Synthesis of AA8050/B<sub>4</sub>C/TiB<sub>2</sub> Hybrid Nanocomposites and Evaluation of Computer-Aided Machining Parameters

Sathish, T.; Vinayagam, Mohanavel; Raja, T.; Seikh, A. H.; Siddique, M. H.; Subbiah, Ram; Hailu, Beruk

doi:https://doi.org/10.1155/2022/9745418

Journal of Nanomaterials

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Abstract Introduction 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 9745418 | https://doi.org/10.1155/2022/9745418

Synthesis of AA8050/B₄C/TiB₂ Hybrid Nanocomposites and Evaluation of Computer-Aided Machining Parameters

T. Sathish,¹Mohanavel Vinayagam,^2,3T. Raja,⁴A. H. Seikh,⁵M. H. Siddique,⁶Ram Subbiah,⁷and Beruk Hailu⁸

Academic Editor: Arpita Roy

Received08 May 2022

Accepted01 Aug 2022

Published31 Aug 2022

Abstract

This research focusses on synthesizing the hybrid nanocomposite samples with AA8050 and the reinforcement of B₄C and TiB₂ nanoparticles at 3 different quality grades. To investigate their machinabilities on the prepared composites in the computer-aided machining centre, the objectives are maximizing the material removal rate (MRR) and minimizing the surface roughness for a specific application. Stir casting process was employed in synthesizing the hybrid nanocomposite samples. Utilizing CNC turning centre was employed to investigate machinability performance on hybrid nanocomposite samples. The PVD-coated HSS tool and dry cutting environment were considered. The quality of machining was investigated by observing the surface roughness on the machined surfaces of samples of hybrid nanocomposite. The machining rate was investigated through the response of material removal rate at as per Taguchi design of experiments L27 orthogonal array. The hybrid nanocomposite synthesizing parameter of contribution of nanoparticle reinforcement (8%, 10%, and 12%) and the Turing parameters include spindle speed (800 rpm, 1000 rpm, and 1200 rpm), machining feed (0.05 mm/rev, 0.10 mm/rev, and 0.15 mm/rev) and depth of cut (0.5 mm, 0.75 mm, and 1 mm). The best performing input levels were identified through Taguchi analysis and the involved input variables were analysed and prediction model developed through ANOVA. The maximum material removal rate and the minimum surface roughness were reordered as 1380 mm³/min.

1. Introduction

Hybrid composites presented the high strength of the aluminium alloy for using reinforced particles. In the statistical analysis, the influence of higher cutting speed reduces the cutting force of the tool material. Results between the experimental work and the predicted values of the SiCp/Al nanocomposites, the cutting force is slightly reduced in the experimental work [1]. Using of carbide cutting tool for turning of E250 steel in the CNC turning process, the material removal rate is increased moderately. Optimum values are attained as 1100 rpm of spindle speed, 0.44 mm of depth of cut, and 0.2 mm/min of feed rate. These optimal output parameters provided better surface finish as well as high MRR [2]. In automobiles, wheel axles are made on hardened alloy steels for giving high strength and absorption of shocks and vibrations. Different parameters such as cutting speed, machining feed, depth of cut, relief angle nose radius, and type of insert are involved in machining of hardened alloy steels. The L18 orthogonal array (OA) is suitable to examine the surface roughness and machinability characteristics of the hardened alloy steels. In this study, the influence of optimal parameters is reducing the tool flank wear such as 53.85%; similarly, the surface roughness also reduced by 15.95%. Optimal flank wear was obtained as 0.057 mm, and the optimum surface roughness value was attained as 1.0248 mm [3]. All the industries like as automotive, aerospace, marine, and structural components the 316L stainless steel was highly influenced. In machining of these materials, the tool has to be highly wear and the tool life also reduced increasing of tool life by the way of applying of lubrication with coolant. Using of coolant, the wear has to be approximately 9%. Dry machining increases the wear of the tool comparing to the coolant applied machining process [4]. Machining of titanium alloys is a difficult one; to overcome this, alternative machining techniques were applied. The tool wear was estimated under the working nature such as dry, wet, and cryogenic surroundings. Comparing the wet and dry nature machining, the cryogenic nature offered higher tool life such as 200% to others. Similarly, the surface roughness was reduced 71% by using cryogenic application. Comparing other methods such as wet and dry offered 64% of reduced surface roughness [5]. Aluminium alloy with reinforcement of silicon carbide nanoparticles is prepared by the stir casting route. Various parameters influencing the CNC turning process decided the surface finish and the MRR. This work concluded the 40 m/min of spindle speed, 0.100 mm/rev of feed rate, 0.3 mm of depth of cut, and 3% SiC, 7% Gr were recorded as optimal parameters [6]. The tungsten carbide inserts are effectively used in the CNC turning process; aluminium alloy (LM6) with silicon carbide particles reinforced composites are machined successfully. They [7] focused to reduce the cutting temperature, vibration, and surface roughness with different optimal parameters. The authors found the poor surface finish was attained due to composite particles sticking to the tool inserts. Many researchers intend to study the aluminium alloy metal matrix composites using coated tool material in the CNC turning process. Only few of the attempts are made on titanium alloy. This work was carried out on titanium metal matrix composites using carbide as well as cubic boron carbide inserts. Machinability study of surface roughness, cylindricity, cutting forces, and tool wear are carried out. In addition, the statistical analysis was included to evaluate the best parameter among the chosen parameters. CBN tool inserts offered good surface finish even in different spindle speeds [8–10]. This article discusses the synthesis of novel aluminium metal matrix composite with composite matrix of AA8050 with equal and hybrid reinforcement of B₄C nanoparticles and TiB₂ nanoparticles at various wt.% and investigates their machinability performance on CNC turning centre. The Taguchi design of experiments and analysis was preferred to optimize the machining parameters for maximizing material removal rate (MRR) and minimizing surface roughness on machined surfaces. With the best of our knowledge, such novel composites were not published or patented so far.

2. Experimental Details

2.1. Materials and Methods

This study conducts the machining process in the CNC turning centre using the material aluminium alloy with reinforcement of boron carbide (B₄C) nanoparticles and titanium diboride (TiB₂) nanoparticles. AA8050 aluminium alloy possesses high strength and excellent mechanical properties; adding of reinforcement nanoparticles, its strength is upgraded in a great level [11–14]. Automotive parts, aerospace components are to be made by using this material. High-strength nanoparticles of boron carbide and titanium diboride nanoparticles are used as reinforcement agent of this study. Boron carbide is a high hard material for antagonism against wear as well as a lightweight material [15]. Titanium diboride is an extreme heat conductivity material and also prevents oxidation, with good stability. The chemical composition of aluminium alloy 8050 is illustrated in Table 1.

Material preparation is conducted through stir casting process; the particles were reinforced at the time of stir casting [16–18]. Stir casted materials are machined through CNC turning using Diamond-Like Carbon- (DLC-) coated tungsten carbide tool [19–21]. CNC turning process is achieved by using different parameters applying L₂₇ orthogonal array (Taguchi route). The outcome of this experimental work is considered as surface roughness and material removal rate [22–24].

2.2. Experimental Procedure

Stir casting process is employed to this research work to produce the hybrid nanocomposite in the form of round rod. In stir casting process, the base material of aluminium alloy (AA8050) and the reinforced nanoparticles of boron carbide and nanoparticles of titanium diboride are mixed well [25–27]. The reinforced material is added to the base material at different weight percentages such as 8%, 10%, and 12%. Stir casting process is carried out using different parameters for producing the effective hybrid nanocomposite [28–30]. Stirring speed of 650 rpm, stirring time of 30 min, and stirring temperature of 900°C are used as parameters of the stir casting process [31]. The stir casting equipment is model SWAM EQUIP bottom pouring type stir casting as shown in Figure 1.

All the samples are machined using CNC turning machine (brand: Ace Micromatic; model: Super Jobber 500-LM CNC Lathe Machine). This machine was used to turn a maximum of 320 mm diameter and maximum of 500 mm length as shown in Figure 2. Diamond-Like Carbon- (DLC-) coated tungsten carbide tool is used for turning hybrid composite materials [32–34].

In the turning process, the different parameters and levels are used such as spindle speed (800 rpm, 1000 rpm, and 1200 rpm), machining feed (0.05 mm/rev, 0.10 mm/rev, and 0.15 mm/rev), and depth of cut (0.5 mm, 0.75 mm, and 1 mm). All these parameters are effectively utilized, and turning operation was successfully carried out; each experimental trial run shows different output results such as MRR and result of surface roughness [35–37]. Figure 3 presents the AA8050/B₄C/TiB₂ of the hybrid nanocomposite material samples before and after machining.

(a)

(b)

Material removal rate was calculated by the volume of material removal from the specimen with specified time period [38]. The surface roughness was checked using a Mitutoyo tester (model: SJ210 Surface Roughness Tester). Surface roughness was estimated through conducting of three trials for each sample and averaging it [39]. Table 2 presents the parameters and their levels of MRR.

3. Results and Discussion

3.1. MRR

Table 3 represents all parameter correlation and the output result of material removal rate in a detailed manner. Maximum material removal rate of 1380 mm³/min was obtained by 10% of nanoparticle reinforcement, 1000 rpm of spindle speed, 0.15 mm/rev of machining speed, and 0.50 mm of depth of cut [40].

Tables 4 and 5 present the response table for means and response table for S/N ratio, respectively. In these tables, the spindle speed was a higher influence factor of this investigation comparing to others [41]. From the rank order, the factor influence was stated as second rank of machining speed, third rank of depth of cut, and fourth rank is hybrid nanoparticle reinforcement percentage. In the MRR investigation, the optimal factors were obtained as 12% of hybrid nanoparticle reinforcement, 1000 rpm of spindle speed, 0.15 mm/rev, and 1 mm of depth of cut.

Figures 4 and 5 illustrate the main effect plot for means and main effect plot for S/N ratio of material removal rate. Increasing of hybrid nanoparticle reinforcement percentage changes the material removal rate, minimum spindle speed offered low MRR. Moderate level of spindle speed such as 1000 rpm offered higher MRR. Initially, the machining speed 0.05 mm/rev produced good level of MRR, further increasing of feed 0.05 to 0.10 mm/rev the MRR rate was reduced slightly. Feed of 0.15 mm/rev recorded as higher MRR. In depth of cut analysis, 0.75 mm of depth of cut registered as a low level of MRR, and higher MRR was obtained by using of 1.00 mm of depth of cut [42].

From the probability analysis, maximum points lie on the mean line or probability line few points only slightly deviated from the mean line as shown in Figure 6. These points were represented that the chosen parameters, and its correlation was excellent one and also produced better MRR. All the points were scattered homogeneously between the upper and lower limits as shown in Figure 7. Scattered points were positioned within the limits; it has to be enlightened about the relations between the parameters and the accurate results such as MRR.

Figure 8 illustrates that the contour plot of spindle speed and percentage of reinforcement of hybrid nanoparticles, the moderate spindle speed and increasing of percentage of reinforcement of hybrid nanoparticles offered excellent MRR. Above 1000 mm³/min of MRR was recorded by influencing 1100 rpm of spindle speed and more than 10% of nanoparticle reinforcement. Figure 9 exemplifies the contour plot of machining feed and spindle speed, higher machining speed such as 0.150 mm/rev and moderate spindle speed provided higher MRR. Figure 10 demonstrates that the contour plot of depth of cut and machining speed, the lower value of depth of cut and higher value of machining speed offered maximum of MRR. Contrary minimum machining feed and higher depth of cut offered excellent MRR. Figure 11 represents the contour plot of reinforcement and depth of cut, moderate reinforcement and low level of depth of cut provided enhanced MRR.

Figure 12 shows the pie charts of material removal rate (MRR); this plot enlightens the all-parameter contribution and the outcome (MRR) of the research work individually.

The mathematical model developed to predict the MRR with respect to the nanoparticle reinforcement contribution and machining parameters for the specific requirements and shown in

3.2. Surface Roughness

Table 6 illustrates each parameter relationship and the yield result of surface roughness in elaborate manner. Minimum surface roughness was found as 0.62 μm in the fourth experimental runs. Reduced surface roughness value was obtained by 8% of hybrid nanoparticle reinforcement, 1000 rpm of spindle speed, 0.10 mm/rev of machining speed, and 0.75 mm of depth of cut.

Tables 7 and 8 offer the response table for means and response table for S/N ratio of surface roughness, respectively. In surface roughness analysis, the machining speed was the major influencing factor compared to remaining factors. From the rank order, the machining feed was first, spindle speed was second, hybrid nanoparticle reinforcement percentage was third, and depth of cut was fourth order. Surface roughness analysis provided optimal parameters such as 12% of hybrid nanoparticle reinforcement, 800 rpm of spindle speed, 0.10 mm/rev, and 0.50 mm of depth of cut.

Figures 13 and 14 show the main effect plot for means and main effect plot for S/N ratio of surface roughness. Higher hybrid nanoparticle reinforcement percentage (12%) offered minimum surface roughness. Minimum spindle speed such as 800 rpm provided better surface roughness, further increasing spindle speed from 800 rpm to 1200 rpm the surface roughness was showed highly on the surfaces of the specimens. Moderate machining speed such as 0.10 mm/rev offered minimum surface roughness, continually increasing the machining speed 0.15 mm/rev maximum surface roughness was observed. From depth of cut analysis, minimum depth of cut (0.50 mm) produced low surface roughness. Increasing of depth of cut increases the surface roughness values.

In the probability investigation, most of the points touch the mean line; few of them deviated from the mean line as shown in Figure 15. All points close and that touch the mean line represented the correlation among chosen parameters. This analysis proved the selected parameters were accurate ones and make a better surface finish. All the experimental runs were converted into scattered plot; the points were scattered homogeneously among the upper and lower limits as shown in Figure 16. Scattered points informed that the points are positioned in correct manner; hence, the parameter relation has enlightened the surface roughness.

Figure 17 demonstrates that the contour plot of spindle speed and hybrid nanoparticle reinforcement percentage, the minimum spindle speed (800 rpm) increasing hybrid nanoparticle reinforcement percentage offered minimum surface roughness. Higher spindle speed affects the surface roughness. Figure 18 illustrates the contour plot of machining feed and spindle speed, increasing machining speed from 0.050 mm/rev and minimum spindle speed presented excellent surface finish. Figure 19 demonstrates the contour plot of depth of cut and machining speed, the higher value of depth of cut and moderate value of machining speed presented minimum surface roughness. Contrary moderate depth of cut and minimum machining feed was increasing the surface roughness. Figure 20 represents the contour plot of hybrid nanoparticle reinforcement percentage and depth of cut, both moderate hybrid nanoparticle reinforcement percentage and depth of cut recorded minimum surface roughness.

Figure 21 illustrates the pie charts of surface roughness; this plot makes clear all parameter involvement and the result (surface roughness) of the investigation individually. The mathematical model developed and shown below to predict the surface roughness with respect to the hybrid nanoparticle reinforcement percentage and machining parameters for the specific requirements and shown in

It was observed that machinability condition requirements vary for each grade (based on hybrid nanoparticle reinforcement percentage) of novel AMMC of AA8050/B₄C/TiB₂. The developed mathematical model will support to make right choice in manufacturing and machining.

4. Conclusion

This research work was carried out for CNC turning with different process parameters that influence to obtain enhanced MRR and surface roughness of hybrid AMMC’s (AA8050/B₄C/TiB₂) successfully. Diamond-Like Carbon- (DLC-) coated tungsten carbide tool was used to conduct the turning process with chosen parameters. The results were concluded as follows: (i)From the MRR analysis, maximum material removal rate of 1380 mm³/min was obtained by 10% hybrid nanoparticle reinforcement, 1000 rpm of spindle speed, 0.15 mm/rev of machining speed, and 0.50 mm of depth of cut. In the MRR investigation, the optimal factors were registered as 12% hybrid nanoparticle reinforcement, 1000 rpm of spindle speed, 0.15 mm/rev, and 1 mm of depth of cut(ii)Moderate level of spindle speed such as 1000 rpm offered higher MRR. Initially, the machining speed 0.05 mm/rev produced good level of MRR, further increasing of feed 0.05 to 0.10 mm/rev the MRR rate was reduced slightly(iii)In the surface roughness investigations, minimum surface roughness was found as 0.62 μm in the fourth experimental runs. Reduced surface roughness value was obtained by 8% hybrid nanoparticle reinforcement, 1000 rpm of spindle speed, 0.10 mm/rev of machining speed, and 0.75 mm of depth of cut. Surface roughness analysis provided optimal parameters such as 12% hybrid nanoparticle reinforcement, 800 rpm of spindle speed, 0.10 mm/rev, and 0.50 mm of depth of cut(iv)From the depth of cut analysis, minimum depth of cut (0.50 mm) formed low surface roughness. Increasing of depth of cut increases the surface roughness values

As aluminium alloys are widely utilized for numerous applications and selection of materials done for the application specific from the range of desired mechanical properties, this novel AMMC type of altering existing mechanical properties like enhanced wear resistance, self-lubrication properties for an automobile spare manufacturing application and this work developed mathematical models for process planning for manufacturing and machining. Hence, this piece of research claims a high social implication.

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 appreciate the supports from Haramaya University, Ethiopia, for providing help during the research and preparation of the manuscript. The authors thank Saveetha School of Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, for providing assistance to this work. The authors would like to acknowledge the Researchers Supporting Project number (RSP-2021/373), King Saud University, Riyadh, Saudi Arabia.

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Copyright © 2022 T. Sathish 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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