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Multi-response optimization of WEDM parameters on machining 16MnCr5 alloy steel using Taguchi technique

  • Tushar Saini
  • Khushdeep GoyalEmail author
  • Deepak Bhandari
Original Paper
  • 177 Downloads

Abstract

In this present work, an attempt has been made to optimize the wire electric discharge machining using muli-response optimization technique based on Taguchi’s design approach. 16MnCr5 Alloy steel was selected as workpiece material. The response parameters viz. material removal rate and surface roughness were optimized by varying types of electrode, pulse on time (Ton), pulse off time (Toff) and peak current (Ip). The Taguchi’s L18 mixed orthogonal array has been used for planning and designing the experiments. ANOVA was used to find the contribution and significance of different process parameters on the response parameters. The results clearly indicated that pulse on time (Ton) was the most influential factor for the material removal rate and surface roughness. Optimal level of process parameters was used to perform the confirmatory experiments which verified the improvements in performance characteristics.

Keywords

WEDM 16MnCr5 MRR SR Signal to noise (S/N) ratio ANOVA L18 OA 

List of symbols

Vc

Cutting Speed

Mt

Thickness of workpiece material (mm)

Wd

Diameter of the wire (mm)

Ton

Pulse on time

Toff

Pulse off time

Ip

Peak current

WEDM

Wire electrical discharge machining

MRR

Material removal rate

SR

Surface roughness

ANOVA

Analysis of variance

S/N

Signal to noise ratio

1 Introduction

Wire electrical discharge machining (WEDM) is a non-traditional machining process used to machine hard and complex shapes with high dimensional accuracy (Ho et al. 2004; Kapoor et al. 2011; Khatri and Rathod 2017). The cutting is done by slow moving wire and the material is removed by producing series of discharges between wire and workpiece (Mahapatra and Patnaik 2007; Singh et al. 2014; Babu and Soni 2017). The removed material is flushed away with the help of dielectric fluid (Benedict 1987). The wires made up of brass, tungsten, zinc coated, or brass coated are generally used in this process (Singh and Garg 2009; Raju et al. 2014). A few researchers had tried to enhance the performance characteristics of WEDM by variation of process parameters. The important parameters in WEDM process are given in Table 1 (Han et al. 2007; Va et al. 2010; Singh et al. 2014; Aggarwal et al. 2015; Khatri and Rathod 2017). These output performance parameters are correlated with input parameters such as pulse on time, pulse off time, spark voltage, and wire feed (Aggarwal et al. 2015; Manjaiah et al. 2016; Pramanick et al. 2016). Careful selection of these input parameters can give higher material removal rate, good surface finish and low electrode wear rate (Han et al. 2007; Singh and Garg 2009; Malik et al. 2015; Rao 2016; Kumar et al. 2017b). The operators select these input parameters based on their individual experiences and from data handbooks. But these selected parameters are not optimum and do not give optimal machining performances. Therefore, some researchers have used different techniques to achieve optimum values of machining parameters. Kumar et al. (2017a) employed Taguchi L27 orthogonal array for the multi-objective optimizations of input process variables viz. wire tension, pulse on time, wire speed, and discharge current on MRR and SR during WEDM of Inconel 718. Result revealed that pulse on time was having major effect on MRR. Kanlayasiri and Boonmung (2007b) performed WEDM operation on DC53 die steel using brass wire as an electrode. They found that pulse on time and peak current had significant effect on the surface roughness. Bobbili et al. (2015) investigated the comparative analysis of MRR and surface roughness of 7017 Aluminium alloy and rolled homogeneous armour (RHA) steel by WEDM machining operation. They concluded that the improvement in the wear rate of brass wire was due to the increase in the input energy of wire breakage. Singh et al. (2017) utilized RSM for evaluating the impact of pulse on and off times, servo voltage, peak current and feed rate of wire on MRR, SR, and cutting rate. The results showed that pulse on time, pulse off time and peak current were the most significant factors. Pasam et al. (2010) developed rectilinear regression equation and GRA algorithm program to predict and select the best cutting parameters of the WEDM process. Kumar et al. (2014) measured the consequences of various wire electrodes materials such as Brass wire, copper wire coated with zinc and steel wire on the cutting rate and cutting time during the machining of D3 steel work material on WEDM. They found that cutting time was minimum with copper-coated steel wire. Va et al. (2010) projected a combination of Taguchi methodology and Gray Relational Analysis to get the optimum input parameters of WEDM. Shah et al. (2013) investigated the effects of the machining input variables for Inconel-600 by WEDM. They used response surface methodology (RSM). They concluded that pulse on time, pulse off time and peak current were most significant factors. Kumar et al. (2012) studied the influences of input variables on the machinability of Nimonic-90. On the basis of experimentation, the results revealed that discharge current and pulse on time had major impact on cutting speed. Huang and Liao (2003) used grey relation and statistical analysis for optimization of machining parameters of WEDM. It was observed that uncertainty, multi-input, and discrete data problems can be solved using grey theory. Reddy et al. (2012) performed experiments on two different materials for same process parameters on WEDM and compared the performance. Process parameters selected for experimentation were pulse off time, pulse on time, bed speed, and peak current. They found that peak current was the major influencing parameter. On the basis of results, they found that higher MRR was achieved in the machining of EN 19 material and better surface finish was achieved in the machining of SS 420 material for same set of parameters.
Table 1

Important WEDM process parameters

Parameter

Definition

Effect

Pulse on time Ton

It is the time duration of flow of current in each cycle

MRR is directly proportional to the amount of energy applied during pulse on time. Increase in pulse on time will lead to generation of more heat energy

Pulse off time Toff

It is the duration of time between two simultaneous sparks and also called pulse interval

With a lower value of Toff, there is more number of discharges in a given time, resulting in increase in MRR

Spark gap

It is the distance between the electrode and the workpiece during machining process.

The spark gap depends on the properties of material and electrode

Peak current

It is the maximum value of the current passing through the electrodes for the given pulse

Increase in the peak current value increases the pulse discharge energy which in turn can improve the cutting rate

Spark gap voltage

It is the specific value of voltage for the actual gap between the workpiece material and the wire

The MRR increases with increase in voltage and then starts to decrease

wire feed

The rate at which the wire travels along the wire guide path and is fed for generating the sparks is called wire feed rate

The MRR remains nearly constant with variation in the wire feed. SR decrease with increase in wire feed rate, because new wire comes in contact rapidly when wire feed rate increases

Wire tension

It determines the extension in wire between upper and lower wire guides

With increase in wire tension, the vibrations in wire reduces, which leads to reduction in inaccuracies during machining

Dielectric pressure

It is the pressure of the dielectric fluid used in WEDM process

The increase in dielectric pressure carries more debris from the machining area resulting in reduction in SR

It has been revealed after the review of literature that materials including alloys of metals like 16MnCr5, 20MnCr5 etc. have not been investigated yet and the research work regarding machining of these materials is limited up to a certain extent. Therefore, 16MnCr5 material has been selected for this research work to generate WEDM data. 16MnCr5 grade steel is low alloy chromium, manganese case hardening steel used extensively for carburizing and carbonitriding. This material is having high hardenability and excellent forge-ability (Çetinkaya and Arabaci 2006). It has varied practical applications such as manufacturing of crankshaft, steering component, axels, gears, heat exchangers and power plant components. An investigation on machining of 16MnCr5 alloy steel by the WEDM process is necessary for understanding the performance characteristics. These data may be helpful for the operators which are working on WEDM of this material. Therefore, the present work was aimed to optimize the machining parameters in WEDM of 16MnCr5 alloy steel (workpiece) using Taguchi optimization technique.

2 Methods and materials

2.1 Workpiece material

The 16MnCr5 grade steel has been used as a workpiece material for the present experiments. Chemical composition is shown in Table 2.
Table 2

Chemical composition of 16MnCr5 in percentage

Element

C

Cr

Mn

Al

P

S

Si

Weight%

0.14–0.19

0.80–1.10

1.00–1.30

0.039 max

0.035 max

0.035 max

0.15–0.40

2.2 Tool material

Diffused and the zinc-coated wires were used in this experimental work as the tool electrodes. The wires were having a diameter of 0.25 mm. Diffused wire is widely used in WEDM due to its good machining properties like electric discharge performance, heat resistance, low clarification and heat release.

2.3 Methodology

The experimentation was designed using the Taguchi’s approach. The methodology used is shown in Fig. 1. The experimentation was based on the L18 (21 × 33) mixed orthogonal array. The experiments were carried out on a Wire cut EDM machine (Electronica Sprint Cut Wire cut EDM 734) at MICRO PRECISION, 101, Industrial Area, Phase-1, Chandigarh, India. A total of 18 experimental trials were carried out. The input parameters were selected by proper pilot run experimentation on 16MnCr5 alloy steel by varying individual factor at a time. Four input process variables viz. pulse on time (Ton), pulse off time (Toff), peak current (Ip) and types of the electrode were selected for experimentation. One parameter, i.e. type of the electrode was varied to two levels and other process parameters were varied to three levels (21 × 33) to examine the true influence of performance parameters. The levels selected for input variables are described in Table 3. Table 4 shows constant parameters. Material removal rate (MRR) and Surface roughness (SR) were calculated for each experimental run to evaluate the machining performance. Figure 2 shows the experimental set up.
Fig. 1

Flow chart of the methodology used

Table 3

Control parameters and their levels

Factor designation

Factors

Levels

DOF

Level 1

Level 2

Level 3

A

Type of wire (Wt)

Diffused wire

Zinc Coated

_

1

B

Pulse on time (Ton)

115

120

125

2

C

Pulse off time (Toff)

40

45

50

2

D

Peak current (Ip)

180

200

220

2

Table 4

Constant parameters

Serial no.

Constant parameters

Selected value

1

Workpiece material

16MnCr5 alloy steel

2

Wire tension (Kg)

8

3

Wire feed (m/min)

8

4

Servo feed setting

2100 units

5

Servo voltage (V)

20

6

Dielectric fluid

De-ionized water

7

Flushing pressure

1

Fig. 2

Experimental setup of the WEDM

2.4 Response variables

Surface roughness and material removal rate were selected as response variables. Mitutoyo Surface Roughness Tester SJ—201 was used to measure surface roughness. The surface roughness was measured six times and the average is reported for analysis purpose. Many researchers tried to increase MRR, because it can assist to increase economic benefits in WEDM appreciably. MRR was calculated using the following expression:
$$ {\text{MRR}} = \, V_{\text{c}} \times \, M_{\text{t}} \times \, W_{\text{d}} $$
(1)
where Vc is the cutting Speed, Mt is the thickness of workpiece material (mm), and Wd is the diameter of the wire (mm).
Figure 3 shows the total 18 specimens after the experimentation.
Fig. 3

A photographic view of specimens

In the present research work, MINITAB-17 software has been used for all the designs, plots and to carry out the analysis. In Taguchi designs, a measure of robustness is used to identify control factors that reduce variability in a process by minimizing the effects of uncontrollable factors (noise factors). Control factors are those process parameters that can be controlled. Noise factors cannot be controlled during production or product use, but can be controlled during experimentation. In a Taguchi designed experiment, noise factors can be manipulated to force variability to occur and from the results, identify optimal control factor settings that make the process robust, or resistant to variation from the noise factors. Higher values of the signal-to-noise ratio (S/N) identify control factor settings that minimize the effects of the noise factors.

3 Results and discussion

The experiments were conducted to prepare 18 rectangular punches of size 16 mm × 10 mm shown in Fig. 4. The experimental results for MRR and SR are given in Table 5.
Fig. 4

Effects of process parameters on MRR (S/N data)

Table 5

L18 orthogonal array and experimental results

Exp. no.

Control factors and levels

MRR (mm3/min)

S/N ratio, MRR (db)

SR (µm)

S/N ratio, SR (db)

Wire type

T on

T off

I p

1

1

1

1

1

11.490

21.2064

3.085

− 9.7851

2

1

1

2

2

9.700

19.7354

3.525

− 10.9432

3

1

1

3

3

6.790

16.6374

3.165

− 10.0075

4

1

2

1

1

11.650

21.3265

3.445

− 10.7438

5

1

2

2

2

13.560

22.6452

3.290

− 10.3439

6

1

2

3

3

10.630

20.5307

3.115

− 9.8692

7

1

3

1

2

14.090

22.9782

3.655

− 11.2577

8

1

3

2

3

15.490

23.8010

3.590

− 11.1019

9

1

3

3

1

13.560

22.6452

3.415

− 10.6678

10

2

1

1

3

11.480

21.1988

2.910

− 9.2779

11

2

1

2

1

10.360

20.3072

3.040

− 9.6575

12

2

1

3

2

7.415

17.4022

3.000

− 9.5424

13

2

2

1

2

12.970

22.2588

2.735

− 8.7391

14

2

2

2

3

13.820

22.8102

3.380

− 10.5783

15

2

2

3

1

10.640

20.5388

3.535

− 10.9678

16

2

3

1

3

13.750

22.7661

3.265

− 10.2777

17

2

3

2

1

15.150

23.6083

3.395

− 10.6168

18

2

3

3

2

13.730

22.7534

3.630

− 11.1981

3.1 Effect of different parameters on MRR

Figure 4 shows the main effect plots for S/N ratios for MRR. It is clear that the MRR increases with increase in pulse on time and decreases with increase in the peak current. This is due to the fact that time period between two successive sparks increases with an increase in pulse on time (Liao and Woo 1997). An increase in pulse on time leads to a maximum number of sparks and therefore more amount of energy is produced (Liao and Woo 1997). The MRR increases slightly with an increase in peak current. Peak current has a very little influence on the material removal rate as it increases slightly with an increase in peak current (Mishra et al. 2016).

Analysis of variance (ANOVA) is used to find out significance of the process variables on response factors. It also helps to find out the percentage contribution of input process parameters on machining response. The results of the ANOVA are shown in Table 6 and it is observed that pulse on time is the major significant factor (contributing 59.01% to performance measure), with pulse off time (contributing 23.57%), peak current (contributing 12.76%) and wire types (contributing 1.98%) as other influencing factors. The factor with a p value less than 0.05 is taken as significant due to 95% confidence level.
Table 6

ANOVA for MRR

Source

DF

Seq. SS

Adj. SS

Adj. MS

F

P

Wire types

1

2.254

2.254

2.254

0.27

0.612

Pulse on time

2

41.316

41.316

20.658

22.25

0.000

Pulse off time

2

17.621

17.621

8.811

8.41

0.007

Peak current

2

10.39

10.39

5.195

0.21

0.814

Residual error

10

9.286

9.286

0.929

  

Total

17

66.8672

    

3.2 Time series plot for material removal rate (MRR)

Figure 5 shows time series plot. This graph shows a plot of material removal rate versus the number of experimental runs. A time series plot consists of the number of runs (time scale) on X-axis and material removal rate (data scale) on the Y-axis. From Fig. 5, it can be observed that the data show an upward trend with respect to the number of runs and there is a periodic fluctuation in the value of materials removal rate. The minimum MRR is obtained at a 3rd run (6.790 mm3/min) and maximum MRR is obtained during the 8th run (15.490 mm3/min).
Fig. 5

Time series plot for MRR

3.3 Contour plots of material removal rate (MRR)

A contour plot is a two-dimensional graphical representation of the relationship among three numeric variables. Two independent variables are indicated along X- and Y-axes and third variable Z is represented by shaded regions, called contour levels. Figure 6a shows the contour plot of material removal rate (MRR) versus pulse off time (Toff) and pulse on time (Ton). It can be inferred that the effect of pulse on time (Ton) is much more than pulse off time (Toff) on material removal rate. The maximum material removal rate is obtained when the pulse on time (Ton) is in between 120.6 and 125 µs and pulse off time (Toff) is in between 40.2 and 49 µs. Figure 6b shows the contour plot of material removal rate (MRR) versus peak current (Ip) and pulse on time (Ton). It is clear from the plot that the effect of pulse on time (Ton) is much more than peak current (Ip) on material removal rate. The maximum material removal rate is obtained when the pulse on time (Ton) is in between 121.9 and 125 µs and peak current (Ip) is in between 195 and 220 A.
Fig. 6

a Contour plot for MRR Vs pulse off time (Toff), pulse on time (Ton). b MRR Vs Peak Current (Ip), Pulse on Time (Ton)

3.4 Confirmation tests for material removal rate (MRR)

After selecting optimum value of process parameters, improvement of the response parameters using the optimum level of process parameters is predicted and verified.

In this work, there are four input parameters and the parameters A, B, C and D scored the highest ranks as shown in Table 7. The predicted optimum response in terms of S/N ratio (ή) can be calculated using the Eq. 2 or by direct using the MINITAB-17. Using this equation, the predicted optimum response in terms of S/N ratio for MRR is calculated as:
$$ {\acute{\eta}} = \overline{\eta } + \left( {\overline{A}_{2} - \overline{\eta } } \right) + \left( {\overline{B}_{3} - \overline{\eta } } \right) + \left( {\overline{C}_{2} - \overline{\eta } } \right) + \left( {\overline{D}_{1} - \overline{\eta } } \right) $$
(2)
In this equation, \( \bar{\eta } \) is the average of S/N ratio for all the observation, and \( \bar{A}_{2} , \bar{B}_{3} ,\bar{C}_{2} \,{\text{and}}\, \bar{D}_{1} \) are the highest mean S/N ratios for the most significant parameters A, B, C and D.
Table 7

Confirmation experiment result of MRR

 

Initial cutting parameters

Optimal cutting parameters

Prediction

Experiment

Level

A1B3C3D1

A2B3C2D1

A2B3C2D1

MRR (mm3/min)

0.219

0.283

0.314

S/N ratio (dB)

− 11.2304

− 10.9659

− 10.2181

Improvement of S/N ratio

 

0.2645

 
Using MINITAB-17 software, the predicted optimum response in terms of S/N ratio for MRR from the optimal values has been calculated:
$$ {\acute{\eta}} = - 10. 9 6 5 9\,{\text{dB}} $$
Putting this value of ή in equation “larger is better” gives the value of MRR (y) corresponding to estimated S/N ratio using the optimal machining parameters (ή):
$$ y = 0.283 $$
It was the predicted value for MRR for optimal machining parameters by taking the initial cutting parameter as A1B3C3D1, i.e. diffused electrode, pulse on time 125 µs, pulse off time 50 µs and peak current 180A. The optimal cutting parameters were A2B3C2D1, i.e. zinc-coated electrode, pulse on time 125 µs, pulse off time 45 µs and peak current 180A. Two experiments were conducted according to these values of parameters by maintaining the same previous experimental conditions. Corresponding to initial cutting parameters (A1B3C3D1) and optimal parameters (A2B3C2D1), the values of MRR were measured as 0.219 and 0.283 mm3/min, respectively. The S/N ratio of MRR was calculated using MINITAB-17:
$$ \begin{aligned} &{\text{For}}\,A_{1} B_{3} C_{3} D_{1} \,{\text{S}}/{\text{N}}\,{\text{ratio}}\, = - \,11.2304 \\ &{\text{For}}\,A_{2} B_{3} C_{2} D_{1} \,{\text{S}}/{\text{N}}\,{\text{ratio}}\, = \, - \,10.9659 \end{aligned} $$
The increment of the S/N ratio from the initial cutting parameters was
$$ = - \, 10. 9 6 5 9+ 1 1. 2 30 4= 0. 2 6 4 5 $$
Table 7 displays the results of the confirmation experiment using the optimal machining parameters of MRR. It shows a good agreement between the predicted and actual machining performances. Based on the confirmation test, the MRR was increased by 1.2869 times and the experimental results confirmed the prior parameters design for the optimal machining parameters with the multiple performance characteristics in the WEDM process.

3.5 Effect of different parameters on SR

Figure 7 illustrates that the surface roughness increases with increase in pulse on time. The surface roughness slightly increases from 40 to 45 µm and then tends to decrease with increase in the pulse off time from 40 to 50 µm. This is due to the fact that when the pulse off time increases, the number of discharge decreases (Zheng et al. 2007). Since the off time means the pause between the two successive sparks, therefore, the time period between the two sparks increases. This pause further helps to flush away the debris and allows the material to cool down, thereby improving the surface finish. Peak current also has a small effect on the surface roughness (Kanlayasiri and Boonmung 2007a; Vikram Reddy et al. 2015). With an increase in the value of peak current from 180 to 220 A, the surface roughness also increases.
Fig. 7

Effects of process parameters on SR (S/N data)

To study the significance of the process parameters towards SR, analysis of variance (ANOVA) was performed. Table 8 shows the effect of process parameters on the surface roughness.
Table 8

Analysis of Variance for SR

Source

DF

Seq. SS

Adj. SS

Adj. MS

F

P

Wire types

1

1.83

1.83

1.83

2.14

0.175

Pulse on time

2

4.87

4.87

2.44

3.86

0.049

Pulse off time

2

2.96

2.96

1.48

1.12

0.364

Peak current

2

1.35

1.35

0.68

0.20

0.824

Residual error

10

3.88

3.88

0.39

  

Total

17

8.74

    

The data revealed that pulse on time was the most influential factor (contributing 46.79% to performance measure), followed by pulse off time (contributing 24.94%), wire types (contributing 16.47%) and peak current (contributing 6.52%). The factor with a p value less than 0.05 is counted significant due to 95% confidence level taken during analysis.

3.6 Time series plot for surface roughness (SR)

Figure 8 shows time series plot. This graph shows a plot of the mean of surface roughness values versus the number of experimental runs.
Fig. 8

Time series plot for surface roughness

From Fig. 8, it can be seen that data show an upward trend with respect to the number of runs and there is a periodic fluctuation in the value of surface roughness on each run. The minimum value of SR is at a 13th run (2.735 µm) and the maximum value of SR is during the 7th run (3.655 µm).

3.7 Contour plot for surface roughness (SR)

Figure 9a shows the contour plot of Surface Roughness (SR) versus pulse off time (Toff) and pulse on time (Ton). It can be observed from the plot that effect of pulse on time (Ton) is much more than pulse off time (Toff) on surface roughness. The minimum surface roughness is obtained when the pulse on time (Ton) was in between 115 and 121.9 µs and pulse off time (Toff) was in between 40 and 42.5 µs. Figure 9b shows the contour plot of Surface Roughness (SR) versus peak current (Ip) and pulse on time (Ton). It can be inferred from the plot that the effect of the pulse on time (Ton) is much more than peak current (Ip) on surface roughness. The minimum surface roughness was obtained when the pulse on time (Ton) was in between 117.9 and 119.9 µs and peak current (Ip) was in between 199 and 208A.
Fig. 9

a Contour Plot for SR Vs Pulse off time (Toff), Pulse on Time (Ton). b SR Vs Peak Current (Ip), Pulse on Time (Ton)

3.8 Confirmation tests for surface roughness (SR)

Using MINITAB-17 software, the predicted optimum response in terms of S/N ratio for SR from the optimal values has been calculated for the present case:
$$ {\acute{\eta}} = \left( {\bar{A}_{1} } \right) + \left( {\bar{B}_{3} } \right) + \left( {\bar{C}_{2} } \right) + \left( {\bar{D}_{1} } \right) - 3\left( {\overline{\eta } } \right) $$
$$ \begin{aligned} &= \left( { - 10. 5 2 4} \right) + \left( { - 10. 8 5 3} \right) + \left( { - 10. 5 40} \right) + \left( { - 10. 40 6} \right)\\ & \quad - 3\left( { - 10. 30 9 5} \right) \\ &= - 4 2. 3 2 3+ 30. 9 2 8 5 \\ &= - 1 1. 3 9 4 5 {\text{ dB}} \end{aligned} $$
Putting this value of ή in equation “lower is better” gives the value of SR (y′) corresponding to estimated S/N ratio using the optimal machining parameters (ή):
$$ y\prime \, = \,3.7130 $$
It was the predicted value for optimal surface roughness by taking initial cutting parameter as A2B2C3D1, i.e. zinc-coated electrode, pulse on time 120 µs, pulse off time 50 µs and peak current 180A. The optimal cutting parameters were A1B3C2D1, i.e. diffused electrode, pulse on time 125 µs, pulse off time 45 µs and peak current 180A. The two experiments were conducted according to these parameters by maintaining the same previous experimental conditions. Corresponding to initial cutting parameters (A2B2C3D1) and optimal parameters (A1B3C2D1), the values of SR were measured as 3.415 and 3.7130 µm, respectively. The S/N ratio was
$$ \begin{aligned} &{\text{For}}\,A_{2} B_{2} C_{3} D_{1} \,S/N\,{\text{ratio}} = - 10.1547 \\ &{\text{For}}\,A_{1} B_{3} C_{2} D_{1} \,S/N\,{\text{ratio}} = - 11.3945 \hfill \\ \end{aligned} $$
The increment of the S/N ratio from the initial cutting parameters to the optimal parameters was calculated as:
$$ = - 10. 1547+ 11.3945 = 1.2398 $$
Table 9 displays the results of the confirmation experiment using the optimal machining parameters of SR. Good agreement between the predicted and actual machining performance has been exposed. Based on the confirmation test, SR was increased by 0.68 times. The experimental results validate the optimal machining parameters with the multiple performance characteristics in the WEDM process.
Table 9

Confirmation experiment result of SR

 

Initial cutting parameters

Optimal cutting parameters

Prediction

Experiment

Level

A2B2C3D1

A1B3C2D1

A1B3C2D1

SR (µm)

3.415

3.7130

3.630

S/N ratio (dB)

− 10.1547

− 11.3945

− 11.1019

Improvement of S/N ratio

 

1.2398

 

4 Conclusions

In this present work, an attempt has been made to optimize the wire electric discharge machining using multi-response optimization technique based on Taguchi’s design approach. The effect of the process parameters viz. Pulse on time, Pulse off time, Peak current and Wire types on response characteristics viz. material removal rate and surface roughness was studied. The following conclusions can be drawn:
  • Pulse on time is the most significant parameter and thereafter order of significance being pulse off time and peak current in MRR.

  • MRR increases with increases in pulse on time. Since the energy released per spark increases with increase in pulse on time, hence higher time for each spark is provided leading to more material removal rate.

  • The optimal parameters for better material removal rate (MRR) are zinc-coated electrode, pulse on time 125 µs, pulse off time 45 µs and peak current 180A.

  • Pulse on time is the most significant factors and thereafter the order of significance being pulse off time and types of the electrode in SR.

  • SR increases with increases in pulse on time. Discharge energy increases with increases in pulse on time due to this much more melting and re-solidification of materials takes place leading to higher SR to be produced.

  • The optimal parameters for better surface finish are diffused electrode, pulse on time 125 µs, pulse off time 45 µs and peak current 180A.

Notes

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest and have not received any funding from any agency.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tushar Saini
    • 1
  • Khushdeep Goyal
    • 1
    Email author
  • Deepak Bhandari
    • 2
  1. 1.Department of Mechanical EngineeringPunjabi UniversityPatialaIndia
  2. 2.Department of Mechanical EngineeringYadavindra College of EngineeringTalwandi SaboIndia

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