Introduction

In advanced material science, metal matrix composites (MMC) play an important role due to their enhanced mechanical and thermal properties (e.g., high fatigue strength, thermal shock resistance, hardness, etc.), dimensional stability, lightweight construction, corrosion and wear resistance, sustainability, and advanced applications, etc.1,2. Due to these properties, MMC has numerous applications in advanced manufacturing industries. MMC is used in the manufacturing of moving structures in high-speed equipment like wind turbine blades, aerospace, high-performance cars, unmanned aerial vehicles (UAVs), fiberglass tanks, etc.3. When it comes to machining, MMCs can present challenges due to the presence of reinforcing materials, that are typically harder and more abrasive than the metal matrix.

Al-SiC MMC combines the lightweight, processable properties of aluminium with the exceptional mechanical and thermal properties of silicon carbide. Different expedient properties of the aluminium alloy, like recycling possibilities, high corrosion resistance4, and good thermal conductivity5, also have a significant impact. Silicon (Si) plays a critical role in Al-SiC MMCs due to its unique properties and its compatibility with other MMC components. The alloying element silicon has good wetting properties, enhances the tensile strength, hardness, and wear resistance of the MMC, increases oxidation resistance, and also plays an key role in bonding and reinforcement. The addition of SiC particles to the aluminium increases its wear resistance6,7. The upgraded properties of Al/SiC MMC pose additional challenges for production engineers in the economical and efficient machining of these materials and also require greater specification accuracy. Therefore, there is a demand to develop efficient, accurate, and cost-effective machining techniques for processing Al/SiC MMC. Henceforth, there is an urgent need to develop an accurate and efficient machining technique for processing Al-SiC MMC accurately and economically. Manufacture of complex shapes with precise tolerances, higher surface finish, and superi production rates for Al/SiC MMC materials using conventional approaches is even more challenging8,9.

Manufacturing sectors face challenges in producing components due to complex, sophisticated profiles and surface requirements, such as high levels of accuracy, surface finish, and material strength. A special class of processing techniques has been established, namely non-conventional or advanced machining processes, to fulfil such demands. One such advanced machining process is electrical discharge machining (EDM), which melts conductive materials by generating sparks between the tool electrode and the workpiece, without direct contact. EDM can effectively and accurately machine difficult-to-cut materials, fibre-reinforced composites, ceramics, etc., with intricate shapes10,11. EDM is the best machining method for processing such hard and electrically conductive materials8,12, which are widely used to machine MMC13,14,15. In EDM, the copper or graphite electrode progressively creates a cavity that is a mirror image of the tool’s shape. Through the dielectric fluid, the sparks flow in a controlled manner16,17,18.

Devi et al.19 show recent exploration of EDM to machine aluminum-based MMCs. Senthilkumar et al.20 effectively used EDM as a tool to machine MMCs that were difficult to machine. Hourmand et al.21 illustrated the effect of current, voltage, pulse-on-time (TON), and duty factor on material removal rate (MRR), electrode wear ratio (EWR), and microstructure changes during EDM of Al–Mg2Si MMC. Maurya et al.22 analyzed EDM parameters, namely supply voltage, TON, peak current, and duty cycle effect on tool wear rate (TWR), MRR, and surface roughness heights. Bhattacharjee et al.23 used spark-EDM with copper electrodes to machine Al–Cu–SiCp MMC and examined the influence of SiCp particle reinforcement regarding its machinability and forgeability. Extensive parametric readings by Aneesh et al.24 showed the relationship between voltage, discharge current, and spark-on time on MRR, TWR, and crater depth during EDM of the superalloy Haynes-25. Through experimental studies, Palanisamy et al.25 show that discharge current has a noteworthy effect on the surface finish and MMR during EDM of LM6-Alumina stir casted MMC. Sahoo et al.26 attempted to model MRR during EDM and revealed that gap voltage is the most influential parameter for MRR.

Sana et al.27 outlined recent efforts in sustainable EDM, alternative dielectrics, and data-driven optimization, particularly for improving machining of hard Ni-based superalloys. Bhuyun et al.28 discussed hybrid modeling and optimization methods for machining nonconductive materials, highlighting the growing use of TW-ECSM and multi-objective optimization techniques to enhance machining performance. Matharou and Bhuyan29 examined the technical challenges and limitations associated with micro-EDM, emphasizing issues such as electrode wear, system scheduling, and precision requirements that hinder its broader industrial adoption. Researchers studied EDM machining parameters and hybrid composite behaviour, establishing how process variables like current, voltage, and pulse characteristics influence material removal in advanced composite materials30.

The main purpose of this research is to find out the influence of variant electrodes, namely copper and graphite, during the EDM of Al-SiC MMC. Different controlling parameters, e.g., TON, current, and TOFF, were considered to examine MRR and TWR. Experimental results were analyzed to achieve enhanced machining performance during the machining of Al/SiC-MMC. A mathematical model was established, and an ANOVA analysis was also conducted to govern the noteworthy parameters stirring the responses.

The uniqueness of this work lies in its systematic comparison of copper and graphite electrodes for EDM of Al–SiC MMCs using a statistically structured Box–Behnken design. Unlike earlier studies focused mainly on theoretical or isolated analyses, this research integrates experimental design and ANOVA to identify key process parameters and provide practical insights for optimizing machining performance.

Methodology

Machine used for the experiment

To carry out the experiments, die-sink EDM is used Fig. 1. In the die sink EDM process, two metal parts, i.e., work material and tool electrode, are submerged in liquid kerosene (dielectric fluid) and connected with the current supply. When a current of a given intensity is applied, an electric field is produced between the tool and the workpiece. If the gap between the two metal parts is maintained, the electrical field discharges a spark that can jump across it. When a spark strikes, the work material is heated to a high temperature, between 8000 °C and 12,000 °C, and it melts and vaporizes. The detailed specifications of the die-sink EDM are given in Table 1.

Table 1 Specification of die-sink EDM.
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Fig. 1
Fig. 1
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Die-sink EDM machine.

Workpiece and tool material

In the present work, Al-SiC MMC is used as workpiece material, which was manufactured MMC of Al6061 reinforced with 3 weight% SiC particulates by the stir casting method, having a commercial grade: oxidized-SiCp/Al6061. The workpiece used in this study exhibits improved hardness, wear resistance, and tensile strength due to the uniform distribution of hard SiC particulates, which enhances load-bearing capability and restricts matrix deformation. Typical composites of this type report hardness values of 75–90 BHN and tensile strengths of 210–240 MPa, attributed to adequate particle–matrix bonding and a refined microstructure31,32. These superior mechanical and thermal properties make Al-SiC MMC well-suited for machining studies, especially under the high thermal stresses encountered in EDM.

Copper and graphite are chosen as electrodes in this study, which are the most common and readily available electrode materials33. Copper (99% pure), a ductile and malleable metal, has excellent electrical conductivity. It is a good conductor of heat, and, when very pure, a good conductor of electricity34. Graphite, a soft, grayish-black, greasy substance, has a metallic luster and is opaque to light, with a specific gravity of 2.3. It is a very stable allotrope of carbon at very high temperatures, which can be transformed into an artificial diamond35. Graphite is a good conductor of electricity due to the presence of free electrons, which are widely used as EDM electrodes36.

Copper (Cu 99% purity round copper) and graphite (SWISSO 99.99% graphite electrode cylinder) rods are properly machined to 7 mm. diameter and used as electrode materials for machining of Al-SiC MMC shown in Fig. 2. Specimens of Al–SiC MMC were cut into rectangular-shaped blocks of 50 mm × 50 mm × 5 mm as shown in Fig. 3.

Fig. 2
Fig. 2
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Copper (a) and graphite (b) tool electrode; schematic representation of tool electrode (c–d).

Fig. 3
Fig. 3
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Al-SiC MMC after machining (a) using copper tool; (b) using graphite tool.

Selection of process parameters

Investigations were piloted based on response surface methodology (RSM)37,38 with a Box Behnken design considering three controllable parameters. Every parameter was studied at three levels. The selected parameters with respective levels are detailed in Table 2. The selection of process parameters and their ranges was based on their established influence on EDM performance when machining Al-based MMCs39. Preliminary trials in the present work further confirmed that the chosen ranges produced consistent sparking and reliable machining responses, thereby justifying their use. The Box Behnken experimental design was selected for its efficiency in exploring nonlinear interactions among EDM parameters with a reduced number of trials. At the same time, ANOVA was employed for its reliability in quantifying the statistical significance and the contribution of each factor to the machining responses.

Table 2 Selected EDM parameters.
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Experimental readings

Experimental values obtained using the copper tool

A set of experiments is conducted by EDM machining Al–SiC MMC using copper tool electrode material as per the design matrix. Experiments readings are given in Table 3.

Table 3 Experimental results using the copper tool.
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Experimental values obtained using the graphite tool

A set of experiments is conducted by EDM machining Al–SiC MMC using copper tool electrode material as per the design matrix. Experiments readings are given in Table 4.

Table 4 Experimental results using the graphite tool.
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Results and discussion

Statistical and regression analysis are carried out in this section to signify the connection between the independent variables and responses.

Estimated regression coefficients for MRR and TWR using copper tool electrode

A combination of control parameters’ significance, in the form of regression coefficients and analysis of variance, on the variables was investigated. The obtained experimental values were fed to the statistical software Minitab, and the response surface design was analyzed. From the experimental data set, a quadratic regression model for MRR and TWR was developed for copper as well as graphite electrodes. At a confidence level of 95%, all the experiments were piloted to get the relevant output.

It was observed that the I, TON, and \(\:{{\:T}_{ON}}^{2}\) significantly affect the MRR when machined using copper electrodes. The ‘p’ values were less than 0.05 for these control variables. The estimated regression coefficients are tabulated in Table 5. Further, from the ANOVA data (Table 6), it may also be seen that significant entities are the linear and square terms, whereas the lack of fit is non-significant.

Table 5 Estimated regression coefficients for MRR copper tool.
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Table 6 ANOVA results for MRR using the copper tool.
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The second-order regression model for response MRR for the copper electrode developed is shown in Eq. (1).

$${\text{MRR}}_{{{\text{Copper}}}} = {\text{ }}0.{\text{129}}000\, + \,0.0{\text{7}}0{\text{638}}I\, + \,0.00{\text{625}}0T_{{ON}} {-}{\text{ }}0.0{\text{147}}00T_{{ON}} \times T_{{ON}}$$
(1)

While analyzing TWR for copper electrodes, it was found that I, TOFF, and\(\:{{\:T}_{ON}}^{2},{{\:T}_{OFF}}^{2}\) significantly affect the TW R. The ‘p’ values were less than 0.05 for these control variables. The estimated regression coefficients are tabulated in Table 7. Furthermore, from the ANOVA data (Table 8), it can also be seen that the linear and square terms are significant, whereas the lack of fit is non-significant.

Table 7 Estimated regression coefficients for TWR using a copper tool.
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Table 8 ANOVA results for TWR using a copper tool.
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The second-order regression model, for response TWR for the copper electrode developed, is shown in Eq. (2).

$${\text{TWR}}_{{{\text{Copper}}}} = {\text{ }}0.00{\text{249}}0\, + \,0.0{\text{11931}}I{-}{\text{ }}0.000{\text{35}}0T_{{OFF}} {-}{\text{ }}0.000{\text{316}}T_{{ON}} \times T_{{ON}} {-}{\text{ }}0.000{\text{246}}T_{{OFF}} \times T_{{OFF}}$$
(2)

While machining using graphite electrodes, it was observed that I, TON, and \(\:{{\:T}_{ON}}^{2}\)significantly affect the MRR. The ‘p’ values were less than 0.05 for these control variables. The estimated regression coefficients are tabulated in Table 9. ANOVA data (Table 10) also shows that the linear and square terms are significant, whereas the lack of fit is non-significant.

Table 9 Estimated regression coefficients for MRR using a graphite tool.
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Table 10 ANOVA results for MRR using a graphite tool.
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The second-order regression model, for response MRR for graphite electrode developed is shown in Eq. (3).

$${\text{MRR}}_{{{\text{Graphite}}}} = {\text{ }}0.{\text{1135}}00\, + \,0.0{\text{5215}}0I\, + \,0.00{\text{4375}}T_{{ON}} {-}{\text{ }}0.0{\text{13162}}T_{{ON}} \times T_{{ON}}$$
(3)

In the case of TWR, while machining using graphite electrodes, it was observed that the control variables I, TOFF, their squares I2, and their interaction TON×TOFF significantly affect the TWR. The ‘p’ values were less than 0.05 for these control variables. The estimated regression coefficients are tabulated in Table 11. ANOVA data (Table 12) also indicate that the linear, square, and interaction terms are significant, whereas the lack of fit is insignificant.

Table 11 Estimated regression coefficients for TWR using a graphite tool.
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Table 12 ANOVA results for TWR using a graphite tool.
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The second-order regression model for response MRR for the graphite electrode developed is shown in Eq. (4).

$${\text{TWR}}_{{{\text{Graphite}}}} = {\text{ }}0.0{\text{1265}}0\, + \,0.0{\text{11931}}I{-}{\text{ }}0.00{\text{2231}}T_{{OFF}} \, + \,0.00{\text{2531}}I \times I\, + \,0.00{\text{1975}}T_{{ON}} \times T_{{OFF}}$$
(4)

Effect of EDM parameters on responses when copper tool electrode is used

The effects of current on MRR and TWR when a copper electrode is used on Al-SiC MMC are shown in Figs. 4 and 5, respectively. It can be detected that the MRR and TWR increase linearly as the current value increases. The increase in the current increases the MRR and TWR, which may be because of the augmented intensity of spark discharge to facilitate more energy density to cause the action of melting and vaporization. MRR is also dependent on the tool electrode material, work material, and dielectric flushing. Between the workpiece and electrode, more electrical discharges are produced due to high current. These phenomena accelerate electrode wear, as shown in Fig. 5.

Fig. 4
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Effect of I on MRR using copper electrode.

Fig. 5
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Effect of I on TWR using the copper electrode.

Figure 6 gives the main effect of TON on MRR. As indicated, the initial level and high MRR at a high level of TON were noticed. For an intermediate range of pulse duration, the MRR value was seen to be maximum. During longer TON, more energy is transferred during each discharge cycle, causing more MRR40. The small pulse causes a lesser amount of vaporization, whereas the extended pulse duration causes the plasma channel to enlarge, which causes less energy density on the workpiece41,42.

Fig. 6
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Effect of TON on MRR using copper electrode.

Figure 7 displays the main effect of TOFF on TWR, which shows that TWR gradually decreases with an increase in TOFF. The time duration between consecutive electrical discharges increases with an increase in TOFF, which reduces thermal energy accumulation and, in turn, reduces TWR. Increased TOFF increases electrode stability by reducing mechanical stresses, resulting in less TWR43,44.

Fig. 7
Fig. 7
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Effect of TOFF on TWR using copper electrode.

Effect of EDM parameters on responses when graphite tool electrode is used

The consequence of the current on MRR and TWR when a graphite electrode is used on Al/SiC MMC is displayed in Figs. 8 and 9, respectively. It was observed that the MRR and TWR increase linearly as the current value increases. Increased current causes more frequent and intense electrical discharges as well as an increased level of machining zone heat generation, which leads to more material removal from Al/SiC MMC. On the other hand, graphite electrodes are inclined to wear due to excessive heat generation, and with an increase in current, this wear only intensifies45.

Fig. 8
Fig. 8
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Effect of I on MRR using graphite electrode.

Fig. 9
Fig. 9
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Main effect of I on TWR using graphite electrode.

The main effect plot of TON on MRR is shown in Fig. 10. MRR initially increases with increasing levels of TON, but beyond an optimal value, no noticeable enhancement in MRR is observed. However, due to the lower thermal conductivity and wear resistance of graphite electrodes compared to copper, less MRR is experienced46.

Fig. 10
Fig. 10
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Main effect of TON on MRR using graphite electrode.

Figure 11 displays the main effect of TOFF on TWR, indicating that increasing the TOFF range decreases the TWR. With an increase in TOFF, the graphite electrode cools between discharge cycles, diminishing thermal effects and thus reducing TWR.

Fig. 11
Fig. 11
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Effect of TOFF on TWR using graphite electrode.

Copper having higher thermal conductivity [approximately 401 W/(m K) at 20 °C or 68 °F] compared to graphite [5 W/(m K) to 200 W/(m K)]47,48. Due to this, during the EDM process, copper electrodes dissipate heat more effectively, which reduces the risk of thermal damage to the workpiece and thus improves machining efficiency by 43%. On the other hand, high localized temperatures are produced during machining using graphite due to its curtailed thermal conductivity. This results in thermal damage and curtails machining efficiency. This becomes more pronounced when machining Al-SiC MMC, which is sensitive to heat. Compared to graphite electrodes, copper electrodes may exhibit steadier machining dynamics. Copper and graphite are known for their exceptional electrical conductivity, their ability to withstand high currents, and their durability, which enable an efficient EDM process.

Microstructural study of workpiece and tool electrode

The Scanning Electron Microscope (SEM) micrograph (Fig. 12a) of the Al–SiC MMC before machining shows a well-defined biphasic structure, with angular SiC particles uniformly embedded in the Al matrix. The interfaces appear clean and well-bonded, with no evidence of particle debonding or interfacial porosity. Such uniform reinforcement distribution is characteristic of properly fabricated stir-cast MMCs and is consistent with observations reported in earlier studies on Al-SiC composites49. The accompanying EDX spectrum confirms the expected elemental composition, with dominant peaks for Al and Si corresponding to the matrix and the SiC reinforcement.

Post-machining, the surface morphology (Fig. 12b) undergoes a significant transformation due to the high-temperature electrical discharges. The SEM image reveals a recast layer characterized by melted and resolidified material, shallow micro-craters, and fragmented surface features. The presence of a carbon-rich layer, as indicated by the EDX spectrum, is attributed to dielectric decomposition and carbon deposition- a commonly reported phenomenon in EDM of aluminium-based MMCs50,51.

Fig. 12
Fig. 12
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Microstructural image of Al-SiC MMC: (a) Before machining; (b) After machining using EDM.

Figure 13 shows SEM images of both electrodes, i.e., the copper electrode [Fig. 13(a-b)] and graphite electrode [Fig, 13(c-d)] before and after machining using EDM. Post-EDM, the electrode surfaces show clear evidence of thermal erosion, with wider porosity, and overlapping recast layers are observed in the graphite electrode compared to the copper electrode.

Fig. 13
Fig. 13
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Microstructural images of: (a) Copper electrode before machining; (b) Copper electrode after machining using EDM; (c) Graphite electrode before machining; (d) Graphite electrode after machining using EDM;

Comparison of the present research with the state of the Art

A comparative summary of recent work with the state of the art is provided in Table 13. This table outlines the materials, electrode types, process parameters, and significant findings reported in previous investigations, and identifies the corresponding research gaps. Table 13 effectively highlights how the current study extends prior research and contributes new insights to the field.

Table 13 Comparison of the present research with the state of the art.
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Conclusions

Al-SiC MMC were machined using EDM, varying copper and graphite electrodes, and based on experimental results and analysis, the following points can be concluded:

  • The effect of TON and I is more significant for both MRR and TWR when using copper and graphite electrodes in EDM.

  • The high range of pulse-off time offers low TWR.

  • Higher MRR can be achieved by using a perfect combination of TON and I, as well as TOFF.

  • It was noticed that copper electrode gives more MRR and less TWR than graphite electrodes.

  • The copper tool is more efficient than the graphite tool for the machining of Al-SiC MMC.

  • Copper tool electrodes give the highest MRR when the intensity of current is 9 A, 30µs TON, and 6µs TOFF. Graphite tool electrodes give the highest MRR at 9 A, 30 µs TON, and 6 µs TOFF.

  • This study helps readers understand how key EDM parameters affect the machining of Al–SiC MMCs and identifies copper as the superior electrode for higher efficiency and lower wear. The results provide practical guidance for optimizing EDM processes and a valuable reference for future research on composite machining.

Limitations and future work

As a future scope, the influence of other controllable parameters, such as sensitivity, control gap, and flushing pressure, can be studied. Different shapes of the tool electrodes, such as round, triangular, and square, can also be studied.