where is the duration of a single pulse (μs), and *m* is the weight lost during the EDM process (kg). Data in the literature [31,51] indicate that *MRR* depends on the value of the interelectrode gap *S* (m), the feed rate *V* (m*/*s), the physical and mechanical properties of the processed material, and the workpiece thickness *h* (m). *MRR* is calculated using where *ton* is the duration of a single pulse (µs), and *m* is the weight lost during the EDM process (kg). Data in the literature [31,51] indicate that *MRR* depends on the value of the interelectrode gap *S* (m), the feed rate *V* (m/s), the physical and mechanical properties of the processed material, and the workpiece thickness *h* (m). *MRR* is calculated using Equation (2):

$$MRR = \Im(R+S)hV\rho\tag{2}$$

 = 2(+)ℎ (2) where *R* is the radius of the ET (m), *S* is the interelectrode gap (m); *ρ* is the density of the processed material (kg/m3 ), *h* is the thickness of workpiece (m), and *V* is the feed rate.

where *R* is the radius of the ET (m), *S* is the interelectrode gap (m); *ρ* is the density of the processed material (kg*/*m3), *h* is the thickness of workpiece (m), and *V* is the feed rate. The spark energy, *W* (J), is released in the interelectrode gap and is distributed between the ET and the workpiece. The material is removed from the workpiece by the The spark energy, *W* (J), is released in the interelectrode gap and is distributed between the ET and the workpiece. The material is removed from the workpiece by the mean of the spark energy [31]. The pulse energy is calculated as:

= න

$$\mathcal{W} = \int\_0^{t\_p} \mathcal{U}Id\_{on} \tag{3}$$

(3)

Equation (2):

where *U* is the voltage, (V); *I* is the current strength, (A); *ton* is the pulse duration, (µs); and *tp* is the pulse width (µs). A correction factor is introduced in Equation (3) to improve the accuracy of the calculations.

$$\mathcal{W} = (\eta\_{\mu}) \mathcal{U} I t\_{\mathcal{on}} \tag{4}$$

The coefficient for a fraction of the energy (*ηu*) utilized in the machining process is represented in Equation (5) [31,50]:

$$
\eta\_{\mu} = (1 - \mathbf{K}\_1)(1 - \mathbf{K}\_2) \tag{5}
$$

where *K*1 is the amount of energy lost during the heating and evaporation of the dielectric fluid; *K*2 is amount of energy lost in the ET. The amount of heat *Q* (J) transferred to the WE for heating and melting is determined by the formula:

$$Q = m(\mathbb{C}\_1 \Delta T\_1 + \lambda + \mathbb{C}\_2 \Delta T\_2 + r) \tag{6}$$

where *m* is the mass of the workpiece (kg); *C*1 is the specific heat capacity of the material in the solid state (J/kg K) *C*2 is the specific heat capacity of the material in the liquid state (J/kg K); ∆T is the temperature difference between the initial and final heating points (K); *λ* is the specific heat of fusion of the material (J/kg); and *r* is the specific heat of vaporization (J/kg).

Taking into the account the equal coefficients of energy loss in Equation (4) and Equation (6) (i.e., *W* and *Q*) and *Z* = *C*1∆*T*1 + *λ* + *C*2∆*T*2 + *r*. Equation (1) is represented as:

$$MRR = \frac{Q\eta\_u \text{UI}}{ZW} = \frac{\eta\_u \text{UI}}{Z} \tag{7}$$

thus equating expressions (2) and (7). The value of the interelectrode gap *S* is calculated as:

$$S = \frac{\eta\_{\mu} \mathcal{U} \mathcal{U}}{2 \mathcal{Z} h \mathcal{V} \rho} - R \tag{8}$$

The value of B (as shown in Figure 4) can be calculated, and thus the cut width (L) is obtained. This theoretical model can be used to estimate the process parameter affecting the cut width (L), and can thus suggest the amount of correction required for accurate machining.

The PCM is made up of both electrically conductive carbon fiber and non-conductive epoxy resin. As a result, machining such composites with prominent machining techniques i.e., WIRE-EDM or EDM is a difficult job. In the literature, research on the WIRE-EDM of PCM is limited to where the effect of WIRE-EDM on PCM is explored for higher machining rate, electrode wear, and performance in the linear cut.

In this work, the authors investigated the performance of WIRE-EDM on a patent carbon fiber-reinforced PCM possibly adopted in the aviation industry. The voltage, pulse duration, and pause time were selected as process parameters. These parameters were statistically evaluated, and the level of the significance of factors affecting the cut width was determined using analysis of variance. Finally, the experimental values obtained for cut width were modeled mathematically in terms of significant factors using response surface methodology.

#### *Purpose of Study*

#### **2. Material and Methods** In this study, a polymer composite material (VKU-39, pl. refer https://viam.ru, 11,

part, such as a gear.

**2. Material and Methods**

#### *2.1. Material* May, 2021) used in the aviation industry was chosen. The workpiece is a laminated fi-

*2.1. Material*

In this study, a polymer composite material (VKU-39, pl. refer https://viam.ru, 11 May 2021) used in the aviation industry was chosen. The workpiece is a laminated fibrous polymer composite made of carbon fiber twill as reinforcement/filler, with epoxy as a binder material. The property of the selected PCM is as shown in Table 1. brous polymer composite made of carbon fiber twill as reinforcement/filler, with epoxy as a binder material. The property of the selected PCM is as shown in Table 1. **Table 1.** Properties of polymer composite material (PCM).

• To determine the trajectory of ET to machine PCM in the form of a complex shaped

**Table 1.** Properties of polymer composite material (PCM). Filler material Carbon fabrics twill (Porcher-3692)

**Property Average Value**

*Micromachines* **2021**, *12*, x FOR PEER REVIEW 6 of 14

A PCM plate of thickness 2 mm was used for the WIRE-EDM experiments. To improve the conductivity of the PCM, a conductive layer of titanium (1 mm) was applied on both sides (Figure 5). The study was carried out on a wire-cut EDM machine, "Electronica EcoCut." The electrode tool (ET) used was a brass wire with a diameter of 0.25 mm. Distilled water was used as a dielectric medium, and was sprayed on the ET (Figure 5) instead of immersing the PCMs in a water bath, as PCM immersion in a water bath causes defects such as swelling and filling of water. A PCM plate of thickness 2 mm was used for the WIRE-EDM experiments. To improve the conductivity of the PCM, a conductive layer of titanium (1mm) was applied on both sides (Figure 5). The study was carried out on a wire-cut EDM machine, "Electronica EcoCut." The electrode tool (ET) used was a brass wire with a diameter of 0.25 mm. Distilled water was used as a dielectric medium, and was sprayed on the ET (Figure 5) instead of immersing the PCMs in a water bath, as PCM immersion in a water bath causes defects such as swelling and filling of water*.*

**Figure 5.** Schematic of the WIRE-EDM of polymer composite material (PCM) with conductive layers used in the present study (1: PCM, 2: conductive Ti-layers, 3: tension roller, 4: ET wire, 5: flushing, 6: gearbox, 7: upper and lower diamond wire guides). **Figure 5.** Schematic of the WIRE-EDM of polymer composite material (PCM) with conductive layers used in the present study (1: PCM, 2: conductive Ti-layers, 3: tension roller, 4: ET wire, 5: flushing, 6: gearbox, 7: upper and lower diamond wire guides).

#### *2.2. Method 2.2. Method*

The process parameters selected were U—voltage, V; Ton—pulse duration, μs; and Toff—the pause time in pulse duration. The experimental runs were carried out by employing the orthogonal central composition design (CCD) matrix, where α denotes the distance between the star point and central point i.e., value of α = 1.215. The design matrix in the study was obtained with the assistance of Design-Expert software. Orthogo-The process parameters selected were U—voltage, V; Ton—pulse duration, µs; and Toff—the pause time in pulse duration. The experimental runs were carried out by employing the orthogonal central composition design (CCD) matrix, where α denotes the distance between the star point and central point i.e., value of α = 1.215. The design matrix in the study was obtained with the assistance of Design-Expert software. Orthogonality in the design assisted in estimating the independent regression coefficients [49,52]. The process parameters are presented in Table 2. The output parameter was the value of the EDM cut width (L). The cut width was the wire diameter of the ET and the size of the side clearance (overcut).

**Table 2.** Process parameters of wire-electrical discharge machining (EDM).

The experiment design matrix and response (cut width) is presented in Table 3. Each experimental run was replicated thrice for more accuracy. A pictorial view of the machining process is shown in Figure 6.

**Table 3.** Experimental central composition design (CCD) matrix with the responses the experiment obtained.

**Figure 6.** Pictorial view of PCM machining setup*.*  **Figure 6.** Pictorial view of PCM machining setup.

#### **3. Results and Discussion 3. Results and Discussion**

and is summarized in Table 4.

gested for further analysis.

**Table 4.** Mathematical model analysis*.*

**ANOVA for Response Surface Source Sum of Squares df Mean Square** *F***-Value** *p***-Value**

level.

Analysis of variance (ANOVA) is a commonly used statistical technique to test the significance of a model and the contribution of each process parameter on the experi-Analysis of variance (ANOVA) is a commonly used statistical technique to test the significance of a model and the contribution of each process parameter on the experimental

mental response. The mathematical model was predicted using Design Expert software

R-square values were higher for the 2-way interaction model. Thus, this model is sug-

Statistical analysis of the experimental data (Table 5) revealed the significance of the process parameters, namely voltage, pulse duration, and pause time, on the measured response i.e., cut width. The results are summarized in Table 5, with a 95% confidence

**Model** *p***-Value Adjusted R2 Predicted R2**

Model 10,655.21 6 1775.87 6.71 0.0085 Significant

U-Voltage 1593.02 1 1593.02 6.02 0.0397 - Ton 6189.06 1 6189.06 23.40 0.0013 - Toff 33.21 1 33.21 0.1256 0.7322 - U x Ton 2485.13 1 2485.13 9.40 0.0155 - U x Toff 55.13 1 55.13 0.2084 0.6602 - Ton x Ton 300.12 1 300.12 1.13 0.3179 - Residual 2116.12 8 264.52 - - - Total 12,771.33 14 1775.87 - - -

Linear 0.0127 0.5061 0.2412 -

Quadratic 0.4820 0.7049 −0.0121 -

**Table 5.** Analysis of variance (ANOVA) of process parameters for 2-way interaction model*.*

2-way interaction 0.0663 **0.7100 0.3711** Suggested Model

The 2-way interaction model F-value of 6.71 implies that the model is significant, and only 0.85% chance of noise exists in this model. From Table 5, the p-value less than response. The mathematical model was predicted using Design Expert software and is summarized in Table 4.

From Table 4, it is observed that the value of adjusted R-square and predicted R-square values were higher for the 2-way interaction model. Thus, this model is suggested for further analysis.

Statistical analysis of the experimental data (Table 5) revealed the significance of the process parameters, namely voltage, pulse duration, and pause time, on the measured response i.e., cut width. The results are summarized in Table 5, with a 95% confidence level.

**Table 4.** Mathematical model analysis.

**Table 5.** Analysis of variance (ANOVA) of process parameters for 2-way interaction model.

The 2-way interaction model F-value of 6.71 implies that the model is significant, and only 0.85% chance of noise exists in this model. From Table 5, the *p*-value less than 0.05 indicated that the model terms of voltage, Ton, and their interaction (i.e., U × T-on) were significant, whereas Toff, U × Toff and Ton × Ton were insignificant. Eliminating insignificant terms results in the improvement in the model, as presented in Table 6.

**Table 6.** ANOVA for the reduced 2-way interaction model.

The model regression statistics for the selected model demonstrated a high R2 value (i.e., 0.80), which is acceptable. The predicted R2 of 0.6677 was in reasonable agreement with the adjusted R2 of 0.7504; i.e., the difference was less than 0.2.

In order to check the adequacy of model, the predicted value and the actual experimental values were compared along a 45◦ line, as shown in Figure 7. This implies that the proposed model is adequate and there is no violation of the independent or constant variance assumptions.

**Table 6***.* ANOVA for the reduced 2-way interaction model.

**Squares df Mean**

with the adjusted R*2* of 0.7504; i.e., the difference was less than 0.2.

**Source Sum of**

variance assumptions.

0.05 indicated that the model terms of voltage, Ton, and their interaction (i.e., U x T-on) were significant, whereas Toff, U x Toff and Ton x Ton were insignificant. Eliminating insignificant terms results in the improvement in the model, as presented in Table 6.

Model 10,266.75 3 3422.25 15.03 0.0003 Significant

The model regression statistics for the selected model demonstrated a high R2 value (i.e., 0.80), which is acceptable. The predicted R*2* of 0.6677 was in reasonable agreement

In order to check the adequacy of model, the predicted value and the actual experimental values were compared along a 45° line, as shown in Figure 7. This implies that the proposed model is adequate and there is no violation of the independent or constant

U-Voltage 1593.02 1 1593.02 7.00 0.0228 - Ton 6188.60 1 6188.60 27.18 0.0003 - U x Ton 2485.13 1 2485.13 10.91 0.0070 - Residual 2504.58 11 227.69 - - - Total 12,771.33 14 3422.25 - - -

**Square** *F***-Value** *p***-Value**

**ANOVA for Response Surface**

The final equation obtained for the prediction of cut-width (L) is represented as Equation (9): The final equation obtained for the prediction of cut-width (L) is represented as Equation (9):

$$L = \text{329.6} - 0.9475 \times \text{U} - 6.45 \times \text{Ton} + 0.141 \times \text{U} \times \text{Ton} \tag{9}$$

The regression equation analysis (9) shows the combination (interaction) of input process parameters (factors) affecting the value of the cut width. The regression equation analysis (9) shows the combination (interaction) of input process parameters (factors) affecting the value of the cut width.

Figures 8 and 9 presents the response surface describing the dependence of the cut width on the voltage and pulse duration. Figures 8 and 9 presents the response surface describing the dependence of the cut width on the voltage and pulse duration. *Micromachines* **2021**, *12*, x FOR PEER REVIEW 10 of 14

**Figure 8.** Contour plot for cut-width dependance on voltage and pulse duration for pause time Toff = 30 μs. **Figure 8.** Contour plot for cut-width dependance on voltage and pulse duration for pause time Toff = 30 µs.

pause time Toff = 30μs.

**Figure 9.** Response surface 3D-plot for cut-width dependence on voltage and pulse duration for

It is depicted from the plots (Figure 8,9) that cut width is directly proportional to the voltage and pulse duration. Thus, for machining complex PCM parts, a high value for WIRE-EDM i.e., U = 100 V, Ton = 15 μs, Toff = 30 μs was selected. The obtained cut width path is presented in Figure 10. The cut width (Figure 10) was measured at various WIRE-EDM zones, i.e., at the entrance to the PCM, at the corner, and the end of pro-

*Examination of the Predicted Model forWIRE-EDM onComplex-Shaped PCM Parts*

cessing. The average value of the cut width was calculated as L = 330 μm.

**Cut-Width (µm)** 255 355 X1 = A: Voltage X2 = B: Pulse Time

**Actual Factor** C: Pause Time = 30

5

7

9

11

B: Pulse Time (µs)

13

15 Cut-Width (µm)

**Figure 9.** Response surface 3D-plot for cut-width dependence on voltage and pulse duration for pause time Toff = 30μs. **Figure 9.** Response surface 3D-plot for cut-width dependence on voltage and pulse duration for pause time Toff = 30µs.

50 60 70 80 90 100

300

280

320

340

A: Voltage (V)

#### *Examination of the Predicted Model forWIRE-EDM onComplex-Shaped PCM Parts*

cessing. The average value of the cut width was calculated as L = 330 μm.

*Examination of the Predicted Model forWIRE-EDM onComplex-Shaped PCM Parts*  It is depicted from the plots (Figure 8,9) that cut width is directly proportional to the voltage and pulse duration. Thus, for machining complex PCM parts, a high value for WIRE-EDM i.e., U = 100 V, Ton = 15 μs, Toff = 30 μs was selected. The obtained cut width path is presented in Figure 10. The cut width (Figure 10) was measured at various WIRE-EDM zones, i.e., at the entrance to the PCM, at the corner, and the end of pro-It is depicted from the plots (Figures 8 and 9) that cut width is directly proportional to the voltage and pulse duration. Thus, for machining complex PCM parts, a high value for WIRE-EDM i.e., U = 100 V, Ton = 15 µs, Toff = 30 µs was selected. The obtained cut width path is presented in Figure 10. The cut width (Figure 10) was measured at various WIRE-EDM zones, i.e., at the entrance to the PCM, at the corner, and the end of processing. The average value of the cut width was calculated as L = 330 µm. *Micromachines* **2021**, *12*, x FOR PEER REVIEW 11 of 14

**Figure 10.** Values of the cut width for PCM wire-cut electro-discharge machining (WIRE-EDM) at U = 100 V, Ton = 15 μs, Toff = 30 μs: L1 = 325 μm, L2 = 345 μm, L3 = 325 μm, L4 = 330 μm. **Figure 10.** Values of the cut width for PCM wire-cut electro-discharge machining (WIRE-EDM) at U = 100 V, Ton = 15 µs, Toff = 30 µs: L1 = 325 µm, L2 = 345 µm, L3 = 325 µm, L4 = 330 µm.

**Figure 11.** Machined PCM surfaces after application of a conductive Ti-layer on the surface of PCM; (**a**)—sheet surface at

the processing zone (at the edge), (**b**)—along the cross-section of sheet width.

PCM sheet at the entrance, at the corner, and at the end.

Figure 11a,b reveals the surface the cutting edge achieved after applying the conductive Ti-alloy layer (plates) to the surface of PCM. It is found that this method of ma-

Thus, the Ti layer sandwich method for PCM enhanced the quality of the machined surface. Additionally, it has the potential to obtain a smooth, defect-free surface within the processed slot without causing any damage to the fibers/binder of the PCM material*.*  The percentage error between the values of the cut-width was calculated using Equation (10). The expected value from Equation (9) was calculated as 349.6μm, and the experimental value (actual value) measured was 330 μm (Figure 10). Thus, the percentToff = 30 μs: L1 = 325 μm, L2 = 345 μm, L3 = 325 μm, L4 = 330 μm.

Figure 11a,b reveals the surface the cutting edge achieved after applying the conductive Ti-alloy layer (plates) to the surface of PCM. It is found that this method of machining results in attaining a defect-free smooth surface on both sides of the processed PCM sheet at the entrance, at the corner, and at the end. Figure 11a,b reveals the surface the cutting edge achieved after applying the conductive Ti-alloy layer (plates) to the surface of PCM. It is found that this method of machining results in attaining a defect-free smooth surface on both sides of the processed PCM sheet at the entrance, at the corner, and at the end.

**Figure 10.** Values of the cut width for PCM wire-cut electro-discharge machining (WIRE-EDM) at U = 100 V, Ton = 15 μs,

*Micromachines* **2021**, *12*, x FOR PEER REVIEW 11 of 14

**Figure 11.** Machined PCM surfaces after application of a conductive Ti-layer on the surface of PCM; (**a**)—sheet surface at the processing zone (at the edge), (**b**)—along the cross-section of sheet width. **Figure 11.** Machined PCM surfaces after application of a conductive Ti-layer on the surface of PCM; (**a**)—sheet surface at the processing zone (at the edge), (**b**)—along the cross-section of sheet width.

Thus, the Ti layer sandwich method for PCM enhanced the quality of the machined surface. Additionally, it has the potential to obtain a smooth, defect-free surface within the processed slot without causing any damage to the fibers/binder of the PCM material*.*  Thus, the Ti layer sandwich method for PCM enhanced the quality of the machined surface. Additionally, it has the potential to obtain a smooth, defect-free surface within theprocessed slot without causing any damage to the fibers/binder of the PCM material. *Micromachines* **2021**, *12*, x FOR PEER REVIEW 12 of 14

> The percentage error between the values of the cut-width was calculated using Equation (10). The expected value from Equation (9) was calculated as 349.6μm, and the experimental value (actual value) measured was 330 μm (Figure 10). Thus, the percent-The percentage error between the values of the cut-width was calculated usingEquation (10). The expected value from Equation (9) was calculated as 349.6 µm, andthe experimental value (actual value) measured was 330 µm (Figure 10). Thus, the percentage error obtained is calculated as 5.906%, which is acceptable and shows the competency of the model. age error obtained is calculated as 5.906%, which is acceptable and shows the competency of the model.

$$\% \, error = \frac{|\text{Expected value} - \text{Actual value}|}{\text{Expected value}} \tag{10}$$

The regression Equation (9) was further examined to accurately machine a gear-shaped PCM part. An "Electronica EcoCut" CNC WIRE-EDM machine was programmed. The program was used to machine a PCM workpiece into the gear shape. The machining parameters were carried out at Ton = 15µs, Toff = 30 µs, and U = 100 V. Based on this regression model (Equation (9)), the trajectory correction value was calculated as B = 0.165 mm. When machining the PCM, the offset was added into the control program using the command "G41 B" = 0.165. In Figure 12, the trajectory of the ET and the finished product "gear" are presented. The regression Equation (9)was further examined to accurately machine a gear-shaped PCM part. An "Electronica EcoCut" CNC WIRE-EDM machine was programmed. The program was used to machine a PCM workpiece into the gear shape. The machining parameters were carried out at Ton = 15μs, Toff = 30 μs, and U = 100 V. Based on this regression model (Equation (9)), the trajectory correction value was calculated as B = 0.165 mm. When machining the PCM, the offset was added into the control program using the command "G41 B" = 0.165. In Figure 12, the trajectory of the ET and the finished product "gear" are presented.

and processing parameters such as voltage, pulse-on time, pulse-off time. These results can be expedited to adjust the size of the ET and ensure precision in the WIRE-EDM of PCM (VKU-39) workpieces. It is ascertained that voltage and pulse duration and their interaction are the significant factors affecting the process parameters for machining the PCM workpiece. Furthermore, a2-way interaction model is developed to estimate the cut-width, which shows excellent adequacy with the experimental values obtained. Based on the developed model, the cut-width correction factor for the trajectory of ET was estimated in a WIRE-EDM CNC machine for the accurate machining of a complex-shaped PCM product. Consequently, it is suggested that the proposed model suc-

**Author Contributions:** Conceptualization, E.S.S. and T.R.A.; methodology, K.R.M. and T.R.A.; validation, S.S.S.; formal analysis, T.R.A., E.S.S. and S.S.S.; investigation, E.S.S. and T.R.A.; resources, E.S.S., T.R.A. and K.R.M.; data curation, E.S.S. and K.R.M.; writing—original draft preparation, E.S.S. and T.R.A.; writing—review and editing, K.R.M. and T.R.A.; visualization, E.S.S. and T.R.A.; supervision, S.S.S. and T.R.A.; project administration, K.R.M. and T.R.A.; funding acquisition, T.R.A., E.S.S. and K.R.M. All authors have read and agreed to the published version of the

**Funding:** This work was supported by the Russian President in encouragement of young Russian

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the man-

**Figure 12.** (**a**)Trajectory of the ET, and(**b**) finished product. **Figure 12.** (**a**) Trajectory of the ET, and (**b**) finished product.

cessfully facilitates the forecasting of WIRE-EDM accuracy.

**4. Conclusions**

manuscript.

scientists (grant MK-566.2021.4).

uscript, or in the decision to publish the results.

#### **4. Conclusions**

The experimental work reveals the dependence between the WIRE-EDM cut width and processing parameters such as voltage, pulse-on time, pulse-off time. These results can be expedited to adjust the size of the ET and ensure precision in the WIRE-EDM of PCM (VKU-39) workpieces. It is ascertained that voltage and pulse duration and their interaction are the significant factors affecting the process parameters for machining the PCM workpiece. Furthermore, a2-way interaction model is developed to estimate the cutwidth, which shows excellent adequacy with the experimental values obtained. Based on the developed model, the cut-width correction factor for the trajectory of ET was estimated in a WIRE-EDM CNC machine for the accurate machining of a complex-shaped PCM product. Consequently, it is suggested that the proposed model successfully facilitates the forecasting of WIRE-EDM accuracy.

**Author Contributions:** Conceptualization, E.S.S. and T.R.A.; methodology, K.R.M. and T.R.A.; validation, S.S.S.; formal analysis, T.R.A., E.S.S. and S.S.S.; investigation, E.S.S. and T.R.A.; resources, E.S.S., T.R.A. and K.R.M.; data curation, E.S.S. and K.R.M.; writing—original draft preparation, E.S.S. and T.R.A.; writing—review and editing, K.R.M. and T.R.A.; visualization, E.S.S. and T.R.A.; supervision, S.S.S. and T.R.A.; project administration, K.R.M. and T.R.A.; funding acquisition, T.R.A., E.S.S. and K.R.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Russian President in encouragement of young Russian scientists (grant MK-566.2021.4).

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

## *Article* **Parametric Optimization of Electric Discharge Machining of Metal Matrix Composites Using Analytic Hierarchy Process**

**Sarabjeet Singh Sidhu 1 , Timur Rizovich Ablyaz 2,\*, Preetkanwal Singh Bains 3 , Karim Ravilevich Muratov 2 , Evgeny Sergeevich Shlykov 2 and Vladislav Vitalyevich Shiryaev 2**

**Abstract:** The present study reports on the method used to obtain the reliable outcomes for different responses in electric discharge machining (EDM) of metal matrix composites (MMCs). The analytic hierarchy process (AHP), a multiple criteria decision-making technique, was used to achieve the target outcomes. The process parameters were varied to evaluate their effect on the material erosion rate (*MER*), surface roughness (SR), and residual stresses (σ) following Taguchi's experimental design. The process parameters, such as the electrode material (Cu, Gr, Cu-Gr), current, pulse duration, and dielectric medium, were selected for the analysis. The residual stresses induced due to the spark pulse temperature gradient between the electrode were of primary concern during machining. The optimum process parameters that affected the responses were selected using AHP to figure out the most suitable conditions for the machining of MMCs.

**Keywords:** analytical hierarchy process; residual stress; metal erosion rate; surface roughness

#### **1. Introduction**

Composite materials have superior properties, such as high strength; high modulus, low coefficient of thermal expansion; and resistance to fatigue, corrosion, and wear. Due to these prominent properties and their high strength-to-weight ratios, composites are extensively used in numerous advanced engineering applications. Composites with different reinforcements (such as fibers or particles) are being researched widely for their use in several applications, including manufacturing and biomedical industries. The composite materials, usually called metal matrix composites (MMCs), consist of a metal or alloy in the ductile phase to absorb and equally distribute the external load and develop a percolating network to position the reinforced fibers or particles. Alongside this, a brittle constituent, i.e., reinforcement, is embedded in the metal matrix [1–3].The combined properties of these constituents in MMCs allow for high strength and fractural properties to be attained, as well as high temperature resistance, making them suitable for applications in the automobile and aviation industries, such as braking systems, piston rods, piston pins, and brake discs [4]. These composites are also used as thermal management solutions for high energy density miniature electronic components, such as microprocessor lids, flip-chip lids, and microwave housing, and can replace high-cost materials such as titanium-based alloy [5–7]. With the presence of a soft matrix phase and hard reinforced particles in MMCs, precise machining with conventional methods is challenging in terms of avoiding degradation of the material properties.

Such difficulties can be overcome by adopting newer machining methods that can achieve the desired geometry with minimum damage to the material properties [8]. One

**Citation:** Sidhu, S.S.; Ablyaz, T.R.; Bains, P.S.; Muratov, K.R.; Shlykov, E.S.; Shiryaev, V.V. Parametric Optimization of Electric Discharge Machining of Metal Matrix Composites Using Analytic Hierarchy Process. *Micromachines* **2021**, *12*, 1289. https://doi.org/10.3390/mi12111289

Academic Editors: Francesco Modica and Irene Fassi

Received: 24 August 2021 Accepted: 19 October 2021 Published: 21 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

such method for machining MMCs is electric discharge machining (EDM), which operates by generating controlled electric sparks to machine composite materials with complex geometries and provides a better surface quality with high dimensional accuracy. In this process, the tool electrode produces its replica in the workpiece material, producing a series of discrete electrical sparks that are generated within the dielectric medium. One of the reasons for the tremendous popularity of this process is its ability to machine complex internal contours, even in hard-to-cut materials, with negligible cutting forces [2,8]; however, in this process, the rapid change in temperature gradient of the machined surface results in sub-surface defects such as cracks, spalling, porosity, residual stresses, and metallurgical transformation [9].

For the effective ED machining of MMCs, a higher value of discharge current and shorter pulse-on time is generally recommended. EDM was highlighted by several researchers in the literature as an effective non-traditional machining technique used for shaping and machining of difficult-to-machine materials such as Al-SiC metal matrix composite [10]. In a similar study, a ZrB2-40% Cu composite electrode was reported as an alternative electrode with a better material removal rate and tool wear rate than a conventionally used copper tool [11]; however, the diametric overcut and surface roughness were better with the copper tool than the composite tool electrodes [12].

The output responses, such as the material erosion rate (*MER*), tool wear rate (TWR), and surface roughness (SR), have been studied and widely reported for the EDM process [2,8]; however, one critical factor that significantly affects the machined component's life is the residual stresses induced while machining. These stresses are quantified using destructive and non-destructive routes [13–15]. Non-destructive testing (NDT) X-ray diffraction techniques have been successfully used to evaluate the residual stresses in the materials, and accordingly the effect of the process parameters [16].

Many optimization techniques have been used to analyze the effects of non-conventional machining process parameters on the output responses, such as the *MER*, TWR, SR, and residual stresses. For instance, grey relational analysis has been applied to optimize EDM process parameters on Al-10% SiC composites [17].The multi-regression method was used to establish the relationships between the input and output parameters of the wire EDM process [18].The lexicographic goal programming method was used for optimization of EDM process parameters while machining MMC [19].

So far, the previous studies have mostly reported on the optimization of *MER*, TWR, and SR, but very few studies have focused on optimizing the process parameters in order to minimize residual stresses in MMCs. EDM has been widely used for metals and alloys, although its application on MMCs and analyses of the resulting residual stresses have been limited. As such, the present study aims to establish the best process parameter settings for 65vol% SiC/A356.2 and hybrid 10vol% SiC-5vol% quartz/Al. Three output responses, namely the *MER*, SR, and residual stresses, are optimized using the analytic hierarchy process (AHP). The AHP is a decision-aiding tool that involves specifying a goal, measuring the relative importance (priorities), and choosing the relevant criteria [20,21]. One of the advantages of this tool is that it merges both qualitative and quantitative factors. The tool was formulated to exhibit the way the decision-maker thinks and determines the options based on weighted values. The tangible (objective) and non-tangible (subjective) factors can be efficiently coordinated and can provide reliable findings utilizing simple calculations. The AHP is also validated in various other fields, such as for issues linked to the economy, the stock industry, aircraft manufacture, transportation, and in the construction industry [22].

The objectives of this study are as follows:

• Analyze the influence of the EDM process parameters on the 65vol% SiC/A356.2 (sample I, procured from CPS System, Dallas, TX, USA) and 10vol% SiC-5vol% quartz/Al composites (sample II, produced by a controlled environmental stir casting process);

• Evaluate the outcomes, such as the *MER* (metal erosion rate), SR (surface roughness), and σ (residual stresses), utilizing the L18 Taguchi experimental design and optimizing the process parameters using AHP.

#### **2. Material and Methods**

#### *2.1. Material*

Two different variants of particulate-reinforced MMCs were used in this study. The material (65vol% SiC/A356.2 metal matrix composite) used in the study was procured in rectangular plates from CPS, Boston, MA, USA. The other specimen used in this experiment was a hybrid metal matrix composite with 10vol% SiC-5vol% quartz in aluminum, which was prepared using the in-house stir-casting method. The material composition was quantified using optical emission spectroscopy (Make: Arun Technology PolySpek-M spectrometer) as 0.384% Zn, 0.498% Cu, 0.424% Fe, 2.063% Si, and 0.354% Pb, with the balance% as Al.

#### *2.2. Method*

The experiments were conducted on an OSCARMAX (SD550 ZNC, Taiwan) diesinking EDM machine using a conventional polarity with the selected tool electrodes. The workpieces were machined in EDM oil as a dielectric fluid and with a suspended powder form of copper (5 g/L) and graphite (5 g/L) in the dielectric medium. To ensure the uniform mixing of the suspended powder, a stirrer pedal fixed at 1400 rpm was used during machining. Three tool electrode materials, namely (i) electrolytic copper, (ii) finedgrained graphite (Poco-EDM 3), and (iii) a copper–graphite composite (50% Cu, Grade 673) were used for the experimental study. The electrodes were machined to a cylindrical shape with a diameter of 18mm.

The responses such as the *MER*, SR, and σ were measured after each experimental trial. The *MER* was measured in terms of weight loss per unit time using a digital weighing machine (Chyo-MJ-300). The surface roughness was measured at three different directions of the machined surface using a Mitutoyo (SJ-400) surface roughness analyzer. The developed residual stresses while machining are quantified by X-ray diffraction technique using a PANalytical'sX'PertPro MPD (Almelo, The Netherlands) diffractometer. The diffractometer used in this study was a horizontal, fixed, laboratory-based system. Table 1 shows the brief experimental conditions for the PANalytical'sX'PertPro X-ray stress analysis.

**Table 1.** Residual stress measurement conditions.

#### *2.3. Experimentation*

On the basis of the pilot study, workpiece material, dielectric type, tool electrode material, pulse-on and pulse-off durations, and current, the machining parameters were selected. The other factors, such as the open-circuit voltage (~135 V) and flushing pressure (0.6 kg/cm2 ), were maintained as constant during the experimental study. Table 2 shows the control factors and settings used for the experiments.

**Table 2.** Parameters with different levels.

Because the chosen factors for the experiments involved a combination of two and three levels, the degree of freedom (dof) for2-level factors was 1 and the doffor3-level factors was 2; hence, the total dof required was 11 (i.e., 1 (one 2-level factor) + 5 × 2 (five 3-level factors) = 11). The mixed-level orthogonal array involving a combination of two- and three-level factors with at least 11 dof was designated experimental design (DOE)L18. This DOE methodology used orthogonally designed arrays that significantly reduced the required number of experimental trials to record the necessary data without compromising the output data quality [23]. L18 signifies the18 distinct orthogonal trial conditions performed randomly to remove any undesirable inclinations in the study. The orthogonal arrays contained the two-level factor in column 1, with the option of assigning three-level factors to the other columns. The conditions of the experimental trials after the assignment of factors to a selected array are listed in Table 3. As can be seen from this design matrix, the first column represents the workpiece materials used in the study; thus, the first nine trials represent 65 vol% SiC/A356.2 MMC, hereafter designated as WP I. The remaining nine trials (trials 10 to 18) represent 10 vol% SiC-5 vol% quartz in aluminum, hereafter referred to as WP II. Other factors are assigned to the remaining columns of the L18 array, as listed in Table 3. The 18 experimental trials with two repetitions are performed as per Taguchi's design in random order. The mean *MER*, SR, and σ are measured at the end of each trial and are presented in Table 3 under output responses. The *MER* is calculated by the weight difference of the workpiece before and after machining, as given by Equation (1):

$$MER = \frac{\left(w\_i - w\_f\right)1000}{T} \text{mg/min} \tag{1}$$

where ∆*w* if the change in weight, i.e., the weights before and after machining (gm); t is the machining time in minutes.

The SR was measured using a Mitutoyo SJ-400 surface roughness tester in terms of the arithmetic average of the absolute value Ra (µm). Each sample was measured from three locations diametrically on the machined surface and was averaged for investigation.

Residual stresses were estimated with the help of the classical X-ray diffraction procedure. The peak diffracted from the (422) plane was selected to measure the shift at a maximum 2θ angle. The change in d-spacing due to strain in the sample was minimal; hence, the highest possible 2θ angle peak was chosen. The relations of the d-spacing (∆d) with the diffraction peak (∆θ) is given by Equation (2):

$$\frac{\Delta \mathbf{d}}{\Delta \theta} = (-)(\Delta \theta)\cot \theta \tag{2}$$

The stresses were calculated using the classical sin2ψ technique [14,16], with the assumption that the stress state is unidirectional. Equation (3) was used to calculate normal the residual stresses:

$$\mathbf{a}^+ = \frac{1}{2} \left( I e\_{\varphi\Psi\psi} + I e\_{\varphi\Psi\psi} \right) = \frac{1}{2} \mathbf{S}\_2 \sin^2 \psi (\sigma\_{\varphi}) + I e \varphi\_0 \,^\circ \tag{3}$$

Here, parameter a+ is the average of the lattice strain for positive ( ϕψ+) and negative (ϕψ−) values and ψ is the sample alignment (herein, ϕ = 0◦ ); 1/2S2 = (1 + ν)/E, 1/2S2 are the X-ray elastic constants (XEC's) and their values are represented in Table 4.

**Table 3.** Experimental layout (L18).

**Table 4.** X-ray elastic constants.

Equation (4) can be utilized to estimate the shear residual stress in further studies:

$$\mathbf{a}^- = \frac{1}{2} \left( I e\_{\varphi\Psi\psi} - I e\_{\varphi\Psi} \right) = \frac{1}{2} \mathcal{S}\_2 \sin \left( 2\psi \right) (\tau\_{\Psi}) \tag{4}$$

The sample calibration for the stress test is represented below.

*Sample calculation of σ for trial 2 (WP I):* The machined specimen was sectioned to 25 × 25 mm using a wire-cut EDM machine. To limit modifications of the machined surface properties by heating in the wire EDM operation, we ensured that the cutting edge was far away from the calibration surface zone. The etching of re-solidified metal on the machined zone resulted in reduced computation errors. Residual stress was estimated in the aluminum matrix phase of the machined specimen. The measurement was accomplished by selecting the isolated peak diffracted at the highest value of 2θ from the plane. Figure 1 shows the X-ray spectra for trial 2. From the obtained X-ray spectra, the peak selected for residual stress determination was at approximately 137.23◦ .

Table 5 represents the various parameters for trial 2 used to measure residual stress at different 20 ψ-tilts (positive and negative).

The normal residual stress was analyzed by comparing the linear fit regression equation obtained from the plot of a+ vs. sin2ψ with Equation (3), i.e., the equation obtained was:

$$\mathbf{a}^+ = \left(5.21 \ast \sin^2 \psi = 0.291\right) \ast 10^{-4} \\ 5.21 \times 10^{-4} = \frac{1}{2} \mathbf{S}\_2 \left(\sigma\_{\varphi}\right)^2$$

where 1/2S2 = 6.98 T−1 Pa, ϕ = 0

As such, the normal residual stress (σ0) for trial 2 was 74.6 MPa.

**Table 4.** X-ray elastic constants.

machined zone resulted in reduced computation errors. Residual stress was estimated in the aluminum matrix phase of the machined specimen. The measurement was accomplished by selecting the isolated peak diffracted at the highest value of 2θ from the plane. Figure 1 shows the X-ray spectra for trial 2. From the obtained X-ray spectra, the

 **Sample I Sample II**

peak selected for residual stress determination was at approximately 137.23°.

**Elastic constants T−1Pa(1/2S2)** 6.98 16.84

**Figure 1.** Selected X-ray spectra peaks for residual stress calibration. **Figure 1.** Selected X-ray spectra peaks for residual stress calibration.

*2.4. Analysis of Variance for MER, SR, Residual Stress*

The *MER*, SR, and σ results were analyzed using analysis of variance (ANOVA). A summary of the ANOVA used for *MER*, SR, and σ is presented in Table 6. The significant parameters were chosen by comparing the F-values with F-critical at a confidence level of 95%. The higher the F-value, the greater the effect of the parameter on the responses.

**Table 6.** Analysis of variance for *MER*, SR, and σ.

\* Significant factor.

*MER*: The ANOVA results show that the current and pulse-on contributed significantly to changes in *MER*. Additionally, the variations in the workpiece material had significant effects on the *MER*. On the contrary, the dielectric medium, pulse-off time, and electrode material had no significant effect. It was observed that with increases in pulse-on time and current, the *MER* increases significantly, since increases in the current and pulse-on time increase the spark energy duration; thus, with increased heat input, the temperature increases, resulting in the workpiece's higher melting or evaporation rate;

*SR*: The surface roughness of the machined surface was significantly influenced by factors such as the powder concentration, current, and pulse-on time. Furthermore, the two materials showed quite different SR values. The roughness increased with increases in current and pulse-on time, whereas the powder in the dielectric medium improved the surface finish. An increase in current or pulse-on time increases the spark energy, which drives the formation of bigger and deeper craters, leading to a rough machined surface. The addition of powder consistently improved the finish of the machined surfaces; the spark becomes more uniform with increased frequency and widens the spark gap. This reduced the magnitude of the impact forces, resulting in small and shallow craters and lowering the surface roughness [24,25];

*Residual Stress*: The ANOVA results show that the pulse off time, powder mixing in the dielectric medium, and current significantly affected the σ. Additionally, the selected MMCs showed different residual stress values for similar parameter settings. It can be seen from the results that the pulse-on time affected the *MER* and SR but had no effect on σ. On the other hand, the pulse-off time hada significant impact on the development of residual stress due to the re-solidification time duration. The presence of suspended particles in the dielectric medium facilitates the easy formation of plasma channels between the electrode and the workpiece, resulting in lower SR and residual stress. The conductivity of suspended particles plays the major role in determining the SR but has no impact on the development of σ.

#### **3. Analytical Hierarchical Process**

Optimizing the responses independently results in vastly different parametric combinations of the machining process parameters. For example, if *MER* was optimized separately, this would cause some parameters to increase *MER* (the higher *MER* is, the better the function); however, these parameters may not result in decreased SR. The opposite would be true if SR was optimized individually. The identical condition involves residual stresses optimization. To obtain a result that is close to the target, the responses must be optimized together according to their priority. The analytical hierarchy process (AHP) offers one technique that suggested the best combination of parameters to reach or nearly reach the target. The AHP is simply structured and widely used for multiple-goal

decision-making techniques and is classified as a decision-making tool for use under conditions of certainty, i.e., the data are obtained deterministically and the tool is designed for situations in which ideas, feelings, and emotions are quantified into a numerical scale [26]. The main steps used in the implementation of the AHP are as follows:

**Table 7.** Saaty's fundamental scale.

**Table 8.** Random consistency index. *k* **1 2 3 4 5 6 7 8 9 10 11 12 13**

The steps are summarized in Figure 2. evaluated by the linear addition function. The steps are summarized in Figure 2.

**Table 8.** Random consistency index.

**Figure 2.** Hierarchy layout of the analytical hierarchy process. **Figure 2.** Hierarchy layout of the analytical hierarchy process.

*3.1. Implementation of AHP in EDM process*  In the present study, AHP was applied to choose the best combination of input The MMCs used in the present study are used for very high-end applications in aerospace and mining, while the residual stresses developed during the EDM process affect

parameters. The criteriawere used to minimize the residual stress and surface roughness and maximize the metal erosion rate. In this experimental design layout, 9 trials are conducted for each type of MMC's and the orthogonality is maintained by selecting the

available alternatives for MMC WP I and trials 10 to 18 are alternatives for MMC WP II. The hierarchy for the AHP in the present study was selected using the steps described

where σmax is the maximum value of the residual stress in the corresponding trial set for each workpiece, σ0 is the residual stress measured with the X-ray diffraction method, and σn is a modified residual stress value (refer to Table 3), which was calculated from

Using the criteria for assigning weights to the residual stress, MER, and SR, a (3 × 1) weight column matrix, as shown in Table 9, was established for pair-wise comparison.

The MMCs used in the present study are used for very high-end applications in aerospace and mining, while the residual stresses developed during the EDM process affect the service life of the product. Residual stress was assigned the maximum weight, followed by the material removal rate and surface roughness. To attain the desired objective, the residual stress results were slightly modified (cost-to-benefit conversion,

= (௫ − )1 (5)

which can be achieved by using the –ve sign), as follows:

above (Figure 2).

Equation (5).

the service life of the product. Residual stress was assigned the maximum weight, followed by the material removal rate and surface roughness. To attain the desired objective, the residual stress results were slightly modified (cost-to-benefit conversion, which can be achieved by using the -ve sign), as follows:

$$
\sigma\_{\mathbf{n}} = (\sigma\_{\text{max}} - \sigma\_0) + \mathbf{1} \tag{5}
$$

where σ*max* is the maximum value of the residual stress in the corresponding trial set for each workpiece, σ0 is the residual stress measured with the X-ray diffraction method, and σn is a modified residual stress value (refer to Table 3), which was calculated from Equation (5).

Using the criteria for assigning weights to the residual stress, *MER*, and SR, a (3 × 1) weight column matrix, as shown in Table 9, was established for pair-wise comparison.

**Table 9.** AHP pair-wise comparison of weighting criteria.

Subsequently a pair-wise comparison of the experimental trials (alternatives) was developed for, σn, *MER*, and SR for each workpiece, with the results shown in Tables 10–15. The synthesized matrix to obtain priority vector of σ for WP I is shown in Table 16. It was also ensured during pair-wise comparisons of alternatives that if the values attained during comparison were greater than the maximum limit of Saaty's fundamental scale, the highest value of the scale (9) was selected to avoid inconsistency.

**Table 11.** Pair-wise comparison of *MER* values with respect to their alternatives for WP I.

**Table 12.** Pair-wise comparison of SR values with respect to their alternatives for WP I.

**Table 13.** Pair-wise comparison of σ values with respect to their alternatives for WP II.

**Table 14.** Pair-wise comparison of *MER* values with respect to their alternatives for WP II.

**Table 15.** Pair-wise comparison of SR values with respect to their alternatives for WP II.

**Table 16.** Synthesized matrix of σ for WP I.

The pair-wise comparison (9 × 9 matrix) of the alternatives was completed by comparing and rounding off the response ratio obtained in experimental trials. For example, if trial 1 gives a value of 16.5 and trial 2 gives 4.5, then the response ratio is the ratio of the values of the two trials (trial1/trial2), which is 3.66 (rounding off = 4). The same procedure was adopted for all the response parameters to assign the weights. In the work [27] adopted percentage change in the state of tool wear while machining medium carbon steel workpiece. The change in percentage was used to assign the weight in pair-wise comparison matrix.

To illustrate this calculation, the pair-wise matrix for residual stress (σ) (WP I) is considered, as shown in Table 16.

*Step 1:* The matrix was normalized by dividing each element of the matrix by its column total. For example, for the T(1,1) element, a value of 0.13434 was obtained by dividing 1 by the column total of 7.444 (1 + 1 + 1 + 1 + 1 + 1/3 + 1 + 1 + 1/9). The same procedure was adopted for each element of the matrix, with the results given in Table 16.

*Step 2:* The estimation of the priority vector was done by taking the row average, i.e., (0.13434 + 0.10405 + 0.10405 + 0.17208 + 0.13433 + 0.12442 + 0.13585 + 0.11843 + 0.12329) and dividing it by 9.

Similarly, the synthesized pair-wise comparison matrix, priority vectors, and validation of the constructed matrix were performed for each response parameter. The overall weight was calculated by multiplying the alternative available priority vectors for each sample with the criteria weight, as given in Table 17.

**Table 17.** Overall weight matrix of WP I for the priority vector.

\* 1st rank, \*\* 2nd rank.

The overall priority for each EDM parameter setting was calculated as demonstrated below:

*Overall weight of T1 (WP I):* Overall Weight = 0.581552 (0.130829) + 0.308996 (0.02080) + 0.109452 0.0834519) = 0.091645.

Similarly, the overall weight was calculated for each trial conducted for the selected workpieces.

The remaining calculations were completed by combining the assigned criteria weight with the alternative priority weight to obtain the overall priority results (Tables 17 and 18), as per the hierarchical steps given in Figure 1.

**Table 18.** Overall weight matrix of WPII for the priority vector.

\* 1st rank, \*\* 2nd rank.

The ideal weight vector was obtained by dividing the priority vector with the largest priority weight element in the matrix. The advantage of using an idealized weight vector is that the ranking of trials does not change due to the influence of a newly introduced non-optimal identical alternative [27].

From the calculated overall priority, the trials were ranked for each type of MMC. The maximum overall weight or composite performance score for sample I was obtained for T4 as given in Table 17 (See also Table 3), which was conducted with a graphite electrode; dielectric medium mixed with Cu powder; pulse-off and pulse-on times of 15 µs and 45 µs, respectively; and current of 8 amps. Similarly, the maximum overall weight or composite performance score for sample II as obtained in Table 18 corresponds to trial number 13 (Table 3) of the original L18 array. This trial was also completed with a graphite electrode; dielectric medium mixed with graphite powder; pulse-off and pulse-on times of 15 µs and 45 µs, respectively; and a current setting of 4 amps. It was observed that the presence of powder in the dielectric medium expanded the area of the spark zone between the electrodes, thereby minimizing the impact of thermal shocks on the machined surface and diminishing the induced residual stresses; thus, the solution that globally optimizes residual stresses, *MER*, and SR for the two types of MMCs (WP I and WP II) used in the experiment was obtained (Table 19).

**Table 19.** Summarized process parameters for the target responses.

#### **4. Conclusions**

In the present study, three output response parameters, namely σ, *MER*, and SR, were optimized using a manageable AHP technique. Due to conflicting parameter settings for different output responses in the EDM process, identifying process parameters is a complex decision-making process. A manageable AHP approach was used in the present study to obtain a more reliable global composite performance score for various trial conditions in powder-mixed electric discharge machining (PMEDM) of MMCs. The process conditions that affected the three responses, namely σ, *MER*, and SR, were identified and optimized using AHP for two different types of MMCs. The current and pulse-on time significantly affected *MER*, while the addition of the powder, current, and pulse-on time influenced

SR. Despite this, the pulse-off duration had no significant effect on *MER* or SR. Still, the pulse-off duration had the most considerable influence on residual stresses, followed by the dielectric medium, current, and type of tool electrode.

The three responses were optimized together according to the predetermined goal using AHP. The optimal process conditions for the selected materials were identified. The overall process for both workpieces revealed that machining the workpiece with a graphite tool electrode and higher pulse-on time setting coupled with lowest pulse-off time in the presence of a suspended particle dielectric medium (PMEDM) contributed to minimizing the residual stress with the desired *MER*. Due to the denser ceramic-reinforced particles in WP I compared to WP II, the target results were achieved at a higher current level (i.e., 8A) than the current required for WP II. The optimal settings for achieving the specified target results involved the graphite tool electrode coupled with pulse-on and -off times of 45 µs and 15 µs, respectively, for both workpieces. The methodology used to obtain optimum EDM process parameters can be extended by prioritizing different responses (i.e., *MER*, SR) according to the end-use application of the product. Overall, the use of AHP will open the horizon for EDM practitioners to determinevarious process parameters, improving their ability to achieve their desired targets.

**Author Contributions:** Conceptualization, S.S.S., P.S.B., E.S.S. and T.R.A.; methodology, S.S.S., K.R.M. and T.R.A.; validation, S.S.S. and T.R.A.; formal analysis, S.S.S., T.RA. and E.S.S.; investigation, S.S.S., E.S.S. and T.R.A.; resources, S.S.S., E.S.S., T.R.A., and K.R.M.; data curation, E.S.S., K.R.M. and V.V.S.; software, V.V.S.; writing—original draft preparation, S.S.S., P.S.B., E.S.S. and T.R.A.; writing—review and editing, S.S.S., K.R.M., T.R.A. and V.V.S.; visualization, S.S.S., E.S.S. and T.R.A.; supervision, S.S.S. and T.R.A.; project administration, S.S.S., K.R.M. and T.R.A. funding acquisition, T.R.A., E.S.S. and K.R.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** Financial support was provided by the Russian Ministry of Science and Higher Education (project FSNM-2020-0028).

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

## *Article* **Impact of Magnetic Field Environment on the EDM Performance of Al-SiC Metal Matrix Composite**

**Timur Rizovich Ablyaz 1,\*, Preetkanwal Singh Bains 2 , Sarabjeet Singh Sidhu 3 , Karim Ravilevich Muratov 1 and Evgeny Sergeevich Shlykov 1**

**Abstract:** In the present work, a hybrid magnetic field assisted powder mixed electrical discharge machining had been carried out on the Aluminum-Silicon Carbide (Al-SiC) metal matrix composite. The aim of the study was to obtain higher surface finish, and enhanced material removal rate. The dielectric mediums employed were plain EDM oil, SiCp mixed and graphite powder mixed EDM oil for flushing through the tube electrode. The magnetic field intensity, discharge current, T-on/off duration and type of dielectric were the control variables used for present investigation. From the results, it was observed that the machining variables for instance, discharge current, T-on/off duration and type of dielectric conditions remarkably affected the material removal rate, micro-hardness and surface roughness of the machined composite material. The MRR augmented considerably with an increase in the magnetic field intensity along with peak current. Subsequently, the composite with lesser vol.% of SiC particulates witnessed sharp rise in MRR in maximum magnetic field environment (0.66T). In addition, quality of the machined surface improved significantly in graphite powder mixed dielectric flushing condition with intermediate external magnetic field environment. Besides, an enhancement of micro-hardness was quantified as compared to base material due to the transfer of the material (SiCp) during powder mixed ED machining.

**Keywords:** electrical discharge machining; metal matrix composite; Taguchi; micro-hardness; surface roughness; material removal rate

#### **1. Introduction**

Metal Matrix Composites (MMCs) are the lightweight materials possessing enormous exceptional mechanical and physical properties such as high specific strength, stiffness and wear resistance [1–3]. SiC particles reinforced aluminum matrix (Al-SiC) is one of the categories of MMCs finding huge applications in the aerospace and automotive sectors and various other industries. The machining of difficult-to-cut materials such as ceramics, super alloys and composites has been a major challenge for manufacturing industries in today's era. New and advanced machining techniques as "non-conventional machining methods" have been the breakthrough to curb the engineering challenges posed by the rapid growth in the development of such materials. These days, these non-conventional machining processes are more commonly employed in the manufacturing for machining such hard-to-process materials class. One such machining method- EDM, is being widely used for machining all kinds of metallic materials to the desired size and shape accurately regardless of their mechanical properties [4]. It is a thermoelectric technique wherein, material removal and desired surface roughness from the workpiece is achieved by the spark generated repeatedly between tool and workpiece submerged in dielectric. Indeed,

**Citation:** Ablyaz, T.R.; Bains, P.S.; Sidhu, S.S.; Muratov, K.R.; Shlykov, E.S. Impact of Magnetic Field Environment on the EDM Performance of Al-SiC Metal Matrix Composite. *Micromachines* **2021**, *12*, 469. https://doi.org/10.3390/ mi12050469

Academic Editor: Irene Fassi

Received: 5 April 2021 Accepted: 19 April 2021 Published: 21 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

the selection of suitable machining parameters is of utmost important to achieve optimal machining responses [5,6]. For instance, as reported [7] surface roughness depended capillary imbibitions phenomenon is very important parameters in many applications of petroleum engineering, textile industries or fuel cell etc. Thus, these desired roughness can easily controlled with the suitable tuning of EDM spark energy parameters. Furthermore, aerospace related prototypes and products, automobile industry, electronics, medical and surgical components are the few prominent fields where applications of this manufacturing technology can be witnessed [8]. Additionally, desired surface modification or topology of numerous parts in medical, aeronautical and aerospace industry has been achieved using the optimized EDM process [9].

However, the reliability and safety of conventional ED machined components is being questioned repeatedly. The surface cracking of the machined components owing to the development of recast layer (also known as white layer) and heat affected zone underneath it has been the prominent issue. The hybrid machining (mixing of two or more conventional machining processes) has proved to be a key future technology to solve this issue. Owing to this, it's never been an uphill task to achieve desired surface integrity as complex shapes and precision with the advent of hybrid EDM. The hybrid EDM is a mechanism to enhance the machining characteristics of a conventional EDM by combining various individual processes together to achieve desired surface integrity and stability of process and to overcome the limitations of the individual constituents [10,11]. From the research studies [12], it is witnessed that the magnetic field incorporation with conventional EDM technique significantly improves the thermoelectric properties, enhancing its machining capability. In addition, various researchers have experimented with EDM to improve for its low MRR, and enhance the surface properties of workpiece by mixing various types of powders in dielectric fluid (known as powder mixed EDM), which has proved to be a major breakthrough [13–15]. Likewise, an increased MRR and micro-hardness of machined surface along with reduced roughness has been witnessed in powder mixed hybrid EDM by researchers [16]. The research community has also been successful in achieving mirror-finish surfaces with a powder-mixed dielectric [17,18].

A novel hybrid EDM methodology using the external magnetic field has showcases its process effectiveness by enhancing the effective debris expulsion from the melt pool. Some researchers and academicians have put forward the impact of magnetic field on MRR and Surface Roughness (SR) [19] experimentally in the spark machining process. Moving a step forward, Heinz et al. [20] fully investigated the possibility to employ external magnetic field to generate a 'Lorentz Force' (LF) affecting the melt pool as a resultant of mutually perpendicular electric and magnetic fields. Recently, Rouniyar and Shandilya [21] experimented with an identical hybrid EDM process assisted by external magnetic field and highlighted that a resultant Force (RF) was witnessed. They demonstrated that the RF was due to the interplay of Magnetic Field (MF) and Electric Field (EF), which as a result increased the density of electrons and enhanced MRR.

This elaborative literature study encouraged the authors to dive deep into this magnetic and electric field interference to explore and uncover its process capabilities. The review of past work recognized the noteworthy effects of magnetic field on the EDM process parameters. Therefore, we aimed the present study towards the investigation of the effects of Magnetic field on the output parameters of Al-SiC composite when coupled with EDM. The quest for more efficient machining techniques necessitated the requirement of hybridization of state-of-the-art technologies with exceptional efficiency and stability. The main motive behind this study was to analyze the performance of magnetic field environment on powder mixed assisted EDM operation on machining characteristics for instance, MRR, MH, and SR of Al-SiC metal matrix composite. The prominent process parameters like peak current, duration of T-on/off, and magnetic field intensity were selected to analyze their effects on process performance.

#### **2. Experimentation 2. Experimentation**

In this study, aluminum matrix-based composite (Table 1) variants reinforced with silicon carbide (SiCp, electronic grade manufactured by CPS Technologies, Norton, MA, USA) were used as workpiece. The distribution of SiC (yellow arrows) in alumnium matrix is presented in Figure 1. In this study, aluminum matrix-based composite (Table 1) variants reinforced with silicon carbide (SiCp, electronic grade manufactured by CPS Technologies, Norton, MA, USA) were used as workpiece. The distribution of SiC (yellow arrows) in alumnium matrix is presented in Figure 1.

**Table 1.** MMCs properties (Source: CPS Tech, Norton, MA, USA). **Table 1.** MMCs properties (Source: CPS Tech, Norton, MA, USA).

**Figure 1.** SEM (Make: JSM-6610LV Joel, Tokyo, Japan) of un-machined surface of Al-SiC workpiece at X650 (SiC particulates are highlighted with yellow color arrows). **Figure 1.** SEM (Make: JSM-6610LV Joel, Tokyo, Japan) of un-machined surface of Al-SiC workpiece at X650 (SiC particulates are highlighted with yellow color arrows).

The electrolytic copper tube electrode (Figure 2) having 15 mm outer and 4 mm inner diameter was used to allow the powder mixed dielectric to flow through the tool. The experimentation was carried out employing the ZNC EDM (Make: OSCARMAX, Taichung City, Taiwan) (Figure 3a). As the past studies witnessed the application of magnetic field enhanced the plasma ionization in EDM spark zone and controlled its expansion, same principle was implemented in present study. Two sets of permanent magnets (0.33 T each) were fixed in such a manner so as to achieve desired magnetic field effect into the machining zone, as shown in Figure 3b. Commercial grade EDM oil (specific gravity = 0.763, freezing point = 94 °C) was used as the dielectric medium. Figure 3c presents the arrangement of dielectric flow through the hollow electrode. Figure 3d shows the machined zone on the Al-SiC workpiece. The merits of powder mixed EDM have been witnessed from literature. Hence, the SiC (220 mesh) abrasive particles and graphite particulates (400 mesh) were used in 30 g/L for circulating through tool while machining. All the experimentation trials were conducted with magnetic field intensity, current, duration of pulse, volume percentage of SiC and type of dielectric medium (Table 2) as prominent parameters. The material removal rate, average surface roughness The electrolytic copper tube electrode (Figure 2) having 15 mm outer and 4 mm inner diameter was used to allow the powder mixed dielectric to flow through the tool. The experimentation was carried out employing the ZNC EDM (Make: OSCARMAX, Taichung City, Taiwan) (Figure 3a). As the past studies witnessed the application of magnetic field enhanced the plasma ionization in EDM spark zone and controlled its expansion, same principle was implemented in present study. Two sets of permanent magnets (0.33 T each) were fixed in such a manner so as to achieve desired magnetic field effect into the machining zone, as shown in Figure 3b. Commercial grade EDM oil (specific gravity = 0.763, freezing point = 94 ◦C) was used as the dielectric medium. Figure 3c presents the arrangement of dielectric flow through the hollow electrode. Figure 3d shows the machined zone on the Al-SiC workpiece. The merits of powder mixed EDM have been witnessed from literature. Hence, the SiC (220 mesh) abrasive particles and graphite particulates (400 mesh) were used in 30 g/L for circulating through tool while machining. All the experimentation trials were conducted with magnetic field intensity, current, duration of pulse, volume percentage of SiC and type of dielectric medium (Table 2) as prominent parameters. The material removal rate, average surface roughness (Ra), and micro-hardness were evaluated as the response outcomes in this study. The surface roughness value was recorded with

surface roughness tester (Surftest SJ-400, Mitutoyo America Corporation, Aurora, IL, USA) at three distinct positions of the machined surface and mean was considered. values of responses for individual machined surface. The observed values, with and without magnetic field were recorded in designated columns as MRR, MH and SR output.

**Figure 2. Figure 2.**  Copper electrode with inner hole for dielectric flushing. Copper electrode with inner hole for dielectric flushing. *Micromachines* **2021**, *12*, x 5 of 14

*Micromachines* **2021**, *12*, x 4 of 14

teracting factors degrees of freedom i.e., faXb = (fa)(fb

chined surface and mean was considered.

Minitab-17 software.

the present study.

(Ra), and micro-hardness were evaluated as the response outcomes in this study. The surface roughness value was recorded with surface roughness tester (Surftest SJ-400, Mitutoyo America Corporation, Aurora, IL, USA) at three distinct positions of the ma-

To achieve accuracy in output responses, two replications were performed at random order to find out the mean. The Taguchi's experimental design matrix [22] assisted the authors to identify and scrutinize the prominent parameters for this study. The orthogonal array of Taguchi's experimental design reduces number of experimental trials to measure the effect of parameters included in the study. Valid conclusions during ED machining of Al-SiC MMCs were drawn and the factor assignment was done using

The selection of Taguchi's orthogonal array depends on the number of factors (i.e., process parameters, herein 6 factors) and interactions of interest (herein, 1 interaction) and the number of levels of process parameters (i.e., 3 levels, refer Table 2). The total degree of freedom (fa) for each factor is the number of levels (la) minus one i.e., fa = la − 1 (i. e. , 2 for each factor) and the degree of freedom for interaction is the product of in-

quired degree of freedom in the experimental design is the sum of the entire factors and the interaction's degree of freedom. Thus, the degree of freedom selected array (fOA) has must satisfy the inequalityfOA ≥ (faXb + fa). Thus L27 orthogonal array was selected for

For the critical discussion on MRR, MH and SR of the powder mixed electrical discharge machining process; the results of the investigations are represented graphically. The various outcomes were analyzed through ANOVA to test the significance of model adopted. Prior to experimentation, the workpiece was designated as negative and tool with positive polarity. A precision electronic balance (Citizen, CY220, Mumbai, India) was used to measure workpiece weights before and after the machining. The investigation consisted of 27 distinct trials that helped to investigate the material removed, micro-hardness and surface quality of machined Al-SiC metal matrix composite using various input variables as tabulated in Table 3. This corresponding table constituted the input variables opted as a part of standard L27 orthogonal array control log and recorded

)(Herein 2 ∗ 2).The minimum re-

**Figure 3.** (**a**) EDM used for experimentation (**b**) Schematic diagram of MFAPEDM set-up and (**c**) Powder mixed dielectric set-up (**d**) machined Al-SiC workpiece. **Figure 3.** (**a**) EDM used for experimentation (**b**) Schematic diagram of MFAPEDM set-up and (**c**) Powder mixed dielectric set-up (**d**) machined Al-SiC workpiece.

Dielectric medium Plain dielectric SiC mixed (220 mesh) Graphite mixed (400 mesh) Workpiece (W/P) Al-37% SiC (W/P-1) Al-55% SiC (W/P-2) Al-63% SiC (W/P-3)

Current (A)/I 4 10 16 Pulse-on (µs)/T-on 30 45 90 Pulse-off (µs)/T-off 30 45 90 Magnetic field intensity (T)/B 0 0.33 0.66

**Table 2.** Input parameters and their levels.

**Table 2.** Input parameters and their levels.

To achieve accuracy in output responses, two replications were performed at random order to find out the mean. The Taguchi's experimental design matrix [22] assisted the authors to identify and scrutinize the prominent parameters for this study. The orthogonal array of Taguchi's experimental design reduces number of experimental trials to measure the effect of parameters included in the study. Valid conclusions during ED machining of Al-SiC MMCs were drawn and the factor assignment was done using Minitab-17 software.

The selection of Taguchi's orthogonal array depends on the number of factors (i.e., process parameters, herein 6 factors) and interactions of interest (herein, 1 interaction) and the number of levels of process parameters (i.e., 3 levels, refer Table 2). The total degree of freedom (fa) for each factor is the number of levels (la) minus one i.e., fa = la − 1 (i.e., 2 for each factor) and the degree of freedom for interaction is the product of interacting factors degrees of freedom i.e., faXb = (fa)(fb)(Herein 2 ∗ 2). The minimum required degree of freedom in the experimental design is the sum of the entire factors and the interaction's degree of freedom. Thus, the degree of freedom selected array (fOA) has must satisfy the inequality fOA ≥ (faXb + fa). Thus L27 orthogonal array was selected for the present study.

For the critical discussion on MRR, MH and SR of the powder mixed electrical discharge machining process; the results of the investigations are represented graphically. The various outcomes were analyzed through ANOVA to test the significance of model adopted. Prior to experimentation, the workpiece was designated as negative and tool with positive polarity. A precision electronic balance (Citizen, CY220, Mumbai, India) was used to measure workpiece weights before and after the machining. The investigation consisted of 27 distinct trials that helped to investigate the material removed, micro-hardness and surface quality of machined Al-SiC metal matrix composite using various input variables as tabulated in Table 3. This corresponding table constituted the input variables opted as a part of standard L27 orthogonal array control log and recorded values of responses for individual machined surface. The observed values, with and without magnetic field were recorded in designated columns as MRR, MH and SR output.

**Table 3.** *Cont.*

#### **3. Results and Discussion**

#### *3.1. Influence on the Material Removal Rate (MRR)*

The experimental design matrix and results obtained, thereafter, are demonstrated in the Table 3. It is clear from Table 4 and Figure 4 that MRR is a function of current, duration of T-on/off and externally applied magnetic field. It has been exhibited that the magnetic field intensity was the most significant input parameter that affected the MRR steeply, followed by peak current and type of workpiece. Duration of pulse and dielectric medium, on the contrary, did not turn to be the significant parameters to influence MRR. At high discharge current, the enhanced MRR can be attributed to an improved volume of erosion in the maximum magnetic field intensity due to the high spark energy. The workpiece variant 1 (37 %vol. fraction of SiC) with comparatively lesser vol.% of SiC particulates resulted in enhanced MRR. This is attributed to the increased conductivity of workpiece owing to decreased vol.% of SiC particulates resulting in abrupt material removal. Furthermore, Figure 5 represented the surface morphology of work pieces of Al-37% SiC (Figure 5a) and Al-63% SiC (Figure 5b) after electrical discharge treatment. Figure 5a morphology witnesses the ablation as a mechanism of material removal of machined surface, whereas the Figure 5b indicates the melting and re-solidification of molten workpiece. The dense vol.% of SiC in the matrix exhibits shielding effect while machining and diminishes the MRR.

**Table 4.** ANalysis of VAriance (ANOVA) for Material Removal Rate (MRR).

\*\* Most significant, \* Significant

Total 26 3256.35

Total 26 3256.35

**Figure 4.** Main effect plot for material removal rate (mg/min). **Figure 4.** Main effect plot for material removal rate (mg/min). **Figure 4.** Main effect plot for material removal rate (mg/min).

**Table 4.** ANalysis of VAriance (ANOVA) for Material Removal Rate (MRR).

**Table 4.** ANalysis of VAriance (ANOVA) for Material Removal Rate (MRR).

*Micromachines* **2021**, *12*, x 7 of 14

\*\* Most significant, \* Significant.

\*\* Most significant, \* Significant.

**Scheme DF Seq SS Adj SS Adj MS F-Value** *p***-Value** Magnetic Field (T) 2 1411.29 1411.29 705.646 12.95 0.001 \*\* Current (A) 2 336.98 336.98 168.488 3.09 0.077 \* T-on (µs) 2 12.71 12.71 6.357 0.12 0.891 T-off (µs) 2 1.61 1.61 0.806 0.01 0.985 Flushing Type 2 89.80 89.80 44.898 0.82 0.459 W/P 2 640.96 640.96 320.480 5.88 0.014 \*

**Scheme DF Seq SS Adj SS Adj MS F-Value** *p***-Value** Magnetic Field (T) 2 1411.29 1411.29 705.646 12.95 0.001 \*\* Current (A) 2 336.98 336.98 168.488 3.09 0.077 \* T-on (µs) 2 12.71 12.71 6.357 0.12 0.891 T-off (µs) 2 1.61 1.61 0.806 0.01 0.985 Flushing Type 2 89.80 89.80 44.898 0.82 0.459 W/P 2 640.96 640.96 320.480 5.88 0.014 \*

**Figure 5.** Scanning electron microscope (SEM) analysis of workpiece after machining (**a**) Al-37% SiC shows ablation mechanism of material removal; (**b**) Al-63% SiC shows melting as the dominating material removal mechanism. **Figure 5.** Scanning electron microscope (SEM) analysis of workpiece after machining (**a**) Al-37% SiC shows ablation mechanism of material removal; (**b**) Al-63% SiC shows melting as the dominating material removal mechanism. **Figure 5.** Scanning electron microscope (SEM) analysis of workpiece after machining (**a**) Al-37% SiC shows ablation mechanism of material removal; (**b**) Al-63% SiC shows melting as the dominating material removal mechanism.

Moreover, the maximum material removal was witnessed in Trial 24, wherein 118% increase in MRR was observed in graphite powder mixed dielectric flushing in extreme magnetic field intensity compared to similar parametric conditions in Trial 5. The probable reason for increase in MRR could be the effective molten debris flushing on the machined surface due to the generation of Lorentz forces by the interplay of magnetic as well as electrical fields which enhanced the plasma pressure. Apart from this, injecting the powder mixed dielectric through the hole in the tube electrode enhanced its pressure, Moreover, the maximum material removal was witnessed in Trial 24, wherein 118% increase in MRR was observed in graphite powder mixed dielectric flushing in extreme magnetic field intensity compared to similar parametric conditions in Trial 5. The probable reason for increase in MRR could be the effective molten debris flushing on the machined surface due to the generation of Lorentz forces by the interplay of magnetic as well as electrical fields which enhanced the plasma pressure. Apart from this, injecting the powder mixed dielectric through the hole in the tube electrode enhanced its pressure, Moreover, the maximum material removal was witnessed in Trial 24, wherein 118% increase in MRR was observed in graphite powder mixed dielectric flushing in extreme magnetic field intensity compared to similar parametric conditions in Trial 5. The probable reason for increase in MRR could be the effective molten debris flushing on the machined surface due to the generation of Lorentz forces by the interplay of magnetic as well as electrical fields which enhanced the plasma pressure. Apart from this, injecting the powder mixed dielectric through the hole in the tube electrode enhanced its pressure, augmented the debris flushing. The interaction between different variables was not evident to be significant while powder mixed magnetic field assisted EDM.

#### *3.2. Influence on the Micro-Hardness (MH)*

The effect of various machining parameters and the surface conditions of the machined workpiece were analyzed by measurement of the indentation hardness. The micro-hardness measurements were carried out at the different locations of the machined surface as well as parent material, cross-sectionally. The machining parameters like magnetic field intensity, type of workpiece and pulse-on/off duration posed considerable effect on MH values, as shown in Table 5, except the discharge current and type of flushing which were the in-significant factors affecting the surface hardness of ED machined MMCs. It is clear from the graph (Figure 6) that machining of W/P-3 without magnetic field effect and in

high-end pulse-on time resulted in the enhanced micro-hardness of the machined surface at minimum pause time. This is due to the reason that the higher concentration of SiC particulates resulted in oxides formation at high frequency (i.e., minimum pulse-off time) of sparks generated while machining. magnetic field effect and in high-end pulse-on time resulted in the enhanced micro-hardness of the machined surface at minimum pause time. This is due to the reason that the higher concentration of SiC particulates resulted in oxides formation at high frequency (i.e., minimum pulse-off time) of sparks generated while machining.

augmented the debris flushing. The interaction between different variables was not evi-

The effect of various machining parameters and the surface conditions of the machined workpiece were analyzed by measurement of the indentation hardness. The micro-hardness measurements were carried out at the different locations of the machined surface as well as parent material, cross-sectionally. The machining parameters like magnetic field intensity, type of workpiece and pulse-on/off duration posed considerable effect on MH values, as shown in Table 5, except the discharge current and type of flushing which were the in-significant factors affecting the surface hardness of ED machined MMCs. It is clear from the graph (Figure 6) that machining of W/P-3 without

**Table 5.** ANalysisof VAriance (ANOVA) for Micro-Hardness (MH). **Table 5.** ANalysisof VAriance (ANOVA) for Micro-Hardness (MH).

\*\* Most significant, \* Significant.

*Micromachines* **2021**, *12*, x 8 of 14

*3.2. Influence on the Micro-Hardness (MH)*

dent to be significant while powder mixed magnetic field assisted EDM.

\*\* Most significant, \* Significant. Total 26 87,844

**Figure 6.** Main Effects plot for means for Microhardness (HV). **Figure 6.** Main Effects plot for means for Microhardness (HV).

The machined surface was further analyzed using X-ray diffraction method on PANalytical's X'PertPro MPD (Netherlands). The X-ray spectra of the workpiece was The machined surface was further analyzed using X-ray diffraction method on PANalytical's X'PertPro MPD (Netherlands). The X-ray spectra of the workpiece was characterized using Cu-Kα radiations (λ = 1.5406 A0 ) with the generator setting of 40 mA and 45 kV. The X-ray diffractograms of the machined surfaces (Trial 8 and Trial 16) are shown in Figure 7. The peak pattern shows the formation of oxides such as Al2SiO5, SiO2, Si3W5 on the surface of machined MMCs, thus resulted in the enhanced hardness of machined surface [23]. On the contrary, the reduced surface hardness could be advantageous, wherein post-EDM additional processing such as grinding, grit blasting, etching etc., could be avoided [24]. Moreover, without magnetic field interference and in high current, more material deposition occurred that led to the enhancement in the micro-hardness (613.6%) as recorded (360.9 HV in Trial 8 as compared to 50.57 HV of parent workpiece) and 19.18% high as observed in Trial 23 (302.8 HV). There was no confinement of plasma and therefore more molten material deposited on the machined surface in the absence of magnetic field which was in line with previous studies related to magnetic field application. However,

*Micromachines* **2021**, *12*, x 9 of 14

characterized using Cu-Kα radiations (λ = 1.5406 A0

the interplay between various parameters and magnetic field were not significant enough affecting the output of the experimentation. in the absence of magnetic field which was in line with previous studies related to magnetic field application. However, the interplay between various parameters and magnetic field were not significant enough affecting the output of the experimentation.

and 45 kV. The X-ray diffractograms of the machined surfaces (Trial 8 and Trial 16) are shown in Figure 7. The peak pattern shows the formation of oxides such as Al2SiO5, SiO2, Si3W5 on the surface of machined MMCs, thus resulted in the enhanced hardness of machined surface [23]. On the contrary, the reduced surface hardness could be advantageous, wherein post-EDM additional processing such as grinding, grit blasting, etching etc., could be avoided [24]. Moreover, without magnetic field interference and in high current, more material deposition occurred that led to the enhancement in the micro-hardness (613.6%) as recorded (360.9 HV in Trial 8 as compared to 50.57 HV of parent workpiece) and 19.18% high as observed in Trial 23 (302.8 HV). There was no confinement of plasma and therefore more molten material deposited on the machined surface

) with the generator setting of 40mA

**Figure 7.** X-ray diffraction of (**a**) Trial 8 and (**b**) Trial 16. **Figure 7.** X-ray diffraction of (**a**) Trial 8 and (**b**) Trial 16.

#### *3.3. Influence on the Surface Roughness (SR)*

The surface morphology of machined surface portrays a true signature of the toolworkpiece interaction while machining and material removal mechanism. The surface roughness was significantly influenced by magnetic field intensity and type of dielectric medium. The relations of SR with various process parameters are shown in Table 6 and Figure 8. The average surface roughness value for graphite powder mixed dielectric was witnessed as the lowest value. Alternatively, a rougher surface is achieved when machining is executed in 0.66 T magnetic field influence in SiC powder mixed dielectric medium. This can be related to the fact that at high magnetic field intensity abrupt material removal took place that turned the surface rough. Comparatively, a smoother machined surface is achieved when EDM is carried out in magnetic field intensity of 0.33 T. In this scenario, Lorentz forces developed while machining for little less duration comparatively with graphite kept the machining zone smoother and decreased the roughness. terial removal took place that turned the surface rough. Comparatively, a smoother machined surface is achieved when EDM is carried out in magnetic field intensity of 0.33 T. In this scenario, Lorentz forces developed while machining for little less duration com-

paratively with graphite kept the machining zone smoother and decreased the rough-

The surface morphology of machined surface portrays a true signature of the tool-workpiece interaction while machining and material removal mechanism. The surface roughness was significantly influenced by magnetic field intensity and type of dielectric medium. The relations of SR with various process parameters are shown in Table 6 and Figure 8. The average surface roughness value for graphite powder mixed dielectric was witnessed as the lowest value. Alternatively, a rougher surface is achieved when machining is executed in 0.66 T magnetic field influence in SiC powder mixed dielectric medium. This can be related to the fact that at high magnetic field intensity abrupt ma-

**Table 6.** ANalysisof VAriance (ANOVA) for Surface Roughness (SR). ness.

*Micromachines* **2021**, *12*, x 10 of 14

*3.3. Influence on the Surface Roughness (SR)*

\* Significant.

The interaction between various process parameters was statistically analyzed at 95% confidence level (i.e., *p* ≤ 0.05). It was observed that the interaction between magnetic field intensity (B) and T-on significantly affected the surface roughness. From Figure 9, it is depicted that the surface roughness enhanced at high magnetic field intensity and low T-on value. From the SEM analysis (Figure 10a), it can be observed that non-uniform surface with significant undulations is visible prominently during machining in SiC mixed dielectric medium in comparison to graphite mixed dielectric (Figure 10b). The high thermal conductivity of graphite particulates is responsible for distributing and dissipating uniform heat to the workpiece surfaces, thereby limiting the size of the craters thus produced. Owing to

the externally applied magnetic field in powder mixed EDM process, the graphite powder particles under the effect of Lorentz forces crowded together to bridge the machining void in the dielectric fluid. Multiple plasma discharges are developed, as a result, from a unit pulse input avoiding deeper craters, resulting in improved surface quality [25]. Moreover, the generation of Lorentz forces with interference of external magnetic field into the EDM current density assisted in channelized path of current carriers from the tool to workpiece. thus produced. Owing to the externally applied magnetic field in powder mixed EDM process, the graphite powder particles under the effect of Lorentz forces crowded together to bridge the machining void in the dielectric fluid. Multiple plasma discharges are developed, as a result, from a unit pulse input avoiding deeper craters, resulting in improved surface quality [25]. Moreover, the generation of Lorentz forces with interference of external magnetic field into the EDM current density assisted in channelized path of current carriers from the tool to workpiece.

The interaction between various process parameters was statistically analyzed at 95% confidence level (i.e., *p* ≤ 0.05). It was observed that the interaction between magnetic field intensity (B) and T-on significantly affected the surface roughness. From Figure 9, it is depicted that the surface roughness enhanced at high magnetic field intensity and low T-on value. From the SEM analysis (Figure 10a), it can be observed that non-uniform surface with significant undulations is visible prominently during machining in SiC mixed dielectric medium in comparison to graphite mixed dielectric (Figure 10b). The high thermal conductivity of graphite particulates is responsible for distributing and dissipating uniform heat to the workpiece surfaces, thereby limiting the size of the craters

*Micromachines* **2021**, *12*, x 11 of 14

**Figure 9.** Interaction Plot between Magnetic field intensity (B) and T-on. **Figure 9.** Interaction Plot between Magnetic field intensity (B) and T-on.

**Figure 10.** SEM analysis for comparative surface texture of various machined samples. (**a**) SiC mixed dielectric at 0.66T (**b**) Graphite mixed dielectric 0.33T. **Figure 10.** SEM analysis for comparative surface texture of various machined samples. (**a**) SiC mixed dielectric at 0.66T (**b**) Graphite mixed dielectric 0.33T.

intensity along with peak current.

als without magnetic field.

version of the manuscript.

The study was carried out on EDM of aluminum composite with plain and powder

The MRR is significantly affected by the machining parameters such as magnetic

The removal rate increased significantly with the incorporation of magnetic field

 It is also evident that the decreased vol.% of SiC particulates led to a sharp rise in MRR. A 118% increase in MRR under the influence of magnetic field was observed in plain dielectric flushing when compared to identical parametric conditions in tri-

An enhancement (613.6%) in the micro-hardness was witnessed due to the transfer

 The surface finish of machined MMCs was greatly affected by magnetic field intensity as well as type of dielectric. The surface finish improved steeply in graphite powder mixed dielectric flushing conditions at intermediate (0.33 T) magnetic field. Future Scope: The literature studies depicted that aluminum matrix composites have been the major choice in the field of EDM of MMCs, but no significant work is available for the composite with other matrix phase such cobalt, steel etc. In addition to this, the experimental research can be extended and analyzed using various other levels of mag-

**Author Contributions:** Conceptualization, P.S.B. and S.S.S.; methodology, P.S.B., and S.S.S.; software, P.S.B., S.S.S. and T.R.A.; validation, P.S.B., S.S.S. and T.R.A.; formal analysis, K.R.M., E.S.S.; investigation, P.S.B., S.S.S. and T.R.A.; validation, P.S.B., S.S.S.; resources, K.R.M., E.S.S.; data curation, K.R.M., E.S.S.; writing—original draft preparation, P.S.B. and T.R.A.; writing—review and editing, P.S.B., S.S.S. and T.R.A.; visualization, T.R.A.; supervision, S.S.S.; project administration, T.R.A.; funding acquisition, T.R.A. and K.R.M. All authors have read and agreed to the published

field environment, peak current and SiC% content of workpiece.

of materials and formation of new phases while ED machining.

netic field of permanent magnets as well as rotating electromagnets.

**4. Conclusions**

#### **4. Conclusions**

The study was carried out on EDM of aluminum composite with plain and powder mixed flushing conditions. The following observations are enlisted.

Future Scope: The literature studies depicted that aluminum matrix composites have been the major choice in the field of EDM of MMCs, but no significant work is available for the composite with other matrix phase such cobalt, steel etc. In addition to this, the experimental research can be extended and analyzed using various other levels of magnetic field of permanent magnets as well as rotating electromagnets.

**Author Contributions:** Conceptualization, P.S.B. and S.S.S.; methodology, P.S.B., and S.S.S.; software, P.S.B., S.S.S. and T.R.A.; validation, P.S.B., S.S.S. and T.R.A.; formal analysis, K.R.M., E.S.S.; investigation, P.S.B., S.S.S. and T.R.A.; validation, P.S.B., S.S.S.; resources, K.R.M., E.S.S.; data curation, K.R.M., E.S.S.; writing—original draft preparation, P.S.B. and T.R.A.; writing—review and editing, P.S.B., S.S.S. and T.R.A.; visualization, T.R.A.; supervision, S.S.S.; project administration, T.R.A.; funding acquisition, T.R.A. and K.R.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Russian Science Foundation, grant number 20-79-00048.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

## *Article* **Micro Electrical Discharge Machining of Ultrafine Particle Type Tungsten Carbide Using Dielectrics Mixed with Various Powders**

**Sai Dutta Gattu and Jiwang Yan \***

Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>Tel.: +81-45-566-1445

**Abstract:** Electrical discharge machining (EDM) is widely used to machine hard materials, such as tungsten carbide; however, the machining rate and surface quality are low. In this research, the effects of mixing electrically conductive carbon nanofiber (CnF), semiconductive silicon (Si) powder, and insulative alumina powder (Al2O3 ) at different concentrations in a dielectric fluid were studied by observing single discharge craters and hole machining performance in the EDM of ultrafine particle type tungsten carbide. Craters obtained using carbon nanofiber and alumina were much smaller than in oil-only conditions. In contrast, The results show that adding CnF significantly improved the material removal rate under all conditions. Si and Al2O3 powders only improved the machining performance at a high discharge energy of 110 V. Furthermore, improvement in surface roughness was observed prominently at high voltages for all the powders. Among the three powders, alumina was found to improve the surface roughness the most.

**Keywords:** micro-EDM; tungsten carbide; carbon nanofiber; silicon; alumina

#### **1. Introduction**

Tungsten carbide (WC) and its composites (WC-Co) possess excellent physical properties, such as high melting and boiling points, wear, and corrosion resistance. As a result, it is useful in various industrial applications, notably in die/mold making, cutting, and surgical tools. Consequently, various studies have focused on machining tungsten carbide economically with high precision. Conventionally, tungsten carbide is machined using turning and grinding [1–3] using PCD and CBN tools. However, the tool wear is high, increasing costs [4]. In recent years, there has been a growing trend of using non-conventional methods, such as electric discharge machining (EDM) [5], electrochemical machining (ECM) [6], and laser machining [7], to process tungsten carbide products.

EDM is a thermo-electric process that removes material by thermal erosion due to repeated high-energy electrical sparks. Since there is no physical contact between the workpiece and electrode in this process, the machinability depends only on the tool and the workpiece's electrical and thermal properties. Thus, this process can be used to machine any given conductive material regardless of its hardness. In addition, this method can also be used to directly transfer complex 3D shapes with high precision onto the workpiece without force-induced deformation.

Consequently, various studies have been performed on the EDM of tungsten carbide. Studies by Hourmand et al. [8] and Assarzadeh et al. [9] have used analytical models, such as response surface methodology and ANOVA analysis, to determine the optimum parameter values of peak current, spark time on and off, and gap voltage. Research on the effect of EDM on hardness and sub-surface damage on tungsten carbide has also been performed [10]. Furthermore, Jahan et al. [11] have studied the effect of different electrodes on EDM efficiency by comparing the material removal and electrode wear rate. However, it has been found that the EDM of WC-Co has low machining efficiency, with a high tool wear rate and surface defects. Recently, using tool rotation or vibration [12,13], servo movement

**Citation:** Gattu, S.D.; Yan, J. Micro Electrical Discharge Machining of Ultrafine Particle Type Tungsten Carbide Using Dielectrics Mixed with Various Powders. *Micromachines* **2022**, *13*, 998. https://doi.org/10.3390/ mi13070998

Academic Editors: Irene Fassi and Francesco Modica

Received: 29 May 2022 Accepted: 23 June 2022 Published: 25 June 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and flushing strategies [14,15], and adding the powder to the EDM of WC-Co, have been studied to improve these shortcomings.

The dielectric separating the electrodes serves many functions during the EDM process. In addition to insulation, it helps transport debris away and acts as a coolant during the discharge phenomenon [16]. It has been studied that adding the powder to the dielectric during the EDM process can alter the machining process by dispersing the discharges and thus, improving machining efficiency [17]. Various studies have shown that powder addition improved material removal rate, electrode wear rate, and surface roughness. Furthermore, a few studies also focused on optimizing the powder-mixed EDM (PMEDM) of WC-Co using analytical models [18,19]. Jahan et al. [20] have also developed a mathematical model to study powder behavior during EDM. Their model underlines that the presence of any additive within the dielectric results in an increase in the discharge gap.

The most commonly used powders in EDM are aluminum (Al) and graphite (Gr). Their high electrical conductivity, low density, availability of particles on a nanometer scale, and low hazardous nature allow them to be widely used. However, in recent years, the effects of many other particles, such as silicon (Si), graphene, molybdenum sulfide (MoS2), and alumina (Al2O3) have also been studied. Pecas et al. [20] have shown that under a few conditions (powder concentration of 2 g/L for 1 mm2 electrode), silicon powder addition can generate a mirror surface finish on AISI H13 steel. Liew et al. [21] have used carbon nanofiber-assisted EDM of reaction-bonded silicon carbide and have significantly improved material removal and electrode wear rates. Sahu et al. [22] and Jahan et al. [23] have used nano-alumina powder in the EDM of Inconel and tungsten carbide, respectively. They have shown an increase in the material removal rate and a decrease in the surface roughness for specific parametric values.

Many studies have been performed on the EDM of tungsten carbide. However, up to date, the effects of using these newly attempted types of powders on the EDM of WC-Co, especially the ultrafine particle type WC-Co, which is a more promising material in the industry, are still unknown. In particular, the effect of powders in the micro-EDM process, where the discharge gap is much smaller than in conventional EDM is unclear. To evaluate the impact of characteristics of different powders, we studied the single discharge craters and their progression during EDM drilling. The finding of this study reveals the process mechanisms and parameters to improve the machinability of ultrafine particle-type tungsten carbide using micro-EDM.

#### **2. Process Mechanisms of Powder-Mixed EDM**

During EDM, an essential concern is the expulsion of debris outside the discharge gap. The debris generated during machining is generally removed by two mechanisms: bubble expansion and dielectric flow [12,16]. When the debris is not properly flushed out, it will result in (a) secondary discharges, where the debris is machined instead of the workpiece, or (b) short-circuiting. This results in frequent tool backtracking, increasing machining time, and increased costs. If the discharge gap widens, the dielectric can easily flow in, promoting the flushing of debris and improving process stability.

When a particle is introduced in an electric field (*E*), as shown in Figure 1a, the polarization of the molecule occurs. This polarization results in an electric field aberration (*E1*) within the vicinity of the particle, as shown in Figure 1b. During EDM, when the electrodes are brought closer to each other, this aberration results in field intensification near the particle, creating a more accessible pathway for electrons to flow, thus, promoting dielectric breakdown. Moreover, when several particles get closer to each other, as shown in Figure 1c, the field is intensified further (*E2*), creating 'bridges' for ions to flow, promoting even faster breakdown. As a result, the interelectrode gap required to form sparks is larger. Several studies [21,23,24] have reported that these promote process stability, thus improving machining efficiency.

**Figure 1.** Schematic of (**a**) electric field in between two electrodes and (**b**) distortion due to presence of a single particle, (**c**) bridge formation with multiple particles.

Since the presence of particles alters the discharge characteristic, it is vital to study the effect of the properties of different powders. It has been reported that particles generally exhibit four different behaviors within the discharge gap: reciprocation, bridging, adherence to the electrode, and agglomeration [25]. Polarized molecules reciprocate between electrodes when an electric field is applied due to electromotive force. The physical and electrical properties of molecules determine the extent of this movement. At the same time, bridging occurs when particles align themselves parallel to the electric field, forming chain-like structures (Figure 1c). Reciprocation and bridging promote dielectric breakdown.

In contrast, the adhesion of particles on the workpiece surface inhibits the machining by preventing energy transfer. Moreover, it has been observed that nanoparticles tend to agglomerate due to high surface energy or Van der Walls forces. The particle agglomeration will decrease polarizability, inhibiting discharge channel formation and increasing the possibility of short-circuiting.

Carbon nanofibers are incredibly light and highly conductive materials. It has been reported that due to these properties, the nanofibers form bridges quickly, improving the machining output [21]. On the other hand, alumina is a dense material with very low electric conductivity. Due to these properties, the bridging of particles will not occur, and the sedimentation of the particles on the bottom surface will be prominent, inhibiting machining. Silicon is a semiconductor with a density and electrical conductivity between alumina and carbon nanofibers. Semiconductors exhibit a unique property of decreasing resistivity with an increase in temperature. Thus, depending on the condition within the working gap, silicon particles may exhibit all kinds of behaviors mentioned above.

Based on the process mechanism and powder behavior during EDM, the differences in the material removal, tool wear, and surface roughness during PMEDM of WC-Co are elucidated.

#### **3. Experimental Methods**

#### *3.1. EDM Setup*

In this study, a micro-EDM machine, Panasonic MG-ED72, was used. The device has a resolution of 0.1 µm in the x, y, and z axes. The machining is performed using a resistorcapacitor (RC) circuit, which is suitable for micromachining [26]. The control parameters are voltage ranging from ±50 V to ±110 V and capacitance of 3300, 1100, 200, and 100 pF. The discharge gap, pulse duration, and peak current are not controllable. A spindle is attached to the micro-EDM machine, rotating at 3000 rpm. An acrylic tank was fabricated to contain the dielectric. Discharge progress was monitored using an oscilloscope. The photograph of the machining setup is shown in Figure 2. During experimentation, no external stirring or circulation of the dielectric was applied.

**Figure 2.** Photograph of (**a**) µ-EDM system, (**b**) electrode and workpiece arrangement.

#### *3.2. Electrode and Workpiece*

Ultra-fine grade cemented Tungsten carbide (WC-Co) KM-10, produced by Toyo Tool, Japan, was used as the workpiece material. The average grain size was around 0.7 µm, and the cobalt binder concentration was about 6%. The mechanical properties of the workpiece are shown in Table 1. The surface of the workpiece was mirror-polished to 0.01 µmSa surface roughness using a diamond powder slurry. A copper (Cu) electrode of a diameter of 1 mm from Nilaco Corporation, Japan, was used as a tool material. The electrodes were deburred and dressed using reverse-polarity EDM before each machining test. The scanning electron micrograph (SEM) of the surface of the workpiece is shown in Figure 3a.

**Table 1.** Properties of tungsten carbide.

**Figure 3.** SEM of (**a**) tungsten carbide, TEM of (**b**) carbon nanofiber, (**c**) alumina, (**d**) silicon particles.

Three different powders of varying shapes, densities and electrical properties were used in this experiment. Commercially available polishing grade alumina powder, Baikalox 1CR, manufactured by Baikowski International Co. Ltd., was used as the insula‐ tor powder. Semiconductive silicon powder, manufactured by Kyocera, Japan, was used. Lastly, carbon nanofibers of diameter 10–20 nm and height 0.1–10 μm, produced by Mitsubishi Chemicals, Japan, were used as a conductive powder in this experiment. The transmission electron micrographs [TEM] of carbon nanofiber, alumina, and silicon are shown in Figure 3b–d, respectively. A laser particle analyzer from Horiba semiconduc‐ tors, LA‐960, was used to measure the particle size. Figure 4a,b show the size distribution of the silicon and alumina powders, respectively. The thermal and electric properties of

> **Property Carbon Silicon Alumina** Density [g/cm3] 1.3–2 2.329 3.965

Electrical Conductivity [μΩ/cm] 50 1 × 105 1 × 10−9 Thermal Conductivity [W/mK] 400 150 30

Melting Point [K] 3823 1687 2345

**Figure 3.** *Cont*.

*3.3. Powder Materials*

the powder are listed in Table 2.

**Table 2.** Properties of different powders.

**Figure 3.** SEM of (**a**) tungsten carbide, TEM of (**b**) carbon nanofiber, (**c**) alumina, (**d**) silicon particles. **Figure 3.** SEM of (**a**) tungsten carbide, TEM of (**b**) carbon nanofiber, (**c**) alumina, (**d**) silicon particles.

#### *3.3. Powder Materials 3.3. Powder Materials*

Three different powders of varying shapes, densities and electrical properties were used in this experiment. Commercially available polishing grade alumina powder, Baikalox 1CR, manufactured by Baikowski International Co. Ltd., was used as the insula‐ tor powder. Semiconductive silicon powder, manufactured by Kyocera, Japan, was used. Lastly, carbon nanofibers of diameter 10–20 nm and height 0.1–10 μm, produced by Mitsubishi Chemicals, Japan, were used as a conductive powder in this experiment. The transmission electron micrographs [TEM] of carbon nanofiber, alumina, and silicon are shown in Figure 3b–d, respectively. A laser particle analyzer from Horiba semiconduc‐ tors, LA‐960, was used to measure the particle size. Figure 4a,b show the size distribution of the silicon and alumina powders, respectively. The thermal and electric properties of the powder are listed in Table 2. Three different powders of varying shapes, densities and electrical properties were used in this experiment. Commercially available polishing grade alumina powder, Baikalox 1CR, manufactured by Baikowski International Co. Ltd., was used as the insulator powder. Semiconductive silicon powder, manufactured by Kyocera, Japan, was used. Lastly, carbon nanofibers of diameter 10–20 nm and height 0.1–10 µm, produced by Mitsubishi Chemicals, Japan, were used as a conductive powder in this experiment. The transmission electron micrographs [TEM] of carbon nanofiber, alumina, and silicon are shown in Figure 3b–d, respectively. A laser particle analyzer from Horiba semiconductors, LA-960, was used to measure the particle size. Figure 4a,b show the size distribution of the silicon and alumina powders, respectively. The thermal and electric properties of the powder are listed in Table 2.

**Figure 4.** Particle size distribution of (**a**) silicon and (**b**) alumina used in this study.

**Table 2.** Properties of different powders.

#### *3.4. Machining Conditions*

Two different EDM tests were performed to determine the effect of powder addition on EDM: fundamental characteristics and hole machining tests. The micro-EDM machine used in this study uses an in-built contact detection program. An edge is detected if a threshold number of discharges occurs within a resistance-capacitance (RC) cycle. This method was used to obtain isolated single craters. Next, machining tests on the evaluation of PMEDM were performed by drilling holes of a depth [h] of 50 µm under different conditions, as shown in Table 3. The discharge energies used in this experiment are used for rough-phase micromachining. Full factorial experiments were performed with two repetitions for each condition. A hydrocarbon-based dielectric, Casty Lube EDS was used. As seen in Figure 3b–d, powders tend to agglomerate. Hence, the dielectric and powder mixture was subjected to an ultrasonic bath for 15 min before each machining to obtain a homogenous mixture.

**Table 3.** Machining conditions.

#### *3.5. Evaluation Conditions*

The machining output of EDM with various powders was evaluated with three parameters: material removal rate, electrode wear rate, and surface roughness. Material removal rate (*MRR*) is defined as the ratio of the volume of the material removed (Volm) to machining time (*Mt*) Equation (1). A laser microscope from Olympus, Japan, was used to map the three-dimensional topography of the machined hole. The analysis software TalyMap, Amtek Corp. was used to determine the mean depth plane [*d*], and the diameter [*D*] at (z = 1/2 *d*) of the hole machined.

$$MRR = \frac{\pi D^2 d}{4 \, M\_t} \tag{1}$$

Electrode wear rate is defined as the ratio of the volume of electrode worn Vole to machining time (*Mt*). Electrode wear is an essential factor in determining the economic feasibility of the machining process. Since the micro-EDM machine used in this study does not use any wear compensation mechanism, the difference between set depth [*h*] and actual machined depth [*d*] is the electrode wear. Since machining time varies based on machining conditions, studying the electrode's relative wear to that of the workpiece is considered in this research. The equation to determine relative electrode wear rate (*REWR*) is shown in Equation (2). The machined region's average surface roughness (Sa) was measured over an area of 256 × 256 µm at intervals of 0.25 µm. The ISO 25178 standard of measurement with no filters, measured over three locations on each machined surface was performed.

$$REWR = \frac{D\_\varepsilon^{\ast^2} [h - d]}{D^2 d} \tag{2}$$

#### **4. Results and Discussion**

#### *4.1. Single Discharge Experiments*

Figure 5a–c shows the single discharge craters obtained using EDM oil without powder addition for 90, 100, and 110 V, respectively. Unlike the craters in the EDM of other materials, such as silicon carbide [12,21,27], which show clear circular boundaries, craters in WC-Co were irregular. Furthermore, enlarged images of the crater surface, as shown in Figure 5d,e, indicate that the machining has not completely progressed to the crater's edge since the polishing marks from the original sample are retained after EDM (A1 and B1). These

marks indicated that at these discharge energies, the heat generated is not sufficient to melt the material at the edges; instead, the grains are pushed outwards. Additionally, as seen in A2–B2, redeposited materials are observed. Since tungsten carbide is a highly dense material, it is difficult to eject the molten materials entirely from the discharge gap, which redeposits onto the surface. The formation of micro-cracks was also observed, as seen in B3. Crack formation is due to the preferential removal of cobalt binder, which has a lower melting point than tungsten carbide, forming voids, which develop into cracks.

**Figure 5.** Single discharge craters for (**a**) 90 V, (**b**) 100 V, (**c**) 110 V using EDM oil without powder addition. Crater edge at (**d**) 90 V, (**e**) 100 V.

Figure 6a–i show the SEM images of single discharge craters observed at various PMEDM conditions. The average diameter of these craters is summarized in Figure 7. In the case of carbon nanofiber and alumina mixed EDM, the average crater diameter reduced, whereas it increased in the case of silicon. In addition, the crater diameter increased with voltage for all conditions.

According to previous research, the discharge gap increased drastically when the carbon nanofibers were added to the dielectric [21]. Furthermore, as explained earlier, the breakdown strength is significantly reduced due to its high conductivity. The plasma, which is formed, has lower energy; as it travels for a longer distance, it weakens further. This results in a narrow crater. In the case of alumina-mixed EDM, the increase in the discharge gap is not significant due to its low electrical conductivity. Additionally, alumina particles absorb plasma energy due to their high thermal conductivity. Since lower energy is transmitted to the workpiece, lower material is removed. However, in the case of silicon, contrary results were observed. Silicon exhibits thermal and electrical properties in between carbon nanofiber and alumina. Although the discharge gap is increased compared to the no powder condition, it is not as large as carbon nanofibers. As a result, an expanded plasma channel is formed, which results in a wider crater. Thus, it can be seen that the properties of powder influence the discharge process significantly.

**Figure 6.** Single discharge craters for (**a**) 90 V, (**b**) 100 V, (**c**) 110 V using EDM oil with carbon nanofiber powder, (**d**) 90 V, (**e**) 100 V, (**f**) 110 V with alumina powder and (**g**) 90 V, (**h**) 100 V, (**i**) 110 V with silicon powder.

**Figure 7.** Average single discharge crater diameter under different conditions.

#### *4.2. Hole Machining by Carbon Nanofiber-Mixed EDM*

The machined profiles and the material removal rate with oil and carbon nanofibermixed EDM are summarized in Figures 8 and 9, respectively. In the only EDM oil condition, the maximum MRR obtained was 0.0026 mm3/min at 110 V condition, which decreased with the decrease in voltage. With an increase in voltage (*V*), the maximum discharge energy per spark (W) increases, and as a result, the material removed per discharge also increases, Equation (3). This can be seen evidently in Figure 7, where the crater diameter increased with an increase in voltage.

$$\mathcal{W} = \frac{1}{2}\mathcal{C}V^2\tag{3}$$

**Figure 8.** Profiles of machined holes at 90 and 110 V using oil and 0.5 g/L CnF-mixed EDM.

**Figure 9.** Material removal rate using CnF-mixed EDM.

It can be seen from the machined profiles in Figure 8 that the machined holes were shallower and wider with the addition of the carbon nanofiber. The increase in machined hole diameter, called overcut, is due to an increase in the discharge gap due to the addition of nanofibers.

It was observed that even though the machined depth with the oil-only condition was large, the MRR increased for all concentrations of CnF and voltages. At a higher discharge energy of 110 V, the maximum MRR observed was 0.017 mm3/min at a concentration of 0.5 g/L, over 6.5 times that of the oil-only condition. This tremendous improvement in MMR can be attributed to the ease of discharge initiation and increased discharge gap width. It was also observed that at 110 V, the machining speed was equal to the set feed speed (0.5 µm/s) at all concentrations. This indicates no ineffective discharge period in this condition, where the tool is forced to retract to prevent short-circuiting, could be observed. This can be monitored by observing the waveforms on the oscilloscope, as seen in Figure 10a. It was observed that during EDM with oil, there was a lot of arcing which had the waveform M1 in contrast to normal EDM (M2). It was also observed that if arcing persisted, it led to the formation of complex waveforms [28] as seen in M3, followed by short-circuiting and tool retraction. In contrast, as seen in Figure 10b, the addition of carbon nanofiber increased the number of sparks, and complex waveforms were not observed.

**Figure 10.** Current waveforms during EDM using (**a**) oil and (**b**) carbon nanofiber mixed EDM (1 g/L) at 110 V.

It can be seen from Figure 10 that the MRR increases with an increase in concentration and then decreases after a peak at 0.5 g/L. In EDM with oil without process alteration, it has been reported that the spark tends to accumulate in one region [12,29]. As debris concentration increases, spark formation in the vicinity is likely, as shown in Figure 1b. Whereas, in powder-EDM, sparks are not localized since particles are dispersed throughout the surface. In CnF-mixed EDM, initially, as the particle concentration increases, there is an increase in the number of discharge pathways dispersing EDM, increasing process stability, and thus increasing MRR. However, as the particles' concentration increased, many nanofibers started to either accumulate or adhere to the electrodes, inhibiting the discharge process.

Similarly, an improved machining efficiency was observed when the voltage was reduced to 90 V. A machining rate of 0.008 mm3/min was achieved, over 18 times that of only oil. Generally, the discharge gap decreases considerably at low voltages, increasing the possibility of debris accumulation within the working gap. This increases the frequency of ineffective discharges, such as short-circuiting due to debris accumulation and secondary discharges on the machined debris. Thus, flushing away debris becomes a prime concern. During CnF-mixed EDM, the machined speed increased from 0.18 to 0.53 times the set machining speed when the concentration increased from 0.25 to 1.0 g/L. There was a significant no discharge period. However, during the tool backtracking period, the fresh

dielectric with powder flows in, removing the debris. This period is essential at low voltage, where the discharge gap is lower, as it resets the dielectric. The proportion of powder entering the gap is higher at higher concentrations so that machining can be restarted quickly. Although the machining efficiency increased, it can be considered that at a feed rate of 0.5 µm/s and 90 V using carbon nanofibers, uninterrupted machining cannot be achieved entirely.

An improvement of over 7.7 times in the MRR without powder addition was observed at 100 V. Similar to 110 V, the machining was almost continuous, the machining speed increasing from 0.68 to 0.92 times the feed speed. Furthermore, the MRR increased with concentration. At 1.0 g/L, the MRR was even higher than 110 V. The brief intermissions during EDM at 100 V allowed new dielectrics to flow in, which is considered to improve the machining rate compared with 110 V.

Figure 11 compares electrode wear for different machining conditions in CnF-mixed EDM. It was observed that, unlike in previous studies, electrode wear rate increased with the addition of CnF to the dielectric, which increases further with the increase in the powder concentration.

**Figure 11.** Relative electrode wear rate using CnF-mixed EDM.

During the EDM process, plasma travels from the cathode and bombards the anode, removing material by heat transfer and abrasive action. However, some electrons also travel in the opposite direction removing material from the anode. Since the kinetic energy of ions is higher than electrons, the amount of material removed from the anode is much higher. In addition, the temperature in the discharge gap can exceed 3000 K [30], removing material from both electrodes. Thus, the tool wear depends on the electrodes' electrical and thermal properties. The melting point of copper is much lower than that of tungsten carbide [1631 K], and thus, the melting of copper will be higher at high temperatures. At a high voltage or discharge energy, the fraction of electrons is high, and the temperature in the working gap is enormous. Hence a higher wear rate is observed. The dielectric flow into the discharge gap, which acts as a coolant and reduces the heat transferred to the electrodes is necessary to prevent electrode wear.

As mentioned above, the machining at higher voltages [100 and 110 V] was almost continuous. Since no external flushing was used, if no electrode backtracking occurs, the flow of the new dielectric is limited. The debris removed is only due to bubble explosion and not debris flow, and as a result, the temperatures within the gap will continue to rise. The electrode wear will increase correspondingly with an increasing machining rate, as seen in Figure 11. Since the discharge energy at 100 V is smaller than 110 V, the temperature reached within the gap is expected to be smaller, resulting in a lower tool wear rate.