# Evaluation on Powder Metallurgy Process Parameters of Ball-Milled AA8079-B4C Nanostructured Composites via Taguchi Grey Relational Analysis

Authors: M. Meignanamoorthy; Mohanavel Vinayagam; M. Ravichandran; T. Raja; Amel Gacem; Amine Mezni; Mohammed Jameel; Manikandan Ganesan
Journal: Journal of Nanomaterials
 (2022)

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## Abstract

This work made an attempt to optimize the powder metallurgy (PM) process parameters of ball-milled AA8079-B4C composites via Taguchi grey relational analysis to attain better mechanical properties. The process parameters are reinforcement weight percentage, compaction pressure, sintering temperature, and sintering time, and the output responses are micro Vickers hardness and compressive strength. The different reinforcement weight percentages are AA8079-x wt.%B4C (X=5, 10, and 15 wt.%). The nanograin-refined green compacts were made at various compaction pressure 200 MPa, 300 MPa, and 400 MPa. The various sintering temperatures are 375°C, 475°C, and 575°C at different sintering times 1 h, 2 h, and 3 h. Taguchi L27 orthogonal array was utilized to examine the powder metallurgy process parameters. It could be understood from the results that higher reinforcement weight percentage, compaction pressure, and sintering temperature were determined as appropriate parameters to obtain maximum hardness and compressive strength.


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## Body

## 1. Introduction

Metal matrix composite (MMC) offers a symbiotic blend of properties; this could not be obtained in traditional materials. The MMCs can be attained by combining particulates B4C, SiC, Al2O3, AlN, and Ash into less weight alloys are the preferred to substitute traditional materials in numerous usages defence, structural, automobile, aerospace, marine, and mining industries [1–3]. Owing to the less density, better specific strength and better thermal conductivity aluminium alloys are extensively utilized [4]. To fulfill the necessities such as better mechanical properties and wear resistance of aluminium matrix composites (AMCs), AMCs are reinforced with outstanding structural, and physical properties are significantly required [5, 6]. Amid the different reinforcements, boron carbide (B4C) possesses extreme hardness subsequent to diamond and cubic boron nitride. Moreover, B4C has lesser specific gravity (2.51 g/cm3), and this is lower than Al (2.7 g/cm3). B4C possesses extreme wear and impact resistance, better resistance to chemical agents, and high melting point [7, 8]. Despite of extreme mechanical properties, the utilization of B4C reinforcement has been improved extremely [9, 10].Powder metallurgy (PM) production method includes various steps, namely, blending of powders, compaction, sintering, and secondary finishing method to manufacture parts with reliable net-shape. Metal components produced through PM can be utilized in automobile, aerospace, defence, and electronic industry because of its superior physical and mechanical properties. Near net shape components can be fabricated via PM method; furthermore, material wastage can be eliminated. Uniform distribution of reinforcement particle with matrix can be attained [11, 12]. Precision metal components can be fabricated through PM route so this technique has been acknowledged as extremely established technique. In the course of the most recent seven eras, the innovation has developed from fabricating bearings for autos to difficult transferor gear set in vehicle transposal and engines connecting rods [13]. Figure 1 shows the process sequence for fabrication of MMC using PM. Nowadays, many of the researchers focused to study the powder metallurgy process parameters such as reinforcement weight percentage, compaction pressure, and sinterability [14–18]. Zakir Hussain et al. [19] examined the powder metallurgy process parameters of diamond–copper composites in terms of compaction pressure, sintering temperature, and holding time and reported that compaction pressure 525 MPa, sintering temperature 900°C, and holding time 2 h are the most influencing parameters. Pravin et al. [20] utilized Taguchi system to optimize the process parameters of Al-10% Cu composites, and the process parameters are compaction load, lubricant, sintering atmosphere, and dwell time and stated that lubricant is a major influencing parameter. Ravichandran and Anandakrishnan [21] examined the PM parameters to obtain higher strength coefficient in aluminium matrix composite via Taguchi method and described that compaction pressure and sintering temperature are major substantial parameters.Figure 1
Process sequence for fabrication of MMC using PM.From the detailed literature study, it has been clearly evidenced that many of the researchers investigated the powder metallurgy process parameters as in Figure1 using different matrix materials and reinforcement particles but none of the work has been carried out in AA8079-B4C composites. Due to that, an attempt has been taken to optimize powder metallurgy process parameters on the mechanical properties. In general, a material should possess excellent hardness and compressive strength properties then only it can be used for desired application; according to that, this two mechanical properties have been studied by grey Taguchi method.


## 2. Experimental Details

In this investigation, AA8079 powder was produced by mingling the elemental powders Cu, Fe, Si, and Zn with Al powder. Purity of Al, Cu, Fe, Si, and Zn powders is 99.5%, and mesh size as 10μm as AA8079 possesses less weight with high strength. B4C was selected as reinforcement particle with 99.5% purity and mesh size as 10 μm as it has excellent hardness; in addition, it has less density, and it is the third hardest reinforcement next to diamond and cubic nitride. The chemical composition of the pure elemental powders is essential to synthesis AA8079 0.05Cu, 1.3Fe, 0.3Si, 0.15Zn, and Al remaining (each one in wt %). The SEM image of the produced AA8079 and as received B4C is displayed in Figures 2(a) and 2(b).Figure 2
(a) SEM images of 10 h ball milled AA8079 powders; (b) as received B4C.
(a)(b)Figure3 shows the details of fabrication of ball milled aluminium alloy and its composites. The needed amount of elemental powders was exactly weighted by utilizing an electronic weight balance machine to produce the composition, AA8079, AA8079-5%B4C, AA8079-10%B4C, and AA8079-15%B4C and ball milled via high energy ball mill for 10 h [22]. The drum speed maintained was 100 rpm [23]. The diameter of the steel ball utilized here was 10 mm, and ball to powder ratio was 10 : 1 [24].Figure 3
Fabrication of ball milled aluminium alloy and its composites.The ball milled powders were compressed into cylindrical billets (Dia24×10mm). A computerized universal testing machine capacity of 10 ton has been used to acquire a green compact. Figure 4 shows the compaction press used and other testing process conducted for the present work. The green compacts were compacted at three different compaction pressure 200 MPa, 300 MPa, and 400 MPa. Then, the green compacts were sintered at three different sintering temperatures 375°C, 475°C, and 575°C at three different sintering times 1 h, 2 h, and 3 h. As per ASTM, E384-08 Vickers hardness test was performed at a load of 0.3 kg and a dwell time of 10 s on the samples [25], and as per ASTM, E9-89a compression strength test was done via computerized universal testing machine [26]. In this investigation, trials were carried out with four parameters, and three level have been selected. Hence, L27 orthogonal array was selected like reinforcement wt. %, compaction pressure, sintering temperature, and sintering time. Table 1 displays the process parameters and their levels, and the details of the experimental plan by means of L27 OA are enumerated in Table 2. Figure 5 shows the procedure followed for Taguchi grey relational analysis in this work.Figure 4
Powder metallurgy process to produce alloy and composite samples.Table 1
Process parameters and their levels.
SymbolsParametersUnitLevel 1Level 2Level 3AReinforcement wt.%%51015BCompaction pressureMPa200300400CSintering temperature°C375475575DSintering timehr123Table 2
Experimental results as per L27 OA.
Expt. noReinforcement (wt%)Compaction pressure (MPa)Sintering temperature (°C)Sintering time (hr)Compressive strengthHardness152003751125.49141.59252004752133.74145.27352005753140.05120.09453003752131.48147.92553004753149149.73653005751142167.3754003753105.33151.25854004751156.03114.89954005752157.11112.6210102003751108.09138.9711102004752110.81164.8512102005753151.24136.0613103003752112.79120.4514103004753145.98128.5315103005751154.77154.716104003753116.22122.0217104004751141.06152.3818104005752148132.4119152003751139.09161.5620152004752120126.4221152005753122.12165.9822153003752147.19145.6923153004753150.06184.4124153005751156.88159.1425154003753145.02151.7726154004751164.33174.227154005752171158.03Figure 5
Procedure for Taguchi grey relational analysis.


## 3. Results and Discussions

### 3.1. S/N Ratio Analysis

Taguchi technique is a dominant utensil in value optimization for fabrication routes. Taguchi technique creates use of an unusual design of OA to inspect the worth characteristics over a nominal amount of experiments [27]. Word “signal” indicates the necessary value for the output characteristic, and “noise” indicates the horrible value for the output characteristic. The S/N ratios could be deliberate using Equations (1) and (2)
(1)SNratio=−10logMSD.MSD is mean square deviation.The MSD for the higher the better quality characteristic is expressed as(2)MSD=1n∑i=1n1Ti2.An entire degree of factor was six to four factors. The tests would permanently be carried out extra in numbers than that of designated DOF. Therefore, L27 OA was chosen based as per Taguchi’s design of experiments [28].


### 3.2. Interaction Effects of Factors

S/N response table for the microhardness and compressive strength is displayed in Table3, and S/N response graph is shown in Figure 6, drawn via the results provided in Table 3. The microhardness and compressive strength increase with increasing the reinforcement wt %, compaction pressure, and sintering temperature [29, 30]. Here, the reinforcement wt % has been found to be the supreme active parameter and compaction pressure; sintering temperature and sintering time have been identified to be a slight consequence on mechanical properties founded on S/N ratio. From the Figure 6, it is clearly witnessed that reinforcement wt % is the major noteworthy factor on the response. It is detected that the interaction of reinforcement wt % with compaction pressure is minor at low sintering temperature and noteworthy at higher sintering temperature, the foremost cause is improper bind amid the particles at low compaction pressure and low sintering temperature; and repeatedly, it decreases the microhardness and compressive strength value of the composites. Interaction of compaction pressure with sintering temperature is minor at low pressure and important at higher pressure. The maximum microhardness and compressive strength are attained when the compaction pressure is higher at sintering temperature [31–35]. The interaction of sintering temperature with sintering time is major at maximum temperature and minor at least temperature for the microhardness, because diffusion of atoms takes place at maximum temperature [36]. They [37] reported that increase in reinforcement wt % and sintering temperature increases the microhardness of the composites. This work [38] reported that rise in compaction pressure and sintering temperature increases the compressive strength.Table 3
Response table normalized SN ratios.
Expt. noNormalized S/N ratiosMicrohardnessCompressive strength10.4035380.30698920.4547990.43261830.1040530.52870440.4917120.39820350.5169240.66499260.7616660.55839870.538097080.031620.772042900.788488100.3670430.042028110.7275390.083448120.3265080.699102130.1090680.113598140.2216190.619004150.5861540.752855160.1309370.165829170.5538380.544084180.2756650.649764190.6817110.514086200.1922270.22339210.7432790.255672220.4606490.637432310.681133240.6480010.784986250.5453410.604386260.857780.898432270.6325391Figure 6
S/N ratio response graph for microhardness and compressive strength.


### 3.3. Grey Relational Analysis

It is one of the easiest and simplest tools to provide exact results. The values of grey relation coefficient, grey relation grade, and its rank of each experiments are arranged in Table4. The author [39] reported maximum the grey relational grade; the superior would be the multirecital characteristics. Figure 7 displays interaction plot for grey relational grade. Figure 8 displays grey relational response for parameters for instance microhardness and compressive strength. The optimal parameter is acquired from trial 27. Table 5 displays response table of the average grey relational grade for every level of the parameters. According to these stages, average grey relational grade has been identified; (i) grey relational grades have been combined via factor equal for every column in the orthogonal array and (ii) average obtained [40]. The abovementioned step has been repeated to determine the normal grey relational grade values for every level of the parameters. They [41] reported that average grey relational grade indicates the level of correlation amid reference sequence and the comparability sequence, the superior the value of the grey relational grade, the stronger the correlation to the reference sequence.Table 4
Grey relational coefficient.
Grey relation coefficientExpt. no.MicrohardnessCompressive strengthGrey relational gradeRank10.4560120.4191080.437562220.4783770.4684360.4734061930.358180.5147760.4364782140.495890.4538040.4748471850.5086080.5987960.553702860.67720.531010.604105670.5198030.3333330.4265682380.3405110.6868530.5136821590.3333330.7027290.51803114100.4413230.3429420.39213324110.6472820.352970.50012616120.4260790.6242990.52518913130.3594710.3606460.36005927140.391120.567540.4793317150.5471380.6692140.6081765160.3652130.3747650.36998926170.528450.5230590.52575512180.4083850.5880720.49822916190.6110310.5071430.5590877200.3823290.3916620.38699625210.6607460.4018230.53128410220.4810690.5796630.530366112310.61060.80531240.5868550.6992870.6430714250.5237470.5582760.5410119260.7785490.8311610.8048552270.57639510.7881983Figure 7
Interaction plot for grey relational grade.Figure 8
GRGs for response parameters (MH, CS).Table 5
Grey relational grade for each level of parameters.
FactorParametersLevel 1Level 2Level 3DeltaRankAReinforcement wt. %-6.195-6.622-4.3672.2551BCompaction pressure-6.601-5.198-5.3841.4033CSintering temperature-6.954-5.269-4.9611.9922DSintering time-5.128-6.160-5.8961.0324The optimal process parameter combination is achieved from Table5. The optimal reinforcement weight percent level 3; optimal compaction pressure level 2; optimal sintering temperature, level 3 and duration, level 1; and the best combination of process parameters, A3 B2 C3 D1. The optimal process parameters values are reinforcement content 15 wt %, compaction pressure 400 MPa, sintering temperature 575°C, and sintering time 1 h. It could be understood from the Table 6 the maximum value is 0.1479, and the equivalent control factor, i.e., the reinforcement wt % has the strongest effect on multiperformance characteristics. The order of consequence of the process parameter is factor A (reinforcement wt %), B (sintering temperature), C (compaction pressure), and D (sintering time), i.e., 0.1479>0.1179>0.0907>0.0620. Factor A is utmost important influence in the process for the multiperformance characteristics.Table 6
Response table for means.
FactorParametersLevel 1Level 2Level 3DeltaRankAReinforcement wt. %0.49320.47320.62110.14791BCompaction pressure0.47140.56210.55400.09073CSintering temperature0.45460.56040.57250.11792DSintering time0.56540.50340.51880.06204


### 3.4. ANOVA

From ANOVA in Table7, it is detected that the reinforcement weight %, sintering temperature, and compaction pressure are vital parameters from the F values. The sintering temperature is the most second influential parameter. The compaction pressure has been considered as third dominant part on the responses. Reinforcement weight % is notable as utmost serious factor with highest contribution percentage as shown in Figure 9. The compaction pressure, sintering temperature, and sintering time are subsidized fair with contributions. Figure 9 shows the contribution plot for all the parameters drawn from ANOVA table.Table 7
Analysis of variance for grey relational grade, using adjusted SS for tests.
SourceDFAdj SSAdj MSF valueP valueReinforcement wt.%20.1159570.0579798.610.002Compaction pressure20.0454030.0227023.370.057Sintering temperature20.0756830.0378425.620.013Sintering time20.0187700.0093851.390.274Error180.1211830.006732Total26S=0.0820511; R−Sq=67.86%; R−Sqadj=53.57%.Figure 9
Contribution plot from ANOVA.Sintering normal for Al2O3-reinforced 2xxx series Al composite powders was explored to acquire improved densification. The dissemination of the fluid stage was speedier in the composite powder sintered example than in the mixed powder sintered example. The outcomes demonstrate that a more prominent measure of fluid stage is expected to improve the sinterability of 2xxx series Al composite materials [42]. From Tables 3–6, the ideal parameters for the compressive strength and hardness can be anticipated as the reinforcement weight 15%, compaction pressure 400 MPa, the sintering temperature 575°C, and the sintering time 1 h. This result is near the S/N and ANOVA comes about.


### 3.5. Confirmation Test

Five examples were finished with A3B2C3D1 parameters, and their normal quality coefficient was found. Table8 reveals the relationship of the predicted quality coefficient and real quality coefficient of this composite preforms. A low rate blunder of 2.2% is gotten amid anticipated; what is more, trial esteem is demonstrating a decent relationship as appeared in Table 8.Table 8
Confirmation results.
Optimal process parametersParameterPredictedExperimentMicrohardnessA3B2C3D1190 VHNCompressive strength177 MPa


### 3.6. Microstructure Analysis of the Composites Produced by Anticipated Parameters

SEM analysis had been conducted for the samples fabricated from the predicted parameters (A3B2C3D1), and the images are shown in Figures 10(a) and 10(b). From the SEM images, the occurrence of weight percentage of B4C particles in the AA8079 matrix was attained. The foremost factors manipulating the microstructure of PM components are compaction pressure along with sintering temperature as per the results obtained from the present study. It is observed from Figures 10(a) and 10(b) that the higher compaction pressure and sintering temperature enhance proper bonding amid B4C particles and AA8079 matrix. This creates denser structure owing to greater diffusion rates results in fine microstructure. The uniform distribution of B4C particles in Figure 10(b) is evident in the proper identification PM parameters from the TGRA. This proper microstructure of the composite sample enhanced the properties such as CS and hardness.Figure 10
(a, b) SEM image of the AA8079-15%B4C composites fabricated as per the optimized PM parameters.
(a)(b)


## 3.1. S/N Ratio Analysis

Taguchi technique is a dominant utensil in value optimization for fabrication routes. Taguchi technique creates use of an unusual design of OA to inspect the worth characteristics over a nominal amount of experiments [27]. Word “signal” indicates the necessary value for the output characteristic, and “noise” indicates the horrible value for the output characteristic. The S/N ratios could be deliberate using Equations (1) and (2)
(1)SNratio=−10logMSD.MSD is mean square deviation.The MSD for the higher the better quality characteristic is expressed as(2)MSD=1n∑i=1n1Ti2.An entire degree of factor was six to four factors. The tests would permanently be carried out extra in numbers than that of designated DOF. Therefore, L27 OA was chosen based as per Taguchi’s design of experiments [28].


## 3.2. Interaction Effects of Factors

S/N response table for the microhardness and compressive strength is displayed in Table3, and S/N response graph is shown in Figure 6, drawn via the results provided in Table 3. The microhardness and compressive strength increase with increasing the reinforcement wt %, compaction pressure, and sintering temperature [29, 30]. Here, the reinforcement wt % has been found to be the supreme active parameter and compaction pressure; sintering temperature and sintering time have been identified to be a slight consequence on mechanical properties founded on S/N ratio. From the Figure 6, it is clearly witnessed that reinforcement wt % is the major noteworthy factor on the response. It is detected that the interaction of reinforcement wt % with compaction pressure is minor at low sintering temperature and noteworthy at higher sintering temperature, the foremost cause is improper bind amid the particles at low compaction pressure and low sintering temperature; and repeatedly, it decreases the microhardness and compressive strength value of the composites. Interaction of compaction pressure with sintering temperature is minor at low pressure and important at higher pressure. The maximum microhardness and compressive strength are attained when the compaction pressure is higher at sintering temperature [31–35]. The interaction of sintering temperature with sintering time is major at maximum temperature and minor at least temperature for the microhardness, because diffusion of atoms takes place at maximum temperature [36]. They [37] reported that increase in reinforcement wt % and sintering temperature increases the microhardness of the composites. This work [38] reported that rise in compaction pressure and sintering temperature increases the compressive strength.Table 3
Response table normalized SN ratios.
Expt. noNormalized S/N ratiosMicrohardnessCompressive strength10.4035380.30698920.4547990.43261830.1040530.52870440.4917120.39820350.5169240.66499260.7616660.55839870.538097080.031620.772042900.788488100.3670430.042028110.7275390.083448120.3265080.699102130.1090680.113598140.2216190.619004150.5861540.752855160.1309370.165829170.5538380.544084180.2756650.649764190.6817110.514086200.1922270.22339210.7432790.255672220.4606490.637432310.681133240.6480010.784986250.5453410.604386260.857780.898432270.6325391Figure 6
S/N ratio response graph for microhardness and compressive strength.


## 3.3. Grey Relational Analysis

It is one of the easiest and simplest tools to provide exact results. The values of grey relation coefficient, grey relation grade, and its rank of each experiments are arranged in Table4. The author [39] reported maximum the grey relational grade; the superior would be the multirecital characteristics. Figure 7 displays interaction plot for grey relational grade. Figure 8 displays grey relational response for parameters for instance microhardness and compressive strength. The optimal parameter is acquired from trial 27. Table 5 displays response table of the average grey relational grade for every level of the parameters. According to these stages, average grey relational grade has been identified; (i) grey relational grades have been combined via factor equal for every column in the orthogonal array and (ii) average obtained [40]. The abovementioned step has been repeated to determine the normal grey relational grade values for every level of the parameters. They [41] reported that average grey relational grade indicates the level of correlation amid reference sequence and the comparability sequence, the superior the value of the grey relational grade, the stronger the correlation to the reference sequence.Table 4
Grey relational coefficient.
Grey relation coefficientExpt. no.MicrohardnessCompressive strengthGrey relational gradeRank10.4560120.4191080.437562220.4783770.4684360.4734061930.358180.5147760.4364782140.495890.4538040.4748471850.5086080.5987960.553702860.67720.531010.604105670.5198030.3333330.4265682380.3405110.6868530.5136821590.3333330.7027290.51803114100.4413230.3429420.39213324110.6472820.352970.50012616120.4260790.6242990.52518913130.3594710.3606460.36005927140.391120.567540.4793317150.5471380.6692140.6081765160.3652130.3747650.36998926170.528450.5230590.52575512180.4083850.5880720.49822916190.6110310.5071430.5590877200.3823290.3916620.38699625210.6607460.4018230.53128410220.4810690.5796630.530366112310.61060.80531240.5868550.6992870.6430714250.5237470.5582760.5410119260.7785490.8311610.8048552270.57639510.7881983Figure 7
Interaction plot for grey relational grade.Figure 8
GRGs for response parameters (MH, CS).Table 5
Grey relational grade for each level of parameters.
FactorParametersLevel 1Level 2Level 3DeltaRankAReinforcement wt. %-6.195-6.622-4.3672.2551BCompaction pressure-6.601-5.198-5.3841.4033CSintering temperature-6.954-5.269-4.9611.9922DSintering time-5.128-6.160-5.8961.0324The optimal process parameter combination is achieved from Table5. The optimal reinforcement weight percent level 3; optimal compaction pressure level 2; optimal sintering temperature, level 3 and duration, level 1; and the best combination of process parameters, A3 B2 C3 D1. The optimal process parameters values are reinforcement content 15 wt %, compaction pressure 400 MPa, sintering temperature 575°C, and sintering time 1 h. It could be understood from the Table 6 the maximum value is 0.1479, and the equivalent control factor, i.e., the reinforcement wt % has the strongest effect on multiperformance characteristics. The order of consequence of the process parameter is factor A (reinforcement wt %), B (sintering temperature), C (compaction pressure), and D (sintering time), i.e., 0.1479>0.1179>0.0907>0.0620. Factor A is utmost important influence in the process for the multiperformance characteristics.Table 6
Response table for means.
FactorParametersLevel 1Level 2Level 3DeltaRankAReinforcement wt. %0.49320.47320.62110.14791BCompaction pressure0.47140.56210.55400.09073CSintering temperature0.45460.56040.57250.11792DSintering time0.56540.50340.51880.06204


## 3.4. ANOVA

From ANOVA in Table7, it is detected that the reinforcement weight %, sintering temperature, and compaction pressure are vital parameters from the F values. The sintering temperature is the most second influential parameter. The compaction pressure has been considered as third dominant part on the responses. Reinforcement weight % is notable as utmost serious factor with highest contribution percentage as shown in Figure 9. The compaction pressure, sintering temperature, and sintering time are subsidized fair with contributions. Figure 9 shows the contribution plot for all the parameters drawn from ANOVA table.Table 7
Analysis of variance for grey relational grade, using adjusted SS for tests.
SourceDFAdj SSAdj MSF valueP valueReinforcement wt.%20.1159570.0579798.610.002Compaction pressure20.0454030.0227023.370.057Sintering temperature20.0756830.0378425.620.013Sintering time20.0187700.0093851.390.274Error180.1211830.006732Total26S=0.0820511; R−Sq=67.86%; R−Sqadj=53.57%.Figure 9
Contribution plot from ANOVA.Sintering normal for Al2O3-reinforced 2xxx series Al composite powders was explored to acquire improved densification. The dissemination of the fluid stage was speedier in the composite powder sintered example than in the mixed powder sintered example. The outcomes demonstrate that a more prominent measure of fluid stage is expected to improve the sinterability of 2xxx series Al composite materials [42]. From Tables 3–6, the ideal parameters for the compressive strength and hardness can be anticipated as the reinforcement weight 15%, compaction pressure 400 MPa, the sintering temperature 575°C, and the sintering time 1 h. This result is near the S/N and ANOVA comes about.


## 3.5. Confirmation Test

Five examples were finished with A3B2C3D1 parameters, and their normal quality coefficient was found. Table8 reveals the relationship of the predicted quality coefficient and real quality coefficient of this composite preforms. A low rate blunder of 2.2% is gotten amid anticipated; what is more, trial esteem is demonstrating a decent relationship as appeared in Table 8.Table 8
Confirmation results.
Optimal process parametersParameterPredictedExperimentMicrohardnessA3B2C3D1190 VHNCompressive strength177 MPa


## 3.6. Microstructure Analysis of the Composites Produced by Anticipated Parameters

SEM analysis had been conducted for the samples fabricated from the predicted parameters (A3B2C3D1), and the images are shown in Figures 10(a) and 10(b). From the SEM images, the occurrence of weight percentage of B4C particles in the AA8079 matrix was attained. The foremost factors manipulating the microstructure of PM components are compaction pressure along with sintering temperature as per the results obtained from the present study. It is observed from Figures 10(a) and 10(b) that the higher compaction pressure and sintering temperature enhance proper bonding amid B4C particles and AA8079 matrix. This creates denser structure owing to greater diffusion rates results in fine microstructure. The uniform distribution of B4C particles in Figure 10(b) is evident in the proper identification PM parameters from the TGRA. This proper microstructure of the composite sample enhanced the properties such as CS and hardness.Figure 10
(a, b) SEM image of the AA8079-15%B4C composites fabricated as per the optimized PM parameters.
(a)(b)


## 4. Conclusions

The subsequent conclusions have been strained from the investigations conducted on the AA8079-B4C composites under various process parameters.The AA8079-B4C composites were fabricated via powder metallurgy manufacturing method.The influence of powder metallurgy process parameters on AA8079-B4C composites was studied.The important parameters reinforcement weight percentage, compaction pressure, sintering temperature, and sintering time were analysed by using Taguchi grey analysis on the responses such as hardness and compressive strength of AA8079-B4C samples.Amid the parameters, reinforcement weight percentage 15%, sintering temperature 575°C, compaction pressure 400 MPa, and sintering time 1 h shows a positive consequence on the mechanical properties.SEM examination on the AA8079-B4C sintered composites fabricated by the optimized parameters shows the homogenous dispersal of the reinforcement with the matrix and good bonding between the matrix and reinforcement.In future, the same results were optimized by using some other optimization tools like genetic algorithm and neural network.



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