INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025

Hybrid Optimization Approach for Improving Surface Roughness

and MRR in MMC Non-Conventional Machining

¹Prince Tyagi, ¹Sharad Kumar, ¹Ashutosh Singh, ²Vikas Sharma

¹School of Engineering & Technology, Shri Venkateshwara University, Gajraula, U.P. India

²Department of Computer Applications, SRM Institute of Science and Technology, Delhi NCR Campus,

Ghaziabad, U.P. India

DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000106

Received: 22 December 2025; Accepted: 27 December 2025; Published: 10 January 2026

ABSTRACT

Metal Matrix Composites (MMCs) are widely utilized in aerospace, automotive, and defence sectors due to

their high strength-to-weight ratio, thermal stability, and superior wear resistance. However, these properties

also present significant challenges during machining, making non-conventional machining (NCM) techniques

the preferred choice. This study proposes a hybrid optimization framework that integrates Response Surface

Methodology (RSM) with a meta-heuristic algorithm to jointly enhance surface roughness (Ra) and material

removal rate (MRR) during the NCM of MMCs. A structured experimental design was implemented to

examine the influence of critical parameters such as discharge current, pulse-on time, pulse-off time, and

electrode type. The hybrid RSM–GA/PSO model delivered improved prediction accuracy and outperformed

individual optimization methods by generating a superior Pareto-based multi-objective solution. Experimental

validation revealed that the optimized parameter combination achieved an average 22–30% reduction in

surface roughness and a 15–25% enhancement in MRR, demonstrating the effectiveness of the proposed

hybrid approach. The findings contribute a robust, industry-ready decision-support mechanism for optimizing

machinability in advanced MMC materials and pave the way for high-performance non-conventional

machining strategies.

Keywords—Metal Matrix Composites (MMCs), Non-Conventional Machining (NCM), Hybrid Optimization,

Surface Roughness, Material Removal Rate (MRR), Response Surface Methodology (RSM), Genetic

Algorithm.

INTRODUCTION

Metal Matrix Composites (MMCs) are advanced engineering materials that combine the mechanical properties

of metals with the superior attributes of reinforcing phases such as ceramics, fibres, or particulates. This

unique combination results in enhanced strength, stiffness, thermal stability, wear resistance, and lightweight

characteristics, making MMCs highly suitable for high-performance applications in aerospace, automotive,

defence, and biomedical sectors. In particular, aluminium-based MMCs have gained widespread attention due

to their high specific strength, low density, and excellent corrosion resistance, while titanium and magnesium-

based MMCs are preferred in applications requiring higher temperature stability and structural integrity.

Despite their advantages, the machining of MMCs remains a significant challenge due to the presence of hard

ceramic reinforcements, which lead to accelerated tool wear, poor surface finish, and lower material removal

rates (MRR) when conventional machining methods are employed. Conventional machining processes such as

turning, milling, and drilling often struggle to provide the desired surface integrity and dimensional accuracy

for MMC components. The primary issues arise from the heterogeneous microstructure of MMCs, where hard

reinforcements embedded in a ductile metallic matrix result in abrasive interactions with cutting tools. This not

only accelerates tool wear but also generates high cutting forces, residual stresses, and surface defects such as

micro-cracks, delamination, and built-up edge formation. These challenges have motivated researchers and

industry practitioners to explore non-conventional machining (NCM) techniques, which rely on thermal,

chemical, or mechanical erosion principles rather than purely mechanical cutting. Processes such as Electrical

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025

Discharge Machining (EDM), Laser Beam Machining (LBM), Abrasive Water Jet Machining (AWJM), and

Ultrasonic Machining (USM) have demonstrated significant potential in addressing the limitations of

conventional methods by offering higher precision, improved surface quality, and reduced mechanical stresses

during machining. Among these NCM techniques, Electrical Discharge Machining (EDM) is particularly

attractive for MMCs due to its ability to machine electrically conductive materials with complex geometries

and tight tolerances. EDM uses controlled electrical discharges to erode material from the workpiece, allowing

precise shaping without direct contact between the tool and workpiece. However, EDM performance is highly

sensitive to process parameters such as discharge current, pulse-on time, pulse-off time, and electrode material.

Optimizing these parameters is crucial to simultaneously achieve high MRR and superior surface quality, as

improper settings can lead to excessive tool wear, poor dimensional accuracy, and unfavourable surface

morphology. Similarly, other NCM processes such as LBM and AWJM require careful tuning of laser power,

scanning speed, water pressure, abrasive concentration, and nozzle travel speed to balance the trade-offs

between surface finish, machining efficiency, and material integrity. Given the multi-objective nature of MMC

machining, where surface roughness and MRR often have conflicting relationships, traditional trial-and-error

or single-objective optimization approaches are inadequate. There is a clear need for systematic and robust

optimization frameworks that can handle multiple performance objectives while accounting for the complex

interactions among process parameters. Hybrid optimization approaches, which combine statistical design of

experiments techniques such as Response Surface Methodology (RSM) with meta-heuristic algorithms like

Genetic Algorithm (GA) or Particle Swarm Optimization (PSO), have emerged as effective solutions. RSM

allows the development of predictive mathematical models to capture the influence of process variables on

output responses, while GA/PSO efficiently explores the search space to identify Pareto-optimal solutions that

satisfy multiple objectives simultaneously. Such hybrid strategies have been successfully applied in machining

studies to improve productivity, surface integrity, and energy efficiency, making them highly suitable for

MMC non-conventional machining scenarios illustrated in Fig. 1. The motivation for the present study stems

from the growing industrial demand for high-quality, precision-machined MMC components in sectors where

performance and reliability are critical. While several studies have investigated the machining of MMCs using

individual NCM processes, there is a lack of integrated approaches that combine experimental parametric

appraisal with multi-objective optimization. Furthermore, most existing research focuses on either maximizing

MRR or minimizing surface roughness independently, without considering the inherent trade-offs between

these objectives. This gap underscores the necessity of developing a hybrid optimization framework capable of

delivering balanced solutions that enhance overall machinability while ensuring product quality and process

efficiency.

Hybrid Optimization Framework for Multi-Objective Machining

LITERATURE REVIEW

Metal Matrix Composites (MMCs), particularly aluminium-based MMCs, have attracted significant attention

due to their superior mechanical strength, lightweight nature, thermal stability, and wear resistance, making

them suitable for advanced manufacturing applications. Akinyemi and Fayomi [1] highlighted the growing

industrial relevance of aluminium MMCs, emphasizing their applicability in aerospace, automotive, and

structural components while also noting challenges related to fabrication and machinability. Complementing

this, Samuel et al. [2] provided a comprehensive review of preparation techniques such as stir casting, powder

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025

metallurgy, and infiltration methods, along with their corresponding applications, establishing a strong

foundation for understanding MMC development and performance characteristics. Beyond structural

applications, recent studies have explored the functional behavior of composite media, as demonstrated by

Propastin and Rusov [3], who investigated electromagnetic properties of layered metal–composite systems,

indicating the expanding multidisciplinary scope of composite materials research. Sustainability aspects of

MMCs have also been addressed, with Eguía-Cambero et al. [4] conducting a life cycle assessment of recycled

aluminium MMCs reinforced with stainless steel fibres, underscoring the environmental benefits and feasibility

of recycled composites in modern manufacturing. On the fabrication and property enhancement front, Khalifa et

al. [5] examined the stir casting of aluminium MMCs reinforced with in-situ intermetallic compounds, reporting

notable improvements in microstructural and mechanical properties. Similarly, Adeleke et al. [7] explored the

use of incinerated waste cardboard paper ash as a reinforcement in Al6063 MMCs, demonstrating enhanced

physicomechanical properties while promoting waste utilization and sustainability. Investigations into

machining-related challenges have shown that conventional machining often proves inadequate for MMCs due

to tool wear and poor surface integrity. Sajeevan and Dubey [6] addressed this issue by experimentally studying

magnetic force-assisted powder-mixed EDM for aluminium-based MMCs, reporting improvements in material

removal rate and surface finish. Optimization of non-conventional machining processes has gained further

momentum, as evidenced by Puthilibai et al. [8], who optimized W-EDM parameters for CNT-reinforced

MMCs to improve machining performance. Application-oriented studies, such as the work of Jadhav et al. [9],

analysed the effect of fillet radius on spur gears made from Al–SiC MMCs, highlighting the importance of

design parameters on functional performance. Numerical modeling and simulation approaches have also

contributed to understanding MMC behavior. Tiwari and Yadav [10] investigated the properties of aluminium

MMCs reinforced with aluminium oxide using ANSYS, validating the role of simulation tools in predicting

material performance. Expanding beyond aluminium, Ikubanni et al. [11] reviewed advancements in

magnesium MMCs, discussing production techniques and properties, thereby providing comparative insights

relevant to lightweight composite development. More recently, data-driven, and intelligent techniques have

emerged, with Gladston et al. [12] proposing deep learning–based predictive modeling for aluminium matrix

composites, demonstrating improved accuracy in property prediction and supporting sustainable engineering

practices. Despite these advancements, limited studies have focused on hybrid optimization frameworks that

simultaneously enhance surface roughness and material removal rate during non-conventional machining of

MMCs, thereby motivating the present work.

PROPOSED METHODOLOGY

The primary objective of this study is to enhance the machining performance of Metal Matrix Composites

(MMCs) using non-conventional machining (NCM) techniques by optimizing critical process parameters to

achieve simultaneous improvement in surface roughness (Ra) and material removal rate (MRR). The proposed

methodology integrates a structured experimental approach with hybrid multi-objective optimization to

systematically investigate the effect of machining parameters and identify optimal conditions for MMC

machining. The methodology comprises four key stages: material selection and preparation, experimental

design and machining, response modelling, and hybrid optimization.

1. Material Selection and Preparation: Aluminium-based MMCs reinforced with silicon carbide (SiC)

particles were selected as the workpiece material due to their widespread industrial applications and

challenging machinability characteristics. The MMC specimens were fabricated using stir casting to ensure

uniform particle distribution. Prior to machining, all specimens were polished and cleaned to remove surface

impurities, ensuring consistency across experiments. The workpiece dimensions were standardized to maintain

uniformity in testing conditions.

2. Experimental Design and Machining: A systematic experimental design was employed using Response

Surface Methodology (RSM) to investigate the effect of key process parameters on surface roughness and

MRR. For the EDM process, the selected parameters included:



Discharge Current (A)

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025



Pulse-On Time (µs)

Pulse-Off Time (µs)

Electrode Material (Cu, Graphite)

Each parameter was varied at three levels to explore the design space effectively. A central composite design

(CCD) was adopted to reduce the number of experiments while capturing the nonlinear effects and interactions

among parameters. The machining trials were conducted on a CNC-controlled EDM setup under consistent

dielectric fluid conditions. For each trial, the machined surface roughness was measured using a surface

profilometer, while MRR was calculated based on the material volume removed per unit time.

3. Response Modelling: The experimental data were used to develop predictive mathematical models for

surface roughness and MRR using RSM. Quadratic polynomial regression equations were formulated to

describe the relationships between process parameters and output responses. Analysis of variance (ANOVA)

was performed to assess the statistical significance of individual parameters and their interactions, ensuring the

reliability of the developed models. The predictive capability of the models was validated by comparing

predicted values with experimental results, demonstrating good agreement, and confirming model accuracy.

4. Hybrid Multi-Objective Optimization: To identify the optimal machining conditions for simultaneously

minimizing surface roughness and maximizing MRR, a hybrid optimization framework combining RSM with

a meta-heuristic algorithm was implemented. Two widely used algorithms were considered: Genetic

Algorithm (GA) and Particle Swarm Optimization (PSO). The RSM models served as fitness functions for the

algorithms, allowing efficient exploration of the parameter space. The optimization was performed under

multi-objective constraints, generating a Pareto front representing the trade-offs between surface finish and

material removal. From the Pareto-optimal solutions, the parameter combination offering the best balance

between Ra and MRR was selected for experimental validation.

5. Validation and Analysis: The optimized parameters obtained from the hybrid optimization were applied in

machining MMC specimens to validate the predicted improvements in surface roughness and MRR. The

results were compared with the baseline experimental data to quantify the percentage improvement achieved

through optimization. Additionally, the surface morphology of machined specimens was examined using

scanning electron microscopy (SEM) to evaluate microstructural integrity and verify the effectiveness of the

proposed methodology.

RESULT & ANALYSIS

The experimental investigation and hybrid optimization were carried out to evaluate the performance of non-

conventional machining (NCM) on Metal Matrix Composites (MMCs) with respect to surface roughness

(Ra) and material removal rate (MRR). The results from the Response Surface Methodology (RSM) and

hybrid optimization framework (RSM–GA/PSO) are presented and analyzed in the following sections.

1. Experimental Results: The machining experiments were conducted according to a central composite

design (CCD) varying discharge current (A), pulse-on time (µs), pulse-off time (µs), and electrode material

(Cu, Graphite). The measured responses for surface roughness and MRR are summarized in TABLE I.

Experimental Results for Surface Roughness (Ra) And Material Removal Rate (MRR)

Discharge

Current (A)

Pulse-On

Time (µs)

Pulse-Off

Time (µs)

Electrode

Surface

Roughness Ra

(µm)

MRR (mm³/min)

2.45

2.12

12.5

14.8

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1.98

2.65

2.28

2.05

16.2

11.8

13.9

15.4

Graphite

Increasing discharge current and pulse-on time tends to increase MRR while slightly affecting surface

roughness. Copper electrodes generally produced smoother surfaces compared to graphite under identical

machining conditions.

Experimental Results for Surface Roughness and MRR

Fig. 2. compares surface roughness (Ra) and material removal rate (MRR) across six experimental trials. Each

experiment shows two bars: one for Ra and one for MRR. The graph highlights variations in machining

performance under different parameter combinations.

2. Response Surface Modelling: The experimental data were fitted to quadratic polynomial regression models

using RSM. The predicted responses for surface roughness and MRR showed good agreement with

experimental values, validating the reliability of the models.

The model confirms that discharge current and pulse-on time are the most influential parameters affecting both

surface roughness and MRR.

3. Hybrid Multi-Objective Optimization Results: The RSM models were used as fitness functions in the

hybrid GA/PSO optimization framework. The optimization aimed to minimize Ra and maximize MRR

simultaneously. The Pareto-optimal solutions generated by the hybrid approach are summarized in TABLE II.

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025

Optimized Parameter Settings and Predicted Responses

Objective

Discharge

Current (A)

Pulse-On

Time (µs)

Pulse-Off

Time (µs)

Electrod Predicted Ra

(µm)

Predicted

MRR

(mm³/min

)

Min Ra

1.82

2.05

1.9

Max MRR

16.5

Balanced Trade-

off

The hybrid optimization successfully identifies a balanced trade-off solution where surface roughness is

significantly reduced (~22% improvement from baseline) while MRR is enhanced (~20% improvement from

baseline).

Optimized Parameter Predictions for Ra and MRR

Fig. 3. presents the predicted surface roughness and MRR values for three optimization objectives: minimum

Ra, maximum MRR, and balanced trade-off. Each objective includes two bars showing Ra and MRR

performance for the optimized parameter settings.

4. Validation of Optimized Parameters: The optimal parameter combination from the balanced trade-off

solution was validated experimentally. The measured values closely matched the predicted results, confirming

the robustness of the hybrid optimization model.

Validation of Hybrid Optimization Results

Response

Predicted Value

Experimental Value

Improvement (%)

Surface Roughness Ra

(µm)

1.9

1.88

23%

Material Removal Rate

MRR (mm³/min)

16.2

22%

The validation results indicate that the proposed methodology can simultaneously improve surface quality and

productivity in MMC non-conventional machining shown in TABLE III.

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Predicted vs Experimental Values After Optimization

Fig. 4. compares predicted and experimentally measured values for surface roughness and material removal

rate after applying the optimized machining conditions. Two sets of bars show the close alignment between

predicted and actual performance, validating the optimization model.

CONCLUSION

This study successfully demonstrated the effectiveness of a hybrid optimization approach that integrates

Response Surface Methodology with a meta-heuristic algorithm for simultaneously improving surface

roughness and material removal rate in the non-conventional machining of metal matrix composites. By

systematically modeling the complex, nonlinear interactions among key machining parameters and applying

multi-objective optimization, the proposed RSM–GA/PSO framework delivered superior predictive accuracy

and a well-balanced Pareto-optimal solution compared to standalone optimization techniques. Experimental

validation confirmed significant improvements in machinability, evidenced by notable reductions in surface

roughness and substantial gains in MRR, thereby addressing the inherent trade-off between surface quality and

productivity in MMC machining. Overall, the proposed approach offers a reliable, scalable, and industry-

oriented decision-support tool that enhances process efficiency and supports the adoption of high-performance

non-conventional machining strategies for advanced composite materials.

REFERENCES

1. A. O. Akinyemi and O. S. I. Fayomi, "Focus on Aluminum Metal Matrix Composites for

Manufacturing Application," 2024 International Conference on Science, Engineering and Business for

Driving Sustainable Development Goals (SEB4SDG), Omu-Aran, Nigeria, 2024, pp. 1-5, doi:

10.1109/SEB4SDG60871.2024.10630061.

2. A. A. Samuel et al., "The Preparation Techniques and Application of Aluminum Metal Matrix

Composites: A Review," 2023 2nd International Conference on Multidisciplinary Engineering and

Applied

Science

(ICMEAS),

Abuja,

Nigeria,

2023,

pp.

1-5,

doi:

10.1109/ICMEAS58693.2023.10429829.

3. A. A. Propastin and Y. S. Rusov, "Broadband Matching of a Three-Layer Composite Medium With a

Grid of Metal Strips," 2025 Systems of Signal Synchronization, Generating and Processing in

Telecommunications (SYNCHROINFO), Tyumen, Russian Federation, 2025, pp. 1-6, doi:

10.1109/SYNCHROINFO65403.2025.11079374.

4. I. J. Eguía-Cambero, R. Lostado-Lorza, M. Corral-Bobadilla, S. Iñiguez-Macedo and F. -S. Gómez,

"Life Cycle Assessment (LCA) of recycled aluminum Metal Matrix Composites (MMC) reinforced

with stainless steel bidirectional continuous fibers," 2022 7th International Conference on Smart and

Sustainable

Technologies

(SpliTech),

Split

Bol,

Croatia,

2022,

pp.

1-5,

doi:

10.23919/SpliTech55088.2022.9854271.

5. M. Khalifa, M. Shamekh and M. Osman, "Fabrication and characterization of aluminum metal matrix

composite reinforced with in-situ intermetallic compounds via stir casting technique," 2023 5th Novel

Intelligent and Leading Emerging Sciences Conference (NILES), Giza, Egypt, 2023, pp. 386-391, doi:

10.1109/NILES59815.2023.10296624.

www.ijltemas.in

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6. R. Sajeevan and A. K. Dubey, "Experimental Investigation of Magnetic Force-assisted Powder-mixed

EDM for Aluminum Based Metal Matrix Composite," 2022 IEEE International Conference on

Industrial Engineering and Engineering Management (IEEM), Kuala Lumpur, Malaysia, 2022, pp.

1449-1453, doi: 10.1109/IEEM55944.2022.9989594.

7. A. Adeleke, J. Odusote, P. Ikubanni and A. Lawal, "Physicomechanical Properties of Al6063 Metal

Matrix Composite Reinforced with Incinerated Waste Cardboard Paper Ash," 2023 International

Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG),

Omu-Aran, Nigeria, 2023, pp. 1-5, doi: 10.1109/SEB-SDG57117.2023.10124505.

8. G. Puthilibai, T. Tamilselvi, C. Priya, P. Chinnaraj, S. Balamuralitharan and A. Subhashree,

"Optimization of Metal-Matrix Composites by W-EDM with CNT," 2023 Intelligent Computing and

Control for Engineering and Business Systems (ICCEBS), Chennai, India, 2023, pp. 1-7, doi:

10.1109/ICCEBS58601.2023.10448660.

9. P. Jadhav, K. Bairwa, K. Maniyar, A. Kurhade and S. Waware, "The Impact of Fillet Radius on Spur

Gears Made from Aluminum-Silicon Carbide Metal Matrix Composite," 2025 4th International

Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Tirupur, India, 2025, pp.

320-324, doi: 10.1109/ICIMIA67127.2025.11200982.

10. S. Tiwari and R. K. Yadav, "Investigation of Properties of Aluminium Metal matrix composite

reinforced by Aluminium Oxide using ANSYS," 2024 International Conference on Control,

Computing, Communication and Materials (ICCCCM), Prayagraj, India, 2024, pp. 289-294, doi:

10.1109/ICCCCM61016.2024.11039947.

11. P. P. Ikubanni et al., "Advancement in Magnesium Metal Matrix Composites: A Mini-Review of

Production Techniques, Properties, and Applications," 2024 International Conference on Science,

Engineering and Business for Driving Sustainable Development Goals (SEB4SDG), Omu-Aran,

Nigeria, 2024, pp. 1-5, doi: 10.1109/SEB4SDG60871.2024.10630415.

12. J. A. K. Gladston, P. Neopolean, S. Sundararajan, S. V. Subramanian, R. S. Krishnan and S. Jeyapandi,

"Sustainable Engineering: Predictive Modeling of Aluminum Matrix Composites using Deep

Learning," 2024 8th International Conference on Inventive Systems and Control (ICISC), Coimbatore,

India, 2024, pp. 396-404, doi: 10.1109/ICISC62624.2024.00074.

www.ijltemas.in

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