# Investigation and Process Parameter Optimization on Wire Electric Discharge Machining of Aluminium 6082 Alloy

Authors: G. Murali; M. Murugan; K. Arunkumar; P. V. Elumalai; D. Mohanraj; S. Prabhakar
Journal: Advances in Materials Science and Engineering
 (2022)

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## Abstract

Wire electrical discharge machining (WEDM) is an unconventional machining process that is being extensively used in the aerospace,medical devices, die, tooling, and automotive industries for machining high-hardness materials with conductivity. In the present work, WEDM of aluminium 6082 alloys was carried out since it influences diversified applications in manufacturing industries. The WEDM process includes an extensive number of variables that influence its execution. However, based on the literature survey, three process parameters such as pulse-on time (PTON), pulse-off time (PTOFF), and wire feed (WF) were taken into consideration. The factorial design was used for the selection of parameter levels and arrived at the 27 trails for the machining. The output responses of the WEDM, namely, surface roughness (SR), kerf width (KF), and metal removal rate (MRR) were measured, and its parameter optimization was also carried out to minimize the significant effect on productivity and the quality of components. The measured output response was compared with the predicted response surface methodology (RSM) results; it was found that the SR and KF values decreased with the increase of PTON. The MRR was increased with the increase of PTON.


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## Body

## 1. Introduction

The WEDM is one of the thermoelectric nontraditional machining processes which are exclusively applicable to conductive materials. The work piece material was eroded in terms of discrete sparks between the workpiece and the tool, and the electrode was placed in a liquid dielectric medium.Jampana and Rao [1] investigated the SR and MRR of stainless steel of grade 630 and copper tungsten by considering the process parameters such as PTON, PTOFF, flushing pressure (PF), and peak current (Ipeak). The analysis of variance (ANOVA) is one of the methods used for the analysis of the effect of process parameters. In addition to the analysis, response surface models were used to establish the optimum process parameters [2]. The graph theory and utility concept (GTUC) was compared with teaching learning-based optimization (TL-BO) for the energy consumption in the WEDM process, and the KF was more in GTUC than TLUC, and the MRR was found to be greater in GTUC than TLUC [3]. The high aspect ratio ofsuperalloys , such as Ti–6Al–4V and Inconel 718 with the tool materials such as brass tubes and copper tubes were studied under rapid electrical discharge machining [4]. The MRR, the Tool Wear Rate (TWR), and the overcut (OC) were investigated on Ti–6Al–4V and Inconel 718 superalloys with brass and copper tube tools. The sudden increase of MRR was noticed on the Ti–6Al–4V while using the copper tube tools, and the reduction of TWR and OC was noticed on the Inconel 718 material using brass tube tools [5]. During the WEDM of AZ31B Mg, 95.4% of alloy similarity was obtained between the regression model and the experimental results, and the PTON was the main determining factor on SR.The Taguchi–Grey method [6] was better to determine the improved performance factors. Mathematical modelling [7] was developed for Monel 400 and artificial neural network (ANN) modelling has greater accuracy with experimental values of KF. Senkathir et al. [8] led an investigation of WEDM on Inconel 718 blocks using a molybdenum wire and evaluated the SR, MRR, and circularity. Shihab et al. [9] studied the effect of WEDM process parameters on friction-stir-welded 5754 aluminium alloy using the Box-Behnken design (BBD). Abbasi et al. [10] developed a surface roughness model on the high strength low alloy (HSLA) steel using a factorial design and studied the relationship of surface roughness to the WEDM process parameters, namely, PTON, PTOFF, and WF. Rajmohan and Kumar [11] have investigated the effects of process parameters on the MRR, SR, and KF of duplex stainless steel (DSS) using WEDM. Scanning electron microscopy (SEM) analysis with welding expert system and software has been used to capture the image of the KF and the measurements were taken, and also the optimizing technique was used to get the optimum WEDM process parameters. Chalisgaonkar and Kumar [12] investigated the finish cutting operation of WEDM of pure titanium. The uncoated brass wire has the pulse energy parameters and the PTOFF, tool modifies the surface finish in the trim cut. Investigation of WEDM of 6061 aluminium alloy in terms of MRR, SR, KF, and wire electrode wear for PTON and PTOFF. The MRR increases with the increase in PTON, and the wire tension does not affect the MRR. Ikram et al. [13] experimented by considering the two different materials with two different tool electrodes, and the parameters were peak current, wire feed, and PTON. Kao et al. [14]experimented with the effects of MRR, SR, and electrode wear ratio (EWR) of Ti–6Al–4V alloy using the performance characteristics of Taguchi and grey relational analysis. The optimized process parameters provide a lower EWR, a higher MRR, and better SR. Kansal et al. [15] studied the machining behaviour of high-speed steel (HSS) through the electrical discharge machining process since it has good toughness, high hardness, high wear resistance, and relatively high abrasion resistance. The positive polarity provides a higher MRR. Many researchers [16, 17] investigated the WEDM process parameters of Inconel 601 using brass wire. The peak current has a significant effect on SR.In the present study, the investigation of WEDM process parameters on the aluminium 6082 alloy was conducted since it has a low melting point and also is malleable. It is a unique approach to investigating the challenges of influential parameters such as PTON, PTOFF, and WF on the output responses such as MRR, SR, and KF. The design of experiments with response surface methodology (RSM) has been used for the experimentation. Taguchi’s technique was used to study the influence of input parameters on the output response and its significance.


## 2. Materials and Methods

### 2.1. Material

The aluminium alloy 6082 has superior strength and excellent corrosion resistance properties. This can be found as a structural alloy with superior mechanical properties in the 6000 series [17]. The aluminium alloy 6082 with the dimensions of 180 mm × 100 mm × 10 mm size has been chosen for the WEGM, and the same has been shown in Figure 1. The chemical composition of the material is listed in Table 1 , and the property of aluminium alloy 6082 is listed in Table 2.Figure 1
Workpiece.Table 1
Chemical composition of aluminium alloy 6082.
CompositionCrSiMnFeMgZnTiAlC(%)0.250.70.60.51.00.200.1950.1Table 2
Property of aluminium alloy 6082.
PropertyValueDensity2700 kg/m³Melting point555°CModulus of elasticity70 GPaElectrical resistivity0.038 ×10−6 Ω·mThermal conductivity180 W/mKThermal expansion24×10−6 K


### 2.2. WEDM Process

The experimentation is conducted on a 5-axis ELEKTRA EL PLUS 50f CNC machine available at VELLORE WIRE CUT Ltd, VELLORE, and the experimental setup is shown in Figure2.Figure 2
WEDM CNC machine.To achieve the ultimate capability level of productivity from WEDM on a constant, repeatable, and reliable basis, regular calibration of WEDM machine components are very much essential. As a part of calibration, the required components are thoroughly serviced as per the standard procedure to work in the standard manner well before using for cutting of workpiece. A total of 27 experiments have been conducted by varying the following three parameters Pulse on-time, Wire feed, and Pulse off-time. Elektra EL PLUS 50 F CNC WEDM has been chosen with a table size of 800 × 450 mm, with Linear and circular Interpolation; Frequency: 0–200 Hz; Pulse peak current: 40 A; Die electric fluid: Pure H2O; Wire electrode diameter: 0.25 mm(Standard) and the operative voltage of 60–420 V for the WEDM process.


### 2.3. Design of Process Parameters Limits

By using factorial design experiments with parameters limit the design of experiments is carried out for the 27 trials. The design of experiments can be used at the point of greatest leverage to reduce design costs by speeding up the design process, reducing late engineering design changes, and reducing product material and labour complexity. By lowering process variance, rework, scrap, and the requirement for inspection, designed experiments are also effective instruments for cutting manufacturing costs. The controllable process parameters and their limits are listed in Table3, and input parameters are listed in Table 4.Table 3
Controllable process parameters and their limits.
ParameterSymbolsLevel 1Level 2Level 3PTON (μs)A106110114WF (mm/min)B123PTOFF (μs)C605652Table 4
Input parameters.
S. noABCABC111415631221143563323110360231411025222351101522136114252323711416031181062521239110356232101102602211110625612212110352233131143603311411025622115110160211161061521131711435233318106360131191141523132010626012121114260321221061561122311425632224106352133251101562122610635613227106160111


### 2.4. WEDM of Aluminium 6082 Alloy

WEDM for the 27 trails has been conducted and the machined work piece as shown in Figure3(a), the machined block with the dimension of 15 mm × 10 mm × 10 mm is as shown in Figure 3(b).Figure 3
(a) Machined work piece. (b) Machined block.
(a)(b)


### 2.5. Output Response Parameters

#### 2.5.1. Kerf Width

Kerf width is the measure of the amount of material removal during machining. It determines the dimensional accuracy of the finishing part. Kerf width is commonly called a working gap as shown in Figure4. The width of this working gap is varied for different cutting parameters, dielectric fluid as well as material to be cut. The Kerf width has been measured using a coordinate measuring machine (CMM). The kerf width is calculated by the following expression:(1)Kf=BLANK WIDTH−PUNCH WIDTH2.Figure 4
Kerf width.A coordinate measuring machine is used to measure the machined work piece and machined block for calculating the kerf width. A coordinate measuring machine with a Model: GRANO 4-5-4 with Moving Bridge type with manual operation with a guided method of Air Bearings on All Axis having the measuring axis of 450 mm × 50 mm × 400 mm; working area: 1040 mm × 550 mm; Resolution: 0.5; Operational Voltage: 230 VAC ±10%, 50/60 HZ, and Operating Temperature of 20°C ± 2 are chosen the following specifications.


#### 2.5.2. Material Removal Rate (MRR)

The ratio of the volume of material removed from the work piece to the total machining time. It can be calculated using the following formula:(2)MRR=Total cutting length×Kerf width×Thickness of work pieceTotal time taken.


#### 2.5.3. Surface Roughness (SR)

The SR often shortened to roughness, is a deviation measured to the machined surface texture. The surface roughness test has been conducted on the SURFCON 1500SD2 machine and the ACC TEE analysis software. The SR is measured at three different locations at the top, centre, and bottom on one side of the machined surface three times. The graphical diagram of surface roughness is shown in Figure5 ,and the photograph of the surface roughness measuring machine is shown in Figure 6.Figure 5
Measurement of surface roughness.
(a)(b)Figure 6
Methodology of WEDM process.


### 2.6. Methodology

Figure 6


## 2.1. Material

The aluminium alloy 6082 has superior strength and excellent corrosion resistance properties. This can be found as a structural alloy with superior mechanical properties in the 6000 series [17]. The aluminium alloy 6082 with the dimensions of 180 mm × 100 mm × 10 mm size has been chosen for the WEGM, and the same has been shown in Figure 1. The chemical composition of the material is listed in Table 1 , and the property of aluminium alloy 6082 is listed in Table 2.Figure 1
Workpiece.Table 1
Chemical composition of aluminium alloy 6082.
CompositionCrSiMnFeMgZnTiAlC(%)0.250.70.60.51.00.200.1950.1Table 2
Property of aluminium alloy 6082.
PropertyValueDensity2700 kg/m³Melting point555°CModulus of elasticity70 GPaElectrical resistivity0.038 ×10−6 Ω·mThermal conductivity180 W/mKThermal expansion24×10−6 K


## 2.2. WEDM Process

The experimentation is conducted on a 5-axis ELEKTRA EL PLUS 50f CNC machine available at VELLORE WIRE CUT Ltd, VELLORE, and the experimental setup is shown in Figure2.Figure 2
WEDM CNC machine.To achieve the ultimate capability level of productivity from WEDM on a constant, repeatable, and reliable basis, regular calibration of WEDM machine components are very much essential. As a part of calibration, the required components are thoroughly serviced as per the standard procedure to work in the standard manner well before using for cutting of workpiece. A total of 27 experiments have been conducted by varying the following three parameters Pulse on-time, Wire feed, and Pulse off-time. Elektra EL PLUS 50 F CNC WEDM has been chosen with a table size of 800 × 450 mm, with Linear and circular Interpolation; Frequency: 0–200 Hz; Pulse peak current: 40 A; Die electric fluid: Pure H2O; Wire electrode diameter: 0.25 mm(Standard) and the operative voltage of 60–420 V for the WEDM process.


## 2.3. Design of Process Parameters Limits

By using factorial design experiments with parameters limit the design of experiments is carried out for the 27 trials. The design of experiments can be used at the point of greatest leverage to reduce design costs by speeding up the design process, reducing late engineering design changes, and reducing product material and labour complexity. By lowering process variance, rework, scrap, and the requirement for inspection, designed experiments are also effective instruments for cutting manufacturing costs. The controllable process parameters and their limits are listed in Table3, and input parameters are listed in Table 4.Table 3
Controllable process parameters and their limits.
ParameterSymbolsLevel 1Level 2Level 3PTON (μs)A106110114WF (mm/min)B123PTOFF (μs)C605652Table 4
Input parameters.
S. noABCABC111415631221143563323110360231411025222351101522136114252323711416031181062521239110356232101102602211110625612212110352233131143603311411025622115110160211161061521131711435233318106360131191141523132010626012121114260321221061561122311425632224106352133251101562122610635613227106160111


## 2.4. WEDM of Aluminium 6082 Alloy

WEDM for the 27 trails has been conducted and the machined work piece as shown in Figure3(a), the machined block with the dimension of 15 mm × 10 mm × 10 mm is as shown in Figure 3(b).Figure 3
(a) Machined work piece. (b) Machined block.
(a)(b)


## 2.5. Output Response Parameters

### 2.5.1. Kerf Width

Kerf width is the measure of the amount of material removal during machining. It determines the dimensional accuracy of the finishing part. Kerf width is commonly called a working gap as shown in Figure4. The width of this working gap is varied for different cutting parameters, dielectric fluid as well as material to be cut. The Kerf width has been measured using a coordinate measuring machine (CMM). The kerf width is calculated by the following expression:(1)Kf=BLANK WIDTH−PUNCH WIDTH2.Figure 4
Kerf width.A coordinate measuring machine is used to measure the machined work piece and machined block for calculating the kerf width. A coordinate measuring machine with a Model: GRANO 4-5-4 with Moving Bridge type with manual operation with a guided method of Air Bearings on All Axis having the measuring axis of 450 mm × 50 mm × 400 mm; working area: 1040 mm × 550 mm; Resolution: 0.5; Operational Voltage: 230 VAC ±10%, 50/60 HZ, and Operating Temperature of 20°C ± 2 are chosen the following specifications.


### 2.5.2. Material Removal Rate (MRR)

The ratio of the volume of material removed from the work piece to the total machining time. It can be calculated using the following formula:(2)MRR=Total cutting length×Kerf width×Thickness of work pieceTotal time taken.


### 2.5.3. Surface Roughness (SR)

The SR often shortened to roughness, is a deviation measured to the machined surface texture. The surface roughness test has been conducted on the SURFCON 1500SD2 machine and the ACC TEE analysis software. The SR is measured at three different locations at the top, centre, and bottom on one side of the machined surface three times. The graphical diagram of surface roughness is shown in Figure5 ,and the photograph of the surface roughness measuring machine is shown in Figure 6.Figure 5
Measurement of surface roughness.
(a)(b)Figure 6
Methodology of WEDM process.


## 2.5.1. Kerf Width

Kerf width is the measure of the amount of material removal during machining. It determines the dimensional accuracy of the finishing part. Kerf width is commonly called a working gap as shown in Figure4. The width of this working gap is varied for different cutting parameters, dielectric fluid as well as material to be cut. The Kerf width has been measured using a coordinate measuring machine (CMM). The kerf width is calculated by the following expression:(1)Kf=BLANK WIDTH−PUNCH WIDTH2.Figure 4
Kerf width.A coordinate measuring machine is used to measure the machined work piece and machined block for calculating the kerf width. A coordinate measuring machine with a Model: GRANO 4-5-4 with Moving Bridge type with manual operation with a guided method of Air Bearings on All Axis having the measuring axis of 450 mm × 50 mm × 400 mm; working area: 1040 mm × 550 mm; Resolution: 0.5; Operational Voltage: 230 VAC ±10%, 50/60 HZ, and Operating Temperature of 20°C ± 2 are chosen the following specifications.


## 2.5.2. Material Removal Rate (MRR)

The ratio of the volume of material removed from the work piece to the total machining time. It can be calculated using the following formula:(2)MRR=Total cutting length×Kerf width×Thickness of work pieceTotal time taken.


## 2.5.3. Surface Roughness (SR)

The SR often shortened to roughness, is a deviation measured to the machined surface texture. The surface roughness test has been conducted on the SURFCON 1500SD2 machine and the ACC TEE analysis software. The SR is measured at three different locations at the top, centre, and bottom on one side of the machined surface three times. The graphical diagram of surface roughness is shown in Figure5 ,and the photograph of the surface roughness measuring machine is shown in Figure 6.Figure 5
Measurement of surface roughness.
(a)(b)Figure 6
Methodology of WEDM process.


## 2.6. Methodology

Figure 6


## 3. Results and Discussions

### 3.1. Optimization of Kerf Width

Table5 shows the optimization results of KF in the WEDM process and also shows the actual value and predicted values.Table 5
Optimization results of kerf width.
Run orderActual valuePredicted valueResidual10.3560.4450.01020.3540.3440.01030.3380.3270.01240.3360.3280.00850.3330.3250.00860.3490.3500.00070.3490.355−0.00680.3210.325−0.00490.3310.3250.006100.3260.3240.002110.3180.3150.002120.3260.337−0.011130.3420.347−0.445140.3060.320−0.014150.3300.3260.003160.3160.317−0.002170.3440.354−0.010180.3170.324−0.008190.3460.350−0.004200.3200.3180.002210.3400.349−0.009220.3040.310−0.006230.3570.3430.014240.3530.3380.015250.3070.319−0.013260.3150.325−0.009270.3250.3160.009In a wire electrical discharge machining, it is increasingly important to focus on achieving higher machining productivity with acceptable precision and surface smoothness. However, even a highly competent operator using a cutting-edge WEDM is rarely able to attain the best performance due to the participation of numerous variable parameters and other adjustable WEDM elements. The right choice of machining settings is necessary for the WEDM process to be used at its full potential. The WEDM is a complicated system that can be influenced by a wide range of characteristics and other elements.Figure7 indicates the effect of PTON and PTOFF, and WF for KF on the 3D response surface and counterplot. In this figure, while increasing the PTONKF also gradually increased. This is due to the applied energy removed from the material leading to cause more MRR [17].Figure 7
Effect on the PTON and PTOFF.Figure8 indicates the effect of WF and PTOFF for KF on the 3D response surface and counterplot. In this figure, the WF increases ,the KF increases, the PTOFF increases, and the KF also be slightly reduced. Increasing the WF leads to the removal of more material from the work piece, hence the KF [17].Figure 8
Effect on PTOFF and WF.Figure9 indicates the effect of PTON and WF for KF on the 3D response surface and counterplot. In this figure, the WF increases the KF slightly modifying the PTON increases the KF also gradually increased. During higher level PTON and the WF more material removal happening leads to an increase in the KF [17].Figure 9
Effect on WF and PTON.


### 3.2. Optimization of MRR

In order to maximise MRR and minimize surface roughness, it was to investigate several parameters, includingTon, Toff, V, I, and WFR dielectric flushing pressure utilised for wire EDM [18]. Table 6 shows the optimization results of MRR in the WEDM process and also shows the actual value and predicted values.Table 6
Optimization results of MRR.
Run orderActual valuePredicted valueResidual124.19922.6571.542224.01422.7611.253313.66913.783−0.114419.41218.1261.286518.82917.9870.843624.36424.937−0.573720.26120.424−0.163810.90111.314−0.413915.22416.024−0.7991012.53813.633−1.096118.8219.050−0.2291218.85718.2640.5931320.69420.5380.1571414.71015.879−1.1701513.48513.4840.0011611.23811.0830.1551723.99124.983−0.993187.5357.0280.5061924.22124.891−0.670207.5486.7860.7622120.21720.481−0.264228.3578.813−0.4562323.52322.7090.8142412.04511.5450.5002513.98515.735−1.750268.6269.287−0.661277.4836.5440.939Figure10 indicates the effect of PTON and WF for MRR on the 3D response surface and counterplot. In this figure, the PTON increased MRR slightly modified the PTON increase the MRR also gradually increased.Figure 10
Effect on the PTON and WF.Figure11 indicates the effect of PTON and PTOFF for MRR on the 3D response surface and counterplot. In this figure, the PTOFF will increase the MRR slightly and modify the PTON increase the MRR also gradually increased.Figure 11
Effect on the PTON and PTOFF.Lower pulse off-time and greater pulse on-time combinations result in a higher MRR incremental rate. This is due to the fact that a longer pulse duration will result in a higher discharge energy and spark intensity, which will remove more material [19].Figure12 indicates the effect of WF and PTOFF for MRR on the 3D response surface and counterplot. In this figure, the PTOFF increases the MRR slightly decreases. It demonstrates how wire feed and pulse off-time affect MRR. As pulse off-time increases, the MRR declines. Additionally, it has been found that the MRR is higher than the canter level of wire feed at both lower and higher levels of wire feed. This is due to the fact that increasing wire feed also increases cutting speed, which in turn raises spark discharge rate. For larger spark discharge, there is more material melting and erosion taking place. In light of this, MRR was greater with higher wire feed and shorter pulse off-times [20].Figure 12
Effect on WF and PTOFF.


### 3.3. Optimization Results of SR

It is measured by how far an actual surface deviates from its ideal form in the direction of the normal vector. While a flat surface produces little variation, a rough one produces a lot [21]. A portable surface roughness measuring device was used to measure the surface roughness of five to six machined surfaces [22]. Table 7 shows the optimization results of surface roughness in the WEDM process and also shows the actual value and predicted values.Table 7
Optimization results in surface roughness.
Run orderActual orderPredicted valueResidual13.4323.497−0.06523.5613.5210.04032.8122.897−0.08543.3313.1790.15253.2863.1920.09463.3313.451−0.12073.4693.4390.03082.3882.410−0.02292.9823.094−0.112102.7582.892−0.134112.1062.180−0.074123.2443.1820.062133.4373.465−0.028142.9593.089−0.130153.0732.9020.171162.3732.438−0.065173.4983.4690.029182.0591.8320.227193.3323.448−0.116201.9421.8420.100213.4143.444−0.030222.4252.2070.218233.763.5010.259242.3842.398−0.014253.0843.101−0.017262.0492.169−0.120271.6171.868−0.251In order to properly machine the Skd 61 alloy, Kumar and Singh [23] concluded that choosing the best combination of WEDM parameters will result in a higher surface quality. Figure 13 indicates the effect of PTON and WF for SR on the 3D response surface and counterplot. In this figure, the WF increases the SR also slightly modifying. PTON increases the SR also gradually increased.Figure 13
Effect on the PTON and WF.Figure14 indicates the effect of PTON and PTOFF for SR on the 3D response surface and counterplot. In this figure, the WF will increase the SR slightly modify. The PTON increase the SR also gradually increased. SR reduced when Pulse off time increased. SR went up as Pulse off went up and down when pulse on went up. It was shown that SR increased with higher peak current and lower pulse off-time. When cutting at a fast speed while the wire is moving at a low speed, the wire breaks. Within a set of constraints, increasing wire tension significantly improves accuracy and cutting speed. The surface roughness was demonstrated to be best controlled by the discharge current, then by pulse duration [24].Figure 14
Effect on the PTON and PTOFF.Figure15 indicates the effect of PTOFF and WF for SR on the 3D response surface and counterplot. In this figure, the PTOFF increases the SR will slightly decrease the WF increase the SR also be slightly modified. Similar results were reported during the EDM of AISI D2 Steel [25].Figure 15
Effect on PTOFF time and WF.


### 3.4. Significance Parameter by ANOVA

#### 3.4.1. ANOVA Analysis for KF

Using the S/N and raw data, the analysis of variance (ANOVA) was utilised to identify significant and nonsignificant components [26]. The parametric effects on the response characteristics are shown by plotting the signal-to-noise data and the raw data for the response curves (principal effect) [27]. The ANOVA table and response curves were examined to find the ideal values of significant process parameters in terms of mean response characteristics [28]. Table 8 shows the ANOVA results for KFin the WEDM process and also shows the sum of the square, F, and P values. PTON has been found the major factor affecting the KF.Table 8
ANOVA for KF.
SourceSum of squaredfMeanF valveP value prob > FModel4.811E − 00395.345E − 0044.600.0034A-pulse on3.458E − 00313.458E − 00329.77<0.0001B-wire feed1.608E − 00411.608E − 0041.380.2556C-pulse off8.149E − 00518.149E − 0050.700.4139AB2.185E − 00412.185E − 0041.880.1881AC3.136E − 00513.136E − 0050.270.6101BC9.919E − 00519.919E − 0050.850.3684A24.914E − 00414.914E − 0044.230.0554B23.267E − 00513.267E − 0050.280.6028C22.369E − 004Residual1.975E − 003171.162E − 004Cor total6.786E − 00326Regression Equation for KF is given as follows:(3)KF=+0.32+0.014A+0.0029B–0.0021C–0.0042AB+0.0016AC–0.002BC+0.009A2+0.0023B2+0.0062C2.Figure16 shows the main effect plot for means. The effect of three control factors (viz. PTON, PTOFF, and WF) on the KF. The kerf is increased with the gradual increase in the pulse on-time. When the pulse current is raised, the kerf width gradually increases, but when the pulse on time is increased, the kerf width drastically changes.Figure 16
Main effects plot for means.Figure17shows the residual plots for means. Normal probability plot, versus fits, Histogram, and versus order of KF values are shown in Figure.The main effects plot for SN ratios are shown in Figure 18.Figure 17
Residual plots for means.Figure 18
Main effects plot for SN ratios.


#### 3.4.2. ANOVA Analysis of MRR

From the ANOVA results for MRR. According to Table9, PTON has been found the major factor affecting the MRR.Table 9
ANOVA for MRR.
SourceSum of squaredfMeanF valveP-valve prob > F% of contributionModel930.816155.14169.04<0.0001A-pulse on839.531839.53914.79<0.000188.44B-wire feed0.3710.370.410.53010C-pulse off90.8190.898.949.6AB0.110.10.11AC3.83E − 0313.83E − 034.18E − 03BC3.44E − 0413.44E − 043.74E − 04Residual18.35200.921.9Total949.1626Regression equation for MRR is given as follows:(4)MRR=+15.8+6.83A+0.14B−2.25C−0.093AB+0.018∗A∗+0.0053BC.Figure19 shows the main effect plot for means. The effect of three control factors (viz. PTON, PTOFF, and F) on the MRR. The MRR increases with the gradual increase in the pulse on-time.Figure 19
Main effects plot for means.Figure20 shows the main effect plot for SN ratios. The effect of three control factors (viz. PTON, PTOFF, and WF) on the MRR. The MRR is decreased with the gradual decrease in the PTOFF. There may not be a peak stream running during the discharge. Since the gap voltage and peak current control the servo, theinter electrode gap will not reduce any more once the gap voltage or peak current are reached. The subsequent rise in peak current will not have any effect if the gap voltage is reached [29].Figure 20
Main effects plot for SN ratios.Table10 shows the response table of means gives the rank order of PTON, PTOFF, and WF. PTON is the major influencing factor in MRR.Table 10
Response table for menas for MRR.
LevelPTOFF (μs)WF (mm/min)PTON (μs)118.20615.7849.173215.71815.78115.634313.71416.07322.831Delta4.4920.29113.659Rank231


#### 3.4.3. ANOVA for SR

Table11 presents the analysis of variance (ANOVA) results for SR. It has been identified that the PTON has been found as the major factor affecting the SR.Table 11
ANOVA for surface roughness.
SourceSum of squaredfMeanF valve% of contributionModel8.8690.9839.78A-pulse on7.8617.86317.5384.6B-wire feed2.35E − 0412.35E − 049.49E − 030C-pulse off0.3710.3715.023.9AB2.88E-0312.88E-030.12AC0.2410.249.582.6BC1.63E − 0511.63E − 056.60E − 04A20.3710.3715.033.9B23.58E − 0413.58E − 040.014C20.01710.0170.71.8Residual0.42170.025Total9.2826Regression Equation for SR is as follows:.(5)SR=+3.09+0.66A–0.0036B−0.14C+0.016AB+0.14AC+0.0011BC−0.25A2+0.0077B2−0.054C2.


#### 3.4.4. Taguchi Graphical Outputs for SR

Figure21 shows the main effect plot for means. The effect of three control factors (viz. PTON, PTOFF and WF) on the surface roughness. The SR increases with the gradual increase in the pulse on-time. The effect of pulse on time (Ton) on surface roughness is depicted in Table 11. As seen from the Table, the surface roughness increases with the increase in pulse on-time and reaches a maximum value. This is explained by the fact that increased pulse on time contributes more discharge energy, causing large number of deeper craters due to the flush out melted material from the machined surface.Figure 21
Main effects plot for means.Figure22 shows the main effect plot for SN ratios. The effect of three control factors (viz PTON, PTOFF, and WF) on the SR. The SR decreases with the gradual decrease in the PTOFF.Figure 22
Main effects plot for SN ratios.In Table12, this response table of means shows the rank and order of the PTON, PTOFF and WF. Pulse on-time is the major influencing factor in MRR. This is due to the fact that longer pulse duration will result in a higher discharge energy and spark intensity, which will remove more material. Higher MRR is also caused by a combination of longer pulse on-time and shorter pulse off-time, which results in increased sparking time. Adjusting the process parameters is a crucial step for achieving excellence without increasing costs [30].Table 12
Response table for means for SR.
LevelPTOFF (μs)WF (mm/min)PTON (μs)12.1492.8993.01823.0592.8872.92933.472.8922.731Delta1.3210.0110.287Rank132


## 3.1. Optimization of Kerf Width

Table5 shows the optimization results of KF in the WEDM process and also shows the actual value and predicted values.Table 5
Optimization results of kerf width.
Run orderActual valuePredicted valueResidual10.3560.4450.01020.3540.3440.01030.3380.3270.01240.3360.3280.00850.3330.3250.00860.3490.3500.00070.3490.355−0.00680.3210.325−0.00490.3310.3250.006100.3260.3240.002110.3180.3150.002120.3260.337−0.011130.3420.347−0.445140.3060.320−0.014150.3300.3260.003160.3160.317−0.002170.3440.354−0.010180.3170.324−0.008190.3460.350−0.004200.3200.3180.002210.3400.349−0.009220.3040.310−0.006230.3570.3430.014240.3530.3380.015250.3070.319−0.013260.3150.325−0.009270.3250.3160.009In a wire electrical discharge machining, it is increasingly important to focus on achieving higher machining productivity with acceptable precision and surface smoothness. However, even a highly competent operator using a cutting-edge WEDM is rarely able to attain the best performance due to the participation of numerous variable parameters and other adjustable WEDM elements. The right choice of machining settings is necessary for the WEDM process to be used at its full potential. The WEDM is a complicated system that can be influenced by a wide range of characteristics and other elements.Figure7 indicates the effect of PTON and PTOFF, and WF for KF on the 3D response surface and counterplot. In this figure, while increasing the PTONKF also gradually increased. This is due to the applied energy removed from the material leading to cause more MRR [17].Figure 7
Effect on the PTON and PTOFF.Figure8 indicates the effect of WF and PTOFF for KF on the 3D response surface and counterplot. In this figure, the WF increases ,the KF increases, the PTOFF increases, and the KF also be slightly reduced. Increasing the WF leads to the removal of more material from the work piece, hence the KF [17].Figure 8
Effect on PTOFF and WF.Figure9 indicates the effect of PTON and WF for KF on the 3D response surface and counterplot. In this figure, the WF increases the KF slightly modifying the PTON increases the KF also gradually increased. During higher level PTON and the WF more material removal happening leads to an increase in the KF [17].Figure 9
Effect on WF and PTON.


## 3.2. Optimization of MRR

In order to maximise MRR and minimize surface roughness, it was to investigate several parameters, includingTon, Toff, V, I, and WFR dielectric flushing pressure utilised for wire EDM [18]. Table 6 shows the optimization results of MRR in the WEDM process and also shows the actual value and predicted values.Table 6
Optimization results of MRR.
Run orderActual valuePredicted valueResidual124.19922.6571.542224.01422.7611.253313.66913.783−0.114419.41218.1261.286518.82917.9870.843624.36424.937−0.573720.26120.424−0.163810.90111.314−0.413915.22416.024−0.7991012.53813.633−1.096118.8219.050−0.2291218.85718.2640.5931320.69420.5380.1571414.71015.879−1.1701513.48513.4840.0011611.23811.0830.1551723.99124.983−0.993187.5357.0280.5061924.22124.891−0.670207.5486.7860.7622120.21720.481−0.264228.3578.813−0.4562323.52322.7090.8142412.04511.5450.5002513.98515.735−1.750268.6269.287−0.661277.4836.5440.939Figure10 indicates the effect of PTON and WF for MRR on the 3D response surface and counterplot. In this figure, the PTON increased MRR slightly modified the PTON increase the MRR also gradually increased.Figure 10
Effect on the PTON and WF.Figure11 indicates the effect of PTON and PTOFF for MRR on the 3D response surface and counterplot. In this figure, the PTOFF will increase the MRR slightly and modify the PTON increase the MRR also gradually increased.Figure 11
Effect on the PTON and PTOFF.Lower pulse off-time and greater pulse on-time combinations result in a higher MRR incremental rate. This is due to the fact that a longer pulse duration will result in a higher discharge energy and spark intensity, which will remove more material [19].Figure12 indicates the effect of WF and PTOFF for MRR on the 3D response surface and counterplot. In this figure, the PTOFF increases the MRR slightly decreases. It demonstrates how wire feed and pulse off-time affect MRR. As pulse off-time increases, the MRR declines. Additionally, it has been found that the MRR is higher than the canter level of wire feed at both lower and higher levels of wire feed. This is due to the fact that increasing wire feed also increases cutting speed, which in turn raises spark discharge rate. For larger spark discharge, there is more material melting and erosion taking place. In light of this, MRR was greater with higher wire feed and shorter pulse off-times [20].Figure 12
Effect on WF and PTOFF.


## 3.3. Optimization Results of SR

It is measured by how far an actual surface deviates from its ideal form in the direction of the normal vector. While a flat surface produces little variation, a rough one produces a lot [21]. A portable surface roughness measuring device was used to measure the surface roughness of five to six machined surfaces [22]. Table 7 shows the optimization results of surface roughness in the WEDM process and also shows the actual value and predicted values.Table 7
Optimization results in surface roughness.
Run orderActual orderPredicted valueResidual13.4323.497−0.06523.5613.5210.04032.8122.897−0.08543.3313.1790.15253.2863.1920.09463.3313.451−0.12073.4693.4390.03082.3882.410−0.02292.9823.094−0.112102.7582.892−0.134112.1062.180−0.074123.2443.1820.062133.4373.465−0.028142.9593.089−0.130153.0732.9020.171162.3732.438−0.065173.4983.4690.029182.0591.8320.227193.3323.448−0.116201.9421.8420.100213.4143.444−0.030222.4252.2070.218233.763.5010.259242.3842.398−0.014253.0843.101−0.017262.0492.169−0.120271.6171.868−0.251In order to properly machine the Skd 61 alloy, Kumar and Singh [23] concluded that choosing the best combination of WEDM parameters will result in a higher surface quality. Figure 13 indicates the effect of PTON and WF for SR on the 3D response surface and counterplot. In this figure, the WF increases the SR also slightly modifying. PTON increases the SR also gradually increased.Figure 13
Effect on the PTON and WF.Figure14 indicates the effect of PTON and PTOFF for SR on the 3D response surface and counterplot. In this figure, the WF will increase the SR slightly modify. The PTON increase the SR also gradually increased. SR reduced when Pulse off time increased. SR went up as Pulse off went up and down when pulse on went up. It was shown that SR increased with higher peak current and lower pulse off-time. When cutting at a fast speed while the wire is moving at a low speed, the wire breaks. Within a set of constraints, increasing wire tension significantly improves accuracy and cutting speed. The surface roughness was demonstrated to be best controlled by the discharge current, then by pulse duration [24].Figure 14
Effect on the PTON and PTOFF.Figure15 indicates the effect of PTOFF and WF for SR on the 3D response surface and counterplot. In this figure, the PTOFF increases the SR will slightly decrease the WF increase the SR also be slightly modified. Similar results were reported during the EDM of AISI D2 Steel [25].Figure 15
Effect on PTOFF time and WF.


## 3.4. Significance Parameter by ANOVA

### 3.4.1. ANOVA Analysis for KF

Using the S/N and raw data, the analysis of variance (ANOVA) was utilised to identify significant and nonsignificant components [26]. The parametric effects on the response characteristics are shown by plotting the signal-to-noise data and the raw data for the response curves (principal effect) [27]. The ANOVA table and response curves were examined to find the ideal values of significant process parameters in terms of mean response characteristics [28]. Table 8 shows the ANOVA results for KFin the WEDM process and also shows the sum of the square, F, and P values. PTON has been found the major factor affecting the KF.Table 8
ANOVA for KF.
SourceSum of squaredfMeanF valveP value prob > FModel4.811E − 00395.345E − 0044.600.0034A-pulse on3.458E − 00313.458E − 00329.77<0.0001B-wire feed1.608E − 00411.608E − 0041.380.2556C-pulse off8.149E − 00518.149E − 0050.700.4139AB2.185E − 00412.185E − 0041.880.1881AC3.136E − 00513.136E − 0050.270.6101BC9.919E − 00519.919E − 0050.850.3684A24.914E − 00414.914E − 0044.230.0554B23.267E − 00513.267E − 0050.280.6028C22.369E − 004Residual1.975E − 003171.162E − 004Cor total6.786E − 00326Regression Equation for KF is given as follows:(3)KF=+0.32+0.014A+0.0029B–0.0021C–0.0042AB+0.0016AC–0.002BC+0.009A2+0.0023B2+0.0062C2.Figure16 shows the main effect plot for means. The effect of three control factors (viz. PTON, PTOFF, and WF) on the KF. The kerf is increased with the gradual increase in the pulse on-time. When the pulse current is raised, the kerf width gradually increases, but when the pulse on time is increased, the kerf width drastically changes.Figure 16
Main effects plot for means.Figure17shows the residual plots for means. Normal probability plot, versus fits, Histogram, and versus order of KF values are shown in Figure.The main effects plot for SN ratios are shown in Figure 18.Figure 17
Residual plots for means.Figure 18
Main effects plot for SN ratios.


### 3.4.2. ANOVA Analysis of MRR

From the ANOVA results for MRR. According to Table9, PTON has been found the major factor affecting the MRR.Table 9
ANOVA for MRR.
SourceSum of squaredfMeanF valveP-valve prob > F% of contributionModel930.816155.14169.04<0.0001A-pulse on839.531839.53914.79<0.000188.44B-wire feed0.3710.370.410.53010C-pulse off90.8190.898.949.6AB0.110.10.11AC3.83E − 0313.83E − 034.18E − 03BC3.44E − 0413.44E − 043.74E − 04Residual18.35200.921.9Total949.1626Regression equation for MRR is given as follows:(4)MRR=+15.8+6.83A+0.14B−2.25C−0.093AB+0.018∗A∗+0.0053BC.Figure19 shows the main effect plot for means. The effect of three control factors (viz. PTON, PTOFF, and F) on the MRR. The MRR increases with the gradual increase in the pulse on-time.Figure 19
Main effects plot for means.Figure20 shows the main effect plot for SN ratios. The effect of three control factors (viz. PTON, PTOFF, and WF) on the MRR. The MRR is decreased with the gradual decrease in the PTOFF. There may not be a peak stream running during the discharge. Since the gap voltage and peak current control the servo, theinter electrode gap will not reduce any more once the gap voltage or peak current are reached. The subsequent rise in peak current will not have any effect if the gap voltage is reached [29].Figure 20
Main effects plot for SN ratios.Table10 shows the response table of means gives the rank order of PTON, PTOFF, and WF. PTON is the major influencing factor in MRR.Table 10
Response table for menas for MRR.
LevelPTOFF (μs)WF (mm/min)PTON (μs)118.20615.7849.173215.71815.78115.634313.71416.07322.831Delta4.4920.29113.659Rank231


### 3.4.3. ANOVA for SR

Table11 presents the analysis of variance (ANOVA) results for SR. It has been identified that the PTON has been found as the major factor affecting the SR.Table 11
ANOVA for surface roughness.
SourceSum of squaredfMeanF valve% of contributionModel8.8690.9839.78A-pulse on7.8617.86317.5384.6B-wire feed2.35E − 0412.35E − 049.49E − 030C-pulse off0.3710.3715.023.9AB2.88E-0312.88E-030.12AC0.2410.249.582.6BC1.63E − 0511.63E − 056.60E − 04A20.3710.3715.033.9B23.58E − 0413.58E − 040.014C20.01710.0170.71.8Residual0.42170.025Total9.2826Regression Equation for SR is as follows:.(5)SR=+3.09+0.66A–0.0036B−0.14C+0.016AB+0.14AC+0.0011BC−0.25A2+0.0077B2−0.054C2.


### 3.4.4. Taguchi Graphical Outputs for SR

Figure21 shows the main effect plot for means. The effect of three control factors (viz. PTON, PTOFF and WF) on the surface roughness. The SR increases with the gradual increase in the pulse on-time. The effect of pulse on time (Ton) on surface roughness is depicted in Table 11. As seen from the Table, the surface roughness increases with the increase in pulse on-time and reaches a maximum value. This is explained by the fact that increased pulse on time contributes more discharge energy, causing large number of deeper craters due to the flush out melted material from the machined surface.Figure 21
Main effects plot for means.Figure22 shows the main effect plot for SN ratios. The effect of three control factors (viz PTON, PTOFF, and WF) on the SR. The SR decreases with the gradual decrease in the PTOFF.Figure 22
Main effects plot for SN ratios.In Table12, this response table of means shows the rank and order of the PTON, PTOFF and WF. Pulse on-time is the major influencing factor in MRR. This is due to the fact that longer pulse duration will result in a higher discharge energy and spark intensity, which will remove more material. Higher MRR is also caused by a combination of longer pulse on-time and shorter pulse off-time, which results in increased sparking time. Adjusting the process parameters is a crucial step for achieving excellence without increasing costs [30].Table 12
Response table for means for SR.
LevelPTOFF (μs)WF (mm/min)PTON (μs)12.1492.8993.01823.0592.8872.92933.472.8922.731Delta1.3210.0110.287Rank132


## 3.4.1. ANOVA Analysis for KF

Using the S/N and raw data, the analysis of variance (ANOVA) was utilised to identify significant and nonsignificant components [26]. The parametric effects on the response characteristics are shown by plotting the signal-to-noise data and the raw data for the response curves (principal effect) [27]. The ANOVA table and response curves were examined to find the ideal values of significant process parameters in terms of mean response characteristics [28]. Table 8 shows the ANOVA results for KFin the WEDM process and also shows the sum of the square, F, and P values. PTON has been found the major factor affecting the KF.Table 8
ANOVA for KF.
SourceSum of squaredfMeanF valveP value prob > FModel4.811E − 00395.345E − 0044.600.0034A-pulse on3.458E − 00313.458E − 00329.77<0.0001B-wire feed1.608E − 00411.608E − 0041.380.2556C-pulse off8.149E − 00518.149E − 0050.700.4139AB2.185E − 00412.185E − 0041.880.1881AC3.136E − 00513.136E − 0050.270.6101BC9.919E − 00519.919E − 0050.850.3684A24.914E − 00414.914E − 0044.230.0554B23.267E − 00513.267E − 0050.280.6028C22.369E − 004Residual1.975E − 003171.162E − 004Cor total6.786E − 00326Regression Equation for KF is given as follows:(3)KF=+0.32+0.014A+0.0029B–0.0021C–0.0042AB+0.0016AC–0.002BC+0.009A2+0.0023B2+0.0062C2.Figure16 shows the main effect plot for means. The effect of three control factors (viz. PTON, PTOFF, and WF) on the KF. The kerf is increased with the gradual increase in the pulse on-time. When the pulse current is raised, the kerf width gradually increases, but when the pulse on time is increased, the kerf width drastically changes.Figure 16
Main effects plot for means.Figure17shows the residual plots for means. Normal probability plot, versus fits, Histogram, and versus order of KF values are shown in Figure.The main effects plot for SN ratios are shown in Figure 18.Figure 17
Residual plots for means.Figure 18
Main effects plot for SN ratios.


## 3.4.2. ANOVA Analysis of MRR

From the ANOVA results for MRR. According to Table9, PTON has been found the major factor affecting the MRR.Table 9
ANOVA for MRR.
SourceSum of squaredfMeanF valveP-valve prob > F% of contributionModel930.816155.14169.04<0.0001A-pulse on839.531839.53914.79<0.000188.44B-wire feed0.3710.370.410.53010C-pulse off90.8190.898.949.6AB0.110.10.11AC3.83E − 0313.83E − 034.18E − 03BC3.44E − 0413.44E − 043.74E − 04Residual18.35200.921.9Total949.1626Regression equation for MRR is given as follows:(4)MRR=+15.8+6.83A+0.14B−2.25C−0.093AB+0.018∗A∗+0.0053BC.Figure19 shows the main effect plot for means. The effect of three control factors (viz. PTON, PTOFF, and F) on the MRR. The MRR increases with the gradual increase in the pulse on-time.Figure 19
Main effects plot for means.Figure20 shows the main effect plot for SN ratios. The effect of three control factors (viz. PTON, PTOFF, and WF) on the MRR. The MRR is decreased with the gradual decrease in the PTOFF. There may not be a peak stream running during the discharge. Since the gap voltage and peak current control the servo, theinter electrode gap will not reduce any more once the gap voltage or peak current are reached. The subsequent rise in peak current will not have any effect if the gap voltage is reached [29].Figure 20
Main effects plot for SN ratios.Table10 shows the response table of means gives the rank order of PTON, PTOFF, and WF. PTON is the major influencing factor in MRR.Table 10
Response table for menas for MRR.
LevelPTOFF (μs)WF (mm/min)PTON (μs)118.20615.7849.173215.71815.78115.634313.71416.07322.831Delta4.4920.29113.659Rank231


## 3.4.3. ANOVA for SR

Table11 presents the analysis of variance (ANOVA) results for SR. It has been identified that the PTON has been found as the major factor affecting the SR.Table 11
ANOVA for surface roughness.
SourceSum of squaredfMeanF valve% of contributionModel8.8690.9839.78A-pulse on7.8617.86317.5384.6B-wire feed2.35E − 0412.35E − 049.49E − 030C-pulse off0.3710.3715.023.9AB2.88E-0312.88E-030.12AC0.2410.249.582.6BC1.63E − 0511.63E − 056.60E − 04A20.3710.3715.033.9B23.58E − 0413.58E − 040.014C20.01710.0170.71.8Residual0.42170.025Total9.2826Regression Equation for SR is as follows:.(5)SR=+3.09+0.66A–0.0036B−0.14C+0.016AB+0.14AC+0.0011BC−0.25A2+0.0077B2−0.054C2.


## 3.4.4. Taguchi Graphical Outputs for SR

Figure21 shows the main effect plot for means. The effect of three control factors (viz. PTON, PTOFF and WF) on the surface roughness. The SR increases with the gradual increase in the pulse on-time. The effect of pulse on time (Ton) on surface roughness is depicted in Table 11. As seen from the Table, the surface roughness increases with the increase in pulse on-time and reaches a maximum value. This is explained by the fact that increased pulse on time contributes more discharge energy, causing large number of deeper craters due to the flush out melted material from the machined surface.Figure 21
Main effects plot for means.Figure22 shows the main effect plot for SN ratios. The effect of three control factors (viz PTON, PTOFF, and WF) on the SR. The SR decreases with the gradual decrease in the PTOFF.Figure 22
Main effects plot for SN ratios.In Table12, this response table of means shows the rank and order of the PTON, PTOFF and WF. Pulse on-time is the major influencing factor in MRR. This is due to the fact that longer pulse duration will result in a higher discharge energy and spark intensity, which will remove more material. Higher MRR is also caused by a combination of longer pulse on-time and shorter pulse off-time, which results in increased sparking time. Adjusting the process parameters is a crucial step for achieving excellence without increasing costs [30].Table 12
Response table for means for SR.
LevelPTOFF (μs)WF (mm/min)PTON (μs)12.1492.8993.01823.0592.8872.92933.472.8922.731Delta1.3210.0110.287Rank132


## 4. Conclusion

Studied the WEDM process parameters on Aluminium alloy 6082 using the Response Surface Methodology (RSM) and the Taguchi method analysis were carried out for the optimization of the process parameters. The following results were drawn from the work. [31, 32].(i)
The optimal parameter combination has been evaluated with the selected experimental domain for achieving minimum KF, minimum SR, and maximum MRR.(ii)
The KF and SR value of the machined surface decrease with the increase in PTON.(iii)
ANOVA result reveals that the interaction between the PTON, PTOFF, and WF significantly affects the KF, MRR, and SR, and the responsible table for means shows the optimized rank values of input parameters with the responses.(iv)
The optimized values of KF and MRR were obtained for the PTON of 114 μs, WF of 3 mm/min, and PTOFF of 52 μs.(v)
The optimized values of SR were obtained with PTON of 106 μs, WF of 1 mm/min, and PTOFF of 60 μs.



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*Source: 5119056-2022-09-24.xml*