Multi-Objective Optimization of Micromachining Parameters for Titanium Alloy Ti-3Al-2.5V Using Grey Relational Analysis ^†

Abstract

This research investigates the multi-objective optimization of micro-milling processes for the titanium alloy Ti-3Al-2.5V (grade 9) through the application of grey relational analysis. The incorporation of nanometer-sized particles in hybrid machining lubricants plays a crucial role in improving heat transfer during machining. The approach aims to increase the efficiency and effectiveness of micro-milling by addressing various performance metrics simultaneously, leading to better machining results for this titanium alloy. Additionally, the integration of nanoparticles into the machining lubricant significantly improves the lubrication properties, reducing friction during the machining process. The study analyzed four machining parameters: machining speed, rate of feed, axial depth of cut, and the weight percentage concentration of hybrid machining lubricants Multi-wall Carbon Nano Tube and Alumina Oxide (MWCNT and Al₂O₃). The machining nanolubricant was formulated by adding 1% and 2% volume concentrations of MWCNT and Al₂O₃ nanoparticles to the industrial machining fluid. In this machining context, the friction between the machining tool and the Ti-3Al-2.5V work piece is a vital factor influencing the output quality. The results demonstrate that the chosen machining parameters and machining lubricants have a direct impact on the coefficient of friction and surface roughness. The study concludes that utilizing machining nanolubrication for machining Ti-3Al-2.5V (grade 9) significantly enhances the quality compared with traditional machining lubricants.

1. Introduction

Titanium grade 9 (Ti-3Al-2.5V) is widely regarded as an excellent material for various industrial applications due to its beneficial properties. It is commonly utilized across multiple sectors, particularly in precision manufacturing, where it is used to fabricate honeycomb structures for advanced aerospace applications and certain biomedical components [1]. Additionally, micro-milling has gained traction in the medical, aerospace, and electronics industries, fueled by the increasing demand for miniature products in these areas. Numerous research studies aimed to explore the impacts of different micro-milling process parameters, such as feed rate, machining speed, and depth of cut, on the surface quality, chip formation, and tool flank wear associated with titanium grade 9. The economics of metal machining processes are heavily influenced by the choice of optimal machining parameters, including machining speed, feed rate, and depth of cut [2]. To maximize performance, it is essential to fully utilize the capabilities of machine tools. Various input factors, including tool path design, work piece material, and machining fluid, affect tool wear, surface finish, machining forces, and other output characteristics, similar to any manufacturing process [3]. At the microscale, the interaction of these process inputs becomes even more significant, especially regarding the microstructure of both the tool and the work piece, which presents challenges for the efficiency of the micro-milling process. The primary objective of extensive research in micro-milling is to enhance productivity and efficiency, thereby broadening the range of applications for this technique [4]. A quantitative approach to evaluating efficiency during experiments is crucial for tracking advancements in micro-milling.

In machining operations, numerous factors play a crucial role in determining performance. It is vital to systematically record these parameters in a proactive manner. These factors can be categorized into machining parameters, including machining speed, rate of feed, and cutting depth, as well as minimum quantity lubrication (MQL) parameters [5]. A range of optimization methods and strategies has been proposed by various researchers to improve both the productivity and quality in modern manufacturing industries. Approaches such as Evolutionary Algorithms, Grey Relational Analysis (GRA), Fuzzy Systems, Multiple Regression Analysis, and Genetic Algorithms, can effectively leverage manufacturing data to produce optimal solutions for machining processes. Earlier research primarily focused on optimizing a single objective function. However, recent developments have introduced several multi-objective optimization techniques designed to enhance machining parameters [6].

Numerous experimental studies have been undertaken to optimize the process variables associated with the machining of titanium alloys. While many researchers have examined how process parameters such as machining speed, feed rate, and depth of cut affect results like surface roughness, there is still potential to deepen the understanding of these variables by employing an appropriate multi-response optimization technique. Furthermore, the studies have also considered the cooling concentration, particularly in relation to minimum quantity lubrication (MQL). The resulting outputs, including the surface finish achieved during machining and the effects of tool vibration, have been evaluated [7]. A comprehensive analysis of the correlation between input parameters and output responses has been conducted using a Taguchi-based orthogonal array and grey relational analysis optimization approach.

The application of conventional machining fluids in machining operations has been linked to negative environmental impacts and considerable health hazards for workers, thereby undermining the sustainability of these processes [8]. Critical machining factors such as machining speed, feed rate, axial depth of cut, and the selection of machining fluid have been shown to have substantial influences on surface roughness. Engineering ceramics, especially aluminum oxide, are recognized for their outstanding mechanical characteristics, including high strength, hardness, and stiffness, which render them ideal for high-temperature applications [9]. Aluminum oxide is favored for its accessibility and cost-effectiveness, with an impact energy value of 17 Joules. A new Al-GnP hybrid nanofluid has been developed by integrating graphene nanoplatelets with a blended aluminum oxide nanofluid in a ratio of 10:90.

The utilization of advanced frameworks significantly enhances the micro-milling process of titanium alloys while also offering critical insights into the various factors that influence machining results. This comprehensive approach is vital for achieving optimal outcomes in the production of components made from Ti-3Al-2.5V, a material commonly employed in the aerospace and medical fields. Numerous studies have explored both types of parameters [10]. The effects of these input variables on tool vibration and surface finish quality have been thoroughly examined. Tool vibration refers to the relative movement between the tool and the work piece, which can influence the machine efficiency to some degree. Surface quality is paramount, as it directly affects the component’s functionality and mechanical properties, such as corrosion resistance, fatigue life, and creep behavior. The implementation of minimum quantity lubrication has also been evaluated due to its increasing effectiveness [11].

The research conducted by Shan et al. highlights the significant impact of tool orientation on the deformation of thin-walled components during multi-axis CNC (Computer Numerical Control) machining. Therefore, it is vital to meticulously choose machining parameters and ball end milling techniques that reduce forces and vibrations. An optimization procedure for the machining process can be utilized to achieve this goal. Analysis of the mechanical properties of titanium alloys during tensile testing reveals necking behavior.

Furthermore, many β-alloys exhibit work softening under appropriate conditions, which, interestingly, improves elongation by mitigating necking. Titanium alloys are particularly susceptible to notches; surface irregularities such as grooves and scratches can serve as initiation points for fatigue cracks, adversely affecting fatigue performance. To ensure consistent outcomes, it is crucial to carefully polish the surface, ideally in the longitudinal direction, or to use electro-polishing methods [12].

This research introduces a method designed to decrease machining forces and vibrations during the precise ball end milling of hardened materials. It emphasizes the importance of selecting the optimal surface inclination angle α and tool overhang [13], which is essential for reducing machining forces and vibrations, thus improving the quality of the machined surface. The experimental phase included measuring machining forces and vibration accelerations during milling tests with different input parameters. Subsequently, the ball end milling process was optimized based on the gathered data. This optimization was performed by minimizing process responses using the signal-to-noise (S/N) ratio and grey relational analysis (GRA). The optimal values for the input parameters were then confirmed through further ball end milling tests that evaluated the topographies of the machined surfaces. The results demonstrate that both the surface inclination angle and tool overhang have a significant impact on the generated forces and vibration levels. Additionally, choosing the optimal values for α and l results in a substantial enhancement in the quality of the machined surface.

Ball end milling of molds and dies made from hardened alloy steels is commonly utilized as a finishing process, which imposes strict requirements on both the quality of the machined surface and the condition of the machining tool [14]. The popularity of curvilinear surface milling can be largely attributed to its cost-effectiveness and higher efficiency when compared with alternative methods, such as electro-discharge machining (EDM), ultrasonic machining, and laser beam techniques. However, a major issue faced during accurate ball end milling is the high surface roughness and geometric inaccuracies of the resulting curvilinear surfaces. This deterioration in surface quality is often linked to the use of unsuitable machining parameters and strategies, which arise from a limited comprehension of the physical phenomena that contribute to surface texture development during the milling process. Gaining a more profound understanding of these phenomena could pave the way for the creation of precise process models, aiding in the identification and selection of parameters that significantly affect the surface texture during the meticulous milling of curvilinear surfaces. Nevertheless, the optimization of surface texture continues to be a significant scientific and technological challenge that requires further exploration [15].

The existing literature highlights that the main goals of optimizing machining processes revolve around aspects such as the surface finish, machining forces, tool longevity, tool degradation, and machining vibrations. Widely used optimization methods include the Taguchi approach, which employs signal-to-noise ratio analysis, grey relational analysis (GRA), and the response surface methodology. For example, Durakbasa et al. [16] utilized the Taguchi method to refine process parameters and various coating materials, along with different tool radii, to minimize the surface roughness during the dry end milling of AISI H13 hot work steel. In a similar vein, Khanna and Davim [17] explored the influence of control factors such as machining speed and feed rate on machining forces, feed forces, and tool temperature in the machining of titanium alloys, applying Taguchi techniques. Furthermore, Kumar et al. implemented the Taguchi optimization method to evaluate the surface integrity after electrical discharge machining of LG-2 leaded gunmetal demonstrating that the drilling character had a significant effect on the hardness and corrosion while using conical electrode [18].

This study focuses on the benefits of hybrid nanolubricants as machining fluids during machining operations. It investigates several machining parameters such as machining speed, feed rate, axial depth of cut, and the application of hybrid machining lubrication that includes MWCNT and Al₂O₃. The hybrid lubricant was created by adding 1% and 2% volume concentrations to the industrial machining fluid. The goal is to identify the ideal parameters for the slot milling process and the appropriate volume concentration of the hybrid machining lubrication to improve the surface quality.

2. Preparation of Nanofluid

Nanofluid samples were produced by mixing base oil, deionized water, and a designated concentration of multi-wall carbon nanotube (MWCNT) and Al₂O₃ nano-sized particle additives. For this research, ‘S’ Servo cut oil from Indian Oil was selected as the base oil to facilitate the dispersion of MWCNT and Al₂O₃ particles. The nanotubes, which measure less than one hundred nanometers, are utilized in powdered form. The base fluid was formulated by combining the base oil with deionized water, in accordance with Metal Working Fluid (MWF) standards [19]. The machining fluid sample, which totaled 100 mL and contained MWCNT and Al₂O₃ nano-sized particles, consisted of 95 mL of deionized water and 5 mL of ‘S’ Servo cut oil, as illustrated in Figure 1. Three different machining fluid samples were examined: the conventional machining fluid, a formulation with 1 wt% MWCNT and Al₂O₃ nano-sized particles, and a formulation with 2 wt% MWCNT and Al₂O₃ nano-sized particles:

• Sample-1: 95 mL of distilled water + 5 mL of Servo ‘S’ cut oil.
• Sample-2: 95 mL of distilled water + 5 mL of Servo ‘S’ cut oil + 1 wt% of MWCNT and Al₂O₃ nano-sized particles.
• Sample-3: 95 mL of distilled water + 5 mL of Servo ‘S’ cut oil + 2 wt% of MWCNT and Al₂O₃ nano-sized particles.

The cooling efficiency of nanoparticles and machining fluids is significantly influenced by their cohesiveness. To ensure a consistent colloidal suspension of the nanofluids, the mixture was agitated using a magnetic stirrer for a duration of 20 to 30 min, followed by ultrasonication with an ultrasonic vibrator set at 50 kHz and 80 watts for 60 min to enhance the diffusion process [19]. This method was maintained until the ‘S’ Servo cut oil was thoroughly blended with MWCNT and Al₂O₃ nanoparticles. The three samples that contained MWCNT and Al₂O₃ nanoparticles at concentrations of 0%, 1%, and 2% are presented in Figure 1 and a representative scanning electron microscopy (SEM) image is given in Figure 2.

3. Materials and Methods

3.1. Experimental Setup

A titanium grade 9 work piece was selected for the experiments. This alloy, known as Ti-3Al-2.5V, is classified as a nearly two-phase material that includes both α and (α + β) phases. It is specifically engineered for applications in the hydraulic and fuel systems of aircraft. In comparison with titanium grade 5 (Ti-6Al-4V), grade 9 titanium offers intermediate mechanical properties and enhanced corrosion resistance. The chemical composition of the workpiece was assessed using a metal analyzer (SPECTROMAXX LMX10, Spectro Analytical Instruments GmbH, Kleve, Germany).

For the slotting experiments, titanium grade 9 (Ti-3Al-2.5V) blocks, each measuring 500 mm in length, 100 mm in width, and 100 mm in height, were utilized on a Haas VF-3 3-Axis CNC vertical machining center. The study involved a two-flute tungsten carbide end mill for the slotting operation, specifically employing a power radius End Mill cutter, model MSXH440R. A K Type Thermocouple Temperature measurement device (TEL96-9001) was also used to monitor the machining temperature. The machining center was operated at a maximum spindle speed of 180 m/min, with process parameters optimized to reach the peak machining efficiency. The spindle demonstrated a runout accuracy of 0.1 µm.

3.2. Design of Experimentation

A comprehensive analysis of the experimental parameters applied in the machining processes is provided. The study utilized a design of experiments approach, specifically the L27 array. To ensure the validity of the findings, eight independent trials (Figure 3) with new tools were conducted, and in cases of notable variations, the experiments were repeated up to five times. A three-component tool dynamometer was used to measure the machining forces, while the TEL96-9001 K-type thermocouple was employed to monitor the machining temperatures. A small amount of nanofluid was conventionally supplied under flood conditions with pressure. However, due to environmental concerns, the use of machining fluids was minimized.

The results obtained from the experiments are utilized to derive regression equations listed below. These equations clarify the connections between input and output parameters and are also used to forecast optimal configurations. A significant advantage of this methodology is its capacity to aid in the creation of mathematical models. Furthermore, this design approach provides benefits such as minimizing the number of experimental trials and concentrating on particular areas of the factors.

$$\begin{array}{c}\mathrm{Cutting} \mathrm{force} \left(\mathrm{CF}\right):\hfill \\ + 370.33 + 130.50\times \mathrm{S} - 40.67\times \mathrm{F} - 47.75\times \mathrm{DoC} - 39.58\times \mathrm{NF} + 50.25\times \mathrm{S}\times \mathrm{F} - 98.75\times \mathrm{S}\times \mathrm{DoC} - 57.50\times \mathrm{S}\times \mathrm{NF} - \hfill \\ 53.25\times \mathrm{F}\times \mathrm{DoC} + 50.00\times \mathrm{F}\times \mathrm{DoC} + 83.75\times \mathrm{DoC}\times \mathrm{NF} + 71.17\times {\mathrm{S}}^2 + 228.17\times {\mathrm{F}}^2 + 87.04\times {\mathrm{DoC}}^2 + 81.29\times {\mathrm{NF}}^2\hfill \\ \mathrm{Material} \mathrm{removal} \mathrm{rate} \left(\mathrm{MRR}\right):\hfill \\ + 0.0523 - 0.0138\times \mathrm{S} + 0.008\times \mathrm{F} + 0.0041\times \mathrm{DoC} + 0.0006\times \mathrm{NF} - 0.0053\times \mathrm{S}\times \mathrm{F} - 0.0106\times \mathrm{S}\times \mathrm{DoC} - \hfill \\ 0.0009\times \mathrm{S}\times \mathrm{NF} - 0.0028\times \mathrm{F}\times \mathrm{NoC} - 0.0024\times \mathrm{F}\times \mathrm{NF} + 0.0043\times \mathrm{DoC}\times \mathrm{NF} - 0.0034\times {\mathrm{S}}^2 - 0.0151\times {\mathrm{F}}^2\hfill \\ \mathrm{Cutting} \mathrm{temperature} \left(\mathrm{CT}\right):\hfill \\ + 379.67 - 37.33\times \mathrm{S} + 22.42\times \mathrm{F} + 34.94\times \mathrm{DoC} + 16\times \mathrm{D} + 26\times \mathrm{S}\times \mathrm{F} - 38.75\times \mathrm{S}\times \mathrm{DoC} + 14.25\times \mathrm{S}\times \mathrm{NF} + \hfill \\ 36\times \mathrm{F}\times \mathrm{DoC} + 31.25\times \mathrm{F}\times \mathrm{NF} - 2\times \mathrm{DoC}\times \mathrm{NF} + 25.37\times {\mathrm{S}}^2 - 4\times {\mathrm{F}}^2 - 51.75\times {\mathrm{DoC}}^2 - 27.87\times {\mathrm{NF}}^2\hfill \end{array}$$

where S—machining speed, F—rate of feed, DoC—depth of cut, and NF—MWCNT and Al₂O₃ nanoparticles.

3.3. Influence of Feed Rate

The feed rate per tooth is defined as the distance the machining edge of a tool travels along the groove’s longitudinal axis during one complete revolution. This measurement directly correlates to the thickness of the material layer that is removed by a single edge of the tool. In the context of micro-milling, the feed rate is a crucial input parameter that significantly impacts the outcomes of the process.

An analysis of the effects of the feed rate on various results in micro-milling titanium grade 9 was conducted using the Grey relational analysis (GRA) approach. The findings indicate that the feed rate has a substantial influence on surface roughness, tool wear, and material removal rates. Specifically, an increase in the feed rate results in higher surface roughness, while a decrease in the feed rate leads to improves the surface quality. Research has shown that elevated feed rates produce more heat, expand the contact area between the tool and the work piece, and increase the machining forces, all of which contribute to a rougher surface finish. During the micro-milling operation, two primary mechanisms of material removal are observed: plowing and shearing. The shearing mechanism, which removes material in distinct chips, is preferred, whereas the plowing mechanism, characterized by plastic deformation, is less favorable. The plowing mechanism tends to occur at lower feed rates, resulting in larger chip sizes, while higher feed rates favor the shearing mechanism, producing smaller chip sizes.

3.4. Influence of Machining Speed

The machining speed is a crucial parameter in the micro-milling process, significantly impacting the overall outcomes. Studies in micro-milling have demonstrated that the machining speed plays a pivotal role in determining the surface roughness, tool wear, and chip formation. The relationship between the machining speed and the micro-milling of titanium grade 9 illustrates that an increase in the machining speed results in a reduction in the chip width, while the tool wear and surface roughness generally increase. Initially, the chip length grows with machining speed, but it eventually declines. The decrease in chip width is linked to the reduction in uncut chip thickness that occurs at higher machining speeds.

Research indicates that elevated machining speeds lead to higher temperatures in difficult-to-machine materials, which can cause abrasive wear of the tool due to the friction between the tool and the workpiece, even over a brief period. This thermal softening at the flank face accelerates the wear process. Furthermore, poor surface finishes at higher speeds are frequently attributed to an increased built-up edge, which results from the material’s enhanced ductility at elevated temperatures and friction, hindering the flow of continuous chips. Chandiramani et al. found that lower machining speeds result in the production of discontinuous chips. The shorter lengths of chips at these speeds may be associated with the generation of discontinuous chips, while an increase in machining speed subsequently reduces the chip length, which can be attributed to a decrease in the uncut chip thickness.

The axial depth of cut is defined as the distance a machining tool penetrates a work piece while moving perpendicular to its axis. Numerous studies have recognized depth of cut as a significant input variable, alongside feed rate and machining speed. The impact of the depth of cut on different results in the micro-milling of titanium grade 9 demonstrates a direct correlation between the depth of cut and both tool wear and chip width; as the depth of cut increases, these factors also elevate. Conversely, the surface roughness generally decreases with an increase in depth of cut. The burr length does not follow a consistent trend with depth of cut; it initially increases before subsequently declining. The increases in chip width and length can be attributed to the growing uncut chip thickness associated with deeper cuts. The improved surface quality at greater depths may result from the work hardening of the surface layer. Wang et al. observed in their study on end milling of titanium grade 5 that higher depth of cut leads to increased heat generation and machining forces, which accounts for the observed rise in tool wear in this research.

3.5. Implementation of GRA

When focusing on a single quality objective, the optimization process is referred to as single-objective optimization. In contrast, when multiple quality objectives are considered, it is termed multi-objective optimization. Due to the vast solution space inherent in manufacturing challenges, optimization strategies frequently involve several quality objectives. In this study, the main quality goal is to simultaneously minimize all process outcomes, specifically the machining force, machining temperature, and material removal. The optimal values for the input parameters associated with each process outcome vary from one another. Therefore, a multi-objective approach is crucial for optimizing the process input parameters to meet quality objectives that include reduced surface roughness, tool wear, and chip size.

Grey relational analysis (GRA) is a technique utilized in scenarios where only partial information is available. This optimization method was first introduced by Deng Julong in 1989. Although GRA was developed several years ago, its application has only recently become more prevalent. It is used to assess the performance of complex projects with relatively limited data. By leveraging experimental data, GRA converts multi-response optimization into a single-objective optimization problem. The GRA process consists of several steps, which are elaborated upon in the following sections.

Grey correlation analysis is a comprehensive decision-making method that determines levels of grey correlation to evaluate performance attributes. A common technique for normalization is linear data preprocessing. When normalizing the factor series, specific criteria, such as “higher-the-better,” “lower-the-better,” “nominal-the-better,” or “best ideal” define the nature of the series. A low value at peak points is deemed beneficial. In the context of linear normalization, points that yield low values are those approaching “1,” while those with higher values trend towards “0.” Generally, the “smaller-the-better” criterion is favored for metrics like machining forces, material removal rate, and machining temperature.

The normalized results were then expressed as in Equation (1):

$$x_i\left(k\right)={\displaystyle \frac{(\mathit{max}{x}^0\left(k\right)-x^0\left(k\right))}{(\mathit{max}{x}^0\left(k\right)-\mathit{min}{x}^0(k\left)\right)}}$$

(1)

where

• $x_i^0\left(k\right)$: the $i$ series $k$ value in the range;
• $x_i\left(k\right)$: the range after normalization;
• $\min x_i^0\left(k\right)$: the minimum range;
• $\max x_i^0\left(k\right)$: the maximum range.

Deng stated that higher normalized results indicate better performance and that the best normalized result should equal one. The relationship between the best and the actual experimental results are expressed by the calculation of the grey relational coefficient ε($x_0$(k),$x_j$(k)), as in Equation (2):

$$\epsilon \left(x_0\left(k\right),x_j\left(k\right)\right)={\displaystyle \frac{(\Delta min+\xi \Delta max)}{({\Delta }_{0}i\left(k\right)+\xi \Delta max)}}$$

(2)

where

• $k$: the GR coefficient at the point;
• $\xi$: coefficient [0, 1].

Then, the grey relational degree ($x_0, x_i$) is calculated using Equation (3):

$$\gamma \left(x_0,x_i\right)={\displaystyle \frac{1}{n}}\sum {\epsilon }^{n}k=1(x_0\left(k\right),x_j\left(k\right))$$

(3)

where

• $\gamma \left(x_0,x_i\right)$: a measure of the geometric similarity.

The size of the grey associative level indicates a strong correlation between $x_i$ and $x_0$. The grey relational level is “1” when the two series are the same. The degree of similarity between the comparison and reference series is denoted by the grey relational degree.

4. Results and Discussion

The machining speed is a critical factor that significantly influences the machining force. An increase in both the machining speed and feed rate results in a rise in machining temperature within the milling area. An inadequate thermal conductivity of the work piece can negatively affect the quality of the machined surface. Consequently, it is vital to control the machining temperature throughout the machining process. High machining temperatures can detrimentally impact the quality of the machined surface, primarily due to the increased friction that occurs between the machining tool and the work piece at elevated machining speeds and feed rates.

The data clearly indicates that the machining speed is the most pivotal factor affecting the machining temperature. Additionally, the use of hybrid machining nanofluid, particularly nano-MWCNT- and Al₂O₃-diffused machining fluid, significantly reduces the machining temperature compared with conventional machining fluids. This decrease is linked to the poor thermal conductivity of the Ti-6Al-4V alloy. Selecting the optimal concentration of nano-MWCNT and Al₂O₃ nano-sized particles in the machining fluid, specifically within the range of 1% to 1.5%, enhances the cooling efficiency and improves the heat transfer in the machining zone, effectively managing the increased machining temperatures between the machining tool and the work piece.

The grey relational grade (GRG) is employed to evaluate the correlation between a measurement space factor and a target sequence by generating a discrete sequence through grey relational analysis. The methodology for calculating the GRG is specified in the equations found in

Table 1. GRA normalization value and deviation sequence.

Exp. No	Normalization Value			Deviation Sequence
	CF (N)	MRR (Gm/min)	CT (°C)	CF (N)	MRR (Gm/min)	CT (°C)
1	1.000	0.466	0.685	0.000	0.534	0.315
2	0.043	0.080	0.690	0.957	0.920	0.311
3	0.387	0.183	0.717	0.613	0.817	0.283
4	0.000	0.196	0.247	1.000	0.804	0.753
5	0.168	0.244	0.717	0.832	0.756	0.283
6	0.000	0.069	0.639	1.000	0.931	0.361
7	0.548	0.211	0.443	0.452	0.789	0.557
8	0.189	0.000	0.680	0.811	1.000	0.320
9	0.344	0.379	0.155	0.656	0.621	0.845
10	0.785	0.364	0.648	0.215	0.636	0.352
11	0.215	0.347	1.000	0.785	0.653	0.000
12	0.011	0.373	0.676	0.989	0.627	0.324
13	0.576	0.440	0.256	0.424	0.560	0.744
14	0.742	0.218	0.881	0.258	0.782	0.119
15	0.129	0.235	0.703	0.871	0.765	0.297
16	0.946	0.737	0.543	0.054	0.263	0.457
17	0.387	0.440	0.648	0.613	0.560	0.352
18	0.634	0.114	0.699	0.366	0.886	0.301
19	0.591	0.558	0.566	0.409	0.442	0.434
20	0.140	0.409	0.877	0.860	0.591	0.123
21	0.860	0.774	0.206	0.140	0.226	0.795
22	0.946	0.728	0.320	0.054	0.272	0.680
23	0.936	0.731	0.206	0.065	0.269	0.795
24	0.944	0.756	0.320	0.056	0.244	0.680
25	0.776	0.918	0.174	0.224	0.082	0.827
26	0.774	1.000	0.000	0.226	0.000	1.000
27	0.387	0.409	0.142	0.613	0.591	0.858

and

Table 2. Gray relational Coefficient value, Grade and Rank.

Exp. No	Grey Relational Coefficient			Grade	Rank
	CF (N)	MRR (Gm/min)	CT (°C)
1	1.000	0.484	0.613	0.699	1
2	0.343	0.352	0.617	0.437	23
3	0.449	0.380	0.638	0.489	16
4	0.333	0.383	0.399	0.372	27
5	0.375	0.398	0.638	0.471	18
6	0.333	0.349	0.581	0.421	25
7	0.525	0.388	0.473	0.462	20
8	0.381	0.333	0.610	0.442	22
9	0.433	0.446	0.372	0.417	26
10	0.699	0.440	0.587	0.575	11
11	0.389	0.434	1.000	0.608	10
12	0.336	0.444	0.607	0.462	21
13	0.541	0.472	0.402	0.472	17
14	0.660	0.390	0.808	0.619	8
15	0.365	0.395	0.628	0.462	19
16	0.903	0.655	0.523	0.694	2
17	0.449	0.472	0.587	0.503	15
18	0.578	0.361	0.624	0.521	14
19	0.550	0.531	0.535	0.539	13
20	0.368	0.458	0.802	0.543	12
21	0.782	0.689	0.386	0.619	9
22	0.903	0.647	0.424	0.658	5
23	0.886	0.650	0.386	0.641	7
24	0.899	0.672	0.424	0.665	4
25	0.691	0.859	0.377	0.642	6
26	0.689	1.000	0.333	0.674	3
27	0.449	0.458	0.368	0.425	24

. Following this, the experimental runs were ranked based on their GRG values, with the highest value receiving a rank of 1 and the lowest a rank of 27. The GRG values shown in Table 2 were used to determine the mean grey relational grade for each level of the input variables, which was subsequently organized into the response table. A higher grey relational grade in this table signifies enhanced performance across various characteristics.

The surface characteristics of machined components were examined using both standard machining fluid and a blend containing 1 wt% of MWCNT and nano-sized Al₂O₃ particles. As shown in Figure 4a,b, several surface defects were observed, such as adhered built-up edges, distinct feed marks, micro-cracks, and scratches. Figure 4b suggests that the more prominent feed marks are a result of utilizing higher feed rates. Conversely, Figure 4a reveals that standard machining fluids contribute to the development of built-up edges on the machined surface. This occurs because traditional machining lubrication reduces the heat transfer efficiency during machining, leading to an increased coefficient of friction and a poorer surface finish. The observed differences can be linked to the enhanced fracture strength and ductility of the material, which effectively reduces the occurrence of micro-cracks.

Confirmatory tests were conducted on the experimental design to ensure the reliability of the results obtained. This research concentrated on evaluating the tool wear, surface roughness, and chip removal through a “smaller is better” methodology. The target values for these input parameters, which were derived from the main effects plot, are presented in the accompanying table. Subsequently, validation experiments were carried out under the specified conditions detailed in the table. It is important to mention that one of the eight combinations of machining parameters had already been tested during the initial experimental design (L27 orthogonal array), while the other conditions were replicated three times each. It was observed that all optimal and suboptimal output response conditions yielded consistent results.

5. Conclusions

The process variables play a crucial role in reducing production costs and enhancing the product quality. This research focused on analyzing three key process parameters in the micro-milling of titanium grade 9. While there is a wealth of studies on titanium grade 5, there is a notable gap in research aimed at optimizing micro-machining parameters for titanium grade 9. Given that titanium grade 9 presents specific advantages over grade 5 in certain applications, this study aimed to pinpoint the critical process parameters that are distinctive to this grade. It was found that minimizing the tool edge radius resulted in a significantly improved surface finish for titanium grade 9. The key conclusions from the research are summarized as follows.

The surface integrity, which is indicative of product quality, was shown to be affected by all the machining variables analyzed. Among these, the feed rate was identified as the most significant factor, accounting for 59.31% of the overall impact. The formation of burrs was primarily influenced by the feed rate, which can be explained by the shift in the machining mechanism from ploughing to shearing as the feed rate increased, effectively facilitating material removal and minimizing chip formation.

The combined effects of the machining speed and feed rate on tool wear were determined to be 53%, highlighting their significance. The machining speed affects the temperature in the machining zone, which, in turn, influences the tool wear mechanisms, while the feed rate governs the rate of heat dissipation, another vital aspect of managing tool wear.

This study thoroughly explored the potential of replacing traditional machining fluids with nano-cutting fluids to enhance machining efficiency. Notably, hybrid nano-cutting fluids, which incorporate two or more types of nanoparticles, have gained traction. The results indicate that using hybrid nano-cutting fluids with 1% MWCNT result in improved surface roughness of the work piece compared with mono-nano-cutting fluids.

6. Future Scope

The further study conducts as part of a thorough evaluation of the machinability of the aerospace alloy Ti-3Al-2.5V (titanium grade 9). Given the complexities associated with machining these alloys and the growing emphasis on sustainable manufacturing, the research focused on achieving cost-effective production by carefully selecting the most suitable machining parameters. The results are encouraging and suggest avenues for further investigation to improve productivity while maintaining sustainability. Future studies could explore transitional and high-speed machining methods for challenging titanium and nickel alloys using MQL and cryogenic cooling techniques. Furthermore, the benefits of dry machining at standard speeds may be comprehensively assessed through the examination of tool wear and energy consumption patterns.