# Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite

Authors: CR Mahesha; R .Suprabha; NPG Bhavani; Prashant Sunagar; Raja Ramesh; P. Balamurugan; Rajasekar Rajendran; Anirudh Bhowmick
Journal: Advances in Materials Science and Engineering
 (2022)

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## Abstract

Improved properties can be found in aluminum alloys containing silicon carbide reinforcement particles. This work studies the machinability of Al 6063 reinforced with silicon carbide particles with wire electrical discharge machining. To attain a high material removal rate, wire EDM constraints such as current (I), pulse-on time (Ton), wire speed (Ws), voltage Iv, and pulse-off time (Toff) can be adjusted with precision. Taguchi L16 orthogonal arrays are used to design the experiments and statistical methods are used to examine. These process characteristics had a significant impact on the overall rate of return, with a 28.2% impact on the MRR, 23.04% impact on the MRR, and 22.86% impact on the MRR. We achieved MRRs of 65.21 mg/min for samples containing 5% and 10% SiCp at optimal conditions, respectively. Linear regression was used to create the statistical model, which then used confirmation trials to verify its accuracy in predicting MRR (R -73.65%). The statistical model is used to estimate MRR based on various process parameter settings.


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## Body

## 1. Introduction

Machining using EDM is an isolated phenomenon and removes material by repeatedly igniting a conductor and the workpiece in a dielectric liquid [1]. This fluid circulates to remove molten debris from the work area while it is being machined. You can use wire EDM to make complex forms out of conductive metals like aluminum, copper, brass, titanium, and other alloys [2]. While other technologies can produce thin walls with low surface roughness and short internal corner radii, wire EDM has the advantage of having the least amount of burrs [3]. This approach's speed and economy are two additional perks. As a result of their low density, aluminum alloys are extensively employed in the transportation industry (aircraft, planes, and ships). Of the total, the transportation sector consumes 23%, followed by construction (25%), packing (8%) and engineering (11%), and electrical appliances (6%). This metal has a low density, high malleability, resistance to corrosion, and electrical conductivity [4, 5]. The aluminum 6000 series' superior mechanical and welding characteristics enable a wide range of goods, including aviation fittings and electrical components and connections, camera lens mounting, and marine fits and accessories.Alloying it with other substances like silicon carbide (SiC), aluminum oxide (Al2O3) magnesium oxide (MgO), water-glass (WC), silicon dioxide (SiO2), or boron carbide (B4C), etc, the inclusion of SiC and Al2O3 particles in an aluminum matrix enhanced yield strength (YS), ultimate tensile strength (UTS), elastic modulus (EM), and hardness (Hd), according to [6]’s research. Electrical conductivity was reduced by the addition of aluminum oxide, zirconium, cerium, and Mg. In comparison to the as-cast alloy, the age-hardened Al 6063-SiCp-reinforced composites demonstrated an increase in hardness of 120–145% [7]. Compared to high-temperature aging, lower-temperature aging demonstrated a significant improvement in wear resistance. Size reduction was achieved with the inclusion of grain refiners (Al–5Ti–1B), which also turned a long, coarse grain into a circular one with improved tensile and wear resistance. Strength/weight ratio, superior thermal stabilization, and abrasive resistance make SiCp a useful reinforcing particle [8, 9]. Al 6063 with SiCp had enhanced Young’s modulus, strength, and toughness (500 nm). An investigation of Al MMC strengthened with silicon and Tungsten carbide was conducted by [10]. Load is transferred to the reinforcement through the robust interface among the matrix and strengthened composites. This procedure enhanced both elasticity and strength. References [11, 12] used continuous mechanical simulations to show that whiskers of Tungsten carbide and aluminum nitride strengthen Al composites. Figure 1 shows the schematic view of the wire cut electrical discharge machine.Figure 1
Schematic view of wire cut electrical discharge machine.The abrasive character of SiCp-reinforced aluminum alloys makes machining them challenging, which results in rapid tool wear and a poor surface finish [13]. Particle detachment and fracture in reinforced particles are what cause voids and cavities on the machined surface to appear. Nontraditional methods such as wire EDM (electrical discharge generated between the tool and workpiece) may be an option for removing metal from the workpiece [14, 15]. The base metal is molten and vaporized by the spark gap's concentrated heat. The spark gap is filled with dielectric fluid, which is used to cool the machine as well as control the discharge of electricity. This procedure has a drawback in that it requires a workpiece that is electrically conducting [16–19]. Among the benefits of wire EDM are its ability to produce complicated profiles regardless of the workpiece's mechanical qualities and the fact that tools and workpieces do not come into direct touch during the machining process. Currently, wire EDM research trends are mostly focused on scientific research of application areas and optimization of processing variables [20]. For vehicle nozzles, turbine blade cooling holes, medicinal devices such as stents, microfluidic channels, and the fabrication of different microelectromechanical devices, EDM has become increasingly popular in recent years. Polishing 3D printed parts with the wire EDM reduces surface roughness by roughly 80%, making it a useful tool for other applications as well [21]. The eradication of balling pits, voids, and porosity could be seen in the 3D printed SS316 L material that had been polished with wire EDM. Because the long discharge time of the high ton produced roughness, the wire EDM polishing application of the low ton was promoted. Electrode wires are visible on the polished surface, accumulating in extremely small amounts [22].Once it came to aluminum composites, [23] studied the effects of hot extrusion and heated treatment on reinforced SiCp size (0.7–13 m). Smaller-sized SiCp and 0% particles were not significantly different in the surface quality after wire EDM. Both the kerf breadth and the electrode wear on the smaller-sized SiCp were greater than on test samples reinforced with larger particles [24, 25]. Moreover, the wire electrode was coated with matrix material during the milling process. Mixing MWCNTs into the dielectric fluid resulted in an increase in machining performance. Shape memory alloy nitinol wire EDM treated with MWCNTs (1 g/L) showed a 75.42% increase in MRR and a 19.15% decrease in SR. Additionally, it was determined that the recast layer's thickness had decreased significantly. Al413 - 9% composite was studied by [26] for its wire EDM machinability. The kerf-width and surface roughness were highly correlated with the machine's voltage, capacitance, and feed rate. Improving the supply enhanced the MRR during SiC/Al composite machining. Matrix SiCp content increases by 15%, whereas subsequent increases in SiC content increase the MRR in the opposite way [27]. The AA6025 composite was tested for its surface coarseness and material removal rate (silicon carbide: 10 wt% and aluminum oxide:10 wt.%) [28]. The conductive powder was shown to improve performance in the dielectric fluid by the researchers, according to their findings. This is because the conductive particles have diminished the dielectric fluid's strength and hence increased the spark gap. As a result of this phenomenon, the spark gap discharge remained more stable and higher during the ignition process. Researchers of [29, 30] used a 6%SiC/A356 surface to better understand EDM surface features. SiC/A359 is 30% and 10% SiC/Al359 is 30%. The harder the surface became, the denser the reinforced particles became. In EDM, copper electrodes transport a lot more metal than graphite electrodes. Researchers of [31–33] enhanced the wire EDM parameter settings to machine an Al 6063/cenosphere composite. In addition to increasing in size, the craters' surface wave smoothness also increased as I and Ton. Electrode wear is increased because of the composite’s poor surface quality.The weight percentage and volume percentage of reinforcing particles in the matrix influence the composite's mechanical properties. When reinforcing particles are consistently distributed throughout the material, the mechanical characteristics of composites improve [34]. Due to increased stress, reinforcing materials began forming aggregates that reduced their mechanical qualities. SiCp up to 10% was shown to boost tensile and hardness; however, after that point, these properties rapidly deteriorated. However, ductility decreased as the amount of SiCp in the alloy rose. More than 8% SiCp loading resulted in aggregated particles in the Al 6063 matrix when stir casting was performed on test samples. As a result, in this project, the weight fraction is capped at 8%. Traditional machining of Al-SiCp is plagued by pits, voids, microcracks, and cracked reinforcements on the cut surface. Wire EDM machining, for example, can get around these problems [35]. Wire EDM process constraints including Ton and Toff, Ws, and Iv are examined in this analysis in order to optimize these settings in order to maximize productivity in terms of the MRR. Fuzzy AHP-ARAS, genetic algorithms, and particle swarm optimization methods like the Grass–Hopper method and moth-flame are more appealing for experiments with large data sets; however, Taguchi and response surface approaches, as used in this study, are better instruments for optimization in small-scale experiments [36]. As a result, the Taguchi technique is used in this study to optimize the MRR and build a regression model that reliably predicts the MRR.


## 2. Materials and Methods

Sample preparation, experimental setup, wire EDM processing parameters, and design of experiments are all covered in this part. The response parameter and optimization approach are also discussed.


### 2.1. Details of the Experiment and the Specimens

In this study, researchers in India employed a CNC 2-axis wire EDM manufactured by accord wire EDM. Mo wire with a width of 0.16 mm is employed to make the tool electrode. Soft water and gel make up the insulator. The regulator has a 0.001 mm resolution. Stir casting is employed to create the SiC particle-reinforced Al-6063 alloy. By mechanically stirring the molten metal, reinforcing particles (5 and 10% by weight) that have been preheated to 650°C are evenly distributed throughout the molten matrix, which is liquefied at 850°C. The molten metal is poured into a mold that has been preheated to 200 °C and allowed to cool to room temperature before being moved. Table1 provides an overview of the Al 6063 composite's chemical makeup.Table 1
Composition (%) of Al 6063.
SiliconIronCopperMagnesiumManganeseOthersAluminum0.410.110.050.490.10.05Remaining


### 2.2. Design of Experiments

There is a numeral factor that affects the MRR of a wire EDM system including current and pulse duration, wire speed, and voltage. Variations in voltage and other parameters are made at two separate levels. Detailed parameters and factors for the wire EDM technique may be found in Table2. Based on the results of the tests, these levels have been chosen. The L16 Taguchi orthogonal array is chosen since the experiments require a total of 13 degrees of freedom. Table 3 outlines the experiment's plan of action. There was a total of two experiments per condition. The thickness of the specimens and the dielectric fluid supply pressure remained persistent during the processing.Table 2
Wire electrical discharge machining process parameters and factors.
FactorsCode1234I (A)A4567Ton (µs)B25354555Toff (µs)C15202530Ws (rpm)D1803607201440IV (V)E90100--Table 3
Design of Experiments and associated mean material removal rate.
Trial no.Process parameters settingsMaterial removal rateItontoffWsV5%10%1425151440900.3820.315243520720900.4120.4023445253601000.3750.3764455301801000.1980.1965525203601000.4950.4846535151801000.4560.4427545301440900.5210.496855525720900.4160.404962525180900.2580.2351063530360900.3240.31811645157201001.1761.126126552014401001.0190.94513725307201000.7120.622147352514401000.5460.5271574520180900.8630.6641675515360900.7420.723


### 2.3. Dimension of Material Removal Rate

For a total of 20 mm, test specimens are put through wire EDM according to the procedure outlined in Table2. (1) calculates MRR based on weight loss during machining for each experimental session. Digital mass stability is employed to determine the weightage of specimens before and after machining (wiand wf) (accuracy: 0.001 g). Five measurements were done for each sample to assure accuracy. The mean MRR of each testing for each sample with varying SiCp compositions is shown in Table 3. Figure 2 depicts cut surfaces of test materials machined under various circumstances.(1)MRR=wi−wftmg/s.Figure 2
(a) Impact of I on material removal rate. (b) Impact ofWs on MRR.
(a)(b)


### 2.4. Optimization of Material Removal Rate

An ANOVA technique was used to analyze wire EDM properties such as I, Ton and Toff, Ws, and V to assess their influence on material removal rate. With a 95% degree of confidence, the F-test is used to identify important process parameters. It is also possible to calculate the average MRR at several levels (Levels). The best conditions for a high MRR are determined for each of the study's process parameters based on the MRR levels that yield the highest MRR. Equations (2)–(5) are used to calculate the MRR under optimal conditions. Finally, regression analysis is used to construct a statistical model that can predict the MRR using regression coefficients.(2)neff=n1+DF,(3)T=∑MRRn,(4)MRROptimum=MRRA3B3C1D3E2−4×T,(5)Confidence interval=±Fα,DFerror×MSerrorneff.


### 2.5. Material Removal Theoretical Modeling

In wire EDM, the material is removed by a spark gap electric discharge. The amount of spark energy generated through the sparking processing is calculated using (6). The quantity of heat generated by a spark is dependent on the spark's I, Ton, and Iv. Researchers employ the Gaussian heat input model to forecast the rsp, despite the fact that other models exist. The axis of a spark has the highest heat intensity (qR) according to this model, and the heat flux related to this is represented by formula (7). The electric field among the electrode and workpiece ionizes the dielectric liquid, allowing electrons to flow freely. Eventually, a spark forms, which is hot enough to melt and evaporate the workpiece. The equation can be used to calculate the size of hemisphere craters created on the machined surface as an outcome of this phenomenon (8).(6)Es=Ip×IV×ton,(7)qfR=4.45×WM×I×VΠ×rsp2×e−4.5R/rsp2,(8)Γ=23πr3.


## 2.1. Details of the Experiment and the Specimens

In this study, researchers in India employed a CNC 2-axis wire EDM manufactured by accord wire EDM. Mo wire with a width of 0.16 mm is employed to make the tool electrode. Soft water and gel make up the insulator. The regulator has a 0.001 mm resolution. Stir casting is employed to create the SiC particle-reinforced Al-6063 alloy. By mechanically stirring the molten metal, reinforcing particles (5 and 10% by weight) that have been preheated to 650°C are evenly distributed throughout the molten matrix, which is liquefied at 850°C. The molten metal is poured into a mold that has been preheated to 200 °C and allowed to cool to room temperature before being moved. Table1 provides an overview of the Al 6063 composite's chemical makeup.Table 1
Composition (%) of Al 6063.
SiliconIronCopperMagnesiumManganeseOthersAluminum0.410.110.050.490.10.05Remaining


## 2.2. Design of Experiments

There is a numeral factor that affects the MRR of a wire EDM system including current and pulse duration, wire speed, and voltage. Variations in voltage and other parameters are made at two separate levels. Detailed parameters and factors for the wire EDM technique may be found in Table2. Based on the results of the tests, these levels have been chosen. The L16 Taguchi orthogonal array is chosen since the experiments require a total of 13 degrees of freedom. Table 3 outlines the experiment's plan of action. There was a total of two experiments per condition. The thickness of the specimens and the dielectric fluid supply pressure remained persistent during the processing.Table 2
Wire electrical discharge machining process parameters and factors.
FactorsCode1234I (A)A4567Ton (µs)B25354555Toff (µs)C15202530Ws (rpm)D1803607201440IV (V)E90100--Table 3
Design of Experiments and associated mean material removal rate.
Trial no.Process parameters settingsMaterial removal rateItontoffWsV5%10%1425151440900.3820.315243520720900.4120.4023445253601000.3750.3764455301801000.1980.1965525203601000.4950.4846535151801000.4560.4427545301440900.5210.496855525720900.4160.404962525180900.2580.2351063530360900.3240.31811645157201001.1761.126126552014401001.0190.94513725307201000.7120.622147352514401000.5460.5271574520180900.8630.6641675515360900.7420.723


## 2.3. Dimension of Material Removal Rate

For a total of 20 mm, test specimens are put through wire EDM according to the procedure outlined in Table2. (1) calculates MRR based on weight loss during machining for each experimental session. Digital mass stability is employed to determine the weightage of specimens before and after machining (wiand wf) (accuracy: 0.001 g). Five measurements were done for each sample to assure accuracy. The mean MRR of each testing for each sample with varying SiCp compositions is shown in Table 3. Figure 2 depicts cut surfaces of test materials machined under various circumstances.(1)MRR=wi−wftmg/s.Figure 2
(a) Impact of I on material removal rate. (b) Impact ofWs on MRR.
(a)(b)


## 2.4. Optimization of Material Removal Rate

An ANOVA technique was used to analyze wire EDM properties such as I, Ton and Toff, Ws, and V to assess their influence on material removal rate. With a 95% degree of confidence, the F-test is used to identify important process parameters. It is also possible to calculate the average MRR at several levels (Levels). The best conditions for a high MRR are determined for each of the study's process parameters based on the MRR levels that yield the highest MRR. Equations (2)–(5) are used to calculate the MRR under optimal conditions. Finally, regression analysis is used to construct a statistical model that can predict the MRR using regression coefficients.(2)neff=n1+DF,(3)T=∑MRRn,(4)MRROptimum=MRRA3B3C1D3E2−4×T,(5)Confidence interval=±Fα,DFerror×MSerrorneff.


## 2.5. Material Removal Theoretical Modeling

In wire EDM, the material is removed by a spark gap electric discharge. The amount of spark energy generated through the sparking processing is calculated using (6). The quantity of heat generated by a spark is dependent on the spark's I, Ton, and Iv. Researchers employ the Gaussian heat input model to forecast the rsp, despite the fact that other models exist. The axis of a spark has the highest heat intensity (qR) according to this model, and the heat flux related to this is represented by formula (7). The electric field among the electrode and workpiece ionizes the dielectric liquid, allowing electrons to flow freely. Eventually, a spark forms, which is hot enough to melt and evaporate the workpiece. The equation can be used to calculate the size of hemisphere craters created on the machined surface as an outcome of this phenomenon (8).(6)Es=Ip×IV×ton,(7)qfR=4.45×WM×I×VΠ×rsp2×e−4.5R/rsp2,(8)Γ=23πr3.


## 3. Results and Discussion

### 3.1. Current and Wire Speed Have an Effect on MRR

As seen in Figure2(a), MRR is influenced by the current. From 4 A to 6 A, the MRR rises significantly. At a current of 5 A, the highest material removal rate is 0.662 mg/s. The breakdown voltage and spark energy rise as the current increases from low to high values, as seen in (6). The high current melts and evaporates more material because of the higher. Other scholars have also found similar patterns. However, the MRR decreased slightly when the current was greater than 5 A. When there is not enough flushing from the molten pool, the recast layer forms on top of previously machined surfaces. During a discharge, debris dissolves from the dielectric fluid and degraded wire electrode particles are deposited as the discharge energy increases to its maximum value. When current parameters are exceeded, the quick deposition of wreckage at the machining gap has a substantial effect on dielectric breakdown characteristics and material removal rate. As indicated in the image, wire speed has an impact on MRR, as illustrated in Figure 2(b). When the spool around which the electrode wire is coiled has a rotating speed of 180 to 1440 revolutions per minute, the MRR is indicated in Figure 2(b). The material removal rate rises from 0.382 mg/s to 0.664 mg/s when the spool rotation speed increases up to 720 revolutions per minute. When the spool's rotational speed was increased even further, the MRR was reduced by 10.52%. This is owing to the fact that when wire speed increases, the quantity of spark energy obtainable for machining declines. Because of this, as wire speeds increase, the MRR falls.


### 3.2. The Impact of Pulse-On Time (Ton) and Pulse-Off Time (Toff) on MRR

MRR is affected by Ton and Toff times in Figure 3(a) and Figure 3(b). From 25 to 45s pulse-on time, the MRR was improved. Each spark's energy (E) is equal to E = VI Ton. As a result, larger craters are formed as a result of a longer pulse-on period. However, the MRR decreased from 0.685 mg/s to 0.582 mg/s when the pulse-on time exceeded 45 s. Longer cycles and lower spark frequencies, as shown in Figure, may have resulted in the reforming of the molten pool surrounding the craters.Figure 3
(a) Impact of ton on material removal rate. (b) Impact of toff on material removal rate.
(a)(b)A consistent drop in MRR was seen as a result of increasing the pulse-off period from 15 seconds to 30 seconds, as depicted in Figure4. During the pulse-off period, there is no sparking. As illustrated in Figure 4, growing the pulse time lengthens the cycle while decreasing the spark frequency. The MRR decreased as pulse-off time increased.Figure 4
Spark frequency at variant levels.


### 3.3. The Impact of Voltage on Material Removal Rate

The impact voltage on material removal rate is shown in Figure5. MRR appears to be directly influenced by voltage. The MRR rises by 24.45% while the spark gap voltage rises from 90 V to 100 V. V, I, and Ton all affect the spark energy (E = V I ton), as previously stated. As a result of the increased voltage, a larger volume of material was melted and vaporized, raising the MRR.Figure 5
The impact voltage on material removal rate.As an outcome of the electrical spark among the wire electrode and the substance, the dielectric fluid experiences an abrupt rise in pressure. The holes on the machined surface appear because of the increasing pressure on the molten pool. There are also a few globules of partly molten metal on the machined surfaces.A few of the SiCp particles may be found on the cut surface of the cast layer, showing that they were relocated during the cutting process. This is because dielectric fluid is circulated during the machining process. To collect the heated reinforced molten particles, dielectric fluid is pumped over the machining surface. As the molten matrix pool hardens, these particles are fused to the machined surface. The majority of the crater looks to have a truncated sphere shape based on these photographs. Using a Gaussian heat distribution model, the predicted spark radius is in agreement with this result. There is an extended feature on the machined surface near the crater boundaries. There is a piece of the particle encased in the matrix material, making it more difficult to extract the reinforced SiCp. The spark gap exposes the remaining protruding portion to high temperatures. Sparks and traveling wire electrodes both have a significant impact on reinforced particles, causing shock waves and a mechanical moment. All the SiCp particles remain attached to the machined surface, and there is no evidence of debonding from the matrix. The quick cooling of molten metal and the segregation of alloying elements Mg, Si, etc. are largely responsible for the existence of blow holes. Blow holes are formed on a machined surface by a sparking process in the dielectric medium.


### 3.4. ANOVA of Material Removal Rate

Table4 displays the outcomes of the Analysis of Variance for the MRR. Process characteristics that significantly affect MRR can be found utilizing the F-test on the Analysis of Variance data with a 95% confidence level. They have a considerable impact on MRR since their F-values are above critical values for the above variables. The percentage of each process parameter's contribution is also taken into consideration. Among the several factors influencing the rate of material removal, current accounted for the largest share (28.2%), tracked by V (23.04%), Toff (22.84%), Ton (16.12%), and Ws (13.02%).Table 4
ANOVA for material removal rate.
SourceDf5% SiC10% SiCSeq SSAdj MSFSeq SSAdj MSFI30.31400.121158.20.31560.105251.9Ton30.18620.058234.160.18620.072430.4Toff30.24160.078545.410.26210.0851241.6Ws30.14400.048227.230.15430.050825.8V20.08120.081247.50.08560.085641.8Residual error20.00360.00190.32440.114-Total160.9412-----


### 3.5. Optimum of Process Parameters

When using wire EDM to machine Al 6063-SiCp composite, the Taguchi approach is used to optimize machining parameters for maximum MRR. Table5 displays the average MRR based on various process parameter configurations. There is a maximum MRR at settings A3B3C1D2E2, i.e., I at level 3 (6 A), Ton at level 3 (45 s), Toff at level 1, Ws set to the level of 3, and voltage set to 100 V, as shown in the following table (level 2). For samples containing 5% and 10% SiCp, the anticipated MRR is 68.23 mg/min and 68.16 mg/min, respectively. Table 6 displays the findings of the confirmation trials, which were also carried out. Experiments have shown that MRR is consistent with the model's predictions. This table also shows the greatest change (delta) in MRR resulting from changes in the settings of a variety of processes. The processing factors are sorted in terms of current impact based on delta values: I, II, III, IV, and V are the pulse-off and pulse-on times, as well as the wire speed.Table 5
The mean material removal rate.
LevelITonToffWsV5% SiCp10.3240.4230.6690.5790.45220.4650.4280.6320.6520.59730.6760.6790.3920.481–40.6490.5720.4120.392–Delta0.3520.2640.2840.2590.15210%SiCp10.3220.4120.6520.3850.44120.4560.4190.6190.4720.58230.6520.6620.3820.631–40.6390.5310.4160.561–Delta0.3410.2510.2670.2510.152Table 6
The predicted and experimental material removal rate (mg/min) at optimal conditions.
Trial No5%10%Predicted MRRExperimental MRRError (%)Predicted MRRExperimental MRRError (%)171.468.45.8068.363.47.12271.367.36.7068.462.68.01371.467.54.6068.464.86.21471.467.47.068.462.48.15


### 3.6. Regression Modeling of Material Removal Rate

In this area, we will go through the specifics of creating a statistical model that can be used to estimate MRR based on a variety of process parameter values.I, Ton, Toff, ws, Iv, and the material removal rate are established by using a statistical model. The modeling takes into account the primary impact of these variables. As shown in (9), the regression model generated for samples with 5% and 10% SiCp can be expressed as (10). The equation has a 73.6% coefficient of determination (R 2).(9)y=c+k1×x1+k2×x2+………+kn×xn,(10)Material Removal Rate=−1.20+0.114×I+0.00685×ton−0.0194×toff+0.000135×ws+0.0142×Iv.The accurateness of forecasting material removal rate by regression model is evaluated by directing confirmation experiments up to the limits of operating factors, that is,4≤I≤7;25≤ton≤55; 15≤toff≤30;380≤ws≤1440;90≤IV≤100. Test samples with a 5% and a 10% MRR have been found to have similar actual MRR values. There is a 4.45% minimal and a mean prediction error material removal rate, respectively. 2.19 standard deviations from the mean indicate a significant deviation. Table 6 depicts the predicted and experimental material removal rate (mg/min) at optimal conditions.


## 3.1. Current and Wire Speed Have an Effect on MRR

As seen in Figure2(a), MRR is influenced by the current. From 4 A to 6 A, the MRR rises significantly. At a current of 5 A, the highest material removal rate is 0.662 mg/s. The breakdown voltage and spark energy rise as the current increases from low to high values, as seen in (6). The high current melts and evaporates more material because of the higher. Other scholars have also found similar patterns. However, the MRR decreased slightly when the current was greater than 5 A. When there is not enough flushing from the molten pool, the recast layer forms on top of previously machined surfaces. During a discharge, debris dissolves from the dielectric fluid and degraded wire electrode particles are deposited as the discharge energy increases to its maximum value. When current parameters are exceeded, the quick deposition of wreckage at the machining gap has a substantial effect on dielectric breakdown characteristics and material removal rate. As indicated in the image, wire speed has an impact on MRR, as illustrated in Figure 2(b). When the spool around which the electrode wire is coiled has a rotating speed of 180 to 1440 revolutions per minute, the MRR is indicated in Figure 2(b). The material removal rate rises from 0.382 mg/s to 0.664 mg/s when the spool rotation speed increases up to 720 revolutions per minute. When the spool's rotational speed was increased even further, the MRR was reduced by 10.52%. This is owing to the fact that when wire speed increases, the quantity of spark energy obtainable for machining declines. Because of this, as wire speeds increase, the MRR falls.


## 3.2. The Impact of Pulse-On Time (Ton) and Pulse-Off Time (Toff) on MRR

MRR is affected by Ton and Toff times in Figure 3(a) and Figure 3(b). From 25 to 45s pulse-on time, the MRR was improved. Each spark's energy (E) is equal to E = VI Ton. As a result, larger craters are formed as a result of a longer pulse-on period. However, the MRR decreased from 0.685 mg/s to 0.582 mg/s when the pulse-on time exceeded 45 s. Longer cycles and lower spark frequencies, as shown in Figure, may have resulted in the reforming of the molten pool surrounding the craters.Figure 3
(a) Impact of ton on material removal rate. (b) Impact of toff on material removal rate.
(a)(b)A consistent drop in MRR was seen as a result of increasing the pulse-off period from 15 seconds to 30 seconds, as depicted in Figure4. During the pulse-off period, there is no sparking. As illustrated in Figure 4, growing the pulse time lengthens the cycle while decreasing the spark frequency. The MRR decreased as pulse-off time increased.Figure 4
Spark frequency at variant levels.


## 3.3. The Impact of Voltage on Material Removal Rate

The impact voltage on material removal rate is shown in Figure5. MRR appears to be directly influenced by voltage. The MRR rises by 24.45% while the spark gap voltage rises from 90 V to 100 V. V, I, and Ton all affect the spark energy (E = V I ton), as previously stated. As a result of the increased voltage, a larger volume of material was melted and vaporized, raising the MRR.Figure 5
The impact voltage on material removal rate.As an outcome of the electrical spark among the wire electrode and the substance, the dielectric fluid experiences an abrupt rise in pressure. The holes on the machined surface appear because of the increasing pressure on the molten pool. There are also a few globules of partly molten metal on the machined surfaces.A few of the SiCp particles may be found on the cut surface of the cast layer, showing that they were relocated during the cutting process. This is because dielectric fluid is circulated during the machining process. To collect the heated reinforced molten particles, dielectric fluid is pumped over the machining surface. As the molten matrix pool hardens, these particles are fused to the machined surface. The majority of the crater looks to have a truncated sphere shape based on these photographs. Using a Gaussian heat distribution model, the predicted spark radius is in agreement with this result. There is an extended feature on the machined surface near the crater boundaries. There is a piece of the particle encased in the matrix material, making it more difficult to extract the reinforced SiCp. The spark gap exposes the remaining protruding portion to high temperatures. Sparks and traveling wire electrodes both have a significant impact on reinforced particles, causing shock waves and a mechanical moment. All the SiCp particles remain attached to the machined surface, and there is no evidence of debonding from the matrix. The quick cooling of molten metal and the segregation of alloying elements Mg, Si, etc. are largely responsible for the existence of blow holes. Blow holes are formed on a machined surface by a sparking process in the dielectric medium.


## 3.4. ANOVA of Material Removal Rate

Table4 displays the outcomes of the Analysis of Variance for the MRR. Process characteristics that significantly affect MRR can be found utilizing the F-test on the Analysis of Variance data with a 95% confidence level. They have a considerable impact on MRR since their F-values are above critical values for the above variables. The percentage of each process parameter's contribution is also taken into consideration. Among the several factors influencing the rate of material removal, current accounted for the largest share (28.2%), tracked by V (23.04%), Toff (22.84%), Ton (16.12%), and Ws (13.02%).Table 4
ANOVA for material removal rate.
SourceDf5% SiC10% SiCSeq SSAdj MSFSeq SSAdj MSFI30.31400.121158.20.31560.105251.9Ton30.18620.058234.160.18620.072430.4Toff30.24160.078545.410.26210.0851241.6Ws30.14400.048227.230.15430.050825.8V20.08120.081247.50.08560.085641.8Residual error20.00360.00190.32440.114-Total160.9412-----


## 3.5. Optimum of Process Parameters

When using wire EDM to machine Al 6063-SiCp composite, the Taguchi approach is used to optimize machining parameters for maximum MRR. Table5 displays the average MRR based on various process parameter configurations. There is a maximum MRR at settings A3B3C1D2E2, i.e., I at level 3 (6 A), Ton at level 3 (45 s), Toff at level 1, Ws set to the level of 3, and voltage set to 100 V, as shown in the following table (level 2). For samples containing 5% and 10% SiCp, the anticipated MRR is 68.23 mg/min and 68.16 mg/min, respectively. Table 6 displays the findings of the confirmation trials, which were also carried out. Experiments have shown that MRR is consistent with the model's predictions. This table also shows the greatest change (delta) in MRR resulting from changes in the settings of a variety of processes. The processing factors are sorted in terms of current impact based on delta values: I, II, III, IV, and V are the pulse-off and pulse-on times, as well as the wire speed.Table 5
The mean material removal rate.
LevelITonToffWsV5% SiCp10.3240.4230.6690.5790.45220.4650.4280.6320.6520.59730.6760.6790.3920.481–40.6490.5720.4120.392–Delta0.3520.2640.2840.2590.15210%SiCp10.3220.4120.6520.3850.44120.4560.4190.6190.4720.58230.6520.6620.3820.631–40.6390.5310.4160.561–Delta0.3410.2510.2670.2510.152Table 6
The predicted and experimental material removal rate (mg/min) at optimal conditions.
Trial No5%10%Predicted MRRExperimental MRRError (%)Predicted MRRExperimental MRRError (%)171.468.45.8068.363.47.12271.367.36.7068.462.68.01371.467.54.6068.464.86.21471.467.47.068.462.48.15


## 3.6. Regression Modeling of Material Removal Rate

In this area, we will go through the specifics of creating a statistical model that can be used to estimate MRR based on a variety of process parameter values.I, Ton, Toff, ws, Iv, and the material removal rate are established by using a statistical model. The modeling takes into account the primary impact of these variables. As shown in (9), the regression model generated for samples with 5% and 10% SiCp can be expressed as (10). The equation has a 73.6% coefficient of determination (R 2).(9)y=c+k1×x1+k2×x2+………+kn×xn,(10)Material Removal Rate=−1.20+0.114×I+0.00685×ton−0.0194×toff+0.000135×ws+0.0142×Iv.The accurateness of forecasting material removal rate by regression model is evaluated by directing confirmation experiments up to the limits of operating factors, that is,4≤I≤7;25≤ton≤55; 15≤toff≤30;380≤ws≤1440;90≤IV≤100. Test samples with a 5% and a 10% MRR have been found to have similar actual MRR values. There is a 4.45% minimal and a mean prediction error material removal rate, respectively. 2.19 standard deviations from the mean indicate a significant deviation. Table 6 depicts the predicted and experimental material removal rate (mg/min) at optimal conditions.


## 4. Conclusions

This analysis examined the impact of factors includingI,ton,toff,ws,Iv on processing of 5 wt% to 10 wt% SiCp−Al6063 alloys by wire electrical discharge machining. It is possible to increase the productivity of wire EDM companies by using the optimal settings to increase the MRR when working with these composites. Results have led to the following conclusions.(i)
MRR was significantly influenced and rose by current, pulse-on time, pulse-off time, wire speed, and voltage but declined with rising pulse-off time and also wire speed over 720 rpm.(ii)
The voltage (23.04%), pulse-on time (22.86%), pulse-off time (16.12%), and wire speed all contributed to MRR fluctuation to a lesser extent than the current (28.2%) wire speed (13.04%).(iii)
The optimal setup which formed the greatest material removal rate (66.32 mg/min and62.41mg/min for samples with 5% and 10% SiCp) is current −6A,Pulse on time−45μ,Pulse off time−15μs,wire speed−720rpm, and voltage-100 V.(iv)
It is possible to estimate the MRR in wire EDM of Al 6063 with SiC, -p. up to 10 wt% within the operational range using the statistical model (mean, R2-73.65%). 4A≤I≤7A;25μs≤ton≤55μs;15μs≤toff≤30μs; 380rpm≤ws≤1440rpm;90V≤Iv≤100V. The proposed regression model's MRR prediction error ranges from 4.45% to 20.46%.



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*Source: 2594974-2022-06-30.xml*