Optimizing end milling parameters for AA7475 composite reinforced with Al 2 O 3 and TiO 2 nanoparticles synthesized via Friction Stir Process

Abstract

Manufacturing industries are substituting or replacing the existing monolithic materials with advanced materials because of their high strength-to-weight ratio. To achieve this, researches are focused on developing composite materials incorporating various types of reinforcements that can be utilized in diverse application. However, the machining process of metal matrix composites using end milling operations is challenging due to the presence of hard and abrasive reinforcements resulting in rapid tool wear and poor surface finish. This necessitates the optimization of machining parameters to achieve superior surface finish, minimal tool wear, and maximum material removal rate. In this study, an AA7475 reinforced with $A l_{2} O_{3}$ / $Ti O_{2}$ metal matrix composite was fabricated via friction stir processing. Subsequently, an end milling machining process was performed on the composite using a vertical CNC milling machine. A total of 27 experiments were conducted based on Taguchi’s orthogonal array experimental design. The experimental investigation revealed that the incorporation of Nano-scale particles enhanced the hardness of the composite, with the hardness increasing up to a tool rotational speed of 1375 rpm and a tool traverse speed of 40 mm/min. The Analysis of Variance (ANOVA) and regression modeling results indicated that the cutting speed had the most significant influence in reducing the surface roughness (Ra). The feed rate was identified as the most influential factor for enhancing the Material Removal Rate (MRR), and the cutting speed was also found to be the predominant factor in minimizing the Tool Wear Rate (TWR). The Taguchi-based Grey Relational Analysis approach determined the optimal parameter settings as: 1375 rpm tool rotational speed, 30 mm/min tool traverse speed, 1900 rpm spindle speed, 0.03 mm/rev feed rate, and 0.6 mm depth of cut (DOC).

Keywords

ANOVA friction stir processing

Introduction

Conventional monolithic materials have been confined to attaining a good combination of strength, stiffness, toughness, and ductility. To overwhelm these deficiencies and meet the ever-increasing property demands, conventional monolithic materials are replaced by the most promising alternative materials called composites. Pure aluminum and its alloys are the primary requirements in transportation, aerospace, and various structural applications due to its versatile properties.^1–3 Aluminum has the property of ductility, is malleable, and can be altered into different shapes simply.⁴ The requirement for further improvement of its properties has led to the emergence of new technologies for the fabrication of aluminum-based metal matrix composites. The hardness and strength increase substantially while reducing the ductility and plasticity in Aluminum metal matrix composites (AMMC).⁵

The manufacturing process of the metal matrix composite mainly depends on reinforcement informality, quality, and matrix metals. Based on the reinforcement materials MMCs can be fabricated by powder metallurgy, diffusion bonding, impregnation process, stir casting, 3D printing, and friction stir process.^6–8 The incorporation of ceramic reinforcements (such as Al₂O₃, SiC, B₄C) into aluminum matrices via techniques like stir casting, powder metallurgy, or FSP has been extensively studied to enhance mechanical properties.^9,10 The properties of the fabricated AMMC vary with the variation of matrix materials and the methods of fabrication along with the parameters used. In an experimental study by Reddy et al.,¹¹ by their study revealed that adding ceramic reinforcements to AA7475 improves its thermal and mechanical properties. Optimal graphite content (up to 9%) enhances tensile strength and hardness, while excess disrupts matrix continuity, reducing performance.¹²

The reinforcements can promote grain refinement in the matrix which enhances the hardness due to Hall-petch effect. Reddy et al.¹³ evaluated the microstructure and mechanical properties of composites fabricated by reinforcing graphite at various concentrations of 3, 6, 9, and 12 wt% and coconut shell ash at a constant weight of 10% into AA7475 developed by stir casting. The results showed that the addition of the reinforcements enhanced the tensile strength and hardness with the higher concentration. The uniform distribution of reinforcements into AA7475 is observed from the SEM image. The increase in temperature and duration increases the molecular bond strength between reinforcements.¹⁴

In an experimental study done by Boopathi et al.¹⁵ friction stir processing method is employed in the fabrication of aluminum alloy-based B₄C reinforced surface composite. The volumetric concentration, rotational speed, and travel speed are varied for the evaluation of the composite in terms of tensile strength and wear. Tool travel speed of 40 mm/min, tool rotational speed of 1400 rpm, and 15% of B₄C resulted in the highest value of 347 MPa in tensile strength and a minimum of 55 µm in wear. Friction stir processing is employed in the fabrication of aluminum-based surface composite with better improvement and least defects.¹⁶

Apparao et al.¹⁷ conducted a machining study on AA7075-SiC-Al₂O₃ composites to assess tool wear and material removal rate, optimizing spindle speed, feed rate, and depth of cut using Response Surface Methodology. Results showed minimal tool wear at 1000 rpm, 0.02 mm/rev, and 1 mm cut depth, while maximum MRR occurred at 1838 rpm, 0.04 mm/rev, and 1.81 mm cut. To evaluate surface roughness, Bhuvanesh Kumar et al.¹⁸ were done machining of AA7075-Zr $O_{2}$ -C MMCs by optimizing spindle speed, feed rate, and depth of cut using Grey Relational Analysis (GRA) and ANOVA technique. The optimal combination of end milling parameters is presented as a spindle speed of 1500 rpm, a feed rate of 0.1 mm/rev, a depth of cut of 0.1 mm, and 2.5% Zr $O_{2}$ 12.5% of graphite by weight in their study. Mahesha et al.¹⁹ compared the effect of reinforcement on AA7475-based ZrB₂ MMC. The MMC properties enhanced with the addition of the reinforcements. Many authors investigated MRR, surface roughness, and tool wear of AMMC for the characterization of the quality of machining and components.^20–23 To obtain the required quality of product and the machining efficiency optimization of the process parameters is the primary task.²⁴

The literature indicates that there are significantly fewer studies on the characterization and machinability of AA7475 reinforced with nanoparticles. In this study, hybrid Al₂O₃-TiO₂ nanoparticles are reinforced into AA 7475 alloy in order to explore the hardness of the composite. Most research focuses on the fabrication but overlooks post-processing steps of hybrid composites, leading to tool wear and surface integrity issues. The optimum process parameters for the fabrication and machining of AA7475-Al₂O₃-TiO₂ composite were identified using the Taguchi method and Grey Relational Analysis (GRA). The characterization was done to assess the input parameters of the Friction Stir Process (FSP) on the hardness. Furthermore, the machining was done to achieve an improved surface finish, enhanced material removal rate, and reduced tool wear during the end milling of the AA7475-Al₂O₃-TiO₂ composite. The effect of the hybrid Al₂O₃ and TiO₂ reinforcement with the AA7475 for matrix fabrication through friction stir process with the milling parameters on the matric is focused in this study.

Materials and methods

Materials

Composite materials

This experimental work utilizes AA7475 rolled alloy as the base metal, while the reinforcement materials are nano $A l_{2} O_{3}$ and $Ti O_{2}$ particles. Since the $A l_{2} O_{3}$ and $Ti O_{2}$ nanoparticles possess high hardness and strength exhibiting excellent thermal stability, they contribute significantly to the fabrication. The chemical composition of the thick rolled AA7475 is shown in Table 1. For the experiment, aluminum plates with dimensions 150 mm in length, 100 mm in width, and 2.5 mm in thickness were utilized. The properties of the TiO₂ and Al₂O₃ nanoparticles are presented in Table 2. It is tested that the base Al 7475 plate has the hardness value of 26.83 HRF before experiment.

Table 1.

Chemical composition of AA7475 plate.

Element	Z	Mg	Cr	Si	Ti	Fe	Mn	Cu	Al
Wt%	5.3	2.0	0.19	0.1	0.03	0.1	0.05	1.3	90.9

Table 2.

Properties of $A l_{2} O_{3}$ and $Ti O_{2}$ .

Specification	Al₂O₃ value	TiO₂ value
Purity (%)	99.9	99.9
Average particle size (nm)	30–50	30–50
Specific surface area (m²/g)	120–140	200–230
Atomic weight (g/mol)	101.96	79.8658
Bulk density (g/cm³)	0.2–0.4	0.15–0.25
Morphology	Near spherical	Near spherical

Tool and equipment

The alloy AA7475 undergoes heat treatment. Initially, the alloy is made as a plate. Then, to improve mechanical properties and refine the microstructure, the Friction Stir Processing (FSP) method is used, which entails plastic deformation and localized heating. This can be utilized to improve its strength and other characteristics. The experimental setup involved several equipment for different tasks. A DMTG CNC machine was used for preparing the workpiece, fabricating composite materials, and performing end milling operations. A lathe machine was employed to fabricate the friction stir tool. Surface roughness was measured using a Zeta Optical Microscope, while composite hardness was assessed with a Vickers hardness tester. An electronic weighing machine was used to measure weight loss during the wear test.

FSP tool

The tool used in this work is H13 and it is a type of steel alloy that is commonly used in the manufacturing of cutting tools due to its high strength, toughness, and resistance to heat and wear. Cutting tools made from H13 steel are known for their ability to maintain their shape and cutting edge even when subjected to high temperatures and heavy loads.²⁵ The FSP tool used has three components namely shank, shoulder, and pin.²⁶ Figure 1 shows the nomenclature of the FSP tool.

Figure 1.

Nomenclature of a straight cylindrical pin of friction stir processing tool.²⁶

The shape and the design of a tool have a significant influence on the amount of heat produced, the force required to move the tool, and the flow of the melted material in the work.²⁶ Table 3 shows the specification of the FSP tool used in this experimental work. The selection of the parameters is based on the amount of reinforcements. The flat face of the shoulder removes material scattering during the process.

Table 3.

Friction stir processsing tool geometry.

Pin dia.	Pin l.	Tilt angle	Tool material	Tool pin geometry	Tool shoulder diameter	Tool profile	D/d ratio
6 mm	2 mm	0°	H 13	Straight cylin. pin	18 mm	Flat	3

Methods

Experimental design

The experiment in this work is done based on Taguchi’s design of experiment. Taguchi’s design of the experiment is a standardized tool for deciding the number of experimental runs.¹⁸ The input variables of the Design of the experiment (DOE) are rotational speed and transverse speed with three levels for friction stir processing. Furthermore, spindle speed, feed, and depth of cut with three levels for end milling are considered based on the related works performed. Based on the input parameters L27 orthogonal array has been utilized for DOE to analyze the effect of input parameters on the Ra, MRR, and tool wear. Table 4 presents all the process parameters and their respective levels selected for the experiment.

Table 4.

Process parameters and values for the experiment.

Parameters		Level 1	Level 2	Level 3
FSP parameters	Tool traverse speed (mm/min)	20	30	40
	Tool rotational speed (rpm)	500	1375	2250
End milling parameters	Spindle speed (rpm)	1000	1450	1900
	DOC (mm)	0.6	0.9	1.2
	Feed rate (mm/rev)	0.03	0.065	0.1

Experimental procedure

Friction stir processing (FSP) is employed in this experimental work for the preparation of the AA7475-based Al₂O₃-TiO₂ hybrid reinforcement composite. The FSP is used for the fabrication of aluminum-based surface composite, which incorporates milling of the surface of the metal by rotating tool generating high heat.²⁷ This heat softens the material; with the rotation of the tool the materials are dispersed with the movement of the tool. Preparation of composite with reinforcement in FSP involves making of groove on the surface of the base metal.²⁸

The following procedures are utilized for the preparation of AA7475-Al₂O₃-TiO₂ composite.

1. Cleaning of base metal: cleaning of the workpiece is crucial for maintaining quality in FSP. Acetone chemical is used for the cleaning of workpieces, which is used to remove plasticized residue that accumulates on the work during FSP.²⁹

2. Groove making: in order to fabricate surface composite using FSP, making a groove on the surface is the primary task. Groove is prepared on the surface of the plate for precise insertion of nanoparticles.³⁰ Among different grooving techniques, straight grooves are made for the uniform mixing of the reinforcement materials along the length of the work.³¹ The groove dimensions depend on the amount of the reinforcement.³² This relation is shown in equation (1).

\begin{matrix} Theoretical volume fraction (vt) \\ = \frac{Area of the groove}{Projected area of the toolpin} \times 100 \end{matrix}

(1)

For 20 volumetric percentage of reinforcement,³² a groove with an area of 2.4 mm² is made on the surface of the AA7475 plate. Two parallel grooves are made on a single plate with a similar volume.

3. Mixing of hybrid nanoparticles: the mixing of the equal ratio of the reinforcements is done by using high-energy ball milling. Figure 2 shows the mixed reinforcements dispersed in the prepared grooves.

4. Fabrication of the composite: AA7475-based hybrid reinforcement metal matrix composites are fabricated using the H-13 tool on a vertical-centered CNC milling machine by varying the FSP parameters. Table 5 presents the constant and variable parameters utilized for the friction stir processing. Inorder to achieve higher amount dispersion in the FSP zone 2 passes of the tool are siutable.^33,34 To prevent the powders from scattering during the FSP, the groove surfaces were covered with a pinless tool before the processing.²⁶ Depending on the design of experiment nine composite samples are prepared by varying the process parameters.

Figure 2.

AA7475 plate with Al₂O₃ and TiO₂ powder before the FSP.

Table 5.

FSP parameters.

Rotational speed (rpm)	Processing speed (mm/min)	Tool tilt angle (°)	Tool penetration (plunge) depth (mm)	Number of passes
500, 1375 and 2250	20, 30 and 40	0	2	2

Machining responses

The machining process parameters significantly impact the surface finish of the machined composite material. To achieve a high-quality surface finish, it is crucial to optimize the cutting parameters. ZETA Surface Roughness Tester has been utilized for measuring the surface roughness of the machined composite. The average of three measured values at different locations is taken for the analysis.

The amount of materials removed is another criterion to measure the machining quality.^17,19,20 For obtaining the material removal rate during the machining of the composite, the well-known formula presented in equation (2) has been used.¹⁸

MRR = \frac{wDd}{t}

(2)

Where w is the width of a specimen in mm, D is the depth of cut in mm, d is the diameter of the milling cutter in mm, and t is the machining time in min.

Furthermore, machining quality is evaluated by tool wear during machining of composite fabricated by FSP.^22,35 Tool wear (Vb) is an ongoing failure of cutting tools due to steady operation. It is also defined as the amount of material removed from the tool per machining time. Tool wear during machining has been obtained by the rate of the difference of cutting tool weight before machining and after machining as presented in equation (3).³⁶

\begin{matrix} Tool wear \\ = \frac{weight of tool before machining - weight of tool after machining}{Machining time} \end{matrix}

(3)

Optimization technique

Taguchi’s orthogonal arrays are a type of highly efficient experimental design that focuses primarily on the estimation of main effects. In the Taguchi method, the variation in the responses is analyzed using the signal-to-noise (S/N) ratio, which provides insights into the process performance depending on the quality characteristics.^37,38 The ANOVA is used also used and it is used by most researchers to identify the most significant factors affecting the responses and to determine the contribution of each factor.³⁹

The Grey Relational Analysis (GRA) is used with the orthogonal array to analyze the combined effect of the process parameters on multiple responses. The GRA is performed by following four main steps.¹⁸ Normalization of the results found between zero and one followed by an absolute difference of the normalized value and grey relational coefficient. The last step is finding the average grey relational analysis.

The normalized values of the responses are calculated using the following Equations based on the performance characteristics. Equations (4) and (5) are used for finding normalized values for larger is better and smaller is better quality characteristics respectively.

x_{1 ij} = \frac{y_{ij} - \min (y_{ij})}{\max (y_{ij}) - \min (y_{ij})}

(4)

x_{1 ij} = \frac{\max (y_{ij}) - y_{ij}}{\max (y_{ij}) - \min (y_{ij})}

(5)

Where, $x_{ij}$ is the normalized value after the grey relational generation of response variables, and min (y_ij) is the minimum value from the experiment. Similarly, max (y_ij) is the maximum value obtained from the L27 Taguchi-designed responses.

From the normalized values, the absolute difference between each normalized value and the reference value $x_{0 j}$ regardless of the response variables is calculated using equation (6).

Δ_{ij} = | x_{0 j} - x_{ij} |

(6)

Where, $x_{0 j}$ is the maximum of the normalized value.

The grey relational coefficients (GRC) of the responses are then obtained using equation (7).

γ (x_{0 i}, x_{ij}) = [\frac{Δ_{\min} + ξ Δ_{\max}}{Δ_{ij} + ξ Δ_{\max}}]

(7)

Where ξ is the distinguishing coefficient with the value in the range of 0 and 1. In this case, medium 0.5 is used for the calculation.

Grey relational grade (GRG) of the responses is calculated with the use of equation (8).

Γ (X_{0 j}, X_{ij}) = \frac{1}{k * i} \sum_{i = 1}^{n} γ (x_{0 j}, x_{ij})

(8)

Where k is the number of replications

i is the number of responses

Results and discussions

Results on hardness of the composite

The hardness of the fabricated samples was evaluated using a Rockwell hardness tester. To ensure reliable and repeatable measurements, the hardness values were recorded at three distinct points on each sample, and an average value was calculated. The measurements were taken with a spacing of 1 mm from tangent to tangent, using a minor load of 10 kg and a major load of 60 kg, with a duration time of 15 s, in accordance with Rockwell hardness scale. Figure 3 presents the results of the hardness tests conducted on the Aluminum Metal Matrix Composites (AMMC) containing 20 wt% of nanoparticles, with varying tool rotational and traverse speeds.

Figure 3.

Rockwell hardness.

Tool rotational and traverse speeds in Friction Stir Processing (FSP) significantly influence grain refinement and mechanical performance. Moderate increases in rotational speed enhance hardness up to 1375 rpm by generating sufficient frictional heat for dynamic recrystallization and better matrix-reinforcement bonding. However, excessive speeds reduce hardness due to reinforcement particle scattering, while higher traverse speeds promote finer grain structure.⁴⁰ Researchers, such as Natrayan,¹² have reported similar findings. The incorporation of the reinforcing ceramic particles into the matrix material enhanced the overall hardness of the composites. The maximum hardness of 112.46 HRF, achieved with 20% Nano-particles, was higher than that of the unreinforced aluminum alloy. It is 46.43% higher than the base plate of Al 7475. The hardness values increased with the addition of the reinforcement particles and by optimizing the tool traverse speed up to 1350 rpm. Further increases in the tool rotational speed, however, led to a decrease in the hardness values causing grain coarsening.⁴¹

Machining results

Depending on the designed experiment, 27 experiments were done at different combinations of the process parameters on the prepared composite. Figure 4 depicts the machined composite materials of 27 experiments.

Figure 4.

Workpiece after machining.

Table 6 shows the results obtained from the machining experiment in terms of surface roughness, tool wear, and MRR.

Table 6.

Experimental results of each experiment.

Ext.	TRS (rpm)	TTS (mm/min)	Spindle speed (rpm)	Feed (mm/rev)	DOC (mm)	Ra (µm)	MRR (mm³/s)	TWR (g/s)
1	500	20	1000	0.03	0.6	0.4887	2.5793	0.00079
2	500	20	1000	0.03	0.9	0.5417	5.1586	0.00158
3	500	20	1000	0.03	1.2	0.5643	7.738	0.00238
4	500	30	1450	0.065	0.6	0.4287	7.8501	0.00241
5	500	30	1450	0.065	0.9	0.5737	15.7003	0.00483
6	500	30	1450	0.065	1.2	0.6056	23.5506	0.00724
7	500	40	1900	0.1	0.6	0.3406	15.8381	0.00974
8	500	40	1900	0.1	0.9	0.3573	31.6763	0.01461
9	500	40	1900	0.1	1.2	0.389	47.5145	0.01949
10	1375	20	1450	0.1	0.6	0.4717	12.0906	0.00372
11	1375	20	1450	0.1	0.9	0.6326	24.1815	0.00744
12	1375	20	1450	0.1	1.2	0.6663	36.2721	0.01116
13	1375	30	1900	0.03	0.6	0.1503	4.7513	0.00438
14	1375	30	1900	0.03	0.9	0.2194	9.5028	0.00584
15	1375	30	1900	0.03	1.2	0.2227	14.2543	0.0073
16	1375	40	1000	0.065	0.6	0.5375	5.4166	0.00166
17	1375	40	1000	0.065	0.9	0.5657	10.8333	0.00333
18	1375	40	1000	0.065	1.2	0.5887	16.25	0.005
19	2250	20	1900	0.065	0.6	0.2647	10.4166	0.00641
20	2250	20	1900	0.065	0.9	0.2911	20.8333	0.00961
21	2250	20	1900	0.065	1.2	0.3325	31.25	0.01282
22	2250	30	1000	0.1	0.6	0.5408	8.3333	0.00268
23	2250	30	1000	0.1	0.9	0.5826	16.6666	0.00512
24	2250	30	1000	0.1	1.2	0.6105	25	0.00769
25	2250	40	1450	0.03	0.6	0.3856	3.6353	0.00111
26	2250	40	1450	0.03	0.9	0.4516	7.2706	0.00223
27	2250	40	1450	0.03	1.2	0.5054	10.906	0.00335

Analysis of surface roughness

S/N ratio of surface roughness

Figure 5 shows the graphical representation of the mean S/N of Ra with the variation in the levels. The increase in the spindle speed from 1000 to 1900 rpm increased the S/N of Ra at a higher rate. This agrees with the findings of Kumar et al.,⁴² increase in cutting speed increases the S/N of Ra. With the increase in the cutting speed, the contact between the tool and work at specified position decreases, this reduces the Ra of the composite. The increase in the feed rate and DOC decreased the mean S/N of Ra. Similar effects were obtained by Bhuvanesh Kumar et al.¹⁸ and Rajeswari and Amirthagadeswaran.⁴³ Depending on the quality characteristics highest S/N ratio is obtained at the optimum parameters of 1900 rpm of spindle speed, 0.03 mm/rev of feed rate, 0.6 mm of DOC, 1375 rpm of tool rotational speed, and 30 mm/min of tool traverse speed.

Figure 5.

Mean S/N ratio graph for surface roughness.

Analysis of Variance (ANOVA) for surface roughness

The computed results of the ANOVA for Ra drawn from Minitab 21 software are shown in Table 7. The confidence level of 95% at the significant level of 0.05 (5%) is used for the analysis.

Table 7.

ANOVA of surface roughness.

Source	DoF	SS	MS	F-value	p-Value	% of contribution	Significance
TRS	2	0.006248	0.003124	3.22	0.067	1.169	Insignificant
TTS	2	0.005720	0.002860	2.95	0.081	1.071	Insignificant
Spindle speed	2	0.397937	0.198968	205.36	0.000	74.517	Significant
Feed rate	2	0.063830	0.031915	32.94	0.000	11.952	Significant
Depth of cut	2	0.044784	0.022392	23.11	0.000	8.386	Significant
Error	16	0.015502	0.000969			2.902
Total	26	0.534020

As shown in Table 7, spindle speed has the greatest impact on surface roughness (74.517%), followed by feed rate (11.952%) and depth of cut (8.386%). Tool rotational and traverse speeds have minimal effects. p-Values below the significance level confirm spindle speed, feed rate, and depth of cut significantly affect surface roughness.¹⁷ While the p-values for tool rotational speed and tool traverse speed are greater than the significance level, this shows that these factors are insignificant for the occurrence of the Ra.

Model Summary for Ra:

S	R-sq	R-sq (adj)	R-sq (pred)
0.0645987	83.59%	79.68%	74.17%

The model explains 83.59% of the variability in surface roughness (Ra), with adjusted and predicted R-squared values of 79.68% and 74.17%, indicating strong predictive accuracy and model reliability.

Analysis of material removal rate

S/N ratio of material removal rate

Figure 6 shows the effect of the variation in the levels of the process parameter on the mean S/N of MRR. Based on the quality characteristics of MRR the larger S/N is obtained at the feed rate of 0.1 mm/rev, spindle speed of 1900 rpm, and depth of cut of 1.2 mm. The increase in the depth of cut increased the mean of S/N of MRR. This is due to the amount of material carried by each pass with the increasing depth of cut. This agrees with the findings of Alam et al.³⁹ when characterizing the MRR. Similarly, the increase in the feed rate increased the mean S/N of MRR. As the material fed by each pass increases the material carried by the tool increases, which results in the increase of MRR. Similarly, the increase in the feed rate increased the mean S/N of MRR. As the material fed by each pass increases the material carried by the tool increases, which results in the increase of MRR.

Figure 6.

Mean S/N ratio graph for material removal rate.

Analysis of Variance (ANOVA) for material removal rate

The computed result for the ANOVA of the MRR drawn from Minitab 21 software is shown in Table 8. The confidence level of 95% at the significant level of 0.05 (5%) is used for the analysis.

Table 8.

ANOVA for material removal rate.

Source	DOF	SS	MS	F-value	p-Value	% contribution	Significance
TRS	2	41.55	20.77	1.11	0.354	1.293	Insignificant
TTS	2	43.89	21.95	1.17	0.335	1.366	Insignificant
Spindle speed	2	430.85	215.42	11.51	0.001	13.41	Significant
Feed rate	2	1279.80	639.90	34.20	0.000	39.83	Significant
Depth of cut	2	1117.45	558.73	29.86	0.000	34.78	Significant
Error	16	299.35	18.71			9.31
Total	26	3212.89

From Table 8, the feed rate is the most affecting parameter followed by the depth of cut, and spindle speed with the contribution of 39.83%, 34.78%, and 13.41% respectively. The tool rotational speed and tool traverse speed have the least influence on the MRR with contributions of 1.293% and 1.366% respectively. The p-values for spindle speed, feed rate, and depth of cut are less than the significant level, this shows these factors are significant for the occurrence of the MRR. As the p-value for the factors is less than the significant level then the significance of the factor is more.⁴⁴ While the p-values for the tool rotational speed and tool traverse speed are greater than the significant level this shows these factors have little effect on the MRR.

Model Summary for MRR:

S	R-sq	R-sq (adj)	R-sq (pred)
4.10922	88.96%	86.34%	78.92%

The model accounts for 88.96% of the variation in Material Removal Rate (MRR), with adjusted and predicted R-squared values of 86.34% and 78.92%, indicating high model accuracy and good predictive capability.

Analysis of tool wear rate

S/N ratio of tool wear

Figure 7 shows the variation of mean S/N of tool wear with the variation in levels of process parameters. Based on the quality characteristics of too wear the higher S/N is obtained at the feed rate of 0.03 mm/rev, spindle speed of 1000 rpm, DOC of 0.6 mm, 500 rpm of tool rotational speed, and 40 mm/min of tool traverse speed. The tool rotational speed and tool traverse speed plot show they have minimum effect on the tool wear.

Figure 7.

Mean S/N ratio graph for tool wear.

Analysis of variance (ANOVA) for tool wear

The computed results for the ANOVA of the tool wear drawn from Minitab 21 software are shown in Table 9. The confidence level of 95% at the significant level of 0.05 (5%) is used for the analysis.

Table 9.

ANOVA for tool wear.

Source	DOF	SS	MS	F-value	p-Value	% contribution	Significance
TRS	2	0.000012	0.000006	3.37	0.060	2.264	Insignificant
TTS	2	0.000010	0.000005	2.74	0.095	1.886	Insignificant
Spindle speed	2	0.000221	0.000110	62.31	0.000	41.69	Significant
Feed rate	2	0.000155	0.000077	43.66	0.000	29.24	Significant
Depth of cut	2	0.000105	0.000053	29.74	0.000	19.81	Significant
Error	16	0.000028	0.000002			5.283
Total	26	0.000530

As described in Table 9, spindle speed is the major influencing factor on the tool wear followed by feed rate, and DOC with the contribution of 41.69%, 29.24%, and 19.81% respectively. Tool rotational speed and tool traverse speed have little contribution to the tool wear. The p-values for spindle speed, feed rate, and depth of cut are less than the significant level, this shows these factors are significant for the occurrence of the tool wear. While the p-values for the tool rotational speed and tool traverse speed are greater than the significant level this shows these factors have little effect on the tool wear. Factors with a p-value greater than the significance level are significant for the occurrence of the responses.¹⁸

Model Summary for TWR

S	R-sq	R-sq (adj)	R-sq (pred)
0.0017142	88.36%	85.59%	79.15%

The model captures 88.36% of the variability in Tool Wear Rate (TWR), with adjusted and predicted R-squared values of 85.59% and 79.15%, demonstrating strong model fit and reliable prediction performance.

Regression model analysis

\begin{matrix} Ra = 0.6898 - 0.000021 Tool rotational speed \\ - 0.00073 Tool traverse speed - 0.000303 Spindle speed \\ + 1.685 Feed rate + 0.1623 DOC \end{matrix}

(9)

\begin{matrix} MRR = - 37.07 - 0.00148 Tool rotational speed \\ - 0.0066 Tool traverse speed + 0.01087 Spindle speed \\ + 240.9 Feed rate + 26.26 DOC \end{matrix}

(10)

\begin{matrix} Tool wear = - 0.01707 - 0.000001 Tool rotational speed \\ + 0.000026 Tool traverse speed + 0.000007 Spindle speed \\ + 0.0836 Feed rate + 0.00806 DOC \end{matrix}

(11)

From the regression model in equation (9), the increase in the speed decreases the Ra by reducing the contact between the tool and work during machining, this reduces the chance of leaving marks on the surface of the work. This agreed with the findings obtained during the machining experiment.^18,45 With the increasing feed rate and DOC, the material carried by the tool increases this leaves scratches on the surface of the work with each pass.⁴⁶ From the regression equation (10), with the increasing feed rate and DOC, the amount of material carried by the tool increases, which increases the MRR. With the increasing speed large chips are generated, which increases the MRR.⁴⁴ The negative constant in the model indicates the fitted line of MRR intercepts at −29.13 as the factors get zero.⁴⁷ From the regression equation (11), the increase in tool rotational speed decreases the tool wear. With the increasing spindle speed, the friction between the tool and the work increases which generates more heat. The heat produced increases the tool wear rate.¹⁷ The increase in the feed rate and DOC increases the contact between the tool and the work, which increases the wear of the tooltip.⁴³ The accuracy of the models can be confirmed by random selection.⁴⁸ In order to confirm the accuracy of the developed models, two experimental runs are selected and the respective percentage error is calculated. Table 10 shows the results of the prediction and the experimental values. The percentages of error are less than 10% this indicates the developed models are good for prediction. The coefficient of determination (R²) of the responses obtained from Minitab in Table 11 indicates the regression model is dependable.

Table 10.

Confirmation results for the regression.

Predicted value			Experimental values			% error
Ra	Tool wear	MRR	Ra	Tool wear	MRR	Ra	Tool wear	MRR
0.3893	0.01326	30.3111	0.3573	0.01461	31.676	8.956	9.24	4.309
0.63311	0.004997	18.0038	0.5826	0.00512	16.666	8.669	2.398	8.023

Table 11.

Response of regression analysis.

Ra		MRR		Tool wear
S	R ²	S	R ²	S	R ²
0.0645987	83.59%	4.10922	88.96%	0.0017142	88.36%

Contour plot

Figure 8(a) shows the effects of the relationship between tool rotational speed and tool traverse speed on Ra. Lower Ra less than 0.2 µm is found in the lighter region within the tool rotational speed around 1375 rpm with respect to the tool traverse speed of 30 mm/min. Also, Figure 8(b) depicts the effects of the relationship between tool rotational speed and spindle speed on Ra. Lower Ra less than 0.2 µm is found in the lighter region within the tool rotational speed around 1375 rpm with respect to the spindle speed of 1900 rpm. Furthermore, Figure 8(c) illustrates the effects of the relationship between tool traverse speed and spindle speed on Ra. Lower Ra less than 0.2 µm is found in the lighter region within the tool traverse speed of around 30 mm/min with respect to the spindle speed of 1900 rpm. Figure 8(d) shows the effects of the relationship between tool rotational speed and feed rate on Ra. Lower Ra between 0.2 and 0.3 µm is found in the lighter region within the tool rotational speed around 1375 rpm with respect to the feed rate 0.03 mm/rev.

Figure 8.

Contour Plots of surface roughness: (a) Contour Plot of Ra versus tool rotational speed, tool travel speed, (b) Contour Plot of Ra versus tool rotational speed, spindle speed, (c) Contour Plot of Ra versus tool travel speed, spindle speed, and (d) Contour Plot of Ra versus tool rotational speed, feed rate.

Figure 9(a) describes the effects of the relationship between tool rotational speed and tool traverse speed on MRR. Higher MRR between 20 and 30 mm³/s is found in the darker region within the tool rotational speed greater than 1375 rpm with respect to the tool traverse speed around 20 mm/min. Similarly, Figure 9(b) depicts the effects of the relationship between tool rotational speed and spindle speed on MRR. Higher MRR between 20 and 30 mm³/s is found in the darker region within the tool rotational speed less than 1375 rpm with respect to the spindle speed greater than 1000 rpm. Also, Figure 9(c) illustrates the effects of the relationship between tool traverse speed and spindle speed on MRR. Higher MRR between 20 and 30 mm³/s is found in the darker region within the tool traverse speed around 20 mm/min with respect to the spindle speed greater than 1000 rpm and in the region within the tool traverse speed around 40 mm/min with respect to spindle speed of 1900 rpm. Figure 9(d) shows the effects of the relationship between tool rotational speed and feed rate on MRR. Higher MRR between 30 and 40 mm³/s is found in the darker region within the tool rotational speed around 500 rpm with respect to the feed rate 0.1 mm/rev.

Figure 9.

Contour Plots of material removal rate: (a) Contour Plot of MRR versus tool rotational speed, tool travel speed, (b) Contour Plot of MRR versus tool rotational speed, spindle speed, (c) Contour Plot of MRR versus tool travel speed, spindle speed, and (d) Contour Plot of MRR versus tool rotational speed, feed rate.

Figure 10(a) indicates the combined effects of different process parameters on tool wear. In Figure 10(a), minimal tool wear (below 0.003 g/s) occurs in dark-shaded regions at higher tool rotational speeds above 1375 rpm and a tool traverse speed of about 40 mm/min, as well as at lower speeds near 500 rpm with a traverse speed around 20 mm/min. In Figure 10(b), low tool wear is observed at a rotational speed of 2250 rpm and spindle speed near 1450 rpm, and also at 500 rpm with a spindle speed of 1000 rpm. Figure 10(c) presents a similar trend between tool traverse speed and spindle speed, showing minimal wear at 40 mm/min with 1450 rpm and 20 mm/min with 1000 rpm. Lastly, Figure 10(d) reveals reduced tool wear below 0.003 g/s when the tool rotational speed is under 1375 rpm and the feed rate is below 0.065 mm/rev, indicating optimal process combinations for reduced wear.

Figure 10.

Contour Plots of tool wear rate: (a) Contour Plot of tool wear versus tool rotational speed, tool travel speed, (b) Contour Plot of tool wear versus tool rotational speed, spindle speed, (c) Contour Plot of tool wear versus tool travel speed, spindle speed, and (d) Contour Plot of tool wear versus tool rotational speed, feed rate.

Grey Relational Analysis

The normalized values for the surface roughness and tool wear are calculated by using equation (5). The normalized value for material removal rate is calculated based on equation (4). The respective absolute differences of the responses are calculated based on the normalized values by using equation (6). Table 13 displays the summary of the calculated normalized and absolute difference values.

The Grey Relational Coefficient (GRC) of the response for each experimental run and the respective Grey Relational Grade (GRG) calculated using equations (7) and (8) are shown in Table 12.

Table 12.

Normalized, absolute difference values, GRC, and GRG.

No	Normalized value			Absolute difference			GRC			Total GRC	GRG	Rank
	Ra	MRR	TWR	Ra	MRR	TWR	Ra	MRR	TWR	Total GRC	GRG	Rank
1	0.344	0	1	0.656	1	0	0.433	0.333	1	1.766	0.589	6
2	0.241	0.057	0.958	0.758	0.943	0.042	0.397	0.347	0.922	1.666	0.555	10
3	0.198	0.115	0.915	0.802	0.885	0.085	0.384	0.361	0.855	1.599	0.533	14
4	0.46	0.117	0.913	0.540	0.883	0.087	0.481	0.362	0.852	1.695	0.565	7
5	0.179	0.292	0.784	0.821	0.708	0.216	0.379	0.414	0.698	1.491	0.497	21
6	0.118	0.467	0.655	0.882	0.533	0.345	0.362	0.484	0.592	1.437	0.479	25
7	0.631	0.295	0.521	0.369	0.705	0.479	0.576	0.415	0.511	1.501	0.500	20
8	0.599	0.648	0.261	0.401	0.352	0.739	0.555	0.586	0.404	1.545	0.515	19
9	0.537	1	0	0.463	0	1	0.519	1	0.333	1.853	0.618	2
10	0.377	0.212	0.843	0.623	0.788	0.157	0.445	0.388	0.761	1.595	0.532	15
11	0.065	0.481	0.644	0.935	0.519	0.356	0.349	0.490	0.584	1.423	0.474	27
12	0	0.750	0.445	1	0.250	0.555	0.333	0.666	0.474	1.474	0.491	24
13	1	0.048	0.808	0	0.952	0.192	1	0.344	0.723	2.067	0.689	1
14	0.866	0.154	0.729	0.134	0.846	0.270	0.789	0.371	0.649	1.810	0.603	4
15	0.859	0.259	0.652	0.140	0.740	0.348	0.781	0.403	0.589	1.774	0.591	5
16	0.250	0.063	0.953	0.750	0.937	0.047	0.399	0.348	0.915	1.662	0.554	11
17	0.195	0.184	0.864	0.805	0.816	0.136	0.383	0.379	0.786	1.549	0.516	18
18	0.150	0.304	0.775	0.850	0.696	0.225	0.370	0.418	0.689	1.478	0.493	23
19	0.778	0.174	0.699	0.222	0.826	0.301	0.693	0.377	0.625	1.695	0.565	8
20	0.727	0.406	0.528	0.273	0.594	0.472	0.647	0.457	0.515	1.619	0.540	12
21	0.647	0.638	0.357	0.353	0.362	0.643	0.586	0.580	0.437	1.603	0.535	13
22	0.243	0.128	0.899	0.757	0.872	0.101	0.398	0.364	0.832	1.594	0.531	16
23	0.162	0.314	0.768	0.838	0.687	0.231	0.374	0.421	0.683	1.479	0.493	22
24	0.108	0.499	0.631	0.892	0.501	0.369	0.359	0.499	0.575	1.434	0.478	26
25	0.544	0.024	0.983	0.456	0.976	0.017	0.523	0.339	0.967	1.829	0.610	3
26	0.416	0.104	0.923	0.584	0.896	0.077	0.461	0.358	0.867	1.686	0.562	9
27	0.312	0.185	0.863	0.688	0.815	0.137	0.421	0.380	0.785	1.586	0.529	17

The average grey relational grade is then calculated by fitting the average GRC of the levels and factors with the L27 orthogonal array in Table 6. Table 13 shows the calculated results of the average GRG.

Table 13.

Average GRG.

Level	Factors
	Tool rotational speed	Tool travel speed	Spindle speed	Feed rate	DOC
1	0.539023	0.534829	0.526984	0.584534	0.5705
2	0.549353	0.547411	0.526523	0.527039	0.5284
3	0.537942	0.544079	0.572812	0.514745	0.5274

From Table 12 experiment number 13 yielded the first rank in GRG. The experiment with the higher GRG has a better quality of product.¹⁸ The effect of the factors and the optimum value of the individual factor are determined from the GRG.

Table 13 depicts the best results that can be obtained with the optimum combination of the parameters. According to the table, a higher average GRG is obtained at the second level of tool rotational speed, second level tool traverse speed, third level of spindle speed, first level of feed rate, and first level of DOC. Milling the composite material made with 1375 rpm of tool rotational speed and 30 mm/min of tool traverse speed by the milling parameters 1900 rpm of spindle speed, 0.03 mm/rev of feed rate, and 0.6 mm of DOC yield better results.

Confirmation test

The validation experiment was performed utilizing the identified optimal parameter arrangement, and the results are presented in Table 14. The corresponding improvement is calculated as reported by Muaz and Choudhury.⁴⁹

Table 14.

Confirmation results.

Responses	Initial value	Experimental value	% improvement
Ra (µm)	0.1503	0.1456	3.12
MRR (mm³/s)	4.7513	4.7513	0
Tool wear (g/s)	0.00438	0.00433	1.14

By using the following formula (12) percentage of improvement was calculated in Table 14.

% improvement = \frac{E x p e r i m e n t a l v a l u e - I n t i a l v a l u e}{I n i t i a l v a l u e} * 100

(12)

While Muaz and Choudhury focused on SiC-reinforced composites, their percentage improvement provides a validated framework for comparing property enhancement.

The validation experiment revealed that the optimal process parameters for the 7475/ $A l_{2} O_{3}$ / $Ti O_{2}$ hybrid metal matrix composite fabricated by friction stir processing with a tool rotational speed of 1375 rpm and tool traverse speed of 30 mm/min by milling parameters spindle speed of 1900 rpm, feed rate of 0.03 mm/rev, and DOC of 0.6 mm better improvements were obtained. Under these optimal conditions, Ra of 0.1456 µm, the MRR of 4.7513 mm³/s, and the tool wear rate of 0.00292 g/s are obtained.

The values obtained from the confirmation experiment were in close agreement with the optimized parameters. However, there was a slight variation in the surface roughness and tool wear rate, as shown in Table 14. This can be attributed to changes in the cutting tool weight and machining conditions. The improvement rates for the surface roughness and tool wear rate were 3.12% and 1.14% respectively. As compared to Rajeswari and Amirthagadeswaran⁴³ results obtained while milling composite prepared from AA 7075 with reinforcement of 5 wt% $A l_{2} O_{3}$ , 63.23% improvements is obtained in this study in terms of surface roughness. Significant improvement in terms of tool wear is achieved than the results presented in milling MMC prepared from Al7075-SiC-Al₂O₃.¹⁷

Conclusion

This study assessed the end milling of Al7475-Al₂O₃-TiO₂ hybrid composites fabricated by friction stir process. The machining performance of the fabricated hybrid metal matrix composite is evaluated in terms of surface roughness, material removal rate, and tool wear rate. The results of machining are analyzed and optimized using Taguchi-based GRA.

The study discovered that the highest hardness values with 46.43% increment from base alloy found in composite fabricated with 1375 rpm tool rotational speed and 40 mm/min tool traverse speed beyond that the value of hardness shows decrease.

The machining responses showed, with the optimum process parameters tool rotational speed of 1375, tool traverse speed of 30 mm/min, cutting speed of 1900 rpm, feed rate of 0.03 mm/rev, and DOC of 0.6 mm, a least Ra of 0.1503 µm is obtained. A maximum MRR of 47.5145 mm³/s is obtained with the process parameters tool rotational speed of 500 rpm, tool traverse speed of 40 mm/min, cutting speed of 1900 rpm, feed rate of 0.1 mm/rev, and DOC of 1.2 mm. Tool wear of 0.00079 g/s is obtained with the optimum process parameters tool rotational speed of 500 rpm, tool traverse speed of 20 mm/min, cutting speed of 1000 rpm, feed rate of 0.03 mm/rev, and DOC of 0.6 mm.

The ANOVA and regression model results showed that the cutting speed has the most significant influence on reducing Ra. The feed rate is the most significant factor for increasing the MRR and it is also found that the cutting speed is the most influencing factor in minimizing tool wear rate.

The Taguchi-based GRA showed the optimum parameters for the evaluation conditions are 1375 rpm tool rotational speed, 30 mm/min tool traverse speed, 1900 rpm speed, 0.03 mm/rev feed, and 0.6 mm DOC. The confirmation experiment by the optimized process parameters showed an improvement rate of 3.12%, and 1.14% in terms of Ra and tool wear rate respectively.

Recommendation

The optimization of AA7475 aluminum alloy reinforced with Al₂O₃ and TiO₂ nanoparticles using friction stir processing shows strong potential for improving material properties. Future research should investigate other aluminum alloys, reinforcement combinations, and additional machining parameters to better understand their influence on performance. This study is valuable for aerospace and automotive industries producing high-strength, lightweight components, as well as for researchers focused on enhancing the characterization and machinability of hybrid metal matrix composites for advanced engineering applications.

Footnotes

Appendix

Handling Editor: Riccardo Nobile

Author note

We, the authors, hereby declare that this manuscript is our original work, has not been previously published, and contains no plagiarized material. All sources used have been properly cited, and we confirm that all authors have read and approved the final version of the manuscript.

ORCID iD

Semegn Cheneke Lemessa

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

All data generated or analyzed during this study are included in the manuscript.

References

Yue

, et al. Development and applications of aluminum alloys for aerospace industry. J Mater Res Technol 2023; 27: 944–983.

Sun

The use of aluminum alloys in structures: review and outlook. Structures 2023; 57: 105290.

Khalid

Umer

Khan

KA.

Review of recent trends and developments in aluminium 7075 alloy and its metal matrix composites (MMCs) for aircraft applications. Results Eng 2023; 20: 101372.

Singh

Jawalkar

Belokar

RM.

Analysis of mechanical properties of AMC fabricated by vacuum stir casting process. Silicon 2020; 12(10): 2433–2443.

Ranga Raj

Yoganandh

Senthil Saravanan

, et al. Mechanical and tribological properties of graphene-reinforced bimodal AA7075 composites. Trans Indian Inst Met 2024; 77(6): 1651–1660.

Sharma

Goyal

Bharadwaj

, et al. Application of metal matrix composite fabricated by reinforcement materials – a review. Mater Today Proc. Epub ahead of print 4 March 2023. DOI: 10.1016/j.matpr.2023.02.287.

Shamsudin

Ali

Kamarudin

, et al. Biocompatibility and strength tests of PLA/SS316L composite material for orthopedic implant applications utilizing the 3D printing technique. AIP Conf Proc 2023; 2955: 020030.

Sheng

Yang

, et al. Microstructure evolution and mechanical properties of Ni₃Al/Al₂O₃ composite during self-propagation high-temperature synthesis and hot extrusion. Mater Sci Eng A 2012; 555: 131–138.

Akgümüş Gök

Bayraktar

Hoşkun

. A review on processing, mechanical and wear properties of Al matrix composites reinforced with Al₂O₃, SiC, B₄C and MgO by powder metallurgy method. J Mater Res Technol 2024; 31: 1132–1150.

10.

Narhari Tekale

Dolas

. Study of fabrication methods and various reinforcements with aluminium for automotive application – a review. Mater Today Proc 2022; 62: 2768–2773.

11.

Reddy

KSK

Kannan

Karthikeyan

. Evaluation of mechanical and thermal properties aluminium-7475 reinforced with graphite and fly ash. E3S Web Conf 2021; 309: 01186.

12.

Natrayan

Hardness of Al₂O₃/Gr hybrid composites reinforced by AA7475-T761 and encapsulated with novel encapsulation techniques. J Surv Fish Sci 2023; 10(1): 855–865.

13.

Reddy

KSK

Kannan

Karthikeyan

, et al. Evaluation of mechanical and thermal properties of Al 7475–CSA–graphite hybrid metal matrix composites. Int J Interact Des Manuf 2024; 18(5): 2983–2990.

14.

Sheng

Yang

, et al. Influence of heat treatment on interface of Cu/Al bimetal composite fabricated by cold rolling. Compos Part B Eng 2011; 42(6): 1468–1473.

15.

Boopathi

Thillaivanan

Pandian

, et al. Friction stir processing of boron carbide reinforced aluminium surface (Al-B4C) composite: mechanical characteristics analysis. Mater Today Proc 2022; 50: 2430–2435.

16.

Choi

Shim

Kim

Reduction of defects by friction stir processing for additively manufactured cast aluminum alloys (AlSiMg). Int J Precis Eng Manuf Technol 2024; 11(4): 1193–1205.

17.

Apparao

Vempala

Shalik

, et al. Process design and optimization of end milling parameters of Al 7075 metal matrix composite. Thesis. Anil Neerukonda Institute of Technology, India, 2021.

18.

Bhuvanesh Kumar

Sathiya

Parameshwaran

. Parameters optimization for end milling of Al7075–ZrO2–C metal matrix composites using GRA and ANOVA. Trans Indian Inst Met 2020; 73(11): 2931–2946.

19.

Mahesha

Suprabha

Suresh Kumar

, et al. Effect of ZrB₂ particles on machining parameters of AA7475 alloy-based composites by optimization technique. Adv Mater Sci Eng 2023; 2023(1): 1–9.

20.

Gopinath

Senthilkumar

Improving productivity through maximizing MRR in machining silicon nitride ceramic reinforced aluminium alloy 5052. Mater Today Proc 2023; 79: 122–126.

21.

Babu

Senthilkumar

Influence of TiN coated carbide inserts in turning AA5052+MoS₂+Si₃N₄ HMMC for minimizing surface roughness and comparing the results with uncoated carbide inserts performance. AIP Conf Proc 2024; 2853(1): 020096.

22.

Pugazhenthi

Dinaharan

Selvam

, et al. Influence of cutting speed and particulate content on polycrystalline diamond tool wear in machining AA7075/TiB2 in situ aluminum matrix composites. Measurement 2024; 234: 114873.

23.

Yahul

Saravanan

A comparative study on CNC contour milling performance of AA7475 with 10 % Fly Ash reinforced novel composite with AA7475 for improving surface finish. Mater Today Proc 2023; 79: 159–163.

24.

Sarmah

Gupta

A review on the machinability enhancement of metal matrix composites by modern machining processes. Micromachines 2024; 15(8): 947.

25.

Thanikodi

Britto

ASF

Dattu

VSNC

, et al. Machining and mechanical characterization of friction stir processed (FSP) surface hybrid composites (AA8014 + TiB₂+ ZrO₂). Int J Adv Manuf Technol. Epub ahead of print 26 August 2023. DOI: 10.1007/s00170-023-12198-z.

26.

Vaidyanathan

Sivaraman

Patel

, et al. A review on the effects of shoulder diameter to pin diameter (D/d) ratio on friction stir welded aluminium alloys. Mater Today Proc 2021; 45: 4792–4798.

27.

Dwivedi

Sharma

, et al. Effect of nano-TiO₂ particles addition on dissimilar AA2024 and AA2014 based composite developed by friction stir process technique. J Mater Res Technol 2023; 26: 1872–1881.

28.

Jain

Yadav

Siddiquee

, et al. Fabrication of surface composites on different aluminium alloys via friction stir process - a review report. Aust J Mech Eng 2023; 21(5): 1489–1512.

29.

Molla Ramezani

Davoodi

. Evaluating the influence of various friction stir processing strategies on surface integrity of hybrid nanocomposite Al6061. Sci Rep 2024; 14(1): 1–15.

30.

Ashokkumar

Sonar

Thirugnanasambandam

, et al. Microstructure and mechanical properties of Al₂O₃/AZ61 Mg alloy surface composite developed using friction stir processing and groove reinforcement filling processes. Mater Test 2024; 66(11): 1920–1930.

31.

Sharma

Patel

Badheka

, et al. Different reinforcement strategies of hybrid surface composite AA6061/(B4C+MoS₂) produced by friction stir processing. Materwiss Werksttech 2020; 51(11): 1493–1506.

32.

Sharma

Tripathi

PK.

Approaches to measure volume fraction of surface composites fabricated by friction stir processing: a review. Measurement 2022; 193: 110941.

33.

Saxena

Bongale

Kumar

, et al. Tribological and hardness analyses of friction-stir-processed composites using the Taguchi approach. Materials 2023; 16(1): 420.

34.

Leszczyńska-Madej

Madej

Wąsik

, et al. Microstructural homogenization and mechanical enhancement of aluminum matrix composites via multi-pass friction stir processing with SiC reinforcements. Int J Adv Manuf Technol 2024; 134(3): 2035–2050.

35.

Vignesh Kumar

Dhandapani

. A study on die-sinking electrical discharge machining behaviour of friction stir processed AA5052/WS₂ surface composite. Proc Inst Mech Eng Part E: J Process Mech Eng. Epub ahead of print 5 May 2024. DOI: 10.1177/09544089241251661.

36.

Kibria

Bhattacharyya

Microelectrical discharge machining of Ti-6Al-4V: implementation of innovative machining strategies. Elsevier Ltd, 2017.

37.

Juliyana

Prakash

Čep

, et al. Taguchi optimization of Wire EDM process parameters for machining LM5 aluminium alloy. PLoS One 2024; 19(10): 1–26.

38.

Mahmood

Tahir

Hikmat

, et al. Experimental investigation of the surface roughness for aluminum alloy AA6061 in milling operation by Taguchi method with the ANOVA technique. J Eng 2024; 30(3): 1–14.

39.

Alam

Tomal

ANMA

Nayeem

MK.

High-speed machining of Ti–6Al–4V: RSM-GA based optimization of surface roughness and MRR. Results Eng 2023; 17: 100873.

40.

Gupta

MK.

Friction stir process: a green fabrication technique for surface composites—a review paper. SN Appl Sci 2020; 2(4): 1–14.

41.

Ragu Nathan

Balasubramanian

Rao

, et al. Effect of tool rotational speed on microstructure and mechanical properties of friction stir welded DMR249A high strength low alloy steel butt joints for fabrication of light weight ship building structures. Int J Light Mater Manuf 2023; 6(4): 469–482.

42.

Kumar

Senthil Kumar

Murugan

, et al. Optimization of CNC end milling process parameters of low-carbon mold steel using response surface methodology and grey relational analysis. Adv Mater Sci Eng 2021; 2021(10): 11–21.

43.

Rajeswari

Amirthagadeswaran

KS.

Experimental investigation of machinability characteristics and multi-response optimization of end milling in aluminium composites using RSM based grey relational analysis. Measurement 2017; 105: 78–86.

44.

Waghmare

Patil

Chaudhari

Application of Taguchi method, ANOVA analysis, and TOPSIS technique in optimization of process parameters for surface roughness and material removal rate in electrochemical machining of Al-SiC MMCs. Indian J Sci Technol 2024; 17(16): 1633–1642.

45.

Harikrishnan

Prediction of surface roughness in end miliing process of Al 7075/B4C using response surface methodology. Int J Sci Eng Res 2016; 7(4): 89–94.

46.

Bhushan

Kumar

Das

Machining performance of 7075 Al alloy SiC metal matrix composite with HSS and carbide tool. J Manuf Technol Res 2021; 5(1/2): 17–41.

47.

Outeiro

Cheng

Chinesta

, et al. Modelling and optimization of machining of Ti-6Al-4V titanium alloy using machine learning and design of experiments methods. J Manuf Mater Process 2022; 6(3): 1–22.

48.

Sivaiah

Chakradhar

Modeling and optimization of sustainable manufacturing process in machining of 17-4 PH stainless steel. Measurement 2019; 134(1): 142–152.

49.

Muaz

Choudhury

SK.

Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel. Measurement 2019; 138(1): 557–569.