Abstract
Metal matrix composites are being used more and more in engineering mainly because they show superior mechanical properties and a strong strength-to-weight ratio. In this work, AA7050/TiO2/BN hybrid composites were made by ultrasonic-assisted stir casting, then microstructure was checked through Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray Spectroscopy (EDX). For EDM drilling, the trials were arranged with a Taguchi L9 orthogonal array, aiming to see how current (I), pulse-on time (Ton), pulse-off time (Toff), and the hybrid composition affect Material Removal Rate (MRR) and Surface Roughness (SR). The outcomes showed that the composite with 1 wt% TiO2 and 1 wt% BN had a more uniform reinforcement distribution. That same sample also delivered the top hardness value, reaching 79.15 HRB. After that, ANOVA together with regression analysis suggested that current turned out to be the most dominant parameter controlling both MRR and SR, while the regression models produced R2 values of 96.15% and 85.98% respectively. Overall, these results suggest that statistical optimization methods can enhance EDM drilling results effectively, and they give a dependable base for choosing the best machining parameters for AA7050-based hybrid composites.
1 Introduction
The growing use of composite materials in place of traditional alloys can be attributed to their ability to offer a greater variety of properties that can accommodate the changing needs of different industries (Abu-Zurayk et al., 2025; Singh et al., 2025). These composites are categorized according to the type of the matrix used in their manufacture, such as metal, polymer, ceramic, and carbon matrices (Chourasiya and Krishna, 2024). The reinforcements are divided into particulate, fiber, laminar, and flake types, and the manufacturing processes are adjusted accordingly (Fanani et al., 2021; Kotteda et al., 2022). The excellent electrical conductivity, the high-temperature stability, and the superior mechanical behavior of metal matrix composites, or MMCs, makes them very suitable for automotive, defense, aerospace, and structural engineering uses where they help in overcoming limitations that are common with standard alloys (Kumar et al., 2025; Kumar et al., 2024).
There are a wide variety of aluminum-based composites used in MMCs as they possess high specific strength, high corrosion resistance, and superior mechanical properties compared to other alloy composites (Baghel et al., 2023). Specifically, aluminum alloy 7050 exhibits excellent properties such as toughness, low density, and a high strength-to-weight ratio (de Salvo and Afonso, 2020). When reinforced with particulates, it achieves an improved microstructure and enhanced mechanical properties. Various ceramic reinforcements, including SiC (Mohanavel et al., 2020), TiB2 (Azhagan et al., 2022; Raviraj et al., 2014), ZrO2 (Younes et al., 2016), B4C, and Si3N4, are commonly used in metal matrix composites to enhance mechanical performance (Ramasamy and Daniel, 2020; Baghel and Krishna, 2023).
This study employs advanced optimization techniques to improve machining performance, where the Taguchi design of experiments and ANOVA are utilized to identify the significance of input parameters and optimize surface roughness (SR) in milling operations, while response surface methodology (RSM) combined with the complex proportional assessment (COPRAS) approach is applied to achieve multi-criteria optimization of material removal rate (MRR) and tool wear in electrical discharge machining (EDM) processes. These methods provide a systematic and efficient framework for modeling, analysis, and simultaneous optimization of multiple performance characteristics in composite machining (Abbas et al., 2023; Abbas et al., 2025).
Aerospace and structural applications can use hard ceramic particle-reinforced AA7050 aluminium composites, which have excellent mechanical properties and strength to weight ratios. TiO2 and BN particles improve hardness and wear resistance when added to the base alloy. The variation in hardness between the EDM surface and composite wear surface is caused by the thermal effect produced when machining with Electrical Discharge Machining (EDM). The material removal rate (MRR) varies as pulse on time increases but decreases past an optimum value. The two parameters critical to the performance of EDM are peak current and pulse duration. This investigation will study the hardness characteristics of AA7050/TiO2/BN hybrid composites and how the drilling parameters of EDM affect machining.
2 Experimental procedure
2.1 Selection of material
Commercially available AA7050 is used as the matrix material, and TiO2/BN particles having a length of 10–20 µm and a diameter of 30–50 nm are used as reinforcement. Stir casting equipped with an ultrasonic probe stirrer is used for the fabrication of AA7050 composites reinforced with various concentrations of TiO2/BN. The stir casting setup is shown in Figure 1. A mechanical stirrer having a 45° blade angle is coated with graphite to avoid any chemical interaction with the melt. AA7050 pieces are placed in a graphite crucible inside an electrical resistance furnace, and the temperature is increased to melt the alloy completely. At the same time, TiO2/BN and dies are preheated in a furnace at 300 °C for half an hour to improve the wettability between TiO2/BN particles and AA7050 melt during mixing. The muffle furnace is used for pre-heating the die. Coverall flux is added for the removal of dross, and dry N2 is used for purging. Mg (2 wt%) is also added to molten AA7050 to enhance wettability. Then, the molten matrix is mechanically stirred to form a vortex, and preheated TiO2/BN particles are added at the edge of the vortex to ensure uniform distribution. Ultrasonic stirring will then be performed to break apart TiO2/BN particle clusters and better distribute them uniformly throughout the AA7050 matrix. During ultrasonic mixing, the composite melt is subjected to a compression–expansion cycles, resulting in the formation of ultrasonic cavitation. High pressure and temperature are generated after the collapse of the cavitation bubbles, which help break up the TiO2/BN clusters. Hence, ultrasonication helps in the uniform dispersion of TiO2/BN in an AA matrix. Mechanical stirring is again performed for some time, and the melt composite is then poured and solidified in preheated dies.
FIGURE 1
2.2 Setup used for machining of composites
For drilling operations on the EDM drill machine, tubular brass electrodes having a diameter of 3 mm are used as drilling tools. Drilling is performed by varying the current, pulse-on time, pulse-off time, and composition. The specimens are prepared in a cuboidal shape with dimensions 50 mm × 50 mm × 10 mm, as shown in Figure 2. It is observed that a very high value of current damages the machined surface of the composites, which is not desirable. Similarly, a large value of Ton increases the size of craters, which further increases surface roughness. MRR is very less at very low values of Ton and current, which is also not desirable. Hence, the ranges of Ton and current are selected from the values available on the machine.
FIGURE 2
After drilling, the surface roughness of the drilled surfaces has been measured using a Mitutoyo portable Talysurf profilometer (Taylor Hobson, Surtronic 3+). The Talysurf profilometer contains a diamond stylus probe for roughness measurement with a cut-off length of 0.8 mm (Figure 3).
FIGURE 3
The stirring parameters influencing the casting quality are TiO2/BN concentration in terms of fraction weight, stirring speed, stirring temperature, and stirring time. These four parameters have been selected as input variables for optimization, and their levels are shown in Table 1. The machining characteristics of EDM drilling machine measure the MRR and SR.
TABLE 1
| S. No. | Level | ||
|---|---|---|---|
| I | II | III | |
| Current | 4 | 6 | 8 |
| Pulse-on time | 11 | 15 | 20 |
| Pulse-off time | 3 | 10 | 15 |
| Composition | 1 (S1) | 2 (S2) | 3 (S3) |
Input parameters and their levels.
2.3 Design of experiment
A Taguchi L9 array was used for the design of experiments (DOE), which provided the combination of experiments (treatments) to be performed. TiO2/BN/AA7050 composites are fabricated using stir casting for each treatment of the input variables, and the response variables (output) are measured. The values of these response variables are also presented in Table 2. The uniformity of TiO2/BN in the AA7050 matrix is studied using scanning electron microscopy (SEM) images (obtained from a ZEISS microscope operated at 20 kV). Sample S1 represents the as-cast AA7050 alloy. Sample S2 consists of AA7050 reinforced with 1 wt% TiO2 and 1 wt% BN, while Sample S3 contains 2 wt% TiO2 and 1 wt% BN.
TABLE 2
| S. No. | Sample | Hardness (HRB) | Error (%) |
|---|---|---|---|
| 1 | S1 | 76.81 | 3.8405 |
| 2 | S2 | 79.15 | 3.9575 |
| 3 | S3 | 70.18 | 3.509 |
Hardness and error percentage of samples.
3 Results and discussion
3.1 SEM analysis
The pictures shown in Figure 4 depict the AA7050 alloy as a cast (Figure 4a), AA7050 with 1 wt% of TiO2 and 1 wt% of BN as reinforcements (Figure 4b), and AA7050 with 2 wt% of TiO2 and 1 wt% of BN as reinforcements (Figure 4c). Reinforcements were uniformly dispersed in Figure 4b, while particle clustering or agglomeration of the reinforcements can be seen in Figure 4c because of the larger amount of TiO2 being used as a reinforcement.
FIGURE 4
The EDX analysis of sample S2 is presented in Figure 5, which includes three selected regions along with one spot analysis. Among these, two regions (area 1 and area 2) are discussed in detail. The micrograph corresponding to area 1 is shown in Figure 6a, while Figure 6b illustrates the micrograph of area 2. These selected areas were analyzed to evaluate the elemental distribution within the composite.
FIGURE 5
FIGURE 6
3.2 Hardness test
The hardness of the samples was measured at room temperature using the Rockwell hardness test. The test was conducted in accordance with ASTM E92 standards on specimens measuring 10 mm in height and diameter with a spherical geometry. Hardness measurements were performed using a Rockwell hardness testing machine (MMT X3A, Japan) available in the Material Testing Laboratory, Department of Materials and Metallurgical Engineering, MANIT Bhopal. A hardened steel ball indenter of 1/16-inch diameter was used to create indentations on the specimen surface. During testing, an initial minor load (preload) of 100 g was applied for 10 s. For each sample, three readings were recorded at different locations, and the average value was calculated and presented in Table 2. Among the tested samples, S2 reinforced with 1 wt% TiO2 and 1 wt% BN exhibited the highest hardness value of 79.15 HRB. Furthermore, the experimental error was evaluated to ensure the accuracy and repeatability of the results.
3.3 EDM machining
Experiments were conducted for the stir casting process for various treatments of input process parameters as per the L9 array, as explained in Section 2.3. It was observed that the values of truth, indeterminacy, and falsity membership for each treatment combinations were in decreasing order, which indicates that treatment combinations yielded uniform mixing of TiO2/BN in the AA7050 matrix. The optimized response table for the stir casting input parameters affecting MRR and SR is presented in Table 3. The output parameter were influenced by current, Ton, Toff, and composition, as shown in the response table.
TABLE 3
| S. No. | Input variable | MRR (mm3/min) | SR (µm) | |||
|---|---|---|---|---|---|---|
| Current | Pulse-on time | Pulse-off time | Composition | |||
| 1 | 4 | 5 | 3 | S1 | 4.370 | 3.512 |
| 2 | 4 | 8 | 10 | S2 | 4.193 | 3.289 |
| 3 | 4 | 11 | 15 | S3 | 4.312 | 3.601 |
| 4 | 6 | 11 | 10 | S3 | 4.817 | 3.262 |
| 5 | 6 | 8 | 15 | S1 | 5.133 | 3.811 |
| 6 | 6 | 5 | 3 | S2 | 4.577 | 4.762 |
| 7 | 8 | 5 | 15 | S2 | 6.028 | 3.586 |
| 8 | 8 | 8 | 3 | S3 | 5.113 | 4.312 |
| 9 | 8 | 11 | 10 | S1 | 5.252 | 5.251 |
Experimental results for EDM machining on composite samples.
3.4 Regression analysis
3.4.1 ANOVA for MRR
The MRR is often analyzed in relation to peak current (I), pulse-on time (Ton), and pulse-off time (Toff) to understand machining performance in processes such as EDM drilling. Typically, MRR increases with higher current and Ton as greater discharge energy enhances material erosion. Conversely, longer Toff reduces MRR since it decreases the number of discharge pulses per unit time. The ANOVA results indicate that the composition factor is statistically insignificant, as evidenced by its relatively high p-value (0.263), which may be attributed to the use of coded levels (S1 = 1, S2 = 2, and S3 = 3) rather than actual composition values. Furthermore, the model summary demonstrates a strong fit, with an R2 value of 96.15% and an adjusted R2 value of 92.30%, indicating good explanatory capability, while the predicted R2 value of 77.19% suggests acceptable predictive accuracy of the developed model. Regression analysis can be used to establish an empirical relationship between MRR and these parameters, often in the form of a regression equation (Equation 1), where the coefficients are determined through experimental data fitting. Equation 1 is used to calculate the predicted MRR under varying machining conditions and optimize process parameters to improve efficiency. Table 4, 5 represent the analysis of variance for MRR, while Figure 7 shows the residual plots for MRR.
TABLE 4
| Source | DF | Adjusted SS | Adjusted MS | F-value | p-value |
|---|---|---|---|---|---|
| Regression | 3 | 0.130065 | 0.043355 | 38.62 | 0.001 |
| Current | 1 | 0.104926 | 0.104926 | 93.47 | 0.000 |
| Ton | 1 | 0.010857 | 0.010857 | 9.67 | 0.027 |
| Toff | 1 | 0.022658 | 0.022658 | 20.18 | 0.006 |
| Error | 5 | 0.005613 | 0.001123 | | |
| Total | 8 | 0.135678 | | | |
Analysis of variance for transformed response.
TABLE 5
| S | R-sq | R-sq (adj) | R-sq (pred) |
|---|---|---|---|
| 0.0361416 | 96.15% | 92.30% | 77.19% |
Model summary for transformed response.
FIGURE 7
The regression equation is provided as follows:
3.4.2 ANOVA for SR
The SR versus current, Ton, and Toff method is used to analyze SR in machining processes based on the influence of pulse current (I), pulse-on time (Ton), and pulse-off time (Toff). This method involves experimentally determining SR values under different machining conditions and using regression analysis to establish a mathematical relationship between these parameters, as shown in the regression equation (Equation 2). The ANOVA results indicate that the composition factor has an insignificant effect on the response, as reflected by its low F-value (0.73) and high p-value (0.442). The model summary shows an R2 value of 85.98% and an adjusted R2 value of 71.97%, suggesting a reasonably good fit to the experimental data; however, the relatively low predicted R2 value of 31.90% indicates limited predictive capability of the model, implying that further refinement or inclusion of additional significant factors may be required for improved prediction accuracy. By analyzing the regression equation, trends can be observed, such as an increase in SR with higher I and Ton due to increased material removal and heat generation, while Toff may have a varying effect depending on the specific machining setup. This approach to calculate the optimized machining parameters for achieving the desired surface quality. Table 6, 7 present the analysis of variance for SR, and Figure 8 shows the residual plots for SR.
TABLE 6
| Source | DF | Adjusted SS | Adjusted MS | F-value | p-value |
|---|---|---|---|---|---|
| Regression | 3 | 0.176118 | 0.058706 | 32.76 | 0.001 |
| Current | 1 | 0.114145 | 0.114145 | 63.70 | 0.000 |
| Ton | 1 | 0.025064 | 0.025064 | 13.99 | 0.013 |
| Toff | 1 | 0.013423 | 0.013423 | 7.49 | 0.041 |
| Error | 5 | 0.008959 | 0.001792 | | |
| Total | 8 | 0.185078 | | | |
Analysis of variance for transformed response.
TABLE 7
| S | R-sq | R-sq (adj) | R-sq (pred) |
|---|---|---|---|
| 0.0805290 | 85.98% | 71.97% | 31.90% |
Model summary for transformed response.
FIGURE 8
The regression equation is provided as follows:
Taguchi analysis was used to calculate the predicted values for the validation of MRR and SR results with respect to current, Ton, Toff, and composition, as shown in Table 8.
TABLE 8
| Prediction value | Experimental value | Percentage % | |||
|---|---|---|---|---|---|
| MRR | SR | MRR | SR | MRR | SR |
| 4.2710 | 6.0365 | 4.0991 | 6.4494 | 4.19 | 6.40 |
Validation for results.
4 Conclusion
The MRR and SR are influenced by input variables such as current, pulse-on time (T
on), pulse-off time (T
off), and composition, which, in turn, enhance the output parameters. Increasing the weight percentage of reinforcement in the AA7050 matrix improves the distribution and bonding of the reinforcement within the matrix. As a result, the microstructure becomes uniform, leading to improved mechanical strength and better machining performance.
The uniform distribution of tungsten carbide reinforcements was attained in the AA7050 matrix material, which was confirmed by the SEM images of AA7050/TiO2/BN composites.
Machining characteristics of composite MRR and SR will be enhanced due to TiO2/BN as reinforcement.
Higher current and longer Ton lead to increased MRR as they provide more energy for material removal.
Increased current and Ton can degrade surface finish, leading to higher SR values, while optimizing Toff can help improve surface quality.
A high F-value and low p-value confirm the statistical significance of certain factors, allowing for confident process optimization. Using analytical methods to identify which EDM drilling parameters have the greatest impact on MRR and SR enables the optimization of machining capabilities and overall production efficiency.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author.
Author contributions
AC: Resources, Formal analysis, Supervision, Writing – original draft, Validation, Data curation. MC: Writing – review and editing. MB: Writing – review and editing, Data curation. AK: Visualization, Writing – review and editing. BS: Data curation, Writing – review and editing, Conceptualization. LČ: Writing – review and editing, Funding acquisition.
Funding
The author(s) declared that financial support was received for this work and/or its publication. The authors extend their acknowledgement to the financial support of the European Union under the REFRESH–Research Excellence For REgion Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition. This work has also been carried out in connection with the Students Grant Competition projects SP2026/061 “Sustainable Manufacturing Technologies” and SP2026/060 “Research of Innovative Manufacturing Technologies”, financed by the Ministry of Education, Youth and Sports and the Faculty of Mechanical Engineering, VSB–Technical University of Ostrava (VSB-TUO).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The reviewer RCS declared a past collaboration with the author LČ to the handling editor.
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The author(s) declared that generative AI was not used in the creation of this manuscript.
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Summary
Keywords
AA7050, design of experiment, electrical discharge machining drilling, material removal rate, TiO2
Citation
Chourasiya A, Charde MM, Baghel M, Kumar A, Swarna B and Čepová L (2026) Optimization and analysis of EDM drilling parameters on AA7050 using statistical techniques. Front. Mech. Eng. 12:1834462. doi: 10.3389/fmech.2026.1834462
Received
19 March 2026
Revised
24 April 2026
Accepted
30 April 2026
Published
17 June 2026
Volume
12 - 2026
Edited by
Viet Q. Vu, Thai Nguyen University of Technology, Vietnam
Reviewed by
Rakesh Chandmal Sharma, Graphic Era Deemed to be University, India
Seshadhri V., Excel Engineering College, India
Updates
Copyright
© 2026 Chourasiya, Charde, Baghel, Kumar, Swarna and Čepová.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Anil Chourasiya, anilcwarrior@gmail.com
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.