Home Multiobjective optimization of EDM machining parameters of TIB2 ceramic materials using regression and gray relational analysis
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Multiobjective optimization of EDM machining parameters of TIB2 ceramic materials using regression and gray relational analysis

  • Karthik Shanmugam EMAIL logo , Sivakumar Annamalai and Nathiya Thangaraj
Published/Copyright: February 18, 2025

Abstract

The aerospace industry widely uses titanium diboride, a tough material that is challenging to machine using conventional methods. We, therefore, process titanium diboride (TiB₂) to determine the best process parameters for electrical discharge machining for TiB₂. The study compares four electrode materials: brass, copper, tungsten, and tungsten copper. In addition, water-based deionized (DI) dielectric fluids are studied, and the input parameters are changed to measure the fluctuations of the output parameters through experimental studies and gain insights into the material removal rate (MRR), tool wear rate (TWR), surface roughness (SR), and overcut during machining of titanium diboride. We then verify this result using gray relational analysis and a regression model. Based on these results, we confirm that the discharge current is the most important factor affecting the MRR and SR. The copper electrode achieves a higher MRR, while the tungsten copper-based electrode leads to a lesser TWR. A copper electrode achieves better SR, while tungsten electrodes with DI water-based dielectric fluid show a lower overcut value. After machining, we used SEM to check the surface quality and elemental makeup and to see if there were any microstructural changes, like craters, recast layers, and maybe even microcracks, that were caused by the high-energy discharge process.

1 Introduction

In recent years, there has been a need for the ability to drill holes in tough materials such as titanium, titanium diboride, and ceramics without compromising their surface integrity and strength [1]. Modern industry prefers titanium diboride ceramic materials due to their excellent mechanical properties, high strength at elevated temperatures, and their use in the wear protection industry, defense manufacturing, and cutting tools [2]. Sivakumar et al. [3] investigated how biosilica–water dielectric made from silane-treated wheat fibers can be used to machine Ti–6Al–4V titanium alloys. By using biosilica particles derived from wheat husk ash biomass, the material removal rate (MRR), tool life, and surface texture were improved. Ni et al. [4] investigated the influence of discharge parameters on the size of discharge craters produced by micro-electrical discharge machining (micro-EDM) with deionized (DI) water. The manufacturing industry is increasingly using machining and other advanced processing techniques to create intricate openings in semiconducting and electrically conductive materials. Hema et al. [5] conducted a study on dielectric fluids such as DI water and kerosene and found that kerosene increased the MRR compared to DI water. The study by Baroi et al. investigated how current and pulse duration affect the MRR, tool wear, and surface roughness (SR) in the electrical discharge machining (EDM) process of grade 2 titanium alloys. Copper is the material of the electrodes, while DI water serves as the dielectric. By replacing harmful emissions with DI water, research strives to promote sustainability and reduce environmental impact. According to the results, MRR and TWR increase in parallel with current and pulse on time, while SR increases in both cases [6]. The ability of conventional and modified biodegradable dielectric fluids to facilitate EDM on a titanium alloy and a copper electrode. To ensure environmental sustainability and long-term viability, the study uses vegetable oil as a dielectric. The results indicate that modified RBD palm oil has a slightly different electrode wear rate (EWR) compared to kerosene. Notably, pulse durations of 6 and 12 A resulted in the lowest EWR values [7].

Le et al. studied the influence of tungsten carbide alloy powder on the microhardness and SR of SKD61 workpieces and found that the use of kerosene as a dielectric fluid has improved surface quality compared to traditional EDM, which can affect the strength of mold machine parts [8]. Aged CuCr1Zr alloy electrodes have excellent machining performance and electrical conductivity, while CuCo₂Be alloy electrodes have moderate to high MRR but the greatest relative wear. The cost-effectiveness of electrolytic copper makes it a more viable electrode material for EDM applications despite its moderate MRR performance [9]. Shanmugam et al. investigated the influence of titanium samples with a copper electrode with different input parameters on performance indicators such as overcuts and MRRs and determined optimal results using Taguchi analysis [10]. Using brass electrodes, Maurya et al. conducted EDM experiments on titanium alloys based on the Box–Behnken design; this study investigated how machining parameters such as pulse on time, duty cycle, peak current, and gap voltage affected the machining response output. According to the results, peak current and gap voltage are the most important input parameters for MRR and TWR. The results of the validation tests show a discrepancy between the two series of numbers of 7.509 and 2.8% [11]. Bhaumik et al. investigated the influence of process settings and electrode types on the surface integrity and dimensional stability of the titanium alloy EDMed Ti–5Al–2.5Sn to measure the surface crack density, radial overcut, recast layer, SR, and electrode performance and compared copper, brass and zinc electrodes. The results of the microstructural study indicate that the use of copper electrodes improves the surface gloss, radial overcut, new cast layer thickness, and surface fracture density. A copper tool can achieve a higher level of accuracy and smoothness [12]. Compared to more traditional production processes, EDM offers a number of advantages. The electrical and thermal properties of copper–tungsten (CuW) make it an ideal material for use in precision surface machining. The combination of electrode material and input parameters determines the output properties, including the MRR, tool wear ratio (TWR), and surface finish. This is a result of an evaluation of studies on processing with copper–tungsten electrodes [13]. The SR of the titanium alloy Ti–6Al–4V, which was machined using die-sinking EDM and a copper–tungsten electrode, was investigated and optimized its values using regression analysis and firefly swarm optimization. After defining a set of parameters, mathematical models were created that took linear and two-factor interaction effects into account. Compared to the linear model with the lowest Ra value, the 2FI model performed better [14]. A study was conducted on the difficult-to-machine Ti–4Al–6V alloy to investigate the effects of keyway opening and machining parameters.

The results show that the discharge current and pulse time have a significant influence on the MRRs, tool wear, and SR. The limited thermal conductivity creates a heat-affected zone of up to 60 µm. Researchers have created artificial neural network models to predict the output of a processing system [15]. Sharma et al. [16] conducted a statistical analysis of EDM performance of pearlitic spheroidal graphite iron material, using gray relation analysis (GRA) and regression models to determine the optimal performance of multi-objective EDM parameters. Based on the literature review, we found that only a small number of researchers have conducted studies on EDM in titanium diboride materials using copper electrodes and DI water as a dielectric to study the effects of MRR and TWR. However, the use of brass, tungsten, or copper electrodes in titanium diboride materials has not been investigated despite their better MRR and TWR compared to copper electrodes. To address this issue, current research examines the optimal dielectric fluids, electrode materials (copper, brass, tungsten–copper, and tungsten), and process variables for working with TIB2 materials using regression and GRA optimization techniques.

2 Materials and methods

2.1 Selection of the workpiece

Titanium diboride material with dimensions 100 mm × 100 mm × 10 mm was selected as the workpiece, which was purchased from Domadia Private Limited (Mumbai, India). Various industries use titanium diboride due to its outstanding properties, including high hardness, melting point, and wear resistance [1,17].

2.2 Details of the equipment

Drilling with EDM, a non-traditional machining technique, uses electrical energy to remove material from a workpiece. The Baoma Suzhou DB703A machine performs EDM machining. Figure 1 depicts the EDM machine that drills holes in titanium diboride materials. Spark erosion drilling involves drilling a small hole into the workpiece using a rotating electrode that conforms to the desired hole geometry. It involves placing the workpiece and electrode in a dielectric fluid (usually DI water and kerosene) and using a high-frequency electrical discharge between them. The discharge creates a spark that vaporizes the material at the point of contact, creating a tiny void or hole. The electrode rotates, continuously removing the material, creating a straight or tapered hole. By adjusting the parameters of the electrical process, such as voltage and current, as well as the distance between the electrode and workpiece, the hole size can be precisely controlled, and the value can be displayed digitally. We considered the pulse switch-on time (T on), discharge current (I p), and flushing pressure as variable input parameters. Table 1 presents the levels and values of these variable parameters. We selected the input parameter values based on a literature review and the device capacity. We drilled holes in the titanium diboride material using hollow electrode rods made of brass, copper, tungsten, and tungsten with a diameter of 3 mm. We listed and compared the input parameters to identify the optimal processing parameters and influencing factors. The dielectric fluid consisted of distilled water with a specific flushing pressure to remove the resulting residue. After the machining conditions reached a steady state, we retrieved the voltage and operating current values from the machine monitor and recorded them while drilling the sample. The drilling length was 4 mm in all tests. As shown in Figure 2, we created 64 holes, 16 of which used brass, copper, tungsten, and tungsten copper electrodes. We recorded the working time for each test with various input parameters. We also measured the weight of the sample and tool electrode after each drilling operation to calculate the MRR and TWR.

Figure 1 
                  A hole-drilling EDM machine.
Figure 1

A hole-drilling EDM machine.

Table 1

Input process parameters and their levels

Input parameters Unit Range Levels
1 2 3 4
Pulse on time (T on) µs 0–9 3 4 5 6
Discharge current (I p) A 0–9 4 5 6 7
Spindle speed (S) rpm 10–120 10 20 30 40
Flushing pressure (FR) mpa 0.6–1.0 0.6 0.7 0.8 0.9
Figure 2 
                  TiB2 workpiece with EDM hole.
Figure 2

TiB2 workpiece with EDM hole.

2.3 Selection of electrodes

In this study, brass, copper, tungsten, copper, and tungsten were selected as electrode materials. The diameter of the electrode was 3 mm, and the length was 400 mm. They were selected to test the performance metrics such as MRR, overcut, TWR, and SR. The electrode materials for this study were selected based on their melting temperature, hardness, physical properties, and chemical properties [12].

2.4 Dielectric fluids

Due to its high dielectric strength, DI water can tolerate higher voltages without malfunctioning. This property is crucial for maintaining stable EDM processes, especially when working with complex geometries or high-performance machines. DI water can replace oil-based dielectric fluids, resulting in commonly machined parts with improved surface finishes. This is particularly helpful when surface quality is important. DI water, among many other dielectric fluids, is less harmful to the environment [18]. It does not contain any hazardous substances or additives that could make disposal difficult. With regard to specific machining requirements, such as the material being machined, the desired surface finish, the speed of the machining process, and safety considerations, DI water has its advantages.

3 Experimental results

Using DI water as a dielectric, Table 2 shows the experimental results of EDM operation in titanium diboride material. We measured the MRR, TWR, overcut, and SR. Copper, brass, tungsten, and copper were chosen as electrode materials. T on, I p, S, and the flushing pressure are the input parameters.

Table 2

Electric discharge machining experimental results

Trial no. Input parameters Copper electrode Brass electrode Tungsten–copper electrode Tungsten electrode
T on (μs) I p (A) S (rpm) FR (mpa) MRR (mm3·min−1) TWR (mm3·min−1) SR (μm) Overcut (mm) MRR (mm3·min−1) TWR (mm3·min−1) SR (μm) Overcut (mm) MRR (mm3·min−1) TWR (mm3·min−1) SR (μm) Overcut (mm) MRR (mm3·min−1) TWR (mm3·min−1) SR (μm) Overcut (mm)
1 3 4 10 0.6 2.011 0.234 4.42 0.109 1.708 0.4 6.99 0.199 1.653 0.144 5.457 0.150 1.328 0.100 4.488 0.16
2 3 5 20 0.7 2.099 0.339 4.98 0.131 1.808 0.412 5.99 0.215 1.696 0.177 5.227 0.155 1.412 0.133 4.302 0.188
3 3 6 30 0.8 2.135 0.459 3.98 0.144 1.741 0.396 5.232 0.210 1.744 0.212 5.272 0.158 1.395 0.154 3.992 0.220
4 3 7 40 0.9 2.184 0.329 5.26 0.165 1.774 0.404 4.92 0.199 1.781 0.241 4.982 0.161 1.425 0.180 4.100 0.236
5 4 4 20 0.8 2.187 0.325 4.79 0.188 1.808 0.412 6.03 0.230 1.753 0.206 5.186 0.182 1.402 0.151 4.232 0.237
6 4 5 10 0.9 2.236 0.534 5.48 0.178 1.974 0.444 5.91 0.260 1.804 0.23 5.235 0.199 1.444 0.180 4.531 0.250
7 4 6 40 0.6 2.663 0.614 5.26 0.196 2.009 0.446 5.26 0.200 2.118 0.269 5.05 0.178 1.668 0.204 4.155 0.211
8 4 7 30 0.7 2.472 0.531 6.15 0.198 2.043 0.465 5.23 0.250 2.032 0.31 5.453 0.201 1.625 0.214 4.487 0.242
9 5 4 30 0.9 2.408 0.659 4.69 0.25 2.089 0.476 4.98 0.280 2.024 0.272 4.506 0.231 1.619 0.200 3.743 0.320
10 5 5 40 0.8 2.693 0.596 5.20 0.28 1.958 0.446 4.66 0.249 2.012 0.299 4.805 0.241 1.595 0.222 3.954 0.290
11 5 6 10 0.7 2.651 0.743 6.12 0.226 2.186 0.498 6.11 0.310 2.140 0.313 6.115 0.237 1.699 0.224 4.923 0.265
12 5 7 20 0.6 2.693 0.729 6.32 0.236 2.089 0.496 5.92 0.270 2.248 0.365 6.135 0.222 1.718 0.267 5.125 0.231
13 6 4 40 0.7 3.035 0.743 5.12 0.32 2.136 0.486 4.53 0.260 2.282 0.344 4.488 0.249 1.826 0.246 3.693 0.300
14 6 5 30 0.6 3.245 0.810 5.98 0.29 2.166 0.496 5.26 0.290 2.471 0.346 5.395 0.258 1.977 0.252 4.440 0.287
15 6 6 20 0.9 3.157 0.863 6.62 0.31 2.546 0.561 4.71 0.320 2.518 0.391 5.554 0.300 2.014 0.290 4.570 0.347
16 6 7 10 0.8 3.239 0.817 7.16 0.28 2.611 0.594 5.55 0.330 2.632 0.391 5.709 0.299 2.106 0.298 4.875 0.312

4 Regression analysis

Regression analysis is a valuable tool for analyzing EDM and gaining insights into how different process parameters affect the machining results. It allows the identification of the most effective combination of input parameters, including T on, I p, the drilling speed, spindle speed, pulse duration, electrode material, and dielectric fluid type. By identifying optimal settings, regression analysis helps achieve desired results such as MRR, TWR, surface quality, and dimensional accuracy. This analysis can show the impact of different input parameters on the EDM process. In the field of EDM, the relationships between input parameters and output variables can be complex and nonlinear. We can effectively model these relationships using regression analysis techniques like polynomial regression or nonlinear regression [15]. Once a regression model is developed and validated, it can be used for prediction and control purposes. Control systems can seamlessly integrate the regression model to automatically optimize process parameters in real-time, ensuring consistent and optimal machining performance. Tables 36 show the regression analysis values for four different electrode materials (copper, brass, tungsten, copper, and tungsten) and DI water based dielectric fluid media. We set the confidence at 95% for all regression analyses [15].

Table 3

Regression analysis on an MRR

Source DF Copper electrode Brass electrode Tungsten copper electrode Tungsten electrode
Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value
T on 1 2.3215 2.32153 161.2 0.000 0.75505 0.755050 87.75 0.000 1.20148 1.20148 183.2 0.000 0.718 0.718 111.9 0.000
I P 1 0.1259 0.12593 8.75 0.013 0.10542 0.105415 12.25 0.005 0.15138 0.15138 23.08 0.001 0.074 0.074 11.64 0.006
S 1 0.0258 0.02585 1.79 0.207 0.05090 0.050904 5.92 0.033 0.00003 0.00003 0.01 0.944 0.000 0.0002 0.03 0.871
FR 1 0.0443 0.04437 3.08 0.107 0.01735 0.017346 2.02 0.183 0.01507 0.01507 2.30 0.158 0.004 0.0049 0.78 0.397
Error 11 0.1584 0.01440 0.09465 0.008604 0.07214 0.00656 0.070 0.0064
Total 15 2.6760 1.02336 1.44010 0.865
R 2 = 94.08%, R 2(Adj) = 91.93% R 2 = 90.75%, R 2(Adj) = 87.39% R 2 = 94.99%, R 2(Adj) = 93.17% R 2 = 91.88%, R 2(Adj) = 88.92%
Table 4

Regression analysis on an TWR

Source DF Copper electrode Brass electrode Tungsten copper electrode Tungsten electrode
Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value
T on 1 0.5022 0.50228 97.4 0.000 0.0371 0.03715 141.23 0.000 0.0677 0.0677 796.27 0.00 0.0370 0.03702 902.23 0.000
I P 1 0.0376 0.03762 7.30 0.021 0.0054 0.00541 20.57 0.001 0.0167 0.0167 196.34 0.00 0.0094 0.00948 231.09 0.000
S 1 0.00005 0.00005 0.01 0.921 0.0032 0.00325 12.36 0.005 0.0006 0.0006 7.50 0.01 0.0002 0.00020 5.07 0.046
FR 1 0.00034 0.0003 0.07 0.802 0.0002 0.00020 0.78 0.396 0.0000 0.0000 0.01 0.94 0.0000 0.00009 2.41 0.149
Error 11 0.05669 0.0051 0.0028 0.00026 0.0009 0.0000 0.0004 0.00004
Total 15 0.59700 0.0489 0.0860 0.0472
R 2 = 90.50%, R 2(Adj) = 87.05% R 2 = 94.08%, R 2(Adj) = 91.93% R 2 = 98.91%, R2(Adj) = 98.52% R 2 = 99.04%, R 2(Adj) = 98.70%
Table 5

Regression analysis on a surface roughness

Source DF Copper electrode Brass electrode Tungsten copper electrode Tungsten electrode
Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value
T on 1 4.6900 4.6899 41.75 0.000 1.2515 1.25150 44.77 0.000 0.01988 0.01988 0.60 0.453 0.07369 0.07369 4.67 0.054
I P 1 4.0275 4.0275 35.85 0.000 0.1311 0.13106 4.69 0.053 1.07069 1.07069 32.5 0.000 0.74228 0.74228 47.03 0.000
S 1 0.9968 0.9968 8.87 0.013 3.8360 3.83600 137.22 0.000 1.52601 1.52601 46.4 0.000 1.32922 1.32922 84.21 0.000
FR 1 0.0133 0.0132 0.12 0.738 1.0392 1.03922 37.17 0.000 0.39074 0.39074 11.8 0.005 0.21466 0.21466 13.60 0.004
Error 11 1.2357 0.1123 0.3075 0.02796 0.36145 0.03286 0.17363 0.01578
Total 15 10.963 6.5653 3.36876 2.53348
R 2 = 88.73%, R 2(Adj) = 84.63% R 2 = 95.32%, R 2(Adj) = 93.61% R 2 = 89.27%, R 2(Adj) = 85.37% R 2= 93.15%, R 2(Adj) = 90.65%
Table 6

Regression analysis on an overcut

Source DF Copper electrode Brass electrode Tungsten copper electrode Tungsten electrode
Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value Adj SS Adj MS F-value P-value
T on 1 0.059 0.059678 1319 0.000 0.0211 0.0211 164.7 0.000 0.0326 0.0326 430.0 0.000 0.027 0.0278 365.3 0.000
  I P 1 0.00001 0.000014 0.30 0.594 0.0008 0.0008 6.90 0.024 0.0006 0.0006 8.93 0.012 0.0000 0.0000 0.26 0.618
  S 1 0.00339 0.0033 75.0 0.000 0.0041 0.0041 32.57 0.000 0.0004 0.0004 5.27 0.042 0.0005 0.0005 7.66 0.018
  FR 1 0.00067 0.0006 15.0 0.003 0.0010 0.0010 7.86 0.017 0.0010 0.0010 13.55 0.004 0.0091 0.0091 120.2 0.000
Error 11 0.00049 0.000045 0.0014 0.0001 0.0008 0.0000 0.0008 0.0000
Total 15 0.06426 0.0286 0.0356 0.0384
  R 2 = 99.23% R 2(Adj) = 98.94% R 2 = 95.07% R 2(Adj) = 93.28% R 2 = 97.65% R 2(Adj) = 96.80% R 2 = 97.82% R 2(Adj) = 97.03%

4.1 MRR

(1) MRR ( c opper ) = 0.863 + 0.3407 T on + 0.0793 I p + 0.00359 S 0.471 FR ,

(2) MRR ( b rass ) = 0.672 + 1.943 T on + 0.072 I p 0.00505 S + 0.294 FR ,

(3) MRR ( tungsten copp er ) = 0.684 + 0.0870 T on + 0.087 I p 0.00013 S 0.275 FR ,

(4) MRR ( t ungsten ) = 0.578 + 0.189 T on + 0.061 I p 0.00030 S 0.158 FR .

4.2 TWR

(5) TWR ( c opper ) = 0.342 + 0.1585 T on + 0.0434 I p + 0.00016 S + 0.041 FR ,

(6) TWR ( b rass ) = 0.1879 + 0.04310 T on + 0.01645 I p 0.001275 S + 0.0320 FR ,

(7) TWR ( tungsten cop per ) = 0.152 + 0.05820 T on + 0.0289 I p + 0.000565 S + 0.0015 FR ,

(8) TWR ( t ungsten ) = 0.130 + 0.0430 T on + 0.0217 I p + 0.000322 S + 0.0222 FR .

4.3 Surface roughness (SR)

(9) SR ( c opper ) = 1.57 + 0.484 T on + 0.448 I p 0.02233 S 0.258 FR ,

(10) SR ( c rass ) = 9.831 0.250 T on 0.081 I p 0.0438 S 2.279 FR ,

(11) SR ( t ungsten c opper ) = 5.61 + 0.031 T on + 0.231 I p 0.02762 S 1.398 FR ,

(12) SR ( t ungsten ) = 4.439 + 0.060 T on + 0.192 I p 0.02578 S 1.036 FR .

4.4 Overcut (OC)

(13) Overcut ( c opper ) = 0.107 + 0.0546 T on + 0.0008 I p + 0.001303 S + 0.0583 FR ,

(14) Overcut ( b rass ) = 0.054 + 0.0325 T on + 0.0066 I p 0.001445 S + 0.0710 FR ,

(15) Overcut ( t ungsten c opper ) = 0.042 + 0.04042 T on + 0.00582 I p 0.000447 S + 0.0717 FR ,

(16) Overcut ( t ungsten ) = 0.091 + 0.0373 T on + 0.0010 I p + 0.000540 S + 0.2140 FR .

5 GRA

EDM uses GRA, an effective multi-criteria decision-making tool, to improve machining parameters under uncertainty [19]. It facilitates the investigation of various performance metrics, such as MRR, SR, tool wear, and overcut, by converting qualitative data into quantitative values. By considering the mean GRC values for each dataset, the GRA approach can convert multiple response variables into a single GRG value. The alternative with the largest GRG result is then considered the most favorable choice [20,21]. Tables 710 display the gray rational optimization results, which are ranked for various electrode materials.

Table 7

EDM optimization of copper electrodes using GRA

Trial no. Normalized value Deviation sequence Gray relational coefficients Gray grade Rank
MRR TWR SR OC MRR TWR SR OC MRR TWR SR OC
1 0.000 0.999 0.862 1.000 1.000 0.001 0.138 0.000 0.333 0.999 0.783 1.000 0.779 1
2 0.071 0.833 0.686 0.896 0.929 0.167 0.314 0.104 0.350 0.750 0.614 0.827 0.635 3
3 0.100 0.642 1.000 0.836 0.900 0.358 0.000 0.164 0.357 0.583 1.000 0.754 0.673 2
4 0.140 0.849 0.597 0.735 0.860 0.151 0.403 0.265 0.368 0.768 0.554 0.653 0.586 5
5 0.143 0.855 0.745 0.626 0.857 0.145 0.255 0.374 0.368 0.775 0.663 0.572 0.594 4
6 0.182 0.523 0.528 0.673 0.818 0.477 0.472 0.327 0.380 0.512 0.515 0.605 0.503 10
7 0.529 0.395 0.597 0.588 0.471 0.605 0.403 0.412 0.515 0.453 0.554 0.548 0.517 7
8 0.373 0.528 0.318 0.578 0.627 0.472 0.682 0.422 0.444 0.514 0.423 0.542 0.481 14
9 0.322 0.324 0.777 0.332 0.678 0.676 0.223 0.668 0.424 0.425 0.691 0.428 0.492 11
10 0.553 0.424 0.616 0.190 0.447 0.576 0.384 0.810 0.528 0.465 0.566 0.382 0.485 12
11 0.519 0.191 0.327 0.445 0.481 0.809 0.673 0.555 0.509 0.382 0.426 0.474 0.448 15
12 0.553 0.214 0.264 0.398 0.447 0.786 0.736 0.602 0.528 0.389 0.405 0.454 0.444 16
13 0.830 0.192 0.642 0.000 0.170 0.808 0.358 1.000 0.746 0.382 0.582 0.333 0.511 9
14 1.000 0.084 0.371 0.142 0.000 0.916 0.629 0.858 1.000 0.353 0.443 0.368 0.541 6
15 0.928 0.000 0.170 0.047 0.072 1.000 0.830 0.953 0.875 0.333 0.376 0.344 0.482 13
16 0.995 0.073 0.000 0.190 0.005 0.927 1.000 0.810 0.990 0.350 0.333 0.382 0.514 8
Table 8

EDM optimization of brass electrodes using GRA

Trial no. Normalized value Deviation sequence Gray relational coefficients Gray grade Rank
MRR TWR SR OC MRR TWR SR OC MRR TWR SR OC
1 0.000 0.980 0.000 1.000 1.000 0.020 1.000 0.000 0.333 0.961 0.333 1.000 0.657 4
2 0.110 0.922 0.407 0.878 0.890 0.078 0.593 0.122 0.360 0.864 0.457 0.804 0.621 7
3 0.036 0.999 0.715 0.916 0.964 0.001 0.285 0.084 0.342 0.997 0.637 0.856 0.708 2
4 0.073 0.961 0.841 1.000 0.927 0.039 0.159 0.000 0.350 0.927 0.759 1.000 0.759 1
5 0.110 0.922 0.390 0.763 0.890 0.078 0.610 0.237 0.360 0.864 0.451 0.679 0.588 9
6 0.294 0.759 0.439 0.534 0.706 0.241 0.561 0.466 0.415 0.675 0.471 0.518 0.520 13
7 0.333 0.750 0.703 0.992 0.667 0.250 0.297 0.008 0.429 0.666 0.628 0.985 0.677 3
8 0.372 0.650 0.715 0.611 0.628 0.350 0.285 0.389 0.443 0.589 0.637 0.562 0.558 10
9 0.422 0.598 0.817 0.382 0.578 0.402 0.183 0.618 0.464 0.554 0.732 0.447 0.549 12
10 0.277 0.748 0.947 0.618 0.723 0.252 0.053 0.382 0.409 0.665 0.904 0.567 0.636 5
11 0.529 0.486 0.358 0.153 0.471 0.514 0.642 0.847 0.515 0.493 0.438 0.371 0.454 16
12 0.422 0.497 0.435 0.458 0.578 0.503 0.565 0.542 0.464 0.499 0.469 0.480 0.478 15
13 0.474 0.544 1.000 0.534 0.526 0.456 0.000 0.466 0.488 0.523 1.000 0.518 0.632 6
14 0.508 0.493 0.703 0.305 0.492 0.507 0.297 0.695 0.504 0.497 0.628 0.419 0.512 14
15 0.928 0.165 0.927 0.076 0.072 0.836 0.073 0.924 0.874 0.374 0.872 0.351 0.618 8
16 1.000 −0.002 0.585 0.000 0.000 1.002 0.415 1.000 1.000 0.333 0.547 0.333 0.553 11
Table 9

EDM optimization of tungsten–copper electrodes using GRA

Trial no. Normalized value Deviation sequence Gray relational coefficients Gray grade Rank
MRR TWR SR OC MRR TWR SR OC MRR TWR SR OC
1 0.000 1.001 0.412 1.000 1.000 −0.001 0.588 0.000 0.333 1.003 0.459 1.000 0.699 1
2 0.044 0.867 0.551 0.966 0.956 0.133 0.449 0.034 0.343 0.790 0.527 0.936 0.649 2
3 0.093 0.725 0.524 0.947 0.907 0.275 0.476 0.054 0.355 0.645 0.512 0.903 0.604 4
4 0.131 0.606 0.700 0.925 0.869 0.394 0.300 0.075 0.365 0.560 0.625 0.869 0.605 3
5 0.102 0.748 0.576 0.790 0.898 0.252 0.424 0.210 0.358 0.665 0.541 0.704 0.567 8
6 0.155 0.653 0.546 0.672 0.845 0.347 0.454 0.328 0.372 0.590 0.524 0.604 0.523 9
7 0.475 0.493 0.659 0.811 0.525 0.507 0.341 0.189 0.488 0.496 0.595 0.725 0.576 7
8 0.387 0.327 0.414 0.662 0.613 0.673 0.586 0.338 0.449 0.426 0.460 0.597 0.483 13
9 0.379 0.481 0.989 0.457 0.621 0.519 0.011 0.543 0.446 0.491 0.979 0.480 0.599 6
10 0.367 0.372 0.808 0.390 0.633 0.628 0.192 0.610 0.441 0.443 0.722 0.451 0.514 11
11 0.498 0.314 0.012 0.417 0.502 0.686 0.988 0.583 0.499 0.422 0.336 0.462 0.430 16
12 0.607 0.105 0.000 0.522 0.393 0.895 1.000 0.478 0.560 0.358 0.333 0.511 0.441 15
13 0.643 0.192 1.000 0.339 0.357 0.808 0.000 0.661 0.583 0.382 1.000 0.431 0.599 5
14 0.835 0.181 0.449 0.279 0.165 0.819 0.551 0.721 0.752 0.379 0.476 0.410 0.504 12
15 0.884 −0.001 0.353 −0.001 0.116 1.001 0.647 1.001 0.811 0.333 0.436 0.333 0.478 14
16 1.000 −0.001 0.259 0.006 0.000 1.001 0.741 0.994 1.001 0.333 0.403 0.335 0.518 10
Table 10

EDM optimization of tungsten electrodes using GRA

Trial no. Normalized value Deviation sequence Gray relational coefficients Gray grade Rank
MRR TWR SR OC MRR TWR SR OC MRR TWR SR OC
1 0.000 1.000 0.445 1.000 1.000 0.000 0.555 0.000 0.333 1.000 0.474 1.000 0.702 1
2 0.108 0.834 0.575 0.849 0.892 0.166 0.425 0.151 0.359 0.751 0.541 0.768 0.605 2
3 0.086 0.728 0.792 0.679 0.914 0.272 0.208 0.321 0.354 0.648 0.706 0.609 0.579 4
4 0.124 0.596 0.716 0.593 0.876 0.404 0.284 0.407 0.363 0.553 0.638 0.552 0.526 8
5 0.095 0.742 0.624 0.586 0.905 0.258 0.376 0.414 0.356 0.660 0.571 0.547 0.533 7
6 0.148 0.595 0.415 0.519 0.852 0.405 0.585 0.481 0.370 0.553 0.461 0.510 0.473 14
7 0.437 0.475 0.677 0.727 0.563 0.525 0.323 0.273 0.470 0.488 0.608 0.647 0.553 6
8 0.382 0.423 0.445 0.564 0.618 0.577 0.555 0.436 0.447 0.464 0.474 0.534 0.480 13
9 0.374 0.497 0.965 0.144 0.626 0.503 0.035 0.856 0.444 0.499 0.935 0.369 0.562 5
10 0.343 0.384 0.818 0.305 0.657 0.616 0.182 0.695 0.432 0.448 0.733 0.419 0.508 11
11 0.477 0.376 0.141 0.437 0.523 0.624 0.859 0.563 0.489 0.445 0.368 0.471 0.443 16
12 0.581 0.155 0.000 0.620 0.419 0.845 1.000 0.380 0.544 0.372 0.333 0.568 0.454 15
13 0.640 0.261 1.000 0.253 0.360 0.739 0.000 0.747 0.581 0.404 1.000 0.401 0.596 3
14 0.834 0.231 0.478 0.320 0.166 0.769 0.522 0.680 0.750 0.394 0.489 0.424 0.514 10
15 0.882 0.040 0.387 0.003 0.118 0.960 0.613 0.997 0.809 0.342 0.449 0.334 0.484 12
16 1.000 0.001 0.175 0.190 0.000 0.999 0.825 0.810 1.000 0.333 0.377 0.382 0.523 9

6 Results and discussion

6.1 MRR

Factors such as electrode materials and dielectric fluid influence the MRR in EDM. According to the experimental results, the MRR in the copper electrodes with DI water removes more material than the dielectric liquid kerosene due to the high thermal conductivity and cooling properties of the DI water [22]. The EDM process improves the MRR of titanium materials by increasing the capacity and voltage [23]. Brass electrodes with DI water achieve a moderate MRR compared to copper electrodes and tungsten–copper composite electrodes produce better MRR in DI water. Using kerosene as the dielectric for the tungsten electrode results in a lower MRR compared to DI water. Figures 3 and 4 show the MRR for the titanium diboride material using four different electrodes and DI water dielectric fluid media with variations in the input parameters. In general, copper electrodes outperform brass, tungsten, and tungsten–copper electrodes in terms of the MRR. The thermal conductivity of the copper tool is higher (402 W·(m·K)−1 compared to those of brass (161 W·(m·K)−1, tungsten copper electrodes (240 W·(m·K)−1, and tungsten electrodes (174 W·(m·K)−1. Higher thermal conductivity would result in a faster rate of heat conduction. The spark discharge then ignites faster than with electrodes made of brass or tungsten. Because of the higher heat dissipation rate, shallow craters form on the workpiece surface, resulting in less heat transfer to the workpiece [18]. The thermophysical properties of the materials used as electrodes have a significant impact on EDM performance [24,25]. This reduces the MRR. From Figure 3, we can see that increasing the T on, spindle speed, and I p increases the MRR of the TiB2 material from 2.011 to 3.918 mm3·min−1 for copper electrodes. Similarly, the MRR improves by 3.3944, 3.3696, and 2.64078 mm3·min−1 for the brass, tungsten copper, and tungsten electrodes.

Figure 3 
                  MRRs for different electrode materials and dielectric fluids.
Figure 3

MRRs for different electrode materials and dielectric fluids.

Figure 4 
                  Influence of input parameters on the MRR: (a) copper electrodes, (b) brass electrodes, (c) tungsten–copper electrodes, and (d) tungsten electrodes.
Figure 4

Influence of input parameters on the MRR: (a) copper electrodes, (b) brass electrodes, (c) tungsten–copper electrodes, and (d) tungsten electrodes.

6.2 TWR

The TWR of the TiB₂ material using different electrode materials is shown in Figure 5. Kerosene-based dielectric fluids exhibit a higher TWR than DI water and dielectric fluids. The higher viscosity of kerosene-based dielectric fluids causes deposit accumulation at the tool–electrode interface, which in turn leads to increased tool wear due to abrasion and erosion [22]. Improved EDM heat dissipation in DI water results in less wear and lower thermal stress on the tool. Kerosene, on the other hand, has lower thermal conductivity and can, therefore, increase the temperature around the tool, leading to increased wear and thermal degradation. Chemical reactions with the tool material can lead to chemical wear. Additives and contaminants in kerosene-based dielectric fluids can increase these reactions. The possibility of chemical reactions with the tool material is usually lower when using DI water because it is chemically pure. DI water’s dielectric fluid strength surpasses that of kerosene, enabling it to sustain the EDM process at lower voltages, thereby reducing the energy transfer to the tool and consequently reducing wear [26]. The TWR increases with increasing I p, T on, and spindle speed, as shown in Figure 6(a)–(d) for four different electrode materials. In the case of tungsten, brass, and tungsten–copper electrodes, the copper electrode material is the softest. It also conducts heat well, which means that it wears down faster than the other three electrode materials when titanium diboride materials are machined. During the experimental study, copper electrodes achieved the highest TWR at 0.863 mm3·min−1, followed by brass electrodes at 0.594 mm3·min−1, tungsten copper at 0.391 mm3·min−1, and tungsten at 0.298 mm3·min−1.

Figure 5 
                  TWRs for different electrode materials and dielectric fluids.
Figure 5

TWRs for different electrode materials and dielectric fluids.

Figure 6 
                  Influence of input parameters on the TWR: (a) copper electrodes, (b) brass electrodes, (c) tungsten–copper electrodes, and (d) tungsten electrodes.
Figure 6

Influence of input parameters on the TWR: (a) copper electrodes, (b) brass electrodes, (c) tungsten–copper electrodes, and (d) tungsten electrodes.

6.3 SR

The Mitutoyo SJ 210 surface Roughness tester is used to measure the SR of EDM holes. The SR of the TiB₂ material using different electrode materials is shown in Figure 7. When DI water is used to machine the titanium diboride material, it can result in minimal SR, particularly when combined with harder electrode materials like tungsten, tungsten–copper, and brass. Copper electrode materials are soft and, therefore, can produce higher SR compared to harder electrode materials. Due to the high material removal, the SR of tungsten and tungsten–copper is lower compared to the copper electrode material [27]. Figure 8(a)–(d) shows the interaction diagram for different electrode materials versus the input parameters. The SR value of the TiB₂ material increased with increasing I p, T on, and spindle speed. The highest SR value of 9.635 μm is achieved for the copper electrode when T on = 6, I p = 7, S = 100, and FR = 10, followed by an SR value of the brass electrode (7.46475 μm), tungsten–copper electrode (6.919 μm), and tungsten electrode (5.9230 μm).

Figure 7 
                  SR for different electrode materials and dielectric fluids.
Figure 7

SR for different electrode materials and dielectric fluids.

Figure 8 
                  Influence of input parameters on the SR: (a) copper electrodes, (b) brass electrodes, (c) tungsten–copper electrodes, and (d) tungsten electrodes.
Figure 8

Influence of input parameters on the SR: (a) copper electrodes, (b) brass electrodes, (c) tungsten–copper electrodes, and (d) tungsten electrodes.

6.4 Overcut

Based on the experimental results, it was found that a DI water-based dielectric medium produces less overcut compared to a kerosene-based dielectric medium due to its better dielectric strength, as shown in Figure 9. The kerosene-based dielectric medium produces a high overcut due to the high viscosity of kerosene and lower thermal properties [28]. Hard materials such as tungsten and tungsten–copper electrodes combined with DI water-based dielectric medium produce a lower amount of overcut compared to softer electrode materials such as copper and brass [29]. Figure 10(a)–(d) displays the overcut interaction diagram for various input parameters, dielectric fluids, and electrode materials. By selecting the appropriate electrode material and dielectric fluid medium, one can reduce the overcut of the EDM hole, considering the machining materials used. We observed a higher overcut rate for copper electrodes with a dielectric fluid value of 0.616 mm, followed by brass, tungsten–copper, and tungsten electrodes.

Figure 9 
                  Overcut for different electrode materials and dielectric fluids.
Figure 9

Overcut for different electrode materials and dielectric fluids.

Figure 10 
                  Influence of input parameters on the overcut: (a) copper electrodes, (b) brass electrodes, (c) tungsten–copper electrodes, and (d) tungsten electrodes.
Figure 10

Influence of input parameters on the overcut: (a) copper electrodes, (b) brass electrodes, (c) tungsten–copper electrodes, and (d) tungsten electrodes.

6.5 GRA grade analysis

Figure 11 illustrates the GRA of the titanium diboride material using various electrode materials. The first step in GRA is to normalize the experimental results to a common scale. The second step in GRA is to assign weights to each criterion based on their significance to the machining process. The third step is to calculate the GRC; this coefficient indicates how closely each alternative meets the desired performance levels. Due to the equal weighting of all parameters, the ξΔmax value is assumed to be 0.5 [16]. The fourth step is to grade the GRA; this grade reflects the overall performance of each alternative based on the calculated GRCs and their respective weights. The fifth step is to rank the different trials based on their gray relational grades, guiding the selection of optimal EDM parameters. We plot Figure 11 based on the grade value, an important criterion in GRA optimization.

Figure 11 
                  GRA grade analysis of four different electrode materials.
Figure 11

GRA grade analysis of four different electrode materials.

Copper and tungsten electrodes have reliable grayscale values owing to their stable response to numerous parameters. For applications that involve stability and precision, the copper electrode is a perfect choice. Brass and tungsten–copper electrodes display greater variations in the EDM tests. Brass achieves top outcomes in certain tests but lacks uniformity and robustness. These indicate the sensitivity of each electrode’s performance to specific EDM parameter settings. The tungsten–copper electrodes offer balanced and competitive performance with relatively high gray values in several tests. Applications that require high material removal and durability can benefit from this technology. The tungsten electrode exhibits a constant performance with relatively small fluctuations, indicating reliability. The tungsten electrode is ideal for processing hard materials and attaining smooth surfaces. For example, brass performs extremely well in Experiment 3 but lags significantly behind in others. The tungsten–copper electrodes in Figure 11 illustrate a peak in Experiment 7 but fluctuate elsewhere. The highest average gray value across all trials identifies the optimal electrode for multi-target performance. Based on their stability and average GRG, we can categorize the performance of electrodes into tungsten–copper, tungsten–copper, copper, and brass. Depending on whether one prioritizes stability or peak performance, this ranking can cater as a guide to the material choice for specific EDM applications. For copper electrode trial 1, the highest-grade value of 0.779 indicates the optimal conditions with a favorable balance among MRR, TWR, SR, and overcut. Trial numbers 15 and 12 have the lowest rank, which leads to an unfavorable condition for machining. For brass electrode trial 4, the highest-grade value of 0.759 indicates that this parameter setting offers the best balance between high MRR, low TWR, smooth SR, and accurate overcut. Trial 16 displays a low gray value, suggesting that this parameter may not be as beneficial for optimal EDM performance. In tungsten copper electrode trial 1, we achieved the best balance of high TWR, low TWR, and accurate OC for this set of experiments. Trials 1–4 rank in the top 4, suggesting that the parameter settings in these trials provide more optimal EDM performance using a tungsten–copper electrode. Trial 1 yields the highest-grade value of 0.702 for the tungsten electrode, indicating the best overall EDM performance with this parameter set. This GRA helps us identify optimal parameter combinations for EDM machining with different electrode materials. The GRA determines T on = 3, I p = 4, S = 100, and DR = 10 as the optimal conditions for machining the TiB₂ material, which gives better MRR and lower or moderate TWR, SR, and OC.

6.6 Material characterization

We performed material characterization tests before and after drilling holes using EDM to determine cracks, porosity, and surface integrity of the workpiece. The DK7735 BORUI CNC wire EDM machine can cut samples with dimensions of 10 mm × 10 mm × 5 mm for SEM analysis. After cutting the sample, we can etch it to eliminate impurities and oxides from the workpiece. This smoothens the surface and improves the contrast between the different layers of a workpiece [15].

Table 11 displays the elemental composition and atomic weight percentage of the titanium diboride material. Figure 12 shows the SEM and EDS analysis of the workpiece before performing EDM machining. Figure 13(a) shows a sample at 250× magnification with a working distance (WD) of 6.9 mm and an accelerating voltage of 20.00 kV. According to the surface morphology, it appears that the sample has an uneven, porous structure with varying depths and irregular features. This type of texture could suggest a high-energy machining process, such as EDM, or an etching process, which leaves the surface with microcraters or pits. Figure 13(b) shows the acquisition of this image at 250× magnification, with an accelerating voltage of 20.00 kV and a WD of 6.9 mm. The surface has a rough and broken texture, similar to the first image, indicating the possible material removal through EDM. Some regions seem denser and darker, suggesting localized differences in the material composition or the presence of residues from the machining process. The EDS spot in Figure 13(c) suggests a potential elemental analysis point. The surface morphology is coarser and has more pronounced, jagged edges, which may indicate the effects of an electrical discharge. The EDS analysis can help identify the elemental composition at that specific point, which is useful for understanding the material changes or contaminant deposition. Figure 13(c) and (d) shows the formation of a small recast layer over the EDM hole area.

Table 11

Atomic weight percentage of the TiB2 material

Element Weight % Atomic % Error % Net int. R A F
B K 43.77 75.91 10.98 137.33 0.8726 0.0646 1.0000
C K 1.78 2.78 21.61 10.20 0.8798 0.0280 1.0000
Si K 0.00 0.00 99.99 0.00 0.9188 0.6212 1.0210
Ti K 54.45 21.32 1.63 10427.95 0.9452 0.9674 1.0144
Figure 12 
                  SEM analysis before machining and EDS analysis of the titanium diboride material.
Figure 12

SEM analysis before machining and EDS analysis of the titanium diboride material.

Figure 13 
                  SEM analysis after EDM machining: (a) copper electrodes, (b) brass electrodes, (c) tungsten–copper electrodes, and (d) tungsten electrodes.
Figure 13

SEM analysis after EDM machining: (a) copper electrodes, (b) brass electrodes, (c) tungsten–copper electrodes, and (d) tungsten electrodes.

7 Conclusion

The goal of this study was to improve the MRR and lower the overcut, SR, and TWR in the EDM process so that the titanium diboride material could be machined. We conducted an experiment on titanium diboride (TiB2) materials using a hole EDM machine. We used four different types of electrode materials and DI water as a dielectric fluid to improve the EDM’s machining properties. T on, I p, and flushing pressure are the main parameters for the MRR of the titanium diboride material in the EDM.

  • The DI water-based dielectric fluid medium produces more MRR compared to the kerosene-based dielectric medium due to its lower viscosity and higher dielectric strength.

  • Copper electrodes produce 24.05% more MRR than brass electrodes and 23.9% more than tungsten–copper electrodes and tungsten electrodes in TiB2 due to their superior thermal and electrical properties.

  • Tungsten has a high melting point (3,422°C) and good wear resistance. It produces 63.5% less TWR than the copper electrodes, 49.83% less than the brass electrodes, and 23.79% less than the tungsten–copper electrodes. This is because tungsten does not lose material easily at high temperatures.

  • Tungsten electrode produces the smoothest surface finishes (lowest SR) across most trials. This is due to their high hardness, high melting point, and low wear rate. It gives a 31.9% smoother surface finish than a copper electrode and 12.16% smoother than a brass electrode.

  • Electrodes made of brass and copper yield a moderate overcut. The copper electrode exhibits the highest increase in overcut, potentially due to its high electrical conductivity, which leads to higher energy input and consequent material removal. Tungsten is durable and resistant to wear but has poorer conductivity compared to copper, leading to less efficient discharge and higher overcut.

  • We use the GRA and regression model to optimize the EDM machining parameters. We compare the results with the conformal test, revealing an error percentage of less than 6% in the experimental results, indicating near-accuracy. Based on the GRA optimization results, it was found that tungsten copper and tungsten electrodes are suitable for high-precision machining and provide reliable performance under various input parameter conditions, while copper and brass electrodes are a cheaper alternative; however, it is subject to restrictions under certain conditions.

  • The SEM analysis of the TiB₂ material provided critical insights into the microstructural changes caused by the EDM process. The high-energy electrical discharges created salient craters and recast layers on the machined surface. The results suggest that the electrode material and dielectric fluid choice had a large impact on the surface morphology and led to changes in SR and thermal damage. They also highlight the significance of optimizing EDM parameters to achieve improved surface quality and fewer flaws in TiB₂ components, which are crucial for high-performance applications.

Acknowledgements

We would like to express our gratitude to the Department of Aeronautical Engineering and Mechanical Engineering at Excel Engineering College for providing all the essential facilities.

  1. Funding information: The authors state no funding involved.

  2. Author contributions: Karthik Shanmugam conducted experiments and authored the manuscript. Sivakumar Annamalai supervised and performed material testing, and Nathiya Thangaraj carried out material characterization.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Ethical approval: The authors confirm that this work does not contain any studies with human participants performed by any of the authors.

  5. Data availability statement: All data generated or analyzed during this study are included in this article.

References

[1] Torres, A., C. J. Luis, and I. Puertas. EDM machinability and surface roughness analysis of TiB2 using copper electrodes. Journal of Alloys and Compounds, Vol. 690, 2017, pp. 337–347.10.1016/j.jallcom.2016.08.110Search in Google Scholar

[2] Torres, A., C. Luis, and I. Puertas. Spacing roughness parameters analysis on the EDM of TiB2. Procedia Manufacturing, Vol. 13, 2017, pp. 579–584.10.1016/j.promfg.2017.09.104Search in Google Scholar

[3] Sivakumar, K., J. Sai Prasanna Kumar, K. Loganathan, V. Mugendiran, T. Maridurai, and K. Suresh. Machining characteristics of silane-treated wheat husk biosilica in deionized water dielectric on EDM drilling of Ti-6Al-4 V alloy. Biomass Conversion and Biorefinery, Vol. 14, No. 1, 2022, Feb 1, pp. 199–206.10.1007/s13399-022-02308-4Search in Google Scholar

[4] Ni, T., Q. Liu, Y. Wang, Z. Chen, and D. Jiang. Research on material removal mechanism of Micro-EDM in deionized water. Coatings, Vol. 11, No. 3, 2021, id. 322.10.3390/coatings11030322Search in Google Scholar

[5] Hema, P., K. Naveena, and Y. Chaitanya. Parametric optimization of process parameters on performance characteristics using die-sinking EDM with deionized water and kerosene as dielectrics. Materials Today: Proceedings, Vol. 62, 2022, pp. 655–664.10.1016/j.matpr.2022.03.629Search in Google Scholar

[6] Baroi, B. K., T. Debnath, Jagadish, and P. K. Patowari. Machinability assessment of titanium grade 2 alloy using deionized water in EDM. Materials Today: Proceedings, Vol. 26, 2020, pp. 2221–2225.10.1016/j.matpr.2020.02.482Search in Google Scholar

[7] Supawi, A., S. Ahmad, N. Talib, L. W. Kiow, H. F. Haw, and M. A. Lajis. Electrode wear rate of RBD palm oil as dielectric fluids on electrical discharge machining (EDM) at different peak current and pulse duration of titanium alloys. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, Vol. 112, No. 2, 2024, pp. 54–63.10.37934/arfmts.112.2.5463Search in Google Scholar

[8] Le, V. T., T. L. Banh, X. T. Tran, and N. Thi Hong Minh. Surface modification process by electrical discharge machining with tungsten carbide powder mixing in kerosene fluid. Applied Mechanics and Materials, Vol. 889, 2019, pp. 115–122.10.4028/www.scientific.net/AMM.889.115Search in Google Scholar

[9] Şimşek, L., C. Çoğun, and Z. Esen. Effects of electrolytic copper and copper alloy electrodes on machining performance in electrical discharge machining (EDM). Machining Science and Technology, Vol. 26, No. 2, 2022, pp. 229–244.10.1080/10910344.2022.2044855Search in Google Scholar

[10] Shanmugam, R., M. Thangaraj, G. Thangamani, and M. Ramoni. Enhancing the performance measures of electrical discharge machining using additive manufactured copper tool electrode on drilling titanium alloy specimens. Volume 4: Advanced materials: Design, processing, characterization and applications; Advances in aerospace technology, ASME, New Orleans, Louisiana, USA, 2023.10.1115/IMECE2023-112922Search in Google Scholar

[11] Maurya, S., V. Dubey, and A. K. Sharma. Optimization of machining parameters in EDM of titanium alloy (Ti6Al4V) using brass electrode. AIP Conference Proceedings, Vol. 2901, No.1, id. 100016.10.1063/5.0178717Search in Google Scholar

[12] Bhaumik, M. and K. Maity. Effect of electrode materials on different EDM aspects of titanium alloy. Silicon, Vol. 11, No. 1, 2018, pp. 187–196.10.1007/s12633-018-9844-xSearch in Google Scholar

[13] He, L., J. Yu, W. Duan, Z. Liu, S. Yin, and H. Luo. Copper–tungsten electrode wear process and carbon layer characterization in electrical discharge machining. The International Journal of Advanced Manufacturing Technology, Vol. 85, 2016, pp. 1759–1768.10.1007/s00170-015-8024-zSearch in Google Scholar

[14] Zainal, N., A. Mohd Zain, S. Sharif, H. Nuzly Abdull Hamed, and S. Mohamad Yusuf. An integrated study of surface roughness in EDM process using regression analysis and GSO algorithm. Journal of Physics: Conference Series, Vol. 892, 2017, id. 012002.10.1088/1742-6596/892/1/012002Search in Google Scholar

[15] Çakiroğlu, R. Analysis of EDM machining parameters for keyway on Ti-6Al-4V alloy and modelling by artificial neural network and regression analysis methods. Sādhanā, Vol. 47, No. 3, 2022, pp. 1–17.10.1007/s12046-022-01926-ySearch in Google Scholar

[16] Sharma, A., V. Kumar, A. Babbar, V. Dhawan, K. Kotecha, and C. Prakash. Experimental investigation and optimization of electric discharge machining process parameters using grey-fuzzy-based hybrid techniques. Materials, Vol. 14, No. 19, 2021, id. 5820.10.3390/ma14195820Search in Google Scholar PubMed PubMed Central

[17] Goodfellow. Titanium diboride (TiB2) - Properties and applications. AzoM, 2023. Retrieved on April 14, 2024 from https://www.azom.com/article.aspx?ArticleID=492.Search in Google Scholar

[18] Chung, D. K., B. H. Kim, and C. N. Chu. Micro electrical discharge milling using deionized water as a dielectric fluid. Journal of Micromechanics and Microengineering, Vol. 17, No. 5, 2007, pp. 867–874.10.1088/0960-1317/17/5/004Search in Google Scholar

[19] Surekha, B., T. Sree Lakshmi, H. Jena, and P. Samal. Response surface modelling and application of fuzzy grey relational analysis to optimise the multi response characteristics of EN-19 machined using powder mixed EDM. Australian Journal of Mechanical Engineering, Vol. 19, No. 1, 2019, pp. 19–29.10.1080/14484846.2018.1564527Search in Google Scholar

[20] Haq, A. N., P. Marimuthu, and R. Jeyapaul. Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method. The International Journal of Advanced Manufacturing Technology, Vol. 37, 2008, pp. 250–255.10.1007/s00170-007-0981-4Search in Google Scholar

[21] Karthik, S. and S. Annamalai. Investigation & analysis of electrical discharge machining on titanium diboride ceramic material with various electrode materials and dielectric fluids. Materials Research Express, Vol. 12, No. 1, 2025, id. 016501.10.1088/2053-1591/ada10eSearch in Google Scholar

[22] Bhaumik, M. and K. Maity. Effect of different tool materials during EDM performance of titanium grade 6 alloy. Engineering Science and Technology, an International Journal, Vol. 21, No. 3, 2018, pp. 507–516.10.1016/j.jestch.2018.04.018Search in Google Scholar

[23] Sawant, S. N., S. K. Patil, D. R. Unune, P. Nazare, and S. Wojciechowski. Effect of copper, tungsten copper and tungsten carbide tools on micro-electric discharge drilling of Ti–6Al–4V alloy. Journal of Materials Research and Technology, Vol. 24, 2023, pp. 4242–4257.10.1016/j.jmrt.2023.04.067Search in Google Scholar

[24] Rajhi, W., S. Ezeddini, S. Alshammrei, and M. Boujelbene. On the identification of the performance characteristics laws in EDM of biomedical Ti-6Al-4 V alloy with different tool electrode materials. Measurement, Vol. 231, 2024, id. 114656.10.1016/j.measurement.2024.114656Search in Google Scholar

[25] Karthik, S., P. Karunakaran, and G. Velmurugan. Experimental investigation of WE43(T6) magnesium metal matrix composites to enhance mechanical properties and EDM process parameters. International Journal of Electrochemical Science, Vol. 19, No. 5, 2024, id. 100553.10.1016/j.ijoes.2024.100553Search in Google Scholar

[26] Hui, Z., Z. Liu, Z. Cao, and M. Qiu. Effect of cryogenic cooling of tool electrode on machining titanium alloy (Ti–6Al–4V) during EDM. Materials and Manufacturing Processes, Vol. 31, No. 4, 2015, pp. 475–482.10.1080/10426914.2015.1037893Search in Google Scholar

[27] Annamalai, S. Influencing parameters of productivity through value stream analysis using optimization techniques. Materials Today: Proceedings, Vol. 33, 2020, pp. 3591–3599.10.1016/j.matpr.2020.05.659Search in Google Scholar

[28] Teimouri, R. and H. Baseri. Study of tool wear and overcut in EDM process with rotary tool and magnetic field. Advances in Tribology, Vol. 2012, 2012, pp. 1–8.10.1155/2012/895918Search in Google Scholar

[29] Moghanizadeh, A. Reducing side overcut in EDM process by changing electrical field between tool and work piece. The International Journal of Advanced Manufacturing Technology, Vol. 90, No. 1–4, 2016, pp. 1035–1042.10.1007/s00170-016-9427-1Search in Google Scholar

Received: 2024-05-06
Revised: 2024-12-15
Accepted: 2025-01-03
Published Online: 2025-02-18

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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