the residual plots for corrosion rate; hanging time. this figure comprises the four plots in a single plot. From 8 Journal of Nanomaterials Table 4: Summary of corrosion test process parameters and corrosion rate. -3 Exp. runs % of reinforcement pH Temperature ( C) Hanging time (hrs) Corrosion rate ×10 (mm/year) 1 4 7 30 30 0.135 2 4 7 30 30 0.178 3 4 7 30 30 0.142 4 4 8 35 40 0.131 5 4 8 35 40 0.118 6 4 8 35 40 0.127 7 4 9 40 50 0.153 8 4 9 40 50 0.149 9 4 9 40 50 0.176 10 8 7 35 50 0.112 11 8 7 35 50 0.119 12 8 7 35 50 0.125 13 8 8 40 30 0.134 14 8 8 40 30 0.142 15 8 8 40 30 0.155 16 8 9 30 40 0.193 17 8 9 30 40 0.143 18 8 9 30 40 0.161 19 12 7 40 40 0.152 20 12 7 40 40 0.149 21 12 7 40 40 0.172 22 12 8 30 50 0.134 23 12 8 30 50 0.121 24 12 8 30 50 0.182 25 12 9 35 30 0.139 26 12 9 35 30 0.127 27 12 9 35 30 0.116 Table 5: Response table for means (corrosion rate). Level % of reinforcement pH Temperature ( C) Hanging time (hrs) 1 0.1454 0.1427 0.1543 0.1409 2 0.1427 0.1382 0.1238 0.1496 3 0.1436 0.1508 0.1536 0.1412 Delta 0.0028 0.0126 0.0306 0.0087 Rank 4 2 1 3 Table 6: Response table for signal to noise ratios (corrosion rate); smaller is better. Level % of reinforcement pH Temperature ( C) Hanging time (hrs) 1 16.76 16.95 16.15 17.01 2 16.96 17.15 18.14 16.54 3 16.83 16.45 16.26 17.01 Delta 0.20 0.70 1.98 0.47 Rank 4 2 1 3 Journal of Nanomaterials 9 Main effects plot for means data means pH % of reinforcement 0.15 0.14 0.13 0.12 4 8 12 7 8 9 Temperature (°C) Hanging time (hrs) 0.15 0.14 0.13 0.12 30 35 40 30 40 50 Figure 7: Main effects plot for means (corrosion rate). Main effects plot for SN ratios data means pH % of reinforcement 18.0 17.5 17.0 16.5 16.0 4 8 12 7 8 9 Temperature (°C) Hanging time (hrs) 18.0 17.5 17.0 16.5 16.0 30 35 40 30 40 50 Signal-to-noise: samaller is better Figure 8: Main effects plot for S/N ratios (corrosion rate). all the four plots, the data points were distributed uniformly a minimum corrosion rate such as 35 C of temperature and within the limits; hence, the selected parameters and and 50 hours of hanging time. Figure 10(c) demonstrates model were suitable one. In the normal probability plot, all the connection between hanging time and % of reinforce- the data points touch the mean line; it was denoted that ment; 50 hours of hanging and 8% of reinforcement offered the model was accurate one. minimum corrosion rate. Figure 10 illustrates the contour plot for corrosion rate; Normally, the magnesium alloy material was light weight Figure 10(a) represents the correlation between % of rein- material; adding of reinforcement particles improves the forcement and pH value. In both of these parameters’ corre- wear properties. In stir casting process, the reinforced parti- lation, the minimum corrosion rate was recorded by 8% of cles, namely, boron carbide (B C) and zirconium dioxide reinforcement and 8 pH value. Figure 10(b) presented the (ZrO2), were highly melted and blended into the magnesium pH value and temperature relations; for that, these parame- alloy. The uniform blending increases the mechanical prop- ters offered a minimum corrosion rate by 8 pH value and erties of the magnesium composites; the enhanced strength 35 C of temperature. Figure 10(c) presented the correlation reduces the wear rate of the composite and also reduces between temperature and hanging time; both were offered the corrosion. The hybrid reinforced particles make superior Mean of means Mean of SN ratios 10 Journal of Nanomaterials –3 Residual plots for corrosion rate × 10 (mm/year) Normal probability plot Versus fits 0.04 0.02 0.00 –0.02 –0.04 –0.050 –0.025 0.000 0.025 0.050 Residual Fitted value Histogram Versus order 0.04 0.02 0.00 –0.02 0 –0.04 2 4 6 8 10 12 14 16 18 20 22 24 26 Observation order Residual Figure 9: Residual plots for corrosion rate. –3 Corrosion rate × 10 (mm/year) –3 Corrosion rate × 10 (mm/year) 9.0 0.1930 40 0.1930 0.1829 0.1829 8.5 0.1728 0.1728 0.1626 0.1626 8.0 0.1525 0.1525 0.1424 0.1424 0.1323 0.1323 7.5 0.1221 0.1221 0.1120 0.1120 7.0 30 4 68 10 12 78 9 % of reinforcement pH (a) (b) –3 –3 Corrosion rate × 10 (mm/year) Corrosion rate × 10 (mm/year) 50 12 0.1930 0.1930 0.1829 0.1829 0.1728 0.1728 0.1626 0.1626 40 0.1525 0.1525 0.1424 0.1424 0.1323 0.1323 0.1221 0.1221 0.1120 0.1120 30 4 30 35 40 30 35 40 Temperature (°C) Hanging time (hrs) (c) (d) Figure 10: Contour plot: (a) % of reinforcement vs. pH value; (b) pH value vs. temperature; (c) temperature vs. hanging time; (d) hanging time vs. % of reinforcement. Hanging time (hrs) pH Frequency Percent –0.03 –0.02 –0.01 0.00 0.01 0.02 0.03 0.04 Residual Residual % of reinforcement Temperature (°C) 0.1400 0.1425 0.1450 0.1475 0.1500 Journal of Nanomaterials 11 strength and offered excellent corrosion resistant to the References composite material; it was evidently showed in the numeri- [1] J. Zhu, J. Qi, Q. Guan, L. Ma, and R. Joyce, “Tribological cal analysis. behaviour of self-lubricating Mg matrix composites reinforced with silicon carbide and tungsten disulfide,” Tribology Interna- 5. Conclusion tional, vol. 146, article 106253, 2020. [2] W. Yu, D. Chen, L. Tian, H. Zhao, and X. Wang, “Self-lubri- In this experimental investigation, magnesium alloy hybrid cate and anisotropic wear behavior of AZ91D magnesium composites were prepared through stir casting process with alloy reinforced with ternary Ti AlC MAX phases,” Journal reinforcement of boron carbide and zirconium oxide. 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Synthesis and Experimental Investigations of Tribological and Corrosion Performance of AZ61 Magnesium Alloy Hybrid Composites

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Copyright © 2022 R. Venkatesh 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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Abstract

Hindawi Journal of Nanomaterials Volume 2022, Article ID 6012518, 12 pages https://doi.org/10.1155/2022/6012518 Research Article Synthesis and Experimental Investigations of Tribological and Corrosion Performance of AZ61 Magnesium Alloy Hybrid Composites 1 2 3 4 5 R. Venkatesh, C. Ramesh Kannan, S. Manivannan, M. Vivekanandan, J. Phani Krishna, 6 7 8 Amine Mezni, Saiful Islam, and S. Rajkumar Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Chennai, 602105 Tamil Nadu, India Department of Mechanical Engineering, Dr. Navalar Nedunchezhiyan College of Engineering Tholudur, 606303, India Center for Material Science, Department of Mechanical Engineering, Karpagam Academy of Higher Education Coimbatore, 642021, India Department of Mechanical Engineering, National Engineering College, Kovilpatti, 628503 Tamil Nadu, India Design Engineering, Powder Handling Solutions, RIECO Industries Ltd., Pune 411005, India Department of Chemistry, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Civil Engineering Department, College of Engineering, King Khalid University, Abha 61413, Saudi Arabia Department of Mechanical Engineering, Faculty of Manufacturing, Institute of Technology, Hawassa University, Hawassa, Ethiopia Correspondence should be addressed to S. Rajkumar; rajkumar@hu.edu.et Received 1 March 2022; Revised 12 April 2022; Accepted 15 April 2022; Published 2 May 2022 Academic Editor: Hiwa M. Ahmed Copyright © 2022 R. Venkatesh 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. Magnesium alloy is the light weight material compared to aluminium alloy, and it possesses high strength; these alloys are used to manufacturing of vehicle parts. Magnesium alloy has extreme mechanical and thermal properties, and it is applied to aerospace applications. This study planned to improve the tribological and corrosion resistance of AZ61 magnesium alloy with reinforcement of boron carbide (B C) and zirconium dioxide (ZrO2). Magnesium alloy hybrid composites are fabricated through stir casting process. Tribological and corrosion performance are analyzed through Taguchi L27 Orthogonal Array. In the tribological analysis, four parameters are involved such as % of reinforcement (4%, 8%, and 12%), disc speed (1 m/s, 2 m/s, and 3 m/s), normal load (30 N, 40 N, and 50 N), and sliding distance (1300 m, 1500 m, and 1700 m). Similarly, in salt spry corrosion analysis, four parameters are influenced used such as % of reinforcement (4%, 8%, and 12%), pH (7, 8, and 9), ° ° ° temperature (30 C, 35 C, and 40 C), and hanging time (30 hrs, 40 hrs, and 50 hrs). From this analysis, percentage of reinforcement is highly influenced in wear test, and in corrosion test, temperature is extremely influenced. 1. Introduction [2]. Magnesium possesses low density, and particular prop- erties are lifting the consuming level of magnesium. For Normally, the composite materials are offered excellent recent trends, the magnesium and its alloys are used in the high end applications. Magnesium is having some good mechanical properties and thermal properties. In composite preparation, the matrix material and reinforced particle quality characters, namely, excellent manufacturability, easy to fusion, and higher machinability [3–5]. All these points selections are highly toughest one due to obtain of the are considered to selection of materials with omitting of alu- desired properties [1]. Naturally, the pure magnesium is the light weight material compared to aluminum; it has minium and such other materials. In machinability point of view, the magnesium has 50% higher than aluminium and 33% lighter in similar manner 75% lighter than steel material 2 Journal of Nanomaterials possesses more energy [6–8]. In automobile sectors, magne- sium is one of the substitute materials for aluminium for making of steering shaft, pistons, and brake components [9]. In major consideration in the engine design, the magne- sium reduces the weight of the engine compared to cast iron [10–12]. It is a suitable one for replacing of cast iron engine and improving the vehicle mileage as well as reducing the fuel consumption. In the magnesium material, the wear occurrence takes place in moderate range but addition of reinforced particles is influenced to reduce the wear rate of the magnesium [13–15]. Hybrid composites generally pos- sess high wear resistance as well as corrosion resistance even influencing of high performance parameters [16]. Taguchi optimization is one of the statistical tools to improve the mechanical properties, increasing the corrosion resistance, reduced the wear rate by the way of parameter optimization [17–20]. The main objective of this experimental work is focusing on to prepare the magnesium hybrid composites Figure 1: Dry sliding wear test apparatus. with influencing of boron carbide and zirconium oxide- reinforced particles. Further study of wear and corrosion rate of the fabricated composites is conducted through Taguchi tool. 2. Materials and Methods In this experimental investigation, 2 kg of AZ61 magnesium alloy is purchased from the Andavan Arul Alloys, Chennai. Reinforced particles of boron carbide and zirconium dioxide are procured from Ashoka Marketing Agencies, Chennai, by each 500 g. Stir casting methodology is considered for this investigation to make a high strength magnesium alloy hybrid composites [21]. Bottom pouring stir casting equip- ment is used for this experimental work. Agitation process is controlled by electric motor in the stir casting process. (a) 3. Experimental Work Stir casting process is one of the economical ways to produce the hybrid composites within a short period. Initially, the reinforced particles are preheated in the crucible; different weight percentages (4%, 8%, and 12%) of boron carbide and zirconium oxide are preheated at 450 C for 5 hours in the crucible [22]. The preheating process remove the impu- rities present in the reinforced particles. Pure magnesium alloy is heated by applying of 800 C in the bottom pouring furnace; further, the preheated reinforce particles are mixed to the pure magnesium alloy. Mixing of preheated reinforced (b) particles and the pure magnesium alloy are mixed well by using stirring action, the stirring process is controlled by Figure 2: (a) Image of wear test specimens. (b) Image of salt spray test specimens. electric controller in the motor [23]. Stirring speed is main- tained as 400 rpm with 1 hour. After stirring action, the mol- standard under the dimensions of 12 mm diameter and ten material is pouring into the die and allowed to cool; 30 mm length. Initially, the disc and specimens are cleaned further, the cooled material is separated from the die. The well by using, and then, each sample is weighted initially required dimensions of wear and corrosion samples are with the help of digital balance [24]. EN 32 steel disc with sliced out from the casted magnesium hybrid composites. 65 HRC is used for conducting of dry sliding wear test; dif- ferent process parameters are involved to conduct the wear 3.1. Wear Test. Wear analysis is conducted through test such as % of reinforcement, disc speed, normal load, DUCOM model dry sliding wear test apparatus as shown and siding distance. in Figure 1. Specimens are prepared as per the ASTM G99 Journal of Nanomaterials 3 Table 1: Wear test process parameters and their levels. S. no. Parameters Level 1 Level 2 Level 3 1 % of reinforcement 4 8 12 2 Disc speed (m/s) 1 2 3 3 Normal load (N) 20 30 40 4 Sliding distance (m) 1300 1500 1700 Table 2: Response table for means (wear rate). Exp. runs % of reinforcement Disc speed (m/s) Normal load (N) Sliding distance (m) Wear rate (mm /m) 1 4 1 20 1300 0.0038 2 4 1 20 1300 0.046 3 4 1 20 1300 0.0034 4 4 2 30 1500 0.0341 5 4 2 30 1500 0.0117 6 4 2 30 1500 0.034 7 4 3 40 1700 0.0062 8 4 3 40 1700 0.027 9 4 3 40 1700 0.0071 10 8 1 30 1700 0.0023 11 8 1 30 1700 0.051 12 8 1 30 1700 0.0086 13 8 2 40 1300 0.0073 14 8 2 40 1300 0.0128 15 8 2 40 1300 0.0148 16 8 3 20 1500 0.0039 17 8 3 20 1500 0.0048 18 8 3 20 1500 0.0156 19 12 1 40 1500 0.0028 20 12 1 40 1500 0.0126 21 12 1 40 1500 0.0018 22 12 2 20 1700 0.0107 23 12 2 20 1700 0.0061 24 12 2 20 1700 0.0037 25 12 3 30 1300 0.0027 26 12 3 30 1300 0.0147 27 12 3 30 1300 0.0025 The sample is placed vertically against to rotating disc; for evaluating the difference of mass loss and also find the time taken to conduct wear test per sample is 20 min. corrosion rate. Figure 2(b) shows the salt spray test speci- Finally, after conducting the wear test, the samples are mens; initially, the specimens are loaded in the salt spray weighted to calculate the mass loss. Figure 2(a) illustrates chamber such as all the specimens are hung in the chamber the 27 numbers of wear test specimens. [25]. The salt spray model is CARELAB with the dimensions of 650 mm × 450 mm × 4010 mm. After loading the samples, 3.2. Salt Spray Test. Salt spray test is one of the faster the chamber cabin door is closed; using an atomizer, the 5% methods to analyze the corrosion resistance of the materials. of NaCl solution is continually sprayed on the specimen’s surfaces [26]. The salt spray is achieved by using of pump In this work, initially, the samples are prepared as per the ASM standard ax (ASTM B117) with the dimensions of circulation with constant flow rate. After reaching the spec- 30 mm diameter and 10 mm thickness. Samples are cleaned ified time period, the specimens are taken out from the well and weighted; the initial weights are noted carefully chamber and cleaned well with using of running water; 4 Journal of Nanomaterials Table 3: Response table for signal to noise ratios (wear rate); smaller is better. Level % of reinforcement Disc speed (m/s) Normal load (N) Sliding distance (m) 1 0.019267 0.014726 0.010683 0.012052 2 0.013246 0.015028 0.018009 0.013282 3 0.006473 0.009233 0.010294 0.013652 Delta 0.012794 0.005795 0.007715 0.001601 Rank 1 3 2 4 Main effects plot for means data means Disc speed (m/s) % of reinforcement 0.020 0.015 0.010 0.005 4 812 1 2 3 Sliding distance (m) Normal load (N) 0.020 0.015 0.010 0.005 20 30 40 1300 1500 1700 Figure 3: Main effects plot for mean (wear rate). further, the specimens are dried. All specimens are weighed Table 2 and Table 3 presented the response tables of and calculate the difference of mass loss [27]. wear rate; these tables presented the higher influence param- Table 1 presented the wear test parameters and their eter in priority order. Higher priority was illustrated by the levels; in wear test, four parameters such as % of reinforce- delta and rank order; the percentage of reinforcement was ment, disc speed, normal load, and sliding distance were higher priority in the wear test. Normal load was the second selected. All four parameters have three levels ton satisfying priority, disc speed was the third priority, and sliding dis- L27 OA. The wear rate is obtained with tance was fourth priority. Optimal parameters of the wear rate was found as 12% of reinforcement, 3 m/s of disc speed, W1 − W2 40 N of normal load, and 1500 m of sliding distance. Wearrate WI = ðÞ : ð1Þ Figures 3 and 4 represented the main effects plot for Time × Density means and S/N ratios of the wear rate; increasing of rein- forcement percentage decreases the wear rate. Maximum 4. Results and Discussion reinforcement 12% offered minimum wear rate; further 4.1. Wear Analysis. Influencing of four parameters and three increasing of disc speed also reduces the wear rate. Maxi- mum level 3 m/s of disc speed offered minimum wear rate. levels was extremely influenced to estimate the wear rate of specimens. Table 2 presented the wear input parameters Influencing of normal load such as 20 N to 30 N wear rates and the response of wear rate. From this wear test analysis, was increased; further, 30 N to 40 N of normal load, the wear rate was decreased. Finally, the 40 N of normal load recorded the minimum wear rate was recorded as 0.0018 mm /m. The minimum wear rates attained by the influence of minimum wear rate. Consideration of sliding distance, 1500 m of sliding distance offered minimum wear rate; fur- parameters were 12% of reinforcement, 1 m/s of disc speed, ther increasing of 1500 m and 1700 m of sliding distance, 40 N of normal load, and 1500 m of sliding distance. On the wear rate was increased. Figure 5 highlights the residual the contrary, the maximum wear rate was occurred as plot for the wear rate. 0.051 mm /m by influencing of 8% of reinforcement, 1 m/s of disc speed, 30 N of normal load, and 1700 m of sliding Normal probability plot represents that all the points were lying on the mean line; it tell about the selected distance. Mean of means Journal of Nanomaterials 5 Main effects plot for SN ratios data means Disc speed (m/s) % of reinforcement 4 8 12 1 2 3 Sliding distance (m) Normal load (N) 20 30 40 1300 1500 1700 Signal-so-noise: smaller is better Figure 4: Main effects plot for S/N ratios (wear rate). Residual plots for wear rate (mm /m) Normal probability plot Versus fits 0.04 0.02 0.00 –0.02 –0.02 0.00 0.02 0.04 0.005 0.010 0.015 0.020 Residual Fitted value Histogram Versus order 0.04 0.02 0.00 –0.02 2 4 6 8 10 12 14 16 18 20 22 24 26 –0.012 0.000 0.012 0.024 0.036 Residual Observation order Figure 5: Residual plots for wear rate. 40 N of normal load produced minimum wear rate. parameters, and the levels are good one. In versus fits plot, all the points were distributed uniformly, and within the Figure 6(c) presents the connection between normal load limits, similar trends were observed in the versus order plot. and sliding distance. Minimum wear rate was registered All these points demonstrate that chosen model is appropri- by 40 N of normal load and 1500 m sliding distance. ate one. In histogram plot, all the rectangles were skewed in Figure 6(d) shows the minimum wear rate by 20 N of nor- mal load and 12% of reinforcement. normal position. Figure 6 demonstrates the parallel set plot for wear rate; Figure 6(a) shows the correlation between % of reinforcement and disc speed. From this parameter con- 4.2. Corrosion Analysis. Table 4 illustrates the corrosion nection, the minimum wear rate was recorded by 12% of input parameters and the response of corrosion rate. From reinforcement and 1 m/s of disc speed. this corrosion test analysis, the minimum corrosion rate Figure 6(b) illustrates the relations between disc speed was registered as 0.112 mm/year. The minimum corrosion and normal load. In this analysis, 1 m/s of disc speed and rates obtained by the influence of parameters were 8% of Frequency Percent Mean of SN ratios Residual Residual 6 Journal of Nanomaterials 0.0038 0.046 0.0034 0.0341 0.0117 0.034 0.0062 0.027 0.0071 0.0023 0.051 0.0086 0.0073 8 2 0.0128 0.0148 0.0039 0.0048 0.0156 0.0028 0.0126 0.0018 0.0107 0.0061 0.0037 0.0027 0.0147 0.0025 % of reinforcement Disc speed (m/s) Wear rate (mm /m) (a) 0.0038 0.046 0.0034 0.0341 1 20 0.0117 0.034 0.0062 0.027 0.0071 0.0023 0.051 0.0086 0.0073 2 30 0.0128 0.0148 0.0039 0.0048 0.0156 0.0028 0.0126 0.0018 0.0107 0.0061 3 40 0.0037 0.0027 0.0147 0.0025 Disc speed (m/s) Normal load (N) 3 Wear rate (mm /m) (b) Figure 6: Continued. Journal of Nanomaterials 7 0.0038 0.046 0.0034 0.0341 20 1300 0.0117 0.034 0.0062 0.027 0.0071 0.0023 0.051 0.0086 0.0073 30 1500 0.0128 0.0148 0.0039 0.0048 0.0156 0.0028 0.0126 0.0018 0.0107 0.0061 40 1700 0.0037 0.0027 0.0147 0.0025 Sliding distance (m) Normal load (N) 3 Wear rate (mm /m) (c) 0.0038 0.046 0.0034 0.0341 20 4 0.0117 0.034 0.0062 0.027 0.0071 0.0023 0.051 0.0086 0.0073 0.0128 30 8 0.0148 0.0039 0.0048 0.0156 0.0028 0.0126 0.0018 0.0107 40 12 0.0061 0.0037 0.0027 0.0147 0.0025 Normal load (N) % of reinforcement 3 Wear rate (mm /m) (d) Figure 6: Parallel set plot: (a) % of reinforcement vs. disc speed; (b) disc speed vs. normal load; (c) normal load vs. sliding distance; (d) normal load vs. % of reinforcement. reinforcement, 7 pH value, 35 C of temperature, and 50 hrs Figures 7 and 8 illustrated the main effects plot for of hanging time. means and S/N ratios of the corrosion rate; increasing of Table 5 and Table 6 presented the response tables of cor- reinforcement percentage decreases the corrosion rate. Min- rosion rate; these tables illustrated the higher influence imum corrosion rate was obtained by 8% of reinforcement; parameter in priority order. Based on the delta and rank similarly, increasing of pH value the corrosion rate was values, the higher priority was decided; from this analysis, decreased. Minimum corrosion rate was recorded by chamber temperature was a higher priority in the corrosion influencing of 8 pH value. Increasing of temperature from ° ° ° test. Further, the parameters were followed by pH value, 30 Cto35 C decreases the corrosion rate, 35 C of tempera- hanging time, and percentage of reinforcement. Optimal ture offered minimum corrosion rate. Higher hanging hours parameters of the corrosion rate was recorded as 8% of rein- such as 50 hours recoded minimum corrosion rate. forcement, 8 pH value, 35 C of temperature, and 50 hrs of Figure 9 presented the residual plots for corrosion rate; hanging time. this figure comprises the four plots in a single plot. From 8 Journal of Nanomaterials Table 4: Summary of corrosion test process parameters and corrosion rate. -3 Exp. runs % of reinforcement pH Temperature ( C) Hanging time (hrs) Corrosion rate ×10 (mm/year) 1 4 7 30 30 0.135 2 4 7 30 30 0.178 3 4 7 30 30 0.142 4 4 8 35 40 0.131 5 4 8 35 40 0.118 6 4 8 35 40 0.127 7 4 9 40 50 0.153 8 4 9 40 50 0.149 9 4 9 40 50 0.176 10 8 7 35 50 0.112 11 8 7 35 50 0.119 12 8 7 35 50 0.125 13 8 8 40 30 0.134 14 8 8 40 30 0.142 15 8 8 40 30 0.155 16 8 9 30 40 0.193 17 8 9 30 40 0.143 18 8 9 30 40 0.161 19 12 7 40 40 0.152 20 12 7 40 40 0.149 21 12 7 40 40 0.172 22 12 8 30 50 0.134 23 12 8 30 50 0.121 24 12 8 30 50 0.182 25 12 9 35 30 0.139 26 12 9 35 30 0.127 27 12 9 35 30 0.116 Table 5: Response table for means (corrosion rate). Level % of reinforcement pH Temperature ( C) Hanging time (hrs) 1 0.1454 0.1427 0.1543 0.1409 2 0.1427 0.1382 0.1238 0.1496 3 0.1436 0.1508 0.1536 0.1412 Delta 0.0028 0.0126 0.0306 0.0087 Rank 4 2 1 3 Table 6: Response table for signal to noise ratios (corrosion rate); smaller is better. Level % of reinforcement pH Temperature ( C) Hanging time (hrs) 1 16.76 16.95 16.15 17.01 2 16.96 17.15 18.14 16.54 3 16.83 16.45 16.26 17.01 Delta 0.20 0.70 1.98 0.47 Rank 4 2 1 3 Journal of Nanomaterials 9 Main effects plot for means data means pH % of reinforcement 0.15 0.14 0.13 0.12 4 8 12 7 8 9 Temperature (°C) Hanging time (hrs) 0.15 0.14 0.13 0.12 30 35 40 30 40 50 Figure 7: Main effects plot for means (corrosion rate). Main effects plot for SN ratios data means pH % of reinforcement 18.0 17.5 17.0 16.5 16.0 4 8 12 7 8 9 Temperature (°C) Hanging time (hrs) 18.0 17.5 17.0 16.5 16.0 30 35 40 30 40 50 Signal-to-noise: samaller is better Figure 8: Main effects plot for S/N ratios (corrosion rate). all the four plots, the data points were distributed uniformly a minimum corrosion rate such as 35 C of temperature and within the limits; hence, the selected parameters and and 50 hours of hanging time. Figure 10(c) demonstrates model were suitable one. In the normal probability plot, all the connection between hanging time and % of reinforce- the data points touch the mean line; it was denoted that ment; 50 hours of hanging and 8% of reinforcement offered the model was accurate one. minimum corrosion rate. Figure 10 illustrates the contour plot for corrosion rate; Normally, the magnesium alloy material was light weight Figure 10(a) represents the correlation between % of rein- material; adding of reinforcement particles improves the forcement and pH value. In both of these parameters’ corre- wear properties. In stir casting process, the reinforced parti- lation, the minimum corrosion rate was recorded by 8% of cles, namely, boron carbide (B C) and zirconium dioxide reinforcement and 8 pH value. Figure 10(b) presented the (ZrO2), were highly melted and blended into the magnesium pH value and temperature relations; for that, these parame- alloy. The uniform blending increases the mechanical prop- ters offered a minimum corrosion rate by 8 pH value and erties of the magnesium composites; the enhanced strength 35 C of temperature. Figure 10(c) presented the correlation reduces the wear rate of the composite and also reduces between temperature and hanging time; both were offered the corrosion. The hybrid reinforced particles make superior Mean of means Mean of SN ratios 10 Journal of Nanomaterials –3 Residual plots for corrosion rate × 10 (mm/year) Normal probability plot Versus fits 0.04 0.02 0.00 –0.02 –0.04 –0.050 –0.025 0.000 0.025 0.050 Residual Fitted value Histogram Versus order 0.04 0.02 0.00 –0.02 0 –0.04 2 4 6 8 10 12 14 16 18 20 22 24 26 Observation order Residual Figure 9: Residual plots for corrosion rate. –3 Corrosion rate × 10 (mm/year) –3 Corrosion rate × 10 (mm/year) 9.0 0.1930 40 0.1930 0.1829 0.1829 8.5 0.1728 0.1728 0.1626 0.1626 8.0 0.1525 0.1525 0.1424 0.1424 0.1323 0.1323 7.5 0.1221 0.1221 0.1120 0.1120 7.0 30 4 68 10 12 78 9 % of reinforcement pH (a) (b) –3 –3 Corrosion rate × 10 (mm/year) Corrosion rate × 10 (mm/year) 50 12 0.1930 0.1930 0.1829 0.1829 0.1728 0.1728 0.1626 0.1626 40 0.1525 0.1525 0.1424 0.1424 0.1323 0.1323 0.1221 0.1221 0.1120 0.1120 30 4 30 35 40 30 35 40 Temperature (°C) Hanging time (hrs) (c) (d) Figure 10: Contour plot: (a) % of reinforcement vs. pH value; (b) pH value vs. temperature; (c) temperature vs. hanging time; (d) hanging time vs. % of reinforcement. Hanging time (hrs) pH Frequency Percent –0.03 –0.02 –0.01 0.00 0.01 0.02 0.03 0.04 Residual Residual % of reinforcement Temperature (°C) 0.1400 0.1425 0.1450 0.1475 0.1500 Journal of Nanomaterials 11 strength and offered excellent corrosion resistant to the References composite material; it was evidently showed in the numeri- [1] J. Zhu, J. Qi, Q. Guan, L. Ma, and R. Joyce, “Tribological cal analysis. behaviour of self-lubricating Mg matrix composites reinforced with silicon carbide and tungsten disulfide,” Tribology Interna- 5. Conclusion tional, vol. 146, article 106253, 2020. [2] W. Yu, D. Chen, L. Tian, H. Zhao, and X. Wang, “Self-lubri- In this experimental investigation, magnesium alloy hybrid cate and anisotropic wear behavior of AZ91D magnesium composites were prepared through stir casting process with alloy reinforced with ternary Ti AlC MAX phases,” Journal reinforcement of boron carbide and zirconium oxide. 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Published: May 2, 2022

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