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.

A hole-drilling EDM machine.
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 |

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.
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 3–6 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].
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% |
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% |
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% |
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
4.2 TWR
4.3 Surface roughness (SR)
4.4 Overcut (OC)
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 7–10 display the gray rational optimization results, which are ranked for various electrode materials.
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 |
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 |
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 |
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.

MRRs for different electrode materials and dielectric fluids.

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.

TWRs for different electrode materials and dielectric fluids.

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).

SR for different electrode materials and dielectric fluids.

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.

Overcut for different electrode materials and dielectric fluids.

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.

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.
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 |

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

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.
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Funding information: The authors state no funding involved.
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Author contributions: Karthik Shanmugam conducted experiments and authored the manuscript. Sivakumar Annamalai supervised and performed material testing, and Nathiya Thangaraj carried out material characterization.
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Conflict of interest: The authors state no conflict of interest.
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Ethical approval: The authors confirm that this work does not contain any studies with human participants performed by any of the authors.
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Data availability statement: All data generated or analyzed during this study are included in this article.
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Articles in the same Issue
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Articles in the same Issue
- Research Articles
- Endpoint carbon content and temperature prediction model in BOF steelmaking based on posterior probability and intra-cluster feature weight online dynamic feature selection
- Thermal conductivity of lunar regolith simulant using a thermal microscope
- Multiobjective optimization of EDM machining parameters of TIB2 ceramic materials using regression and gray relational analysis
- Research on the magnesium reduction process by integrated calcination in vacuum
- Microstructure stability and softening resistance of a novel Cr-Mo-V hot work die steel
- Effect of bonding temperature on tensile behaviors and toughening mechanism of W/(Ti/Ta/Ti) multilayer composites
- Exploring the selective enrichment of vanadium–titanium magnetite concentrate through metallization reduction roasting under the action of additives
- Effect of solid solution rare earth (La, Ce, Y) on the mechanical properties of α-Fe
- Impact of variable thermal conductivity on couple-stress Casson fluid flow through a microchannel with catalytic cubic reactions
- Effects of hydrothermal carbonization process parameters on phase composition and the microstructure of corn stalk hydrochars
- Wide temperature range protection performance of Zr–Ta–B–Si–C ceramic coating under cyclic oxidation and ablation environments
- Influence of laser power on mechanical and microstructural behavior of Nd: YAG laser welding of Incoloy alloy 800
- Aspects of thermal radiation for the second law analysis of magnetized Darcy–Forchheimer movement of Maxwell nanomaterials with Arrhenius energy effects
- Use of artificial neural network for optimization of irreversibility analysis in radiative Cross nanofluid flow past an inclined surface with convective boundary conditions
- The interface structure and mechanical properties of Ti/Al dissimilar metals friction stir lap welding
- Significance of micropores for the removal of hydrogen sulfide from oxygen-free gas streams by activated carbon
- Experimental and mechanistic studies of gradient pore polymer electrolyte fuel cells
- Microstructure and high-temperature oxidation behaviour of AISI 304L stainless steel welds produced by gas tungsten arc welding using the Ar–N2–H2 shielding gas
- Mathematical investigation of Fe3O4–Cu/blood hybrid nanofluid flow in stenotic arteries with magnetic and thermal interactions: Duality and stability analysis
- Topical Issue on Conference on Materials, Manufacturing Processes and Devices - Part II
- Effects of heat treatment on microstructure and properties of CrVNiAlCu high-entropy alloy
- Enhanced bioactivity and degradation behavior of zinc via micro-arc anodization for biomedical applications
- Study on the parameters optimization and the microstructure of spot welding joints of 304 stainless steel
- Research on rotating magnetic field–assisted HRFSW 6061-T6 thin plate
- Special Issue on A Deep Dive into Machining and Welding Advancements - Part II
- Microwave hybrid process-based fabrication of super duplex stainless steel joints using nickel and stainless steel filler materials
- Special Issue on Polymer and Composite Materials and Graphene and Novel Nanomaterials - Part II
- Low-temperature corrosion performance of laser cladded WB-Co coatings in acidic environment
- Special Issue for the conference AMEM2025
- Effect of thermal effect on lattice transformation and physical properties of white marble