Conventional Machining of Metal Matrix Composites towards Sustainable Manufacturing—Present Scenario and Future Prospects

Abstract

Metal matrix composites (MMCs) epitomize a promising class of resources in modern manufacturing, offering an enhanced strength-to-weight ratio and high-temperature performance which make them ideal for applications demanding over conventional metals. However, their machining presents significant challenges due to their inherent material properties. The conventional machining methods including turning, milling, drilling, shaping, and the grinding of MMCs pose several challenges, facing limitations in terms of sustainability and efficiency. This paper explores the current perspective and prospects of the conventional machining techniques applied to MMCs, emphasizing sustainable manufacturing practices. Key aspects include the challenges posed by MMCs’ inherent heterogeneity, the MMC materials used, the MMC manufacturing process, the cutting constraints employed, tool wear, surface unevenness, surface integrity, and high energy consumption throughout machining. The study also explores promising advancements in tooling materials, cutting parameters’ optimization, innovative machining techniques aimed at minimizing the environmental impact and maximizing material utilization, and the strategies developed to overcome these challenges. The paper concludes by highlighting optimizing tools, and processes, and adopting emerging optimization techniques and opportunities for further research aimed at the industry, allowing it to move towards more efficient, eco-friendly production methods.

1. Introduction

The conventional machining process emerged from the industrialization technique of metal processing that removes materials from the workpiece to achieve the desired shapes and dimensions. Machining methods often employ cutting tools, which mechanically shear materials like metal, plastic, polymers, or composite materials [1]. Composite materials are man-made or naturally generated materials composed of two or more constituent materials with considerably differing physical and chemical characteristics that remain distinct and separate at the macroscopic or microscopic scale in the final construction.

A metal matrix composite (MMC) consists of at least two components properly scattered to provide the properties of one as a metal matrix and the other as a ceramic reinforcement [1]. The reinforcing phases might include particle-reinforced, short fiber- or whisker-reinforced, continuous fiber- or sheet-reinforced, and laminated or layered phases [2,3,4,5]. Ceramics and fibers are often used as reinforcements to improve mechanical characteristics and provide more strength, stiffness, and wear resistance than conventional metals.

The reinforcing phases of MMCs might include particle-reinforced, short fiber- or whisker-reinforced, continuous fiber- or sheet-reinforced, and laminated or layered phases [6]. This is discussed in Ref. [7] as shown in Figure 1.

Composite materials are also categorized depending on the matrix materials utilized in the manufacturing processes.

• Metal Matrix Composites (MMCs) are made of a metallic matrix (usually aluminum, magnesium, titanium, or copper) reinforced with dispersed particles, fibers, or whiskers of a second phase. These reinforcing phases can be ceramic (e.g., SiC, Al₂O₃), carbides (e.g., TiC, WC), or other metals [7].
• Polymer Matrix Composites (PMCs) combine a polymer with reinforcing carbon fiber and glass fiber fabrics. Examples include epoxies and polyester resins [8].
• Ceramic Matrix Composite (CMCs) are a ceramic matrix with incorporated ceramic fibers. Examples include the use of SiO₂, Al₂O₃, or carbon fibers.
• Carbon Matrix Composites (CAMCs) consist of carbon reinforced with fibers, whiskers, or particles [9,10].

The reinforcing materials are classified as nanocomposites. Nanomaterials, such as carbon nanotubes or graphene added to a polymer matrix or silicon nanoparticles added to steel, are frequently utilized for reinforcement [11].

• Glass Fiber Reinforced Polymers (GFRPs) are matrix composites reinforced with epoxy and polyester-bound glass fibers.
• Hybrid Composites (HCs) combine at least two types of matrices or reinforcement to accomplish specific design requirements [12].
• Natural Fiber Composites (NFCs) include jute, flax, cotton, and wood to enhance strength and provide a wood-like appearance.
• Carbon Fiber Reinforced Polymers (CFRPs) are a form of polymer matrix composite. The sticky polymer is often a thermoplastic or thermoset resin like epoxy, but it can also be a thermoset or thermoplastic polymer like polyester, vinyl ester, or nylon.
• Aramid Fiber Reinforced Polymers (AFRPs) are polymer matrix composites that employ aramid as a reinforcing material.
• Functionally Graded Composites (FGCs) are subsets of composites that allow for the adjustment of parts based on the application or form.

Composite materials, whether natural or man-made, consist of two or more unique components that provide improved physical, mechanical, chemical, and tribological qualities [3]. As a result, typical light metal alloys are used as matrix materials to create MMCs for a variety of applications [4].

The matrix is often constructed of lightweight metal, such as aluminum; magnesium and titanium are the most commonly used metal matrix composite materials. This leads to weight savings, enhanced dimensional stability, higher temperature stability, better cycle fatigue characteristics, increased electrical and thermal conductivity, and increased specific strength. These properties of MMCs make them ideal for high-performance applications. Moreover, high-temperature applications commonly use iron, cobalt, and cobalt-nickel alloy matrices. Matrix materials include standard cast alloys, wrought alloys, and exotic alloys [5]. However, machining MMCs presents unique challenges due to their complex microstructure, inherent material properties, heterogeneity, and abrasive nature resulting in severe tool wear surface impacts [6]. Primarily, the hard ceramic particles in the matrix cause numerous problems during the machining of MMCs. This review examined several experimental studies by different authors utilizing traditional machining procedures and MMCs manufactured from various reinforced materials. Numerous scholars have studied comparisons of various input parameters, performance measurements, computing, and optimization strategies.

To conduct this study, diverse published papers on this subject were reviewed. Recent progress on conventional MMC machining methods has been included in this review article. As illustrated in Figure 2, a total of 184 research papers on traditional MMC machining were reviewed using diverse databases such as Elsevier, Springer Nature, MDPI, IOP, and others. Our search revealed that the major portions of the retrieved articles are those published in Elsevier and Springer with 54 journal articles (24%) and 40 journal articles (22%), respectively.

2. Machining of Metal Matrix Composites

MMC materials have been employed in a wide range of industries, including aerospace, automotive, biotechnology, energy, military, optics, and the like because of their reinforced high-performance mechanical properties and low weight. For instance, MMCs are attractive for use in aircraft engine parts, rocket nozzles, and other structural parts of aerospace applications due to their high strength-to-weight ratios and high temperature resistance. Their low weight combined with high strength has also been employed in the automotive industry to enhance fuel efficiency and performance. The combined lightweight property with high strength and durability under extreme conditions has also made MMCs preferred candidates in military and defense applications such as lightweight armor and ballistic applications. These vast possible applications of MMCs are however not hand in hand with the production process. Composite materials are one of the most difficult to machine because of their inherent inhomogeneity, abrasive reinforcements, and anisotropic nature, which causes significant tool wear and surface impact.

This paper examines the studies conducted by several authors utilizing traditional machining methods such as turning, milling, drilling, and grinding, as well as metal matrix composites (MMCs) using various reinforced materials and optimization techniques. This section delves into the common conventional machining processes applied to MMCs, outlining their associated challenges and potential solutions. Their unique blend of properties makes them valuable for various demanding applications, driving innovation and progress across various industries.

2.1. Aluminum Metal Matrix Composites (AlMMCs)

Aluminum may be the most frequently utilized MMC as a matrix, due to its low density, high stiffness, and inexpensive processing costs, in addition to its great adaptability [13]. In practice, aluminum reinforcing materials include Al₂O₃, SiC, B₄C, graphite, boron, etc.

2.1.1. Aluminum Reinforced with Alumina (Al/Al₂O₃)

Aluminum oxide, also known as alumina, particulate reinforced aluminum composites have outstanding mechanical characteristics such as a high strength-to-weight ratio, better tribological characteristics, and outstanding cast-ability throughout the base materials. Thus, it is considered one of the most productive MMCs available to date [14].

In the work reported in [15], an analytical model was developed using AA7039/Al₂O₃ MMC and AA7039 to forecast the surface finish and the cutting force (Fc) during the milling. The optimization was conducted utilizing the Taguchi method, Analysis of Variance (ANOVA), and an Artificial Neural Network (ANN). The results on Fz indicated that one of the influencing parameters is Al₂O₃, which consists of white particles uniformly embedded in an MMC. An experiment was conducted to develop cost-effective machining and compare the result with TiN-coated and uncoated tools during the turning of an Al alloy reinforced with Al₂O₃ and suggested that machining an MMC can result in fracture and pull-off particles, challenging the achievement a smooth surface finish [16]. Interactions between cutting tools and reinforcements are studied and the results are compared with the changes in F_c, loss of machined surface quality, and reduced tool life during the machining of Al6061 reinforced with Al₂O₃ [17]. A comparison work was performed between micro-mechanical FE analysis and experimental data using thermal–elastic–plastic failure models. The composite materials are fabricated using the squeeze cast method of an Al 7075/BN/Al₂O₃ hybrid nanocomposite to investigate the machinability characteristics and exhibit a greater Brinell hardness and tensile strength than an unreinforced aluminum alloy [18]. Experimental research was performed by [19] to analyze machinability on Al2014 reinforced with Al₂O₃ during the turning process to predict the surface finish (Ra), tool wear, temperature, and cutting force using linear regression analysis (LRA) and the Taguchi method. The study results indicated that the reinforced material has superior mechanical characteristics. At low cutting speed (V_c), the unstable BUE edge is formed during the machining of LM25 aluminum alloy/nano Al₂O₃ and it is noted that increasing the reinforcing mass fraction increases the composite’s hardness and tensile strength [20]. The optimized Ra and MRR values mainly depend on the process settings and machining range during the machining of A359/B₄C/Al₂O₃ hybrid MMCs [21]. A comparison work was conducted using two negative square double-sided ceramic inserts (SNGN) in a turning experiment utilizing the EN AC-44000 AC-AlSi11 alloy with Al₂O₃ to minimize the F_c, tool wear, and Ra. The result emphasizes that the structure of the MMCs built into the composites causes problems during machining [22]. As reported in [23], adding ceramic particles to hybrid MMCs can increase their ultimate tensile strength compared to monolithic materials. Higher reinforcement ratios (>2.5 vol. %) have a significant impact on the cutting procedure by regulating milling force formation, and they also increase the average force magnitudes proportionately during the milling of A356/Al₂O₃ aluminum nano-composites [24]. To reduce tool wear, the Al 6061 reinforced with Al₂O₃ and graphite (Gr) material was used to design the milling experiment and optimization was performed using ANOVA and Scanning Electron Microscope (SEM) analysis. The weight percentage of alumina has a limited impact on tool wear compared to other factors [25].

Table A1. Summaries of research conducted using aluminum (Al) reinforced with aluminum oxide (Al₂O₃).

Author	Particulate Reinforced Metal Matrices	Conventional Machining Process	Input/Output Parameters	Optimization Method	Result
Karabulut (2015) [15]	AA7039/Al2O3 MMC and AA7039	Milling	Input: Vc, Fz, and Dc Output: Ra and Fc	ANOVA, Taguchi, and ANN	Ra (Ra) was improved between 196% and 312% during the milling of the AA7039/Al2O3 alloy
Kannan et al. (2015) [16]	LM6 aluminum alloy/Al2O3	Turning	Input: Vc, Fz, and Dc Output: Fc and surface finish	Comparison of tool	The NCD tool performs well compared to TiN-coated and uncoated tools
Ghandehariun et al. (2016) [17]	Al6061/Al2O3	Turning	Input: Rake angle, clearance angle, Vc, Dc, and width of cut Output: Fc	Finite element analysis (FEA)	Increase in plastic deformation around particle results in debonding and fracture during the cutting process
Kannan et al. (2018) [18]	Al 7075/hexagonal BN/Al2O3	Turning	Input: Tool geometry, tool material, coating type, Vc, feed, Dc, and approach angle Output: Fc, tool wear, and Ra	Dry, MQL, and SEM	MQL machining of the hybrid nanocomposite produces better results over dry machining in terms of surface finish, forces, and tool wear
Prakash and Iqbal (2018) [19]	AA2014/Al2O3	Turning	Input: Vc, Fzs, Dc Output: Fc, surface finish, and temperature	Linear regression analysis and Taguchi method	Ra and Fc decrease with increasing Vc and increase with increasing Fz and Dc
Thankachan (2019) [20]	LM25 Al alloy/nano Al2O3	Turning	Input: Vc, Fz, and Dc Output: Fc and surface finish	RSM and ANOVA	At a low Vc, an unstable built-up-edge (BUE) is formed, which leads to a poor surface finish
Srivastava et al. (2021) [21]	A359/B4C/Al2O3 hybrid MMCs	Turning	Input: Rotational speed, Fz, and Dc Output: MRR, Ra, and tool flank wear	RSM	SR is influenced by both feed and rotational speed. At a higher rotational speed, the machining time decreases. Flank wear also increases when increasing the rotational speed, Dc, and Fz
Szymański et al. (2022) [22]	EN AC-44000 AC-AlSi11/Al2O3	Turning	Input: Dc, Fz, and Vc Output: Fc, tool wear, and Ra	SEM	Fz influences the geometrical structure of the MMC
Ravikumar and Suresh (2023) [23]	Al7075/SiC/Al2O3	Turning	Input: Weight percentages of SiC, Al2O3, and heat treatment Output: Machining force and Ra	Taguchi	The weight percentage of nano-sized reinforcements and ageing temperature increased, and the machining force and Ra changed linearly
Sunar et al. (2023) [24]	A356/Al2O3	Milling	Input: % of reinforcement, feed, and radial and axial Dc Output: Fc and Ra	ANOVA	Surface quality can be improved by higher reinforcement ratios
Arun Premnath et al. (2015) [25]	Al 6061/Al2O3/graphite (Gr)	Milling	Input: Fz, Vc, Dc, and weight fraction Output: Tool wear	ANOVA and SEM	The feed and weight fraction are found to be greater contributing factors and increase the tool wear

shows short summaries of research conducted using aluminum (Al) reinforced with aluminum oxide (Al₂O₃). Al/Al₂O₃ has strong ionic inter atomic bonds which provides it with significant material characteristics and excellent machining characteristics. There is limited research available on the conventional machining of Al/Al₂O₃ MMCs. Further research is needed using dissimilar types of aluminum reinforced with Al₂O₃.

2.1.2. Aluminum Reinforced with Silicon (Al/Si)

Silicon is produced by decreasing sand and carbon in an electric furnace. The thermal breakdown of ultra-pure trichlorosilane is followed by recrystallization to yield high-purity silicon for use in electronics. Nowadays, researchers have conducted an impressive study on combining aluminum and silica to create a hybrid composite with improved mechanical properties. Some of the MMCs reinforced with silicon include the following:

• Al reinforced with silicon (Si);
• Al reinforced with silicon carbide (SiC);
• Al reinforced with silicon carbide and graphite (SiC-Gr);
• Al reinforced with silicon and magnesium (Al-Si10Mg);
• Al reinforced with silicon and aluminum oxide (Si-Al₂O₃);
• Al reinforced with silicon nitride and graphene (Si₃N₄ and C);
• Al reinforced with silicon and multi-wall carbon nanotubes (SI-MWCNT);
• Al reinforced with silicon nitride and molybdenum disulfide (SiN–MoS₂);
• Al reinforced with Silicon with magnesium (AlSi9Mg).

Silicon carbide materials offer exceptional qualities, including a high thermal rigidity and resistance to creep, wear, and oxidation. Al/SiC MMCs have a poor machinability, a fast worn cutting edge of the tool, and surface finishing that is undesirable and considerably harder than the WC tool material [26]. An experimental examination was performed on Al/SiC to determine the tool wear mechanisms, Fc, and spindle power consumption using an X-ray diffraction method and it was reported that higher percentages of SiC material resulted in tool wear [27]. The surface quality, pits, voids, micro cracks, grooves, and protuberances were measured during the turning of SiCp/2024Al and SiCp/ZL101A and it was proposed that smaller SiC particles have a larger particle–matrix contact area and more flaws [28,29]. The authors of [30,31] performed a milling experiment utilizing Al061/SiC and suggested the use of ceramic particles which include SiC and alumina. The 15% SiC particle reinforcement causes brittleness in the material, leading to crack formation and reduced ductility. A drilling experiment was conducted utilizing Al356/SiC-mica composites to assess the thrust force, Ra, wear on the tool, and burr size using the Taguchi method and grey relational analysis (GRA) and the results suggested that an increase in the wt% of SiC reduces surface roughness [32,33], and RSM [34]. To investigate the machinability and influence of the particle size of SiC, the authors suggested that the interface friction among coarser SiC materials raises the temperature of the work material while lowering the interface’s bonding strength among the matrix and reinforcement. The Box–Behnken method is used to calculate F_c and Ra [35] and RSM [36]. The Taguchi method and DFA approach are used to mill Al 356/SiC/B4C and to reduce the surface fineness and Fc. Built-up edge (BUE) development during aluminum machining, especially for Al-SiC-B4C hybrid MMC, has a negative impact on surface quality [37]. The authors of [38] carried out a milling experiment using a SiCp/Al 6063 composite material. An ANOVA and RSM technique was used to predict F_c. A turning experiment was conducted by [39] using a SiC-reinforced A356 aluminum metal matrix to measure Ra and it was suggested that the presence of SiC in the matrix produces an abrasive phase, simultaneously increasing hardness, tensile strength, and thermal resistance. The researchers in [40] carried out an experiment using SiC- and B₄C-reinforced aluminum 356 hybrid MMCs, which resulted in an increased weight percentage of SiC particles increasing both force and Ra. The influence of variations in the size and volume percentage of reinforcement in composites increases F_c and Ra [41] and improves the tensile and yield strength as well as milling force and tool wear of Al7075 reinforced with open-cell SiC MMCs. While spindle speed and feed rate are found to be more important influencing parameters for the tool wear [42,43], the surface finish is mostly affected by V_c and the weight fraction of the SiC nano particulate [44]. Moreover, the authors of [45] conducted an experimental investigation and analyzed the results using ANOVA and RSM. An FE model was developed to investigate A359/SiCp MMC F_c components using a Johnson–Cook thermal–elastic–plastic constitutive equation [46], the Taguchi method [47], and a microstructure-based finite element model [48]. The authors of [49] used SEM and TEM to examine the tool wear of AlSi9Mg0.3 reinforced with SiC particles, and significant damage to the machined surface was caused by the fracture and pluck-out of SiC reinforcement from the cutting tools. To examine the impact of various parameters during machining, the authors of [50,51,52] carried out experimental investigations on Al/SiC MMCs and measured tool flank wear and surface irregularity using fuzzy logic and suggested that the SiC particles increase the complexity during the cutting.

The appearance of the milled surface is determined not only by the combined movement of the feed rate (Fz) and machining speed, but also by the existence of hard SiC [53]. Based on the FE simulations, stress, hard reinforced grains, and shattered particles are the primary causes of tool surface wear [54]. A milling investigation was conducted to measure the Ra of Al 6061 MMCs reinforced with irregular-shaped SiC particles [55]. The tool’s cutting power, surface integrity, chip formation, and wear were measured in the laser-assisted micro milling of Al 2024/SiCp composites and the results suggested that a considerable rise in cutting tool wear resistance may be reached with an increase in laser power [56]. In order to quantify the stress/strain distribution, tool–particle interaction, and machined surface morphology, the authors of [57] compared micro and nano Al/SiC MMCs and suggested that a better machined surface is obtained with fewer flaws. Using an ultrasonic cavitation-aided casting process, Al7075 MMCs filled with SiC and B₄C are produced to increase the mechanical qualities and fine-grained architectures [58]. The FEM and RSM techniques are used to determine the surface quality by examining SiCp/Al and B₄Cp/Al MMCs [59]. In terms of chip shape, simulation findings show that continuous chips are generated while cutting aluminum; however, discontinuous chips are formed for MMCs reinforced with both rounded and polygonal SiC particles. Al6061/SiC/B₄C/talc composites were produced using a stir casting method with a constant weight % to predict Ra, thrust force, and circularity using the Taguchi method, ANOVA, and GRA [60]. The vacuum-based solidification method was effectively used to manufacture nano SiC-reinforced Al matrix composites. Grinding experiments were undertaken to determine the influencing parameters [61]. The researcher [62] developed an experiment to improve machining parameters utilizing the Taguchi and Placket–Burman methods of aluminum reinforced with SiC particles. Tool wear was analyzed by the authors of [63] using Al6063 reinforced with equal weight fractions of SiC and zirconia (ZrO₂) and is fabricated using the stir casting process.

The authors of [64,65] conducted an experiment using Al7075 reinforced with SiC, B₄C, graphene, and CNT and suggested that the maximum F_c is required for graphene-based composite. To measure Ra [66] in machined LM24-SiCp-coconut shell ashes in a turning process, optimization was performed using the Taguchi method and a genetic algorithm. The impact of tool wear, Ra, and the surface morphology of SiCp/Al MMCs were calculated using laser-assisted micro-cutting and the results suggested that process parameters should be enhanced to produce better results [67]. The turning operation of Al-SiC MMCs was conducted to compare the coated tool with PCD tools [68]. Using the Taguchi technique and ANOVA, a numerical model was employed to measure the workpiece temperature and Ra of an Al-SiC composite [69]. The effect of several drilling parameters on the thrust force and drilled hole was investigated using the Taguchi approach and ANOVA of an Al-SiC composite [70].

The researchers in [71,72] developed a multilayer perceptron (MLP) ANN model to predict tool wear during Al/SiC milling. A micro milling experiment was conducted using SiCp/Al composites and the results revealed that the SiC particles result in fast abrasive wear on the cutting tool [73,74]. The Levenberg–Marquardt back propagation training method and ANOVA were used to predict the parameters of Al/SiC during milling, and it was concluded that the formation of the BUE edge causes roughness on the work material [75]. An analytical modeling method was developed to forecast the subsurface depth damage during the cutting of SiCp/Al composites [76]. A metallographic investigation of the SiC material was performed to examine the high percentage of reinforcement [77]. The researchers in [78,79] examined the machining of Al/SiCp MMC using the Taguchi method and principal component analysis (PCA) to reduce F_c and flank wear. The authors of [80] carried out an experimental study using Al 7075/SiC to measure the temperature, roughness, and tool flank wear by employing ANOVA, the Taguchi method, and Particle Swarm Optimization (PSO). During the turning of an Al 7075/SiC composite, the Dc (Dc) affects the tangential force, while the Fz influences the feed force [81], increasing the Vc, and feed increases the flank and crater wear [82]. The researchers in [83] used the NSGA-II method to minimize the surface imperfection and burr height of aluminum reinforced with nano-sized SiC.

During the machining of Al 2024/B₄C/SiC, reinforcement particles fill the matrix pores, leading to improvements in the performance [84]. The RSM, ANOVA, and DFA methods were utilized to improve the machinability properties of Al7075/SiC/Gr [85]. The Al-4032 matrix contains hard SiC particles, rendering the composite challenging to fabricate. Nonetheless, turning becomes much easier at high Vc values and low Fz, resulting in a superior finish. The process of optimization was conducted using Taguchi-based grey relational analysis (TGRA) and ANOVA [86]; the Taguchi method and ANN were utilized in [87]; and RSM was implemented to forecast [88]. The study identified the most effective spindle speed, flow rate, and cut length during the cutting of the Al 7071 alloy reinforced with SiC to accomplish the lowest tribological characteristics [89].

The Al/SiC/Cr composite was produced using the stir casting process. The Taguchi method, GRA, and ANOVA methods were used to optimize Ra and MRR, and the tool wear rate (TWR) occurred at some areas and intensified with higher Mo concentrations [90,91]. The impacts of process factors such as laser power, Dc, and Vc on the Ra, F_c, and temperature were evaluated using the DOE approach. The Taguchi method was used to assess the process variables of aluminum reinforced with SiC and Al₂O₃ [92]. The authors of [93] demonstrated that intermittent cutting minimizes F_c and removes scratches on the surface produced by SiC at the workpiece interface, resulting in improved surface superiority. The authors of [94] performed an experimental investigation utilizing an Al 6061 alloy with SiC reinforcement and concluded that the hole diameter affects the localized stress and chip breakability. The authors of [95] developed a tool wear rate model for machining SiCp/Al composite material that takes into account the abrasive particle features, wear processes, and topological structure during drilling operations.

The stir casting method is used to manufacture Al7075 reinforced with a SiC/tungsten carbide (WC) composite to measure the thrust force, Ra, and roundness error. The optimization was performed using RSM, Multiple Linear Regressions (MLRs), ANN, and DFA [96]. This study used dry machining to examine the cutting parameters that affect the tool tip temperature, wear, and Ra in an Al alloy reinforced with 15% silicon carbide [97]. Increasing the amount of SiC reinforcement with Al/Si/Mg/Cu ultimately increases the mechanical characteristics [98]. The experiment was designed using FE ABAQUS software (Version 2022) to turn 217 XG, 225XE aluminum reinforced with SiC, and Comparison research was conducted using ultrasonically aided turning and conventional turning [99]. The investigation was performed using the Al7075 alloy with nano-sized SiC and Al₂O₃ employing the Taguchi method [23]. The authors of [100,101] investigated the topological and machining features of milling SiCp/Al MMC using Energy Dispersive Spectrometer (EDS) microscopic examination. A linear model was developed and optimization was carried using the RSM of Al-6061-SiC-Gr nanocomposites, resulting in the weight percentage of nanoparticles having the greatest influence on the F_c [102,103]. Adding SiC and Gr particles to Al7075 improves its micro hardness and milled surface quality [85].

The MoS_2p to SiCp/matrix decreases the hard SiCp and improves the tribological properties, lowering force and roughness [104]. The study provides guidelines for selecting tools and minimizing damage during the precision machining of Si/Al composites with a high mass percentage of SiC [105]. The researchers in [106] performed a machinability study to test the performance of an Al-12Si based hybrid reinforced (TiB₂-Al₂O₃) composite employing environmentally friendly cooling materials in milling. The authors of [107] used the Taguchi approach and ANOVA to optimize the MRR, Ra, and roundness error through the turning of Al-Si/Al₂O₃ and Al-Si/MWCNTs.

Aluminum alloy reinforced with Si₃N₄ and graphene is manufactured utilizing ultrasonic-assisted stir-casting technology to measure the wear in the tool, Ra, and F_c throughout the turning process [108]. The RSM is utilized to calculate the % contribution of the machining parameters in the proposed MMC to predict Ra, MRR, and power consumption [109]. The presence of SiN enhances the force, Ra, tool wear, and the % of reinforcement of Al2219-based nanoparticles of SiN/MoS₂ [110]. The authors of [111] fabricated aluminum metal matrix nano (n-B₄C) and nano hybrid composites (n-B₄C/MoS₂) utilizing the stir casting process to detect Ra and F_c using CCD in turning operation. The researcher [112] conducted an experimental work using EN AC-43330 (AlSi9Mg) cast aluminum reinforced with SiC, which enhances the machining parameters and quality of the machined workpiece. A Duralcan aluminum matrix reinforced with SiC composites was used to predict Ra in end milling [113].

Most previous research focuses on enhancing Al/SiC MMCs and cutting parameters that influence Ra, F_c, and tool wear. The majority of studies focus solely on MMCs prediction utilizing ANOVA, RSM, Taguchi, and FEM, with just a few types of research conducted in GRA and GA. More research is needed to address the machinability problem of Al/SiC composites.

Table A2. Summaries of conducted research using aluminum (Al) reinforced with silicon carbide (SiC).

Author	Particulate Reinforced Metal Matrices	Conventional Machining Process	Input/Output Parameters	Optimization Method	Result
Palanikumar et al. (2014) [39]	SiC-reinforced A356 aluminum metal matrix	Turning	Input: Vc, feed, and Dc Output: Ra	RSM, ANOVA, and DFA	An increase in Vc reduces Ra
Venkatesan et al. (2014) [40]	SiC and B4C-reinforced aluminum 356 hybrid MMC	Turning	Input: Weight fraction, speed, Fz, and cutting depth Output: Ra	RSM	Roughness decreases at a higher Vc during machining
Dabade et al. (2014) [41]	Al/SiCp composites	Turning	Input: Tool nose radius, insert geometry speed, Fz, and cutting depth Output: Ra	Taguchi method	Ra is higher at denser reinforcement composites
Vivek et al. (2015) [42], Vijayraj et al. [43]	Al with Si, Mg, and Fe reinforced with SiC	Turning	Input: Spindle speed, Fz, and Dc Output: Tool wear, Ra, and MRR	ANOVA, Taguchi, and RSM	Spindle speed and feed are the more important influencing parameters for the tool wear
El-Kady et al. (2015) [44]	Aluminum–silicon cast alloy	Turning	Input: Vc and the weight fraction percent Output: Fc, tool wear, and Ra	Different weight fractions of SiC nanoparticulate	Improvements in Ra were found by increasing the Vc and the weight fraction percent of the SiC nano-particulates
Karabulut et al. (2016) [45]	Al7075 and the open-cell SiC foam MMC	Milling	Input: Milling speed, Fz, and Dc Output: Milling force and tool wear	ANOVA and RSM	The cutting depth affected the milling force, the cutting tool wear was affected by the cutting depth, and the Fz was the most important factor for the tool wear
Bushlya et al. (2017) [49]	AlSi9Mg0.3 reinforced with SiC	Turning	Input: Vc Output: Tool wear	SEM and TEM	The dimensions and stability of this tool protective layer were found to decrease at a higher Vc
Elsadek et al. (2017) [50]	Al/SiC	Turning	Output: Ra and tool flank wear Output: Fz, Vc, Dc, and % volume of SiC	Fuzzy logic	The surface finish can be increased by increasing the Vc Flank tool wear increases upon increasing the SiC
Ghoreishi et al. (2018) [51]	Al A356-T6 alloy reinforcement with SiC	Milling	Input: Vc, Fz, Dc, and coolant Output: Ra and Fc	CCD	Vc is more than 1800 m/min, Fz is less than the particle size per tooth, and Dc is between 1 and 1.1 mm
Ghoreishi et al. (2019) [52]	Al A356-T6 alloy reinforcement with SiC	Milling	Input: Vc, Fz, Dc, and coolant Output: Ra and tool wear	CCD	Increasing the Vc does not lead to a higher tool wear value Increasing the Dc and Fz results wear in tool.
Xiong et al. (2018) [53]	Situ TiB2/7050Al composites	Milling	Input: Vc, Fz, and axial depth Output: Ra and Fc	ANOVA	Fc are influenced by the axial Dc. Increasing the milling speed reduces Ra
Deng et al. (2018) [54]	SiCp/Al	Milling	Input: Feed, spindle speed, axial depth, and slope angle Output: Tool wear and surface quality	FEA, SEM, and EDS	During MQL operation, the process improves the tool wear and tool life and reduces Ra
Pramanik et al. (2018) [55]	Al 6061/SiC	Milling	Input: Vc and Fz Output: Ra	Chip morphology technique	Fz is the crucial factor that reduces Ra
Wang et al. (2018) [56]	SiCp/Al 2024	Milling	Input: Cutting depth, Vc, and laser power Output: Fc, surface integrity, chip formation, and tool wear	Taguchi and ANOVA	Laser power and cutting depth have a great effect on roughness
Kumar and Sood (2019) [58]	Al7075/SiC/B4C	Turning	Input: Vc, Fz, Dc, and percentage reinforcement Output: Ra and Fc	RSM, ANOVA, and DFA	Increases were found in the hardness, tensile strength, and grain size, as well as more fine-grained structures
Niu (2018) [59]	SiCp/Al and B4Cp/Al	Turning	Input: Spindle speed, Vc, and Fz Output: Chip formation, Fc, temperature, and tool wear	FEM and RSM	The cutting spindle speed and Fz are considered as the main factors
Kumar et al. (2019) [60]	Al6061/SiC/B4C/talc	Drilling	Input: Vc, feed, Dc, and percentage of reinforcement Output: Thrust force, circularity, and Ra	ANOVA, Taguchi technique, and GRA	Ra and Fc is mainly influenced by Fz
Nandakumar et al. (2019) [61]	Aluminum and nano SiC	Grinding	Input: Wheel speed, Dc, and workpiece speed Output: Grinding forces and temperature	RSM	High Dc causes increases in grinding forces and temperature
Mirshamsi et al. (2019) [62]	Aluminum/SiC	Turning	Input: Vc, Dc, Fz, laser power, laser frequency, laser light angle, and percentage of particles Output: Flank wear and Ra	Placket–Burman method, Taguchi, and ANOVA	An increase in the reinforcement ratio leads to an increase in the flank wear of the cutting tool and an increase in Fz resulted in increasing Ra and tool wear
Tripathy and Maity (2019) [63]	Al6063/SiC/ZrO2	Milling	Input: Feed, Vc, and Dc Output: Tool wear	Taguchi	A cryo-treated end mill cutter improves the tool wear rate
Ajithkumar and Xavior (2019) [64,65]	(i) Al7075/SiC/B4C, (ii) Al7075/SiC/graphene, (iii) Al7075/SiC/CNT	Turning	Input: Tool, Vc, Fz, and Dc Output: Flank wear, crater wear, and chip morphology	Taguchi method	The reinforcement particles of SiC, B4C, graphene, and CNT had a higher influence on chip morphology and shape
Arulraj et al. (2019) [66]	LM24-SiCp-coconut shell ash	Turning	Input: Vc, Fz, Dc, and tool-nose radius Output: Surface roughness	Taguchi method and genetic algorithm	Vc is an important factor to reduce Ra
Wang et al. (2019) [67]	SiCp/Al	Turning	Output: Tool wear and Ra	Taguchi method	The tool is damaged seriously due to the large Fc
Ramasubramanian et al. (2019) [68]	AA2124-SiC MMC	Turning	Input: Different diamond-coated tool Output: Tool failure, temperature, chip morphology, and Ra	Raman spectroscopy	BMTN-coated tools outperformed MCD, NCD, BDD, and PCD tools
Thirukkumaran et al. (2020) [69]	Al–SiC	Drilling	Input: Speed, Fz, and point angle in degrees Output: Temperature, Fz, and Ra	Taguchi method and ANOVA	The tool and workpiece temperatures increase with an increase in spindle speed at different point angles
Abbas et al. (2020) [70]	Al/SiC	Drilling	Input: Spindle speed and Fz Output: Thrust force, hole diameter, delamination factor, Ra, tool wear, and chip analysis	SEM and EDS	The result reveal that the thrust force varies directly with the Fz, but inversely with the spindle speed
Wiciak-Pikula et al. (2020) [71,72]	Al/SiC	Milling	Input: Vc, feed, spindle speed, axial and radial infeed, and depth Output: Tool wear	Multilayer perceptron (MLP) and ANN	The effectiveness of predicting wear is based on the forces and vibrations
Zhao et al. (2020) [73,74]	SiCp/Al	Milling	Input: Laser power, scanning speed, and track displacement Output: Fc,	DOE	The reduction in Fc is attributed to the thermal failure of the interface layer
Zhou et al. (2020) [75]	Al/SiC	Milling	Input: Vc, Fz, Dc, and volume fraction of SiC Output: Roughness	ANN and ANOVA	Roughness decreases with cutting speed and increases with an increase in the volume fraction of SiC and Fz
Liu et al. (2020) [76]	SiCp/Al composites	Turning	Input: Fz Output: Tool wear and thermal–mechanical stress	The analytical modeling approach model	The mechanical stress and thermal stress are induced by the mechanical load and thermal load
Repeto et al. (2020) [77]	5083 Al/SiC	Shaper	Input: Vc and Dc Output: Tool wear	SOM and SEM	Machining time is an influential factor and an increase in the wear causes an increase in the forces
Swain et al. (2020) [78]	Al/SiCp	Turning	Input: Vc and feed Output: Flank wear and Ra	Principal component analysis (PCA) and Taguchi	Vc and Dc are the influencing parameters of flank wear, and Dc and feed influence the responses for Ra
Swain et al. (2020) [79]	Al/SiCp	Turning	Input: Vc, feed, and Dc Output: Fc	Taguchi and quadratic regression model	The feed force, Fc, and radial force decrease by increasing the Vc at constant Fz
Das et al. (2020) [80]	Al 7075/SiCp	Turning	Input: Vc, feed, and Dc Output: Temperature, Ra, and tool flank wear	ANOVA, Taguchi, and PSO	At higher machining speeds, the surface quality is improved
Bhushan (2022) [81], Bhushan (2021) [82]	7075 Al alloy/SiC	Turning	Input: Vc, feed, and Dc Output: Crater wear, tangential force, feed force, and radial force	ANOVA and RSM	Dc is the influencing parameter of the tangential force, whereas Fz is the influencing parameter of the feed force [82]; increasing Vc and feed enhances the flank and crater wear [83]
Chakravarthy et al. (2021) [83]	Nano SiC/Al	Drilling	Input: Spindle speed, Fz, point angle, Wt% of SiC, and type of cooling Output: Ra and burr height	FESEM, EDAX, AFM, Vickers micro hardness test, and NSGA-II	The minimal SR and BH are 0.29 μm and 0.23 mm, respectively, under 500 rpm, 50 mm/min feed, and a 900 point angle
Çevik et al. (2021) [84]	A2024-B4C-SiC	Milling	Input: % of SiC and B4C reinforcements Output: Surface morphology, hardness, MRR, and Ra	EDX analysis	Ra values increased with the addition of nanoparticles within the AA2024 alloy matrix
Shihab et al. (2021) [85]	Hybrid Al7075/SiC/Gr	Milling	Input: Spindle speed, feed, Dc, and % of SiC/Gr Output: Ra, micro hardness, and MRR	RSM, ANOVA, and DFA	The weight percentage of the reinforced materials significantly affects the surface integrity
Saini and Singh (2021) [86], (2022) [87], (2022) [88]	Al-4032/SiC	Milling	Input: Spindle speed, Fz, and power supply Output: MRR and Ra	TGRA and ANOVA [87], Taguchi, ANN [88], and RSM [89]	The result established that the inclusion of SiC in the base matrix demonstrates improved mechanical properties and a better machined surface with optimized machining parameters
Patil and Lila (2021) [89]	7071Al/SiC	Turning	Input: Fz and Dc Output: Ra, Fc and tool wear	Metallurgical microscope	PCD tools showed a smaller machined surface than at other Fc levels
Kumar et al. (2021) [90], Kumar et al. (2021) [91]	Al/SiC/Cr [91] Al/SiC/Mo [92]	Turning	Input: Vc, Fz, Dc, and Wt.% of Mo Output: Ra, MRR, and TWR	Taguchi, GRA, and ANOVA	The Vc and feed are the most influential parameter for Ra Dc is the most significant parameter for the highest MRR
Abedinzadeh et al. (2021) [92]	SiC/Al2O3 and aluminum powder	Turning	Input: Vc and Dc Output: Ra and Fc	Taguchi	Vc and Dc exhibited a major influence on Ra and Fc
Zhou et al. (2021) [93]	SiCp/Al	Turning	Input: Volume fraction of SiC (%), Vc, Fz, and Dc Output: Fc and surface topography	Finite element (FE) model	Fc increases with the increase in cutting depth; the larger the Fz or the smaller the cutting depth, the better the hydrophobicity of the machined surface
Devaraj et al. (2021) [94]	Al 6061/SiC	Turning	Input: Hole diameter, hole depth, and pitch between the hole Output: Ra, power consumption, and tool flank wear	Taguchi	The hole depth and pitch between the holes significantly affect the machining performance
Liu et al. (2022) [95]	SiCp/Al	Drilling	Input: Fz and spindle speed Output: Tool wear mechanisms and the tool’s geometrical structure	Tool wear models	Tool wear modeling in the drilling of SiCp/Al should consider the effect of the tool wear mechanisms
Babu et al. (2022) [96]	Al7075/SiC/WC	Drilling	Input: Point angle, feed, and speed Output: Thrust force, Ra, and roundness error	RSM, MLR, ANN, and DFA	Fz and point angle are found to have a significant influence during the drilling process
Nagarajan and Kamalakannan (2022) [97]	AA7075/SiC	Turning	Input: Vc, feed, and Dc Output: Tool tip temperature, Ra, and tool wear	RSM technique	Vc plays the most imperative role affecting Ra
Behera et al. (2022) [98]	Al/Si/Mg/Cu/SiC	Turning	Input: Dc, feed rate, and Vc Output: Ra	Taguchi, ANOVA, and RSM	Feed and Vc are important controllable and dominant factors
Kim et al. (2022) [99]	217 XG, 225XE aluminum/SiC	Turning and UAT	Input: Vc, feed, Dc, and tool radius Output: Fc and Ra	FE and comparison work	Ra was improved in ultrasonically-assisted turning (UAT)
Laghari et al. (2023) [100,101]	SiCp/Al	Milling	Input: Vc, feed, and axial and radial Dc Output: Tool wear and Ra	EDS	At an Fz of 0.06 mm/rev, 160 m/min, and MQL, there is an Ra improvement
Priyadarshi and Sharma (2016) [102,103]	Al-6061-SiC-Gr	Turning	Input: Nanoparticle type, quantity %, and feed Output: Tool wear and Ra	ANOVA and RSM	An increase in the weight percent also significantly affected the magnitude of Fc
Shihab et al. (2021) [85]	Al7075/SiC/ Gr	Milling	Input: Spindle speed, Fz, Dc, and the weight percentage of the reinforced materials Output: MRR, Ra, and micro hardness	ANOVA and RSM	Spindle speed, Fz, and the weight percentage of reinforced materials significantly affect the surface integrity
Kannan and Kannan (2018) [104]	Al-Si10Mg	Turning	Input: Vc and Dc Output: Fc and Ra	RSM and DFA	An increase in force is obtained for all samples at a higher speed and cutting depth
Guolong et al. (2023) [105]	Si/Al	Milling	Input: Vc, radial Dc, axial Dc, feed Output: Cutting-edge radius on the Fc	Damage formation mechanism	Cracks and scratches are the most common damage of the machined surface, and the cutting-edge radius has a great influence on machining damage
Şap (2023) [106]	Al–12Si based reinforced (TiB2–Al2O3)	Milling	Input: MMC type, cooling conditions, Vc, and Fz Output: Ra, tool wear, cutting temperature, and energy consumption	Taguchi method and ANOVA	The most effective parameters on the Ra, flank wear, cutting temperature, and energy consumption were determined as cooling/lubrication (54.41%)
Al-Kandary et al. (2019) [107]	Al-Si/MWCNT	Turning	Input: Vc, Fz, and the rake angle of the cutting tool Output: MRR, Ra, and roundness error	Taguchi method and ANOVA	Vc has the most significant effect on the roundness error and MRR of the nanocomposites
Raj et al. (2023) [108]	Al/Si3N4/graphene	Turning	Input: Feed, machining speed, depth, and nose radius Output: Tool wear, Ra, and Fc	LSOA, MOORA, and TLBO	The resultant Fc rises with a higher nose radius due to the increase in flow stress, as a higher radius at the nose produces more wear
Puttaswamy and Venkatagiriyappa (2021) [109]	Al 6065-Si-MWCT	Turning	Input: Cutting velocity, feed, and Dc Output: MRR and Ra	DOE, ANOVA, and RSM	Ra was mainly affected by the feed and Vc
Kannan et al. (2019) [110]	Al2219/SiN/MoS2	Turning	Input: Vc, feed, and Dc Output: Fc and Ra	CCD and DFA	Ra and Fc are found to better for HMMC than in the other two reinforced base metals
Siddeshkumar et al. (2022) [111]	Aluminum/nano (n-B4C)/Nano HMMC (n-B4C/MoS2)	Turning	Input: Vc and feed Output: Fc and Ra	CCD, ANOVA, and RSM	Fz is the major influencing factor on the Vc. The addition of (n-B4C and n-B4C/MoS2) particles increases the Ra and Fc in nanocomposites
Ghalme and Karolczak (2023) [112]	EN AC-43330 (AlSi9Mg)	Drilling	Input: Drill speed and feed Output: Ra and roundness error	EWTOPSIS	Drill speed has a significant effect on Ra and roundness error
Wiciak-Pikuła et al. (2021) [113]	Duralcan (Al/SiC/10p)	Milling	Input: Vc, spindle speed, feed, and axial and radial depth Output: Ra	CART and ANN	The prediction of Ra based on Fc is conceivable

shows summaries of research studies conducted using aluminum (Al) reinforced with silicon carbide (SiC).

2.1.3. Aluminum Reinforced with Boron (Al-B)

Boron compounds are utilized in organic synthesis, the manufacture of specific kinds of glass, and as a wood preservative. Boron filaments are employed in advanced aircraft constructions due to their high strength and lightweight nature. Boron carbide is one of the hardest ceramic materials, and its Young’s modulus, along with its low density, leads to a strong resistance to ballistic impact [114]. Nowadays, experts have conducted an amazing study on the mixing of aluminum with chemicals to generate a hybrid composite with increased mechanical capabilities. Some of the MMCs reinforced with boron are as follows:

• Al reinforced with boron carbide (B₄C);
• Al reinforced with magnesium diboride (MgB₂);
• Al reinforced with titanium diboride (TiB₂);
• Al reinforced with hafnium diboride (HfB₂);
• Al reinforced with zirconium diboride (ZrB₂).

Al-B₄C composites were produced using a stir casting procedure with reinforcements weighing 10% and 15%, respectively. The addition of B₄C changes the properties of the composites which in turn affect the machining parameters [115]. An experimental investigation demonstrated that increasing the weight % of B₄C significantly increased the thrust force during the experimental investigation of drilling B₄C-reinforced MMCs [116]. The experiment was carried out on Al6061 reinforced with various weight % (5–9 weight) values of B₄C. The findings show that increasing the weight percentage of B₄C particle strengthening leads to decreased F_c and also increases in porosity, stiffness, and density dislocations [117].

A machine-learning approach was utilized to determine the optimum predictive model structures, suggesting that Ra output is difficult to forecast and manage due to the interplay of several complex phenomena in composite machining [118]. An experimental study was conducted using the AA8050 aluminum alloy with B₄C and TiB₂ nanoparticles to form a hybrid nanocomposite material that maximizes MRR and minimizes Ra [119]. The author found that adding B₄C and graphene nanoplate (GNP) to an aluminum 6061 alloy matrix reduces F_c and Ra through the turning operation [120]. A study was carried out to observe the effect of various cutting tools, lubrication procedures, and drill geometries of Alumix 123-B₄C-nickel-coated graphite composites [121].

Some of the researchers used aluminum as a base material and reinforced it with boron materials to conduct experiments: The work reported in [21] used A359/B₄C/Al₂O₃ hybrid MMCs; the work in [37,40] used Al 356/SiC/B₄C; the work in [59] used Al7075/SiC/B₄C; the work in [60] used SiCp/Al and B₄Cp/Al MMCs; the work in [61] used Al6061/SiC/B₄C/talc; the work in [65,66] used Al7075/SiC/B₄C/graphene/CNT; the work in [85] used A 2024/B₄C/SiC composites; and the work in [113] used Al 2219/nano B₄C and nano B₄C/MoS₂. There has not been much study on the experimental examination and analysis of different machining factors such as the Dc, Fc, and cutting tool speed which influence the microstructure and surface quality of Al/B₄C composites. More research is required by researchers employing Al/B₄C MMCs.

Table A3. Assessment of aluminum reinforced with boron (Al-B).

Author	Conventional Machining Process	Particulate Reinforced Metal Matrices	Input/Output Parameters	Optimization Method	Result
Hiremath et al. (2022) [117]	Turning	Al6061/B4C	Input: Feed and Dc Output: Ra, Fc, and microstructural studies	Electron microscopy	The quality of the machined surface is significantly impacted by the weight percentage of reinforced boron carbide particles
Sekhar et al. (2022) [118]	Turning	AA6063-Mg/B4C/MWCNT	Input: Vc, feed, and Dc Output: Ra	Machine learning	The minimum settling times of the controller responses for consistent surface quality and machining productivity were determined
Sathish et al. (2022) [119]	Turning	AA8050/B4C/TiB2	Input: % nanoparticle reinforcement, spindle speed, machining speed, and Dc Output: MRR and Ra	Taguchi design	A moderate level of spindle speed results in a higher MRR The minimum Dc results in a low Ra
Pul and Yağmur (2022) [120]	Turning	Aluminum 6061/B4C/GNP/	Input: Vc and Fz Output: Fc, Ra, and tool wear	SEM	The Fc increased with the increase in the Fz The highest tool wear was observed in tools where 10% B4C reinforced composites without GNP reinforcement were machined
Ekici et al. (2017) [121]	Drilling	Al/B4C/Graphite	Input: Vc and three different Fz values Output: Fc, Ra, thrust force, dimensional accuracy, and burr height	ANOVA	With an increase in Vc, thrust forces decreased. In the drilling of the Al/B4C/Gr hybrid composite, Gr reinforcement was observed

displays the assessment of aluminum reinforced with boron (Al-B).

2.1.4. Aluminum Reinforced with Titanium (Al–Ti)

Titanium aluminum alloy is considered to be one of the most intriguing high-temperature materials owing to its remarkable qualities, including anti-creep, antioxidant, rigidity, and yield toughness. A comprehensive study was conducted by [122] to turn titanium diboride (TiB₂) ceramic-reinforced AA7075 to optimize the machining process. Correlation analysis was performed to study the association between cutting parameters, material properties, specific energy, and Ra during the milling operation using different materials such as Al 2024, Al 7075, and Al 2024 reinforced with TiB₂ composites and Al 7075 reinforced with TiB₂ composites [123]. This reinforcement improves the mechanical properties of the in situ Al7075/TiB₂ composites under various cryogenic MQL conditions [124]. The statistical analysis and regression models used to identify significant factors influencing F_c and Ra through drilling of AA7075/TiB₂ [125]. The researchers in [120] conducted a study using an AA8050 matrix reinforced with B₄C and TiB₂ nanoparticles to enhance properties like the hardness, wear resistance, and thermal stability. EDX and SEM tests were performed on Al6061 reinforced with 2% and 4% TiC, providing insights into the machining characteristics in the turning process using a Taguchi design [126]; a turning operation was performed using PCD and uncoated tungsten carbide [127]. The authors of [128] explore the effects of coconut oil via the MQL turning of Al-7079/7 using TiC MMCs.

The Al/n-TiC/MoS₂ sintered nanocomposite was manufactured using a powder metallurgy process and an experimental design was developed using CCD and optimized using a genetic algorithm [129]. The addition of TiCp enhances the high strength and hardness qualities and also increases the flank wear and Ra during the turning of the Al/TiCp/Gr hybrid composite [130]. The authors in [131] optimized AA7075 reinforced with TiO₂ composite using the Taguchi method, ANOVA, and a decision tree algorithm. To investigate the mechanical qualities and machinability features, the authors of [132] employed Al 6061 as the basis material with graphite, altering the TiO₂ particle weight percent.

Table A4. Assessment of aluminum reinforced with titanium (Al–Ti).

Author	Conventional Machining Process	Particulate Reinforced Metal Matrices	Input/Output Parameters	Optimization Method	Result
Pugazhenthi et al. (2018) [122]	Turning	AA7075/TiB2	Input: Fz, Vc, and Dc Output: Fc and Ra	FESEM	The increase in Vc enhanced Ra due to a reduction in transferred material
Yu et al. (2021) [123]	Milling	Al 2024, Al 7075, Al 2024/TiB2, Al 7075/TiB2	Input: Spindle speed, Fz, and width of cut Output: Fc and Ra	Frequency spectrum	Cutting parameters at a lower cutting specific energy can be selected to obtain a good surface quality in the milling of TiB2/Al composites
Chen et al. (2021) [124]	Milling	TiB2/Al7075	Input: Cutting fluid—pressure Output: Tool life	CMQL	Through a combination of excellent cooling and lubrication performance, the tool life was extended by 198.08% compared with dry conditions
Parasuraman et al. (2021) [125]	Drilling	AA7075/TiB2	Input: Vc, Fz, and weight percentage Output: Fc and Ra	FESEM, EDAX, and EBSD Statistical regression	Fc was observed to be reduced as Vc increased
Kishore et al. (2014) [126] Kishore et al. (2014) [127]	Turning	Al6061/TiC	Input: Vc, Fz, and Dc Output: Fc and Ra [135], surface integrity, and flank wear [136]	EDX, SEM, and Taguchi	Fc was decreased with the increase in Vc. Ra was decreasing by the increase in Vc
Thangavel et al. (2019) [129]	Milling	Al/n-TiC/MoS2 sintered nanocomposite	Input: Spindle speed, Fz, and weight percentage Output: Fc and Ra	FESEM, CCD, RSM, and GA	The relationship between the machining parameters and responses shows a quadratic and linear relationship
Sozhamannan et al. (2018) [130]	Turning	Al/TiCp/Gr hybrid composite	Input: Dc and Fz Output: Flank tool wear and Ra	SCM	A sudden increase in flank wear was observed during the increase in Fz and Ra
Maganti and Potturi (2023) [132]	Turning	Al 6061/graphite/TiO2	Input: Vc, Dc, and Fz Output: Hardness, tension test, and Fc	EDAX	Using Al 6061 in the machining of 3% Gr and 5% TiO2, an increase in Fc of about 50% was observed

displays the assessment of aluminum reinforced with titanium (Al–Ti).

2.1.5. Other Aluminum Metal Matrix Composites

The above subsections presented the most recent studies on aluminum MMCs reinforced with materials such as aluminum reinforced with zirconium [63,133,134,135,136,137]; aluminum reinforced with bismuth [138]; aluminum reinforced with boron nidride [139]; aluminum reinforced with copper [140,141]; aluminum reinforced with magnesium [142,143,144]; aluminum reinforced with graphene [64,65,120,145,146]; and aluminum reinforced with graphite [147]. The machining characteristics of other aluminum MMCs are less reported; the reinforcements are summarized in

Table A5. Other aluminum metal matrix composites.

Aluminum Reinforced with Zirconium (Al–Zr)
Author	Conventional Machining Process	Particulate Reinforced Metal Matrices	Input/Output Parameters	Optimization Method	Result
Ruban et al. (2020) [134], Ruban et al. (2023) [135]	Turning	AA6061/ZrB2-ZrC	Input: Vc, Dc, and Fz Output: Microstructural analysis	Taguchi, ANOVA [143] XRD, and FESEM analysis [144]	Ra and Fc had a major impact on Vc
Yerigeri and Biradar (2022) [136]	Turning	Al/ZrB2	Input: Vc, Dc, and Fz Output: Tool wear	Comparison	The wear rate of an uncoated tool significantly impacted the tool wear
Mahesha et al. (2023) [137]	Turning	ZrB2/AA7475	Input: Vc, Dc, and Fz Output: Ra	Box–Behnken design and ANOVA	Ra decreased as Vc was increased
Aluminum reinforced with boron nidride (Al7075/BN)
Raja (2020) [139]	Drilling	AA7075/BN	Input: % of reinforcement, spindle speed, feed, and point of angle Output: Thrust force and Ra	RSM and ANOVA	Hardness was increased with the increase in the wt% of the reinforcement materials
Aluminum reinforced with chromium (Al/Cr)
Kumar et al. (2021) [91]	Turning	Al/SiC/Cr	Input: Vc, feed, Dc, coating thickness, and chromium Output: Ra, tool wear rate, and MRR	Taguchi, ANOVA, and GRA	The results reveal that the optimal value obtained for the material removal rate is 15,674.32 mm3/min, having Ra and tool wear rate values of 0.39 μm and 0.917 mg/min
Aluminum reinforced with copper (Al/Cu)
Arulkirubakaran et al. (2019) [140]	Turning	Al/Cu/TiB2	Input: Vc and feed Output: Fc, tool wear rate, Ra, and chip morphology	OM and SEM	The specific cutting energy was reduced by an amount of 25% in dry and 30% in lubricating conditions during machining of an Al-Cu/TiB2composite
Aluminum reinforced with magnesium (Al/Mg)
Prasad et al. (2022) [142]	Milling	Al 8081-Mg/Zr/nano TiO2	Input: Vc and feed Output: surface morphology and tool failure	SEM	The evolution of displacement in the PCBN tool was 24.7 μm, which is better compared to 34.3 μm in the PCD tool at 3000 r/min
Jaswanth and Anbuchezhiyan (2022) [143]	Drilling	Al-Mg-Cr alloys	Input: Tool diameter, spindle speed, and load capacity Output: Ra	Statistical analysis using SPSS	WC-reinforced Al-Mg-Cr composites had a higher Ra than Al-Mg-Cr alloys
Pul (2018) [145]	Turning	Al-MgO	Input: Fz, Vc, and Dc Output: Ra	SEM	Ra values were reduced with the increased Vc
Aluminum reinforced with graphene
Joel and Anthony Xavior (2017) [145,146]	Turning	Al 2024, 6061, 7075/reinforced with graphene	Input: Vc, Dc, and Fz Output: Ra	HRSEM, XRD, Taguchi, and ANOVA	The detachment of reinforcement materials from the composite and their impact on surface quality were examined
Aluminum reinforced with graphite
Lagisetti and Sukjamsri (2022) [147]	Turning	AA6061/SiC/graphite	Input: Vc, Dc, and Fz Output: Fc and Ra	ANOVA	High Vc values accompanied by low feed and Dc resulted in a reduced Fc and better surface finish

.

2.2. Copper Metal Matrix Composites

Copper MMCs use copper as the matrix and are reinforced with SiC, Al₂O₃, B₄C, graphite, fibers, or whiskers. These reinforcements are used to improve qualities including durability, rigidity, resistance to wear, and thermal insulation. In the investigation of Si₃N₄ reinforced with copper alloy, Ra was maximized by regulating the Vc and reinforcing %, and the V_c and % of reinforcement were directly influenced to maximize the Ra value from the analysis [148]. Investigations were performed by [149] to predict wear in the tool, Ra, temperature, and chip development during the turning of Cu/B/CrC using the Taguchi method and full factorial design (FFD) method [150]; fuzzy logic was used to predict the minimum energy consumption [151]. The authors of [152] conducted comprehensive investigations using the Taguchi method, SEM, and EDX analysis to predict tool wear and machinability features during the turning of Cu/Mo-SiCp hybrid composites to predict Ra, tool wear, and cutting temperature [153]; Cu-based, B-Ti-SiC_P material was used to turn, considering MQL-assisted and cryogenic LN₂ [154]; and a Cu-based, B-Ti-SiC_P material was used turn the workpiece [155].

Table A6. Assessment of copper CMMCs with other nanoparticles.

Author	Conventional Machining Process	Particulate Reinforced Metal Matrices	Input/Output Parameters	Optimization Method	Result
Sathish et al. (2021) [148]	Turning	Si3N4-reinforced copper alloy composite	Input: Vc, Dc, Fz, and wt. % of Si3N4 reinforcement Output: Fc, tool wear, and Ra	P/M route	The Vc and wt. % of reinforcement were directly influenced to maximize the Ra value from the analysis
Usca et al. (2021) [149]	Turning	Cu-B-CrC composites	Input: Reinforcement ratio, Vc, Fz, and Dc Output: Tool wear, Ra, cutting temperature, and chip formation	Taguchi method	Vc and Fz have a contributing impact on cutting temperatures and Ra
Usca et al. (2022) [150]	Milling	Cu-B-CrC composites	Input: Reinforcement ratio, Vc, and Fz Output: Ra, tool wear, chip morphology, and cutting temperatures	Full factorial design	Vc alterations play an important role in the machinability characteristics
Usca et al. (2022) [151]	Milling	Cu-B-CrC composites	Input: Reinforcement ratio, Vc, and Fz Output: Minimum energy consumption	Fuzzy inference system	An increasing reinforcement ratio, Fz, and Vc increases the energy consumption
Şap et al. (2021) [152]	Turning	Cu/Mo-SiCp hybrid composites	Input: Reinforcement ratio, Vc, Fz, and Dc Output: Ra, tool wear, and cutting temperature	Taguchi, ANOVA, SEM, and EDX	Reinforcement ratio is the dominant factor on all response parameters
Şap et al. (2021) [153]	Turning	Cu-based, B-Ti-SiCP hybrid composite	Input: Vc and Fz Output: Tool wear, temperature, energy, Ra, surface texture, and chip morphology	MQL-assisted and cryogenic LN2-assisted machining	Cryogenic cooling was found to be the most efficient method
Şap et al. (2021) [152,153,154]	Turning	Cu reinforced with Ti-B-SiC powder particles	Input: Reinforcement ratio, Vc, Fz, and Dc Output: Ra, flank wear, and cutting temperature	Full factorial design, SEM, and EDS	Through Fz and Vc, it was determined that the Ra values decreased with the increase in the reinforcement rate

gives the assessment of copper MMCs with other nanoparticles.

2.3. Magnesium Metal Matrix Composites

Magnesium MMCs are made from a magnesium matrix reinforced through strong fibers, particles, or whiskers. These reinforcements can include materials like SiC, Al₂O₃B₄C, and TiB₂. The volume percentage of SiC nanoparticles in reinforced Mg-MMCs significantly affects F_c [156]; an experiment was designed using DOE and analysis was carried out using ANOVA and RSM [157]. The researchers in [158] used FFD and the SEM technique and the authors of [159] used ANOVA to elucidate the influence of Mg/TiB₂ and Mg/Ti MMC nano-sized reinforcements on the micro-machinability characteristics and suggested that the nanoparticles’ volume fraction has a considerable effect on the F_c. The Fc would increase with the Dc and feed per tooth in all specimens during the SEM experimental investigation using Mg/BN and Mg/ZnO MMCs in micro-milling [160]. The FE model was developed by [161] using Mg/Ti/TiB₂ and Mg/BN/ZnO nanomaterials and optimized using the ANOVA method. The author suggests the smaller tool wear and highest R_a observed at a medium V_c. The FEM model is used to design the experiment using Mg/Ti MMCs to measure flank wear and edge chipping. The largest tool wear was generated at the lowest feed per tooth [162]. The authors of [163] conducted a factor analysis to predict F_c during the micromachining of graphene-reinforced magnesium MMCs. The severe fluctuation of F_c in each case was revealed with relation to the serrated or even fragmentized chip formation. A micro-drilling experiment was conducted by [164] using Mg reinforced with SiO₂ nanoparticles to predict chip formation, surface morphology, and F_c using FFD and SEM, resulting in an increase in the rotation speed causing an increase in the fluidity of the material, which in turn causes a decrease in Ra.

The research reported in [165] effectively used Taguchi and ANOVA to optimize the machining parameters during the drilling of Mg/SiC/CNTs to improve process efficiency, quality, and reliability. Spindle speed and Fz were factors that impacted Ra. The experimental investigation was performed to optimize the machining parameters for AZ91/SiC composites to predict the Ra, life of the tool, and F_c. The author concluded that increasing the Fz leads to a tool life reduction [166]. Machinability studies were conducted during the turning of Mg/SiCp MMCs to calculate the affecting factors on forces, Ra quality, and the microstructure of the chip sand tool. The results show that V_c had a significant influence on all F_c values [167]. Another study examined the machinability of the RZ5/8 weight % TiB₂ of in situ MMCs under different cutting conditions [168] and suggested that the R_a also increased with an increase in F_z and D_c, whereas it decreased with an increase in V_c. An investigation was carried out by [169] on the machinability characteristics of Mg/RZ5/TiB₂ MMC. An investigation was carried out on the end milling of a Mg/TiO₂ nanocomposite [170] and suggested that the increase in the % concentration of TiO₂ affected the machinability characteristics and D_c directly influenced the performance of the machinability of the composite [171]. A further overview of the evaluation conducted on the recent literature on magnesium MMCs is appended in

Table A7. Evaluation of magnesium metal matrix composites.

Author	Conventional Machining Process	Particulate Reinforced Metal Matrices	Input/Output Parameters	Optimization Method	Result
Teng et al. (2015) [158], Teng et al. (2016) [159]	Milling	Mg/TiB2 and Mg/Ti MMC	Input: Spindle speed, Fz, Dc, and feed per tooth Output: Fc and surface morphology	FFD, SEM [160], and ANOVA [161]	Dc and spindle speed have a significant effect on Ra
Teng et al. (2017) [160]	Milling	Mg/BN and Mg/ZnO	Input: Spindle speed, Fz, and Dc Output: Fc, Ra, and chip morphology	SEM	Fc increased with the Dc and feed per tooth in all specimens
Teng (2018) [161]	Milling	Magnesium-based Ti, TiB2, BN, ZnO	Input: Spindle speed, Fz, and Dc Output: Fc, Ra, and chip morphology	FE model and ANOVA	Smaller tool wear and the highest Ra were observed at a medium Vc
Teng et al. (2018) [162].	Milling	Mg/Ti MMC	Input: Vc and Fz Output: Fc and Ra	FE model and SEM	The largest tool wear was generated at the lowest feed per tooth
Gao and Jia (2017) [163]	Milling	Mg/GR MMC	Input: Size of GNPs, weight fraction of GNPs, Vc, Dc, rake angle of tool, and edge radius Output: Fc and chip morphology	FE model and factor analysis	The severe fluctuation of Fc in each case was revealed with relation to the serrated or even fragmentized chip formation
Sun et al. (2020) [164]	Drilling	Mg/SiO2	Input: Rotation speed and Fz Output: Chip formation, surface morphology, and Fc	FFD and SEM	The increase in the rotation speed causes an increase in the fluidity of the material, which in turn causes a decrease in Ra
Babu et al. (2020) [165]	Drilling	Mg/SiC/CNTs,	Input: Spindle speed, Fz, drill diameter, and point angle Output: Burr formation and Ra	Taguchi and ANOVA	Spindle speed and Fz were the influencing factors for Ra
Asgari and Sedighi (2023) [166]	Turning	AZ91/SiC	Input: Vc, Fz, and Dc Output: Ra, tool life, and Fc	DOE and RSM	Increasing the Fz leads to tool life reduction
Gobivel and Vijay Sekar (2022) [167]	Turning	Mg/SiCp	Input: Spindle speed, Vc, Fz, and Dc Output: Machining forces, machined surface quality, chip microstructure, and tool morphology	SEM analysis	Vc had a significant influence on all Fc
Meher et al. (2022) [168]	Turning	RZ5/TiB2 MMC	Input: Vc, Fz, and Dc Output: Fc and surface quality	Taguchi and ANOVA	Ra also increased with an increase in Fz and Dc, whereas it decreased with an increase in Vc
Meher and Mahapatra (2023) [169]	Turning	RZ5/TiB2 in situ magnesium	Input: Vc, Fz, and Dc Output: Fc, Ra, chip morphology, and tool wear	FESEM	Fc increased with increases in Vc, Fz, and Dc
Radhakrishnan (2023) [170], Radhakrishnan et al. (2023) [171]	Milling	Mg/TiO2 nanocomposite	Input: Vc, Fz, and Dc Output: Fc, Ra, chip morphology, and tool wear	ANOVA and regression equation	Dc directly influenced the performance of the machinability of the composite

.

2.4. Titanium Alloy Metal Matrix Composites

Titanium matrix composites are appropriate for an extensive selection of high-performance presentations requiring lightweight materials, high strength, and corrosion resistance. Titanium MMCs have applications in the aerospace, automotive, marine, biomedical, and sports goods sectors.

Experimental investigations were conducted using Ti-6Al-4V/TiC MMCs on turning operation and modeled using the Kriging meta-modeling technique (KMMT) and Strength Pareto Evolutionary Algorithm (SPEA) to optimize the machining characteristics [172]. Microstructural and compositional investigations were carried out using a JSM 7600 TFE SEM equipped with an Oxford Energy-Dispersive X-ray Spectroscopy (EDX) [173], and wear in the tool was assessed with an Olympus SZ-X12 microscope [174]. SEM and EDX measurements were conducted on Al6061-4wt% TiC MMCs to analyze the dispersion of TiC [175]. The authors concluded that V_c becomes increasingly more important than F_z. Despite TiC reinforcement remaining in the workpiece material during the machining of Ti-6Al-4V/TiC MMCs; they may not have been sufficiently damaged to prevent adhesive wear in the initial wear zone and TiC reinforcement in the workpiece material is still there in the form, although these particles may not have been much harmed [176]. Microstructural assessments were performed to discover the wear mechanisms during the turning of the Ti-6Al-4V/TiC MMC. The F_z and V_c are considered as the major machining factors affecting flank wear and surface finish [177]. The milling experiment was conducted using in situ TiB₂/7050Al composite materials to investigate the Ra, residual stress, micro hardness, machining defects, and chip formation using ANOVA analysis [178]. The research was designed using Taguchi analysis to analyze the drilling parameters of Ti/TiB MMCs. The authors concluded that the Fz parameter influences thrust force and Ra [179]. The researchers in [180] conducted a milling experiment in a systematic approach combining experimental investigations and analytical techniques to minimize the tool wear of Ti-6Al-4V/TiC MMCs. The machinability of Ti and MWCNTs powders were used to minimize Fc, tool wear, and facial morphology. As a result, the explosive force blew away the hard TiC particles [181]. A machining analysis was performed using graphene nanoplatelets (GNPs) reinforced with Ti6Al4V and the investigation was conducted to predict the effect of the reinforcements during machinability. The results indicate that the GNPs content has a significant effect on R_a and F_c [182]. A face milling experiment was performed using Ti6Al4V MMCs using Taguchi-based GRA and ANOVA [183], and the results demonstrated that the tool feed has the highest contribution rate in the experimental work. A comparative study was conducted in [99] using the ultrasonically assisted turning (UAT) and conventional turning of SiCp/Al composites. Other recent studies on the evaluation of titanium alloy MMCs are appended in

Table A8. Evaluation of titanium alloy (Ti) MMCs.

Author	Conventional Machining Process	Particulate Reinforced Metal Matrices	Input/Output Parameters	Optimization Method	Result
Kishore et al. (2015) [175]	Turning	Al6061-TiC in situ MMC	Input: Vc, Fz, and Dc Output: Fc and Ra	Taguchi, ANOVA, EDX, and SEM	Fc and Ra are low at higher Vc values and lower feed and lower Dc values.
Duong et al. (2016) [176]	Turning	Ti-6Al-4V/TiC	Input: Vc, Fz, and Dc Output: Tool wear	SEM and EDX	TiC reinforcement in the workpiece material is still found in the form, but these particles were not affected strongly
Niknam et al. (2018) [177]	Turning	Ti-6Al-4V/TiC	Input: Vc, Fz, and Dc Output: Ra, flank wear, Fc, feed force, and thrust force	DOE and orthogonal array L9	Fz and Vc are considered as the major machining factors affecting flank wear and Ra
Ramkumar et al. (2019) [179]	Drilling	Ti/TiB	Input: Spindle speed, Fz, and processing technique Output: Trust force and Ra	Taguchi and ANOVA	The influencing parameter on thrust force and Ra is Fz
Kamalizadeh et al. (2019) [180]	Milling	Ti-6Al-4V/TiC MMC	Input: Spindle speed, Fz, and processing technique Output: Tool wear and Ra	Orthogonal array L9 DOE and ANOVA	Vc and Dc are the main cutting parameters affecting tool wear and Ra
Li et al. (2020) [181]	Milling	Ti/MWCNTs	Input: Spindle speed, Fz, and processing technique Output: Fc, tool wear, and facial morphology (Ra, hardness, flaws, and element distribution)	FFT analysis	Hard TiC particles were blown away by the explosion force
Nasr et al. (2020) [182]	Milling	Graphene nanoplatelets (GNPs)-reinforced Ti6Al4V	Input: GNP percentage, Fz, and Vc Output: Fc components, Ra, surface morphology, micro hardness, and chip morphology	SEM	Ra and Fc are considerably affected by the GNPs content
Das et al. (2022) [183]	Milling	Ti6Al4V MMCs	Input: Fz, Vc, and Dc Output: Longitudinal force, radial force, tangential force, Ra, and material removal rate	Taguchi, GRA, and ANOVA	Tool feed has the highest rate contribution

.

2.5. Molybdenum (Mo) MMCs

Molybdenum (Mo) composites are intended to improve certain qualities such as strength, hardness, wear resistance, and thermal conductivity, making them ideal for a variety of high-performance applications. Ceramic particles such as Al₂O₃, SiC, and TiB₂ can be used to reinforce structures.

An investigation was performed on the dry turning of Cu/Mo-SiCp composites [154]. The incorporation of SiC and Mo into an Al-Si alloy was fabricated using the stir casting method to calculate the influence of reinforcements [91].

3. Discussion on Future Trends of Conventional Machining on MMCs

The conventional machining of MMCs remains demanding, but it is a necessary facet of production, particularly in industries that demand high-performance materials. From this review, the base metals considered by most of the researchers during conventional machining are Al, Mg, Ti, Cu, and Mo alloys as summarized and shown in Figure 3.

3.1. Comparison of Base Materials

The majority of the 151 research papers (79.5%) conducted by the researchers used an aluminum alloy as a base metal, viz. 6061, 6063, 7075, 6065, 356, 7079, 7077, 2024, etc. As shown in Figure 3, the study also shows that the majority of research has focused on Al6061 or Al6063 as a base metal, with less emphasis on alternative alloys. The MMC used in conventional machining is also shown in Figure 4.

Among the studied conventional machining of CMMs, about 8.5% (Figure 4) used magnesium as a base metal, where SiC, TiB₂, BN, ZnO, Ti, SiO₂, and graphene are added in the form of reinforcement materials. The use of titanium alloy as a base metal is low (about 7%). The reinforcement materials used in titanium-based CMMCs are TiC, TiB₂, TiB, MWCNT, and GNPs. Copper alloys and molybdenum alloys are used as a base metal to a low extent (about 4% and 1%, respectively).

Furthermore, most of the research work was performed by the researchers by considering aluminum, magnesium, titanium, copper, and molybdenum as base metals during the conventional machining process. There is a lot of scope to work on iron, lead, manganese, nickel, silver, tin, bismuth, chromium, cobalt, gallium, gold, indium, etc., as the base metal. In this regard, it is the authors’ ambition that future research trends in this topic area focus on improved tooling development, cutting parameter optimization, and adaptive machining strategy implementation, additive manufacturing method integration to tackle machining challenges, sustainable conscious manufacturing, and improving the performance of MMC components.

3.2. Reinforcement Materials

The reinforcement phase in the MMCs is considered as a secondary phase. Figure 5 shows materials used as reinforcement in the MMCs’ fabrication. As shown below, 58% of the literature used silica as a reinforcement material; 11% of the literature used boron as a reinforcement material, 8% of the literature used titanium as a reinforcement material; 7.3% used alumina as a reinforcement material; 4% of the literature used zirconium as a reinforcement material; 4% of literature studies used graphene as a reinforcement material; and 2.6% used magnesium as a reinforcement material.

3.3. Machining Input and Output Responses

Most researchers consider machining characteristics such as the spindle speed, Dc, Vc, Fz, varying % of reinforcement, and tool material as parameters. The output response factors studied include surface roughness, temperature, cutting force, and tool wear. Moreover, there are also several research opportunities available, considering the tool geometry, input power, tool point angle, MQL, and output responses such as hardness, minimum energy consumption, flexural strength, surface integrity, corrosion, toughness, stress, etc. The hard and abrasive nature of the reinforcement materials within MMCs, such as ceramic particles or fibers, in particular, has a significant impact on tool wear, surface unevenness, and surface integrity problems, which can cause significant wear on cutting tools and affect the quality of the machined surface. As discussed earlier and summarized in Appendix A, several solutions are suggested or implemented by earlier research studies to mitigate these problems. While the selection of a proper tool material, optimization of the cutting parameters, the use of CO₂ cryogenic cooling [124], and regular tool maintenance are recommended to mitigate tool wear, high-precision tooling and advanced finishing processes are often recommended to improve the surface unevenness problem that may occur due to the inhomogeneous distribution of reinforcement particles in MMCs.

Surface integrity in MMC machining is a rather more complex issue since it manifests in the form of microcracks, residual stresses, and thermal damage [135], which can compromise the performance and durability of the machined part. These issues often arise due to the high cutting forces and temperatures involved in machining MMCs. As reported, several methods are suggested to remedy this problem including proper control of the cutting environment, implementing stress relief techniques such as post-machining heat treatment and laser surface annealing, tool path optimization, and the use of advanced machining methods such as EDM and LAM.

3.4. Sustainable Manufacturing Practices of MMCs

Even under typical cutting circumstances, excessive energy and resource consumption leads to the production of hazardous gases, particle emissions, and other pollutants; risks to one’s health and safety; excessive tool wear; and a decline in the surface quality of the machined elements. To address the issues of machinability and sustainability associated with traditional machining, creative sustainable methods have been developed, including heat-aided machining, tool treatment and texturing, dry and near-dry (minimal quantity lubrication and cryogenic) machining, etc.

Sustainable manufacturing practices in the machining of MMCs are vital to minimize the environmental impacts of the machining process, conserve resources, and ensure economic viability. These practices encompass various strategies and techniques that address the challenges associated with machining MMCs, which are typically more difficult to process than conventional metals due to their hardness and abrasiveness. In the machining of parts made of MMCs, the tool material and design optimization, coolant and lubricant management, waste management, process optimization, and automation are considered to be among the methods implemented to ensure sustainable manufacturing practices.

4. Metal Matrix Composites Computing Techniques

Several authors employ both hard and soft computing technologies in the conventional MMC machining process. “Hard computing” refers to traditional computing in which mathematical models are required for problem solutions. Several authors such as Karabulut [15], Thamizharasan [46], Wu et al. [48], Liu et al. [76], Liu et al. [95], Priyadarshi and Sharma [102,103], Sekhar et al. [118], Teng [161], Teng et al. [162], and Aramesh et al. [172] have used hard computing to achieve the results.

In addition, soft computing with approximation modeling techniques is increasingly being used in MMCs’ machining to handle the inherent complexity and challenges associated with the machining of advanced materials. Most of the researchers in this study use RSM, ANOVA, the Taguchi method, SEM analysis, LRA, DFA, ANN, and GRA. These approaches are very beneficial for handling complicated real-world issues when exact answers are difficult or impossible to find and constantly improving as computational intelligence and machine learning progress.

Some of the soft computing techniques can be used in future to optimize MMCs-related research, including fuzzy computing, neural networks, genetic algorithms, associative memory, adaptive resonance theory, classification, clustering, probabilistic reasoning, Bayesian networks etc. The optimization tasks commonly target the performance of the cutting tool such as the tool material and tool geometry optimization, as well as optimization of the machining processes such as the cutting parameters and optimized use of coolants and lubrication techniques. Nowadays, emerging technologies such as artificial intelligence and machine-learning techniques are being employed to conduct predictive modeling, process optimization, and the monitoring of machining operations to define cutting parameter sensitivity [32]. The emerging digital twin technology is also a technology that can be exploited in virtual machining simulations and real-time machining process monitoring leading to the optimum machining of parts made from MMCs.

5. Conclusions

In summary, the conventional machining of MMCs provides unique problems due to the heterogeneous nature of the advanced materials, which comprise a metal matrix supplemented with secondary phases such as nanomaterials, ceramics, and fibers. Despite these challenges, traditional machining processes remain crucial for shaping MMCs into the complex components needed for numerous high-performance applications in sectors such as aerospace, automotive, and electronics. Throughout this discussion, the authors examined the complexities and unique issues of machining MMCs, such as accelerated tool wear, surface imperfections, inadequate machinability, and process instability. However, researchers and industry practitioners are currently tackling these difficulties through innovative methods and using advances in machining technology.

Manufacturers can implement sustainable machining practices whilst benefiting from MMCs’ performance in a wide range of applications by improving resource utilization, minimizing waste generation, reducing the environmental impact, employing life cycle considerations and technological innovation, and ensuring sustainable manufacturing standards.

Key strategies for improving the conventional machining of MMCs include the development of advanced cutting tools with improved wear resistance and toughness, the optimization of cutting parameters to minimize the parameters, the implementation of effective coolant and lubrication techniques, the integration of adaptive machining strategies for real-time monitoring and control, and sustainable manufacturing.

This study revealed that the topic area and research opportunities of the machining of composites are very vast. Therefore, the literature review is limited to selected MMCs to shed light on the existing conventional machining techniques and identify some research directions. For instance, developing cutting tools with enhanced wear resistance, such as polycrystalline diamond or cubic boron nitride tools, as well as investigating novel coatings like nano-coatings or multi-layer coatings could be recommended for future research targeting improvements in tool performance when machining MMCs.

Future research can also explore hybrid machining processes such as laser-assisted machining, ultrasonic vibration-assisted machining, and electro-discharge machining. These methods have shown promise in reducing tool wear, improving surface finish, and managing the challenging properties of MMCs. Investigating eco-friendly coolant and lubrication methods, such as minimum quantity lubrication and cryogenic cooling, could help to reduce the environmental impact while enhancing machining performance. These advancements have not only helped to achieve sustainability, but have also increased productivity. Future studies could focus extensively on the optimization of machining processes to enhance the performance of these sustainable solutions.