# Effect of SiC Particle Incorporated Dielectric Medium on Electrical Discharge Machining Behavior of AA6061/B4Cp/SiCp AMCs

Authors: Johny Khajuria; N. Nagabhooshanam; Pankaj Sharma; Atul Kumar; Santosh Kumar Sahu; Peyyala Sree Devi; Kuma Gowwomsa Erko
Journal: Advances in Materials Science and Engineering
 (2022)

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## Abstract

In this work, it was observed that using motor oil as the dielectric fluid when producing AA6061/SiCp/B4Cp hybrid composites provided wealth from waste. In these studies, modifications in silicon carbide (SiC) concentration, electrode (copper and brass), current, pulse time, and reinforcing weight percentage were tested. Surface roughness and machined hardness are measured and reported for each removed piece of the material. The bridging effect of silicon carbide particles raised the material removal rate (MRR), while a thorough dielectric fluid flush enhanced the Ra value. On the machined topography, the oil carbon content left dark smudges. Machining performance is superior to that of copper electrode-processed specimens. The MRR, TWR, and Ra all rise as the discharge current and pulse duration increase. Because there is no remelted layer, composites with low machining surface hardness had better finishes. The parameters were optimized using the TOPSIS approach, and it was observed that the efficiency of machining was enhanced by utilizing engine oil by the concentration of 4 gl−1. Moreover, during machining, optimized parameters like pulse on time 36 seconds, current 4 Amps, and also a brass electrode.


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## Body

## 1. Introduction

Standard machining methods were useless in composite materials because of the occurrence of strengthening elements [1]. EDM and other unconventional methods of machining are frequently used to attain high precision and accuracy. The key aims of the EDM are MRR, TWR, and Ra. Process variables such as electrical current, pulse on time, and tool material are all significant in determining machining characteristics [2, 3]. Machine tools can be made from a variety of materials, including copper, brass, graphite, Cu-W, and ZiC. The correct tool and workpiece combination must be determined to optimise machining performance [4–6]. It is possible to increase machine output by mixing foreign particles into the dielectric fluid [7].There have been several research altering the particle size, powder materials, and powder concentration [8]. Powders including SiC, B4C, Al2O3, and graphite are commonly used in PMEDM. Adding powder particles, according to the majority of findings, reduces the gap growth and thus raises MRR [9–11]. Because of the homogeneous distribution of discharge energy, an improvement in MRR was attained. Short circuits are formed when powder particles are added above the limit, resulting in a significant decrease in MRR. Hong et al. [12] tested the kerosene dielectric by adding chromium powder to it. Results show that the addition of chromium powder to dielectric fluid raises material removal rate and surface roughness by 45 and 68%. Abdudeen et al. [13] investigated the dielectric fluid breakdown using powders for four distinct materials. The breakdown of dielectric was reduced when the concentration of powder was increased [14–16].In terms of dielectric fluids, hydrocarbon, vegetable, and synthetic oil-based fluids are maximum and frequently used [13, 17]. Dielectric fluids such as kerosine, EDM oil, and deionized water are the most common. Machined by these traditional fluids, poisonous gas is released into the environment [18–20]. HSS-M35 was machined using argon, nitrogen, and air as dielectric media by [21]. Nitrogen and argon gas was found to be the finest dielectric medium for removing more material and producing the highest surface quality. Imran et al. [22] used distilled water and paraffin oil as dielectric media to manufacture AA6061 aluminium alloy. Using purified H2O as dielectric resulted in a thicker white coating than using paraffin oil. Larger globules and surface pitting have been noticed under paraffin medium, according to the report as well [23–25].Dielectric fluid properties of Jatropha biodiesel were examined by [26]. If you compare it to synthetic EDM oil, it performs better than kerosine in terms of machining, but it performs worse than kerosine in terms of cutting. Pure water and kerosine can be used as the dielectric medium to mill high-carbon and high-chromium steel, according to [27–29]. In this machining method, copper, graphite, and brass tools were employed, and the current and pulse duration were both dynamically modified. Carbon was thought to be deposited on the surface of machines that were operated in kerosene environment [30]. When specimens were fabricated in distilled water using a graphite tool, the MRR was larger and the Ra was lower. Deionized tap water was employed by the authors of [31] as an electric discharge medium. A decrease in surface quality was found as a result of machining that resulted in craters. Dielectric fluid made from multiwall carbon nanotubes and mixed with Kusum edible oil by the authors of [32] was utilized by them to manufacture EN-31.The multicriteria decision-making (MCDM) technique is used in TOPSIS to be preferred because they resemble the deal scenario [33, 34]. When it came to solving a material election problem, TOPSIS was one of the most effective MCDM techniques. Finding the top solution and the nastiest solution yielded the top and worst values [35]. Finding these two perfect values led to the discovery of the optimum parameters. Four separate process parameters were varied by their EDM experiments [36]. The TOPSIS technique was used to discover the ideal process parameters for abrasive water jet machining and powder mixing to electric discharge drilling [37, 38]. Numerous studies using a variety of dielectric fluids and powders in combination with electric discharge machining have been published, according to scientific research [39–41]. There has never been any research on unused engine oil as a dielectric fluid. These projects sought to make money from waste by using engine oil as dielectric fluid for EDM on AA6061 hybrid composites [42].


## 2. Materials and Methods

The matrix material was AA6061, which is employed in the fabrication of aerospace structural parts because of its weldability and dimensional stability, and it is excellent for a wide range of diverse uses. Its element percentage is shown in Table1. An average particle size of 5 μm was chosen as strengthening for the SiC and B4C materials. Modified stir casting was used to create the composite. In a graphite receptacle, the alloy was heated to 850°C. At 1000 rpm, the mixture is agitated for 180 seconds after injecting preheated SiC and B4C particles. Before reheating the composition to 850°C, the same was cooled to 420°C for 120 seconds and violently agitated. Engineering composites of various weight percentages have been made using the same method for releasing the charge into the preheated die steel. Figure 1 shows a schematic view of the electrical discharge machine.Table 1
Chemical composition of AA6061.
ZnMnCuMgFeAl0.240.850.240.920.04RemainingFigure 1
Schematic view of the electrical discharge machine.The dielectric was made up of copper and brass, with different concentrations of SiC powder (0, 4, and 8 gl−1) added to improve the machining properties. Table 2 depicts the dielectric characteristics of the used engine oil obtained from the automotive exhibition room. A mechanical stirrer, submersible flushing pump, and a 12-liter tank make up the PMEDM tank [43]. The variables’ ranges were determined through preliminary tests. We assumed that all of our studies would display a steady discharge. Table 3 depicts the values of the input variables. Several responses are recorded, including MRR, TWR, Ra, and surface hardness (SH). For each trial, the machining time was set at 10 minutes. The specimen Ra was determined using an SJR210 surface roughness tester, which measured it 10 times and averaged the results. The Rockwell hardness testing machine was used to evaluate the surface hardness value following machining in accordance with ASTM standard E18. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), a multicriteria decision analysis optimization method, was used to discover the ideal process parameter value as follows:(1)materialremovalrate=Wb−Wa,(2)toolwearrate=Tb−Ta.Table 2
Dielectric fluid properties.
PropertyElectrical discharge machining oilEngine oil after usedRemarksStrength (KVA)4765Strength raises the efficiency of machiningColourClearBlackThe workpiece is invisibleCarbon (%)997Black dot deposition on the surface of the machine due to carbon contentFlashpoint (ºC)>93188Enhancing the safety of work by higher flash pointThermal conductivity (Wm−1 k−1)0.1140.140Heat dissipation raises due to thermal conductivityDensity (kg m−3)830876Blushing expands the densityViscosity at 45°C (mm2/s)2.462.5Heat transfer raises due to low viscositySpecific heat (kJ kg−1 K−1)2.121.824Specific heat is less in used oil compared to EDM oilTable 3
Levels of process parameters.
ParameterLevelsCurrent2,4,6AElectrode materialCopper, brassPowder concentration0, 4, 8 gl−1Pulse on time12,24,36μsDielectric fluidUsed engine oilWork pieceAA6061/SiC/B4CVoltage32VThe weight percentage of composite5,10,15wt.%


## 3. Results and Discussion

When powder concentration is 4 gl−1, the MRR increased, and when it was 8 gl−1, the MRR decreased. Particles suspended in a space between a tool and its workpiece will increase spark frequency and intensity if applied a voltage. As a result, the MRR will rise. Table 4 illustrates the results of the inquiry. The findings were in line with prior research. As the spark gap widened further, more heat was released due to an increase in powder concentration. Machining the workpiece was not possible due to insufficient heat generated, thereby decreasing the MRR. With copper electrodes, samples cut with brass tools had a lower MRR.Table 4
EDM experimental results of AA7075 hybrid composites.
S. no.ToolConcentration of the powderCurrentPulse on time% of weightMaterial removal rateTool wear rateSurface roughnessHardnessExpected valuesMaterial removal rateTool wear rateSurface roughnessHardness1Cu02125292594.3594300.952.94.889.62Cu042410342724.497321.669.95.194.73Cu063615343835.30103346.678.45.9101.84Cu421210332442.1289367.349.12.688.35Cu442415403633.6097393.860.73.793.46Cu46365507713.6684488.479.63.785.17Cu621210393694.7291374.564.64.886.98Cu642415334765.1295346.869.65.297.99Cu66365294694.8499275.666.75.198.510Br02125297835.6796292.787.75.797.311Br042410325863.8293306.985.74.191.212Br063615395884.2997302.487.74.994.013Br421215327682.5190336.167.92.689.414Br44245443741.7385469.174.51.485.715Br463610352732.1495356.368.92.491.516Br621210379793.9895356.978.54.494.017Br642415284843.9296292.379.34.397.618Br66365304774.1791307.872.44.689.9In addition, it was discovered that the current MRR climbed until a saddle point of 4 A, at that time began to fall again. MRR increased more rapidly because the current flowed through a denser plasma channel and had a higher intensity, which naturally rises as the current increases as shown in Figure2. As current flows across the plasma channel, MRR lowers. For a current of 6 A, however, the MRR was larger when the powder concentration was 4 gl−1. Plasma channels shrink and produce greater heat as spark gaps increase owing to powder addition, according to this theory. Because of this, melting and vaporization are accelerated, increasing MRR. MRR was maximum at a powder concentration of 4 gl−1. According to the data, the MRR decreases as the weight % of strengthening particles increases.Figure 2
Differences in MRR concerning diverse process parameters.Copper electrodes, as seen in Figure3, have a lower TWR than brass electrodes. This decrease in TWR was nominally lower for electrodes with greater melting temperatures because of the material’s physical characteristics. As a result, copper has a lower TWR than brass due to its higher melting point. TWR decreases until it reaches the saddle point of 4 gl−1 and then begins to rise again. From the result, TWR can easily reduce due to the bulk of the negative ions passing over the process gap. Short circuits develop by increasing the powder concentrations, resulting in a rise in TWR. Increased discharge current erodes a greater amount of electrode material, increasing TWR. Increasing the pulse-on-time causes a raise in spark energy and an increase tool wear rate as previously noted. At an outlet current of 2 A, a pulse on time of 15 s, and a concentration of powder of 4 gl−1, TWR was determined to be modest. tolerable work ratio (TWR) is minimum for composites with 5 and 10% reinforcing particles, but it rapidly rises when samples with 15% reinforcing particles are machined.Figure 3
Differences in tool wear rate with respect to diverse process parameters.Figure4 shows that the addition of SiC powder to the dielectric phase considerably reduces Ra. The distance separating the electrode and the workpiece widens as the particles are added. There are no more “globules” or “layers” of remelted material to be formed, which helps to increase the Ra of the gap. As a result of the increased strength of the powder, the gap distance grows even further, which reduces heat generation. As a result, the quality of the surface is lowered because of the production of voids and microcracks. A greater Ra value is created as the discharge strength is raised, and the surface is creating pits. The even distribution of discharge energy provided by 4 gl−1 particles results in a finer surface finish. Because of the greater spark bombardment caused by the longer pulse-on-time, the surface’s Ra value increased. Composites have a deteriorating surface quality with increasing weight fractions. Micropits emerge on the surface when reinforcing substances are removed from the surface during machining, which is consistent with prior observations.Figure 4
Differences in the roughness of surface with respect to diverse process parameters.Hardness values before machining ranged from 86, 93 to 97 HRB for composites containing 5, 10, and 15% reinforcing particles. A higher weight percentage led to higher hardness readings because of the presence of hard ceramic fragments. Machining composites with powder particles at 4 gl−1 results in a loss of hardness as shown in Figure 5. The surface quality is raised by a more significant spark gap generated by the silicon carbide particles, reducing the production of remelted layers. The machined surface’s hardness changes when reinforcing particles are removed from the surface.Figure 5
Differences in machined surface hardness concerning diverse process parameters.Remelted layers are formed when the powder is added to composites while they are being machined, which makes the globules less stable. As a result of the high pulse on time, most of the molten material is deposited again on the surface due to insufficient flushing. Adding SiC particles to the dielectric fluid remedied this issue by increasing the spark gap and allowing for adequate flushing. The experimental data used in the prediction calculation was used to determine the regression coefficients of the second-order equation. This study indicated that the difference between actual and projected results was less than five percent.


## 4. Surface Topography

The occurrence of carbon in wasted engine oil had discovered to be the cause of black patches on the composite surface. Due to the irregular spreading of discharge energy, a significant fracture was detected on the surface; the acquired data are exactly matched to the experimental data, as shown in the Figure5. It is possible to discern globules, remelted layers, and deeper pits all over the surface, all of which significantly impair the surface. The surface topography at a higher magnification of 2000x, reveals craters, tiny holes, and micropits. Because of this, the surface roughness value was increased by approximately 20 μm. Machined composites have a reduced hardness because they lack a remelted layer on the surface. The spark gap’s surface quality improves because melting debris is washed away by the extra space between the electrode and the workpiece. Other researchers have made similar discoveries. There is an increase in the production of carbon black spots, fracturing, and pitting at higher magnifications. This is compensated for by a higher spark frequency. Thus, for fine-finish machining of hybrid composites, an increased spark gap was shown to be preferable.


### 4.1. TOPSIS

Due to identifying the optimum possible mixing of parameters, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) optimization approach was applied. Because policymakers and end-users are both involved, this solution has an advantage. There were 18 experiments with three responses, thus a crucially in a matrix form of 18 × 3 has been produced as displayed in Table4. As a first step, data sets are normalized by equations (3) and (4). The normalizing matrix is Bij, where i denotes the number of experiments and j is the number of responses. As a result, these normalized matrices were used to generate a weighted normalized judgment matrix Cij as follows:(3)Bij=Aij∑,(4)Cij=wj∗Bij.To calculate eigen values, the weighted normalized decision matrix is weighted, with£+ being the highest value and £− being the lowest value in equation (5). According to formula (6), for nonbeneficiary qualities, the calculation was reversed.For beneficiaries,(5)￡+=MaxCiji=1n,￡−=MinCiji=1n.For nonbeneficiaries,(6)￡+=Mini=1n,￡−=MaxCiji=1n.Using this equation, we were able to determine the optimal P+ and ideal in the nastiest P− solutions in equation (7). According to equation (8) indicated in Table 5, the dispersion between ideal and nonideal is determined.(7)P+,P−=∑Cij−￡+2+Cij−￡n2,(8)Oi=P−P++P−.Table 5
Enhanced values in investigational data set and technique for the order of preference by similarity to ideal solution.
Normalized matrixNormalized decision matrix by weightP+P–PiRank0.193210.786540.2645580.064530.0634540.125860.080.050.42170.216740.2287650.2948750.085480.0674160.112560.090.040.3380.217240.2643240.3187480.085700.0871240.1257630.100.030.22130.263400.1683040.1295880.094230.0578610.0534750.040.100.8050.282430.1993450.2165900.101820.0675430.1876020.060.070.6320.353460.2575280.2191470.124160.0851970.1835600.060.080.6460.261070.2187650.2873650.0966280.0734820.1123160.080.050.41140.231030.2416850.3063610.0838190.0795020.1168340.090.040.28100.202230.2213490.2885450.0738680.0743560.1162340.090.040.32160.212600.2968120.2345850.0765240.0965570.1312490.110.020.10150.225230.2839120.2288260.0817350.0928670.0825460.080.050.42180.202340.3065440.2565100.0751960.0996880.1012270.090.040.3190.226570.2384530.1534820.0821690.0728300.0638240.060.080.6430.311240.2612870.1140980.1082100.0861540.1491460.040.100.8040.243210.2314960.1320340.0877690.0785260.0924160.050.090.7110.260150.2378890.2386080.0935650.0874090.0843410.070.050.47120.197960.2723470.2348120.0727670.0898250.0996830.090.050.3970.201670.2487210.2496120.0763140.0851530.0942160.080.050.3811Wt.0.430.40.4Eigen values0.1242680.0587530.0485230.0727680.0987690.131249It was discovered that the discharge current of 4 A and also pulse on time of 45μs were the ideal parametric values when the electrode was brass and the powder mixture concentration was 4 gl−1.


## 4.1. TOPSIS

Due to identifying the optimum possible mixing of parameters, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) optimization approach was applied. Because policymakers and end-users are both involved, this solution has an advantage. There were 18 experiments with three responses, thus a crucially in a matrix form of 18 × 3 has been produced as displayed in Table4. As a first step, data sets are normalized by equations (3) and (4). The normalizing matrix is Bij, where i denotes the number of experiments and j is the number of responses. As a result, these normalized matrices were used to generate a weighted normalized judgment matrix Cij as follows:(3)Bij=Aij∑,(4)Cij=wj∗Bij.To calculate eigen values, the weighted normalized decision matrix is weighted, with£+ being the highest value and £− being the lowest value in equation (5). According to formula (6), for nonbeneficiary qualities, the calculation was reversed.For beneficiaries,(5)￡+=MaxCiji=1n,￡−=MinCiji=1n.For nonbeneficiaries,(6)￡+=Mini=1n,￡−=MaxCiji=1n.Using this equation, we were able to determine the optimal P+ and ideal in the nastiest P− solutions in equation (7). According to equation (8) indicated in Table 5, the dispersion between ideal and nonideal is determined.(7)P+,P−=∑Cij−￡+2+Cij−￡n2,(8)Oi=P−P++P−.Table 5
Enhanced values in investigational data set and technique for the order of preference by similarity to ideal solution.
Normalized matrixNormalized decision matrix by weightP+P–PiRank0.193210.786540.2645580.064530.0634540.125860.080.050.42170.216740.2287650.2948750.085480.0674160.112560.090.040.3380.217240.2643240.3187480.085700.0871240.1257630.100.030.22130.263400.1683040.1295880.094230.0578610.0534750.040.100.8050.282430.1993450.2165900.101820.0675430.1876020.060.070.6320.353460.2575280.2191470.124160.0851970.1835600.060.080.6460.261070.2187650.2873650.0966280.0734820.1123160.080.050.41140.231030.2416850.3063610.0838190.0795020.1168340.090.040.28100.202230.2213490.2885450.0738680.0743560.1162340.090.040.32160.212600.2968120.2345850.0765240.0965570.1312490.110.020.10150.225230.2839120.2288260.0817350.0928670.0825460.080.050.42180.202340.3065440.2565100.0751960.0996880.1012270.090.040.3190.226570.2384530.1534820.0821690.0728300.0638240.060.080.6430.311240.2612870.1140980.1082100.0861540.1491460.040.100.8040.243210.2314960.1320340.0877690.0785260.0924160.050.090.7110.260150.2378890.2386080.0935650.0874090.0843410.070.050.47120.197960.2723470.2348120.0727670.0898250.0996830.090.050.3970.201670.2487210.2496120.0763140.0851530.0942160.080.050.3811Wt.0.430.40.4Eigen values0.1242680.0587530.0485230.0727680.0987690.131249It was discovered that the discharge current of 4 A and also pulse on time of 45μs were the ideal parametric values when the electrode was brass and the powder mixture concentration was 4 gl−1.


## 5. Conclusion

Hybrid composites were successfully fabricated using AA6061/SiCp/B4Cp and dielectric fluid based on motor oil. A range of variables, including dielectric powder concentration, pulse time, and electrode material, was used to analyze machining performance. Following research, the following conclusion was reached:(1)
The bridging activity of SiC particles increased spark frequency and energy strength, improving MRR. When an amount is applied beyond the saddle point, short circuit occurs. Brass-tool manufactured specimens exhibited the highest MRR when compared to copper-tool manufactured specimens. This raises the MRR by increasing discharge current and pulse on time, which increases plasma channel density and spark energy.(2)
With the increased spark gap, the flushing of dielectric fluid enhances surface quality when suspended SiC particles are present. Due to bombardment and surface blitzkrieg, the Ra value raises with raising pulse on time and current. When increased beyond the threshold, this results in reduced heat generation and the formation of voids and pits.(3)
Its higher melting point makes it possible for the copper electrode to have a more efficient total work rate (TWR). SiC particles reduce TWR by reducing the mobility of negatively charged ions across the gap. TWR rises in proportion to longer pulse on times and higher discharge currents.(4)
The mechanical characteristics of composites are adversely affected by increases in wt.% of reinforcing elements. In terms of machining surface hardness, remelted layer and globule formation have a greater impact than normal. Higher surface quality can be achieved with lower machined hardness specimens.(5)
Carbon in the used engine oil causes a black spot on the machined surface. Surface features such as pits, remelted layers, crater valleys, and globules were apparent. Adding SiC particles reduce surface roughness by preventing the creation of the remelted layer. The surface quality deteriorates when the length of the crater valley is extended by SiCp at a concentration of 6 gl−1.(6)
The TOPSIS optimization technique is used to optimize the input variables. Current, 6 amps; pulse on time, 45μs; and brass tool are the best process variables for the machining of AA6061/SiCp/B4Cp underutilized engine oil dielectric medium.



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*Source: 2661158-2022-07-21.xml*