A hybrid optimization technique to control the machining performance of graphene/carbon/polymer (epoxy) nanocomposites

Abstract

The machining behavior of polymers is substantially different from metal and their alloys due to non-homogeneity and anisotropy. This article describes a hybrid optimization method during machining (milling) of epoxy nanocomposites reinforced by graphene oxide/carbon fiber (G/CF). The milling experimentation was staged according to the L₉ orthogonal array of the Taguchi theory. The control of process constraints, namely, cutting speed (V_c), feed (F), depth of cut (D), and G weight % (G) have been appraised to acquired the desired machining response such as material removal rate (MRR), cutting force (F_c), and surface roughness (R_a). Grey-coupled principal component analysis (grey-PCA) effectively tackled the response priority weight during aggregation of conflicting responses. The optimal condition secured by the grey-PCA module are found as V_c = 25.12 m/min, F = 240 mm/min, D = 0.5 mm, and G = 1 %. The supplement of graphene enhances the machining characteristics of the nanocomposites, which in turn, minimizes the damages that occur during the milling process. Scanning electron microscopy (SEM) analysis was conducted for microstructure investigation of the machined component. The findings of confirmatory experiments show good agreement with the actual ones. The preferred solution values of grey-PCA are observed as 1.027, which confirms the proposed technique’s higher practicability.

Keywords

Grey Milling Polymer Carbon fiber graphene

Introduction

In the 21st century, hybrid polymeric materials play a primary function in the production sector due to improved properties like high strength-to-load proportion, anti-corrosive features, dimensional tolerances, high elasticity, and damping coefficient, etc. The development of novel lightweight composites with improved mechanical features leads to the synthesis of a new generation of composites; sometimes, their improved engineering properties may create critical problems during machining processes. In this series, epoxy-based composites are widely used due to their higher cross-linked nature, which possesses a high aspect ratio.¹ Graphene oxide is considered a potential filler material that creates a tremendous change in epoxy matrix features such as stiffness, strength, shock-absorbing capacity, and fracture toughness of the composites.² The stability of graphene is due to its densely packed carbon atoms and an orbital sp² hybridization. Graphene oxide improved the aspect ratio and dispersion between the matrix and reinforcement.³ In recent decades, manufacturing industries have acknowledged the satisfaction of customers with various polymer composites’ material to obtain sustainable machining performance of graphene oxide/carbon-modified polymers through the control of process constraints. The multi-criteria optimization modules consider technical and economic aspects and environmental requirements to be simultaneously optimized. Optimization of multiple machine constraints plays a vivacious role in enhancing the quality and productivity indices of machining procedures.⁴ The manufacturing industries of aerospace, automotive, marine, and construction generally use fiber-reinforced polymers (FRP) to design problems requiring weight savings, accuracy, close tolerances, and production simplification, as well as the precise component assembly.⁵ During the machining of epoxy composites, the manufacturers face critical issues such as matrix debonding; fiber pull out, delamination, plastic deformation, etc.⁶ It creates a complicated situation during the manufacturing and final assembly of epoxy composites. Machining aspects of epoxy composites significantly differ from metal and their alloys due to non-homogeneity and anisotropic performance. The proposed works investigate the machining aspects of epoxy composites modified by G/CF in this context. An effort has been proposed to overcome the limitations for conflicting response optimization. The control of constraints through the optimization tool has been one of the remarkable concerns in the industrial and manufacturing technology, wherever the productive and cost-effective machining procedures offer a vital role in the marketplace.⁷ Kumar et al.⁸ proposed a mathematical model to predict the milling indices of Al-6061. The grey concept was aggregated, and the multiple conflicting indices obtained through the Taguchi L₁₈ orthogonal array design. The PCA theory evaluates the real weight of the milling indices and the correlation between them. The findings of the validation test reveal the practicality of the intended model in the manufacturing environment. Lin⁹ explored the Taguchi theory for optimization of machining performance during milling test of stainless steel. But, the appropriate application of any composites is not possible without understanding its machinability aspects and machining performance optimization. Hence, machining these novel composites becomes a latent research area for industry, research organizations, and academia.

During polymer machining, various complex issues are encountered by manufacturers like circularity error, roughness, burr, fiber pull, matrix debonding, etc.^10,11 The machined samples’ surface roughness plays a critical role in affecting the quality and productivity indices. It acts as a primary concern during polymers’ machining because various aesthetic requirements are dependent upon the surface quality. Various distinguished researchers explored the different tools and techniques to control the machined surface roughness. However, still, polymer trades and manufacturing segments are too concerned about the quality of machined surfaces. In the case of proposed graphene-modified fiber polymers, it is highly required to control the process constraints to minimize these complex milling issues through statistical tools and robust optimization techniques. During milling, cutting force is the most critical response to maintain as it depends upon different process constraints such as feed rate, cutting speed, etc. It is also strongly correlated with other cutting performances such as surface finishing, tool wear, and interface temperature.^11,12 Besides this, the carbon fiber/epoxy composites are anisotropic and non-homogeneous; in turn, the machining concept completely differs from other polymers and metals. The reinforcing fiber and cutting tool contact area develop a deformation zone for the cutting of material. The hardness of matrix-fiber and cutting tool alters the cutting force intermittently. Hence, the control of cutting force depends on the various cutting conditions, and simultaneous control is highly required in fiber-polymer machining.^10,13 The commercialization of G/CF is highly required to develop a cost-effective and durable product. From an exhaustive literature survey, it has been observed that machining aspects of polymer composites reinforced by macro fiber (carbon/glass/aramid) were fruitfully explored. Also, parametric optimization was performed using different statistical modules. Still, the multiple exceptional features of these fiber/polymer can be expanded by adding graphene oxide. Hence, this article’s objective is to identify the effect of graphene on the improvement of machining performance. The control of process constraints can significantly enhance machining indices using a hybrid optimization tool for parametric appraisal. For smooth machining of the hybrid polymer and modified nanocomposites, it is highly desired to control the conflicting machining response such as cutting force, MRR, and surface roughness.

The grey concept staged the aggregation of all the conflicting indices. The evaluation of real weight through a statistically proven approach is evaluated using PCA theory, and accordingly, importance is given to each response. The process constraints, namely, cutting speed (S), feed (F), depth of cut (D), and wt. % of graphene (G) is varied at three different levels, and milling was performed with a silicon carbide–coated titanium-aluminum nitride (SiC-TiAlN) tool. The Taguchi theory was used to design the milling experimentations and the grey-PCA hybrid module, to estimate the favorable milling environment machining indices. In this article, these machining characteristics are explored in a detailed way with compelling process parameter studies. An effort has been proposed to reduce the limitations of existing modules, modify the conventional optimization tools, and present an efficient method to control the contradictory milling characteristics.

Experimentation

Materials and the nanocomposite fabrication method

For the development of the graphene/carbon/polymer nanocomposite, 400 GSM plain woven carbon fabrics were used and epoxy resin-520 in the form of the matrix material. The graphene oxide (G)–reinforcing material was used in three weight ratios (1, 2, and 3%) and is stirred for up to 30 min at a temperature of 60°C. After 30 min stirred modified epoxy mixture at 27 C, the hardener-D introduced with the ratio of 10:1 into the solution. Lastly, the combination was poured and laid up on the carbon fibers into a mold die $(100 \times 100 \times 10$ ) mm. In this study, a total of 18 layers were used for the fabrication of samples (as mentioned in Figure 1). Graphene oxide’s (G) physical properties are described in Table 1. The properties of carbon fiber, and epoxy and hardener are described in Table 2 and Table 3, respectively. The milling tests were performed on a vertical CNC setup (Figure 2) at three different levels (Table 4) of machine constraints, namely, cutting speed (S), feed (F), depth of cut (D) and wt. % of graphene (G) with a silicon carbide–coated titanium-aluminum nitride (SiC-TiAlN) milling tool (5 mm). The Taguchi theory was used to design the experimental array, and the response values are mentioned in Table 5. The Taguchi array is a useful experimentation pattern method that minimizes the test run in a statistically balanced way. It is a highly commended experimental design method in the manufacturing process, multi-criteria analysis, decision science, etc.

Figure 1.

Nanocomposites fabrication process.

Table 1.

Physical properties of graphene oxide.

S. No.	Specification	Value
1	Density	0.18 g/cc
2	Diameter average (X and Y)	5 micron
3	Thickness average (Z)	2–3 micron
4	Carbon purity	> 99%
5	Number of layers	Avg number 2–3
6	Surface area	255 m²/g

Table 2.

Properties of carbon fiber.

S. No.		Warp	Weft
S. No.	Type	12K carbon fiber	12K carbon fiber
1	Density (g/cm³)	1.8	1.8
2	Tensile strength (MPa)	5516	5516
3	Tensile modulus (GPa)	250	250
4	Elongation (%)	2.2	2.2
5	Filament dia. (μ)	7	7
6	Size compatibility	Epoxy	Epoxy

Table 3.

Properties of epoxy resin 520 and hardener.

S. No.	Property	Unit	Epoxy resin 520	Hardener
1	Type		Solvent modification	Polyamine
2	Appearance		Colorless	Clear liquid
3	Mix viscosity at 27°C	mPa-s	650 ± 100	650 ± 100
4	Specific gravity	g/cc	1.5–2	1.5–2
5	Pot life at 27°C	h	1.5–2	1.5–2
6	Storage stability	Month	12	6

Figure 2.

CNC milling setup.

Table 4.

Process parameters.

Parameter	Nomenclature	Level 1	Level 2	Level 3
Cutting speed	V_c (m/min)	12.56	25.12	37.68
Feed	F (mm/min)	80	160	240
Depth of cut	D (mm)	0.5	1	1.5
Wt.% graphene	G (wt.%)	1	2	3

Table 5.

Taguchi design and corresponding observed data.

Exp. No.	Process parameter				Machining response
Exp. No.	V_c (m/min	F (mm/min)	D (mm)	G (wt.%)	MRR (mm³/min)	F_c (N)	R_a (μm)
1	12.56	80	0.5	1	3.793	15.78	1.12
2	12.56	160	1	2	11.78	42.579	1.487
3	12.56	240	1.5	3	11.181	60.075	1.397
4	25.12	80	1	3	10.782	17.757	1.313
5	25.12	160	1.5	1	12.379	16.78	1.483
6	25.12	240	0.5	2	15.973	5.602	0.783
7	37.68	80	1.5	2	9.184	53.675	1.54
8	37.68	160	0.5	3	4.592	9.534	1.02
9	37.68	240	1	1	7.587	4.735	1.93

The surface roughness was evaluated by the Taylor Hobson Surtronic S-128 surface tester, as shown in Figure 3(a). It is surface unevenness–assessing equipment with a stylus traversed within the surface; its vertical motion can be converted into an electrical signal. The arithmetic means of the roughness value at three distinct locations on the machined surface estimates the final R_a value.¹⁴ For calculation of MRR, the initial weight and final weight are obtained through digital weighing balance, as displayed in Figure 3(b). The MRR was computed by applying the expression¹⁵:

M R R = \frac{w_{i} - w_{f}}{ρ \times t_{m}}

(1)

where:

w_{i}

w_{f}

= Initial and final weight of the machined sampleρ = Density of the prepared sample (1.31 g/cm3)

t_{m}

= Machining time

Figure 3.

(a) Digital balancing meter and (b) cutting force measurement setup.

The computerized dynamometer was used to acquire the online data for thrust force, radial force, and torque. The achieved signals are moved to the data acquisition system through the analog–digital converter.¹⁶

Cutting force was computed by using the expression

F_{c} = \sqrt{(F_{x}^{2} + F_{y}^{2} + F_{z}^{2})}

(2)

Where:

F_{x,}, F_{y}, a n d F_{z} =

axial, radial, and torque force.

Methodology

The Taguchi experimental design computed the effect of the input parameters and responses.¹⁷ This system is utilized for the statistical scale for the single function/performance data used to calculate the signal-to-noise ratio (S/N ratio) and then predict the S/N ratio.^18,19 Finally, the S/N ratio provides the optimal condition from the graph. In this case study, the MRR was considered as maximization function, that is, higher the better, using the expression:

Higher the better (HB)

{(\frac{S}{N})}_{ij} = - 10 \log (\frac{1}{n} \sum_{j = 1}^{n} \frac{1}{X_{i j}^{2}})

(3)

The cutting force (F_c) and surface roughness (R_a) desired values correspond to the minimization function, lower the better (LB), using the expression

{(\frac{S}{N})}_{ij} = - 10 \log (\frac{1}{n} \sum_{j = 1}^{n} X_{i j}^{2})

(4)

where (S/N)_ij is the S/N ratio and

X_{i j}^{2}

is measured significance data of the i^th experimentation at the j^th assessment.

Results and Discussion

The grey-PCA hybrid optimization strategy explores the critical task of weight assessment. Due to less computation and complexity, researchers have introduced this module in a large scale of optimization case studies like power controller, energy optimization, biomedical studies, economics, commercial problems, and other multicritical decision-making issues of industrial engineering.²⁰ The grey approach study initially normalizes the observed data to reduce the inconsistency among contradictory Milling indices are known as pre-processing data phase. This phase mainly requires the desired characteristics of the responses based on “higher the better-HTB” and “lower-the-better-LTB” using the equations below.²¹ The normalized data for milling indices are computed in Table 6.

x_{i} (k) = \frac{x_{i} (k) - \min x_{i} (k)}{\max x_{i} (k) - \min x_{i} (k)}

(5)

x_{j} (k) = \frac{\max x_{j} (k) - x_{j} (k)}{\max x_{j} (k) - \min x_{j} (k)}

(6)

Table 6.

Normalized data and deviation sequence for each response.

Exp. No.	Normalize data			Deviation sequence
Exp. No.	N-MRR	N-F_c	N-R_a	MRR	F _c	R _a
1	0	0.8	0.706	1	0.2	0.294
2	0.656	0.316	0.387	0.344	0.684	0.613
3	0.607	0	0.465	0.393	1	0.535
4	0.574	0.765	0.538	0.426	0.235	0.462
5	0.705	0.782	0.39	0.295	0.218	0.61
6	1	0.984	1	0	0.016	0
7	0.443	0.116	0.34	0.557	0.884	0.66
8	0.066	0.913	0.794	0.934	0.087	0.206
9	0.311	1	0	0.689	0	1

However, the deviation value is estimated through equation (7)

x_{i} (k) = 1 - \frac{x_{i} (k) - x}{\max x_{i} (k) - x_{i}}

(7)

Where i = 1,2,3…n (number of experiments), k = 1,2,3…m (machining performance),

x_{i} (k)

represents the selected value of the response, and

\min x_{i} (k)

and

\max x_{i} (k)

represent the minimum and maximum value of the selected response.

The particular response weight is estimated after the normalization process (data pre-processing) by exploring the PCA tool.²² Computation of the eigenvalue for PCs using the following expression are mentioned in Table 7

(R - γ_{k} I_{m}) \times V_{i k} = 0

(8)

Where,

R

= decision matrix,

γ_{k} =

eigenvalue,

I_{m}

= identity matrix and

V_{i k}

= eigenvector.

Table 7.

Eigenvalues and proportions for PCs.

Principal component	Eigenvalue	Proportion (%)
First	1.2704	42.3
Second	1.0919	36.4
Third	0.6378	21.3

Then, the eigenvector for PCs’ values was calculated using the following expression and tabulated in Table 8

Y_{m k} = \sum_{i = 1}^{n} x_{m} (i) \times V_{i k}

(9)

Where y_m1, y_m2…., are first, second principal component, etc. The PCs are fitted for variance in the decreasing order, and hence, the first major element y_m1 is the most variant of the data. The square of eigenvalue implies the corresponding response weight of PCs. Table 9 shows that the first PC’s variance influence is found the highest at 42.3%, and the second and third PC is 36.4%, and 21.3%, respectively. The square of its consequent eigenvectors, that is, 1.2704 for MRR, 1.0919 for cutting force, and 0.6378 for R_a, is stated as the weight value for the associated response. Table 10 shows the contribution for MRR, cutting force, and R_a as 0.0163, 0.5358, and 0.4475, respectively.

Table 8.

Eigenvectors for PCs and contributions.

Output parameters	Eigenvectors
Output parameters	PC1	PC2	PC3	Contribution
MRR	−0.128	−0.88	0.457	0.0163
F _c	0.732	0.227	0.643	0.5358
R _a	0.669	−0.417	−0.615	0.4475

Table 9.

Computation of GRC, grey-PCA, and corresponding rank.

Exp. No	GRC			Grey-PCA	Rank
Exp. No	MRR	F _c	R _a	Grey-PCA	Rank
1	0.333	0.715	0.63	0.670	4
2	0.592	0.422	0.449	0.437	7
3	0.56	0.333	0.483	0.404	8
4	0.54	0.68	0.52	0.606	5
5	0.629	0.697	0.45	0.585	6
6	1	0.97	1	0.983	1
7	0.473	0.361	0.431	0.394	9
8	0.349	0.852	0.708	0.779	2
9	0.421	1	0.333	0.692	3

Table 10.

ANOVA for grey-PCA.

Source of analysis	DF	Adj SS	Adj MS	F-value	p-value
V_c (m/min)	1	0.020862	0.020862	1.37	0.307
F (mm/min)	1	0.027886	0.027886	1.83	0.248
D (mm)	1	0.183624	0.183624	12.03	0.026
G (wt.%)	1	0.004188	0.004188	0.27	0.628
Error	4	0.061036	0.015259
Total	8	0.297596
S = 0.1235, R-sq = 79.49%

The grey procedure calculated the significance between the two structures and systems; next, the GRC $(ξ)$ was evaluated using the expression below and corresponding values are described in Table 9:

ξ (k) = \frac{Δ m i n - ς Δ m a x}{Δ_{i} (k) - ς Δ m a x}

(10)

Where

Δ_{i}

denotes the current value of the sequence as

Δ_{i} (k) = (X_{0} (k) - X_{i} (k)

and the deviation sequence

(ς)

between

X_{i} (k)

and

X_{j} (k)

, generally

(ς) = 0.5

is used.

Finally, we assigned the real weight priority vector into the GRC-PCA to calculate the grey-PCA using the following equation and tabulated the results in Table 9:

α = \frac{1}{n} \sum_{k = 1}^{n} ξ (k)

(11)

Finally, the grey-PCA function calculation and their respective ranks are evaluated, as revealed in Table 9. A larger grey-PCA value is highly desired, indicating that results are closer to the optimal value and demonstrating the optimization module’s efficacy for enhancing the quality and productivity indices.²³ A larger grey-PCA value is highly desired to achieve the parametric combination for favorable milling conditions. The main effect plot of the S/N ratio (Figure 4) shows that V_c (25.12 m/min, Level 2), F (240 mm/min, Level 3), D (0.5 mm, Level 1), and G weight % (1 wt.%, Level 1) are the efficient process constraints. The most critical machining constraints are calculated through the variance among the highest and lowest value of the mean grey-PCA. Table 9 shows the initial value of grey-PCA observed as 0.670, which relates to the initial setting as V_c = 12.56 m/min, F = 80 mm/min, D = 0.5 mm, and G = 1%. The experimental and predicted value shows the grey-PCA function in Figure 5.

Figure 4.

Optimal setting by grey-PCA.

Figure 5.

Response graph for grey-PCA.

ANOVA test for grey-PCA

ANOVA is implemented to calculate the model prediction correctness and appraise the substantial parameter. All the tests have been presented with the 95% confidence level supposition to get the preferred output.²⁴ It is a statistical procedure employed to investigate information about parameters and their effects on responses. Table 10 illustrates that the depth of cut is the most significant process parameter. Also, it shows that the error calculation using the regression equation is in good agreement among predictions through the linear model and experimental data.

Regression equation

G r e y - P C A = 0.765 + 0.000074 \times V_{c} + 0.000852 \times F - 0.350 \times D - 0.0264 \times G

(12)

The grey-PCA means analysis is stated in Table 11, and the values in the orthogonal design at each level and their means are presented in the response table, the depth of cut having the highest delta value of 0.3499, which significantly affects the grey-PCA value. The second influencing factor is cutting speed, and then feed and graphene oxide with the delta values of 0.2210, 0.1363, and 0.0528, respectively.

Table 11.

Average grey-PCA values of process parameters.

Level	V_c	F	D	G
1	0.5038	0.5568	0.8110	0.6491
2	0.7248	0.6004	0.5782	0.6049
3	0.6217	0.6931	0.4611	0.5963
Delta	0.2210	0.1363	0.3499	0.0528
Rank	2	3	1	4

Surface plot

The surface plot for the grey-PCA function is described in Figure 6 with the varying constraints of cutting speed, feed, and wt.% of G. It has been remarked from Figure 6(a) to (e) that the grey-PCA value increases considerably with an increase in cutting speed and feed, and lowers with decreasing depth of cut and wt.% of G. It is possible due to their dominant control over the input parameters. Thus, the depth of cut and cutting speed are significant parameters for milling operation. The depth of cut is noticed as the most significant interaction effect, followed by cutting speed, feed and wt.% of graphene oxide. Figure 6(a) to (e) displays the contour diagram between these parameter interactions. From analysis of these 3-D surface plots of interaction it was found that the optimal machining parameters lead to maximize the grey-PCA value. Two parameters for each graph are varied, whereas the two other parameters are retained at their constant value.²⁵ The depth of cut and feed effect on grey-PCA values are displayed in Figure 6(a) to (e). When cutting speed is 25.12 m/min and feed is 240 mm/min, it is observed that the highest grey-PCA can be reached. It is also detected that the depth of cut effect on the module is higher than that of the feed, cutting speed, and wt.% of G. Depth of cut is the most influencing factor for affecting the machining quality; it was confirmed by Jogi et al.²⁶ Furthermore, a combination of cutting speed and feed increases has a slight reducing effect on the surface roughness function during milling.

Figure 6.

(a)-(f) 3-D surface plot for grey-PCA versus cutting speed, feed, depth of cut, and wt.% GAuthor agree with this edited sentence.

Confirmatory test and comparative study

Prediction and confirmation of the optimal results obtained from G/CF epoxy nanocomposite machining was done through a confirmatory test. The predicted grey-PCA was computed using the optimum milling condition as

\begin{array}{l} ξ (k) = ξ_{m} + \sum_{k = 1}^{n} (ξ_{0} - ξ_{m}) \\ = 0.617 + (0.7248 - 0.617) + (0.6931 - 0.617) + (0.8110 - 0.617) + (0.6491 - 0.617) \\ = 1.027 \end{array}

(13)

where

ξ (k) =

entire grey-PCA mean value,

ξ_{0} =

mean grey-PCA at the optimum level, and n = number of experiment parameters.

This confirmatory test verified the optimal parametric setting outcomes, as mentioned in Table 12. Comparison of the initial setting response outcomes with the optimal setting response was presented to substantiate the process. The grey-PCA confirmatory test results are revealed in Table 12, which demonstrate the significant improvement of MRR, F_c, and R_a from 3.793 to 17.64 mm³/min, 15.78 to 4.65 N, and 1.12 to 0.750 μm, respectively. The outcomes reveal the proposed module’s improved applicability and robustness for efficient machining (milling) environment.

Table 12.

Confirmatory test.

S. No.	Optimal machining condition of Grey-PCA
	Machining response	Initial setting	Predicted	Confirmatory
	Setting	V_c12.56F80D0.5G1	V_c25.12F240D0.5G1
1	MRR	3.793		17.64
2	F _c	15.78		4.65
3	R _a	1.120		0.750
Preferred solution value		0.670	1.027

Comparative analysis has been done to examine the feasibility of proposed approaches. From Table 12, it is remarked that machining performance results for MRR, F_c, and R_a improved from 3.793 to 17.64 mm³/min, 15.78 to 4.65 N, and 1.120 to 0.750 μm. Consequently, these test outcomes inferred that the hybridization approach of grey-PCA is more effective and efficient for solving the multicriteria decision-making (MCDM) machining problem. The proposed module shows an improvement in responses compared to the initial machining condition, which confirms the grey-PCA hybrid module’s higher application potential and robustness in real practice.

Microstructure analysis: SEM test

Microstructure analysis of the machined samples was performed by a scanning electron microscopy (SEM) test at the optimal machining conditions. The machined component’s quality and productivity based upon the product surface features and morphology have been significantly affected by the process parameters, namely, V_c, F, D, and G%. ANOVA and means analysis were used to perform statistical analysis of factorial effects. Figure 7(a) and (b) show red marks that show cavity, damages, and poor surface finish due to fiber breakage and fiber pull out in the machined composites. Figure 7(a) indicates more defects, while Figure 7(b) shows the satisfactory surface quality due to fewer cracks and damages during machining of G/CF epoxy nanocomposites.²⁷ Table 12 shows that the optimal combination for the proposed modules differed in cutting speed and feed only. At the grey-PCA machined sample’s optimal condition, a combination of higher cutting speed and feed value provides an improved surface finish and reduced generation of defect and cracks.^28,29 Figure 7(b) shows evidence of fiber breakage, pull out, and protrusion of epoxy and fibers, which result in fewer values of surface roughness (R_a) in grey-PCA’s optimal condition (V_c = 25.12, F = 240, D = 0.5, and G = 1%). The higher value of cutting speed and feed shows a significant role in the machined component’s surface features.^30,31

Figure 7.

(a) SEM analysis at GR-PCA initial setting (at the scale of 2 mm, 200 μm, and 20 μm). (b) SEM analysis at GR-PCA optimal setting (at the scale of 2 mm, 200 μm, and 20 μm).

Conclusions

The current research deals with the machining (milling) aspects of G/CF epoxy nanocomposites. Multicriteria optimization of machining attributes such as MRR, F_c, and R_a have been efficaciously tackled by the grey-PCA hybridization method. With the microstructure and morphology study of the machined component done by SEM analysis to examine the proposed methodology’s outcomes, the following point may be concluded from this research:

1. The optimization module, the grey-PCA hybrid method, successfully combined the multiple conflicting responses into a single function, which is not viable by the customary Taguchi method.

2. Exploring the PCA technique with the Grey approach has effectively tackled the critical tasks of calculating weight among conflicting machining responses.

3. The optimal setting from the proposed hybrid grey-PCA module, found as V_c = 25.12 m/min, F = 240 mm/min, D = 0.5 mm, and G = 1%, shows the good improvement in MRR, F_c, and R_a from 3.793 to 17.64 mm³/min, 15.78 to 4.65 N, and 1.120 to 0.750 μm, respectively.

4. The cutting speed and feed perform a critical role for surface roughness because, at a higher cutting speed and feed, the defect and cracks’ growth is found less, which has been further validated by the SEM test.

5. The grey-PCA method significantly improved the overall assessment value, which shows the robustness of the proposed module. Also, grey-PCA is a comprehensive optimization module that may be adapted for other manufacturing processes.

The developed hybridization module (grey-PCA) shows higher efficiency during the optimization of conflicting machining performance. The contribution of other aspects such as distinct types of tool material, tool wear, mechanism of chip growth, and health and safety issues can further enhance the quality and productivity of the machining process. In the future, the machinability aspects can also be attempted by varying the CFRP fiber orientation, weaves, matrix properties, etc. Grey-PCA can be used for the drilling, turning, etc. of G/CF nanocomposites.

Footnotes

Acknowledgments

The authors are very grateful to the National Project Implementation Unit (NPIU), Ministry of Education (MoE), Govt. of India for the sanctioned project (ID-1-5755587331) under the CRS scheme.

Declaration of conflicting interests

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

Funding

This research is supported under the CRS project scheme of the National Project Implementation Unit (NPIU), Ministry of Education (MoE), Govt. of India for the sanctioned project (ID-1-5755587331).

ORCID iDs

Jogendra Kumar

Rajesh K Verma

Arpan K Mondal

Vijay K Singh

References

Henriques

Borges

Pinto

, et al. Fabrication and antimicrobial performance of surfaces integrating graphene-based materials. Carbon 2018; 132: 709–732.

Kunrath

Kerche

Rezende

, et al. Mechanical, electrical, and electromagnetic properties of hybrid graphene/glass fiber/epoxy composite. Polym Polym Compos 2019; 27: 262–267.

Gangineni

Yandrapu

Ghosh

, et al. Mechanical behavior of graphene decorated carbon fiber reinforced polymer composites: an assessment of the influence of functional groups. Compos Part A Appl Sci Manuf 2019; 122: 36–44.

Ali

Mia

Khan

, et al. Multi-response optimization of face milling performance considering tool path strategies in machining of Al-2024. Materials (Basel); 12: 1013. Epub ahead of print 2019. DOI: 10.3390/ma12071013.

Bagotia

Choudhary

Sharma

. A review on the mechanical, electrical and EMI shielding properties of carbon nanotubes and graphene reinforced polycarbonate nanocomposites. Polym Adv Technol 2018; 29: 1547–1567.

Panchagnula

Palaniyandi

. Drilling on fiber reinforced polymer/nanopolymer composite laminates: a review. J Mater Res Technol 2018; 7: 180–189.

Campatelli

Lorenzini

Scippa

. Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel. J Clean Prod 2014; 66: 309–316.

Kumar

Pradhan

Kumar

. Modeling and optimization of end milling parameters on aluminum 6061 alloy using GRA based Taguchi method coupled with PCA. In: 5th International and 26th All India Manufacturing Technology, Design and Research Conference AIMTDR 2014At: Department of Mechanical Engineering, IIT Guwahati, Guwahati, India, 12–14 December 2014, p 10–15.

Lin

. Optimisation technique for face milling stainless steel with multiple performance characteristics. Int J Adv Manuf Technol 2002; 19: 330–335.

10.

Qing

Zhang

, et al. The cutting force and defect analysis in milling of carbon fiber-reinforced polymer (CFRP) composite. Int J Adv Manuf Technol 2017; 93: 1829–1842.

11.

Gara

M’hamed

Tsoumarev

. Temperature measurement and machining damage in slotting of multidirectional CFRP laminate. Mach Sci Technol 2018; 22: 320–337.

12.

Haiyan

Xuda

Hao

, et al. Analysis of cutting forces in helical milling of carbon fiber-reinforced plastics. Proc Inst Mech Eng Part B J Eng Manuf 2013; 227: 62–74.

13.

Sheikh-Ahmad

Zhu

, et al. Cutting force analysis considering edge effects in the milling of carbon fiber reinforced polymer composite. J Mater Process Technol 2020; 279: 116541.

14.

Torres

Luis

Puertas

. EDM machinability and surface roughness analysis of TiB2using copper electrodes. J Alloys Compd 2017; 690: 337–347.

15.

Equbal

Sood

. An investigation on the feasibility of fused deposition modelling process in EDM electrode manufacturing. CIRP J Manuf Sci Technol 2019; 26: 10–25.

16.

Ray

Puri

Nagahanumaiah . Investigation on cutting forces and surface Finish in mechanical micro milling of Zr-based bulk metallic glass. J Adv Manuf Syst 2019; 18: 113–132.

17.

Palanikumar

. Cutting parameters optimization for surface roughness in machining of GFRP composites using Taguchi’s method. J Reinf Plast Compos 2006; 25: 1739–1751.

18.

Shunmugesh

Panneerselvam

. Optimization of process parameters in micro-drilling of carbon fiber reinforced polymer (CFRP) using taguchi and grey relational analysis. Polym Polym Compos 2016; 24: 499–506.

19.

Shunmugesh

Panneerselvam

. Optimization of machining process parameters in drilling of CFRP using multi-objective Taguchi technique, to PSIS and RSA techniques. Polym Polym Compos 2017; 25: 185–192.

20.

Siddiquee

Khan

Mallick

. Grey relational analysis coupled with principal component analysis for optimisation design of the process parameters in in-feed centreless cylindrical grinding. Int J Adv Manuf Technol 2010; 46: 983–992.

21.

Chakraborty

Zavadskas

Antucheviciene

. Applications of WASPAS method as a multi-criteria decision-making tool. Econ Comput Econ Cybern Stud Res 2015; 49: 1–17.

22.

Sahu

Mohanty

Mahapatra

. A DEA approach for optimization of multiple responses in electrical discharge machining of AISI D2 steel. Procedia Eng 2013; 51: 585–591.

23.

Kharwar

Verma

. Machining performance optimization in drilling of multiwall carbon nano tube/epoxy nanocomposites using GRA-PCA hybrid approach. Meas J Int Meas Confed 2020; 158: 107701.

24.

Agung

Sapuan

Hamdan

, et al. Optimization of the mechanical properties of abaca fibre-reinforced high impact polystyrene (HIPS) composites using box-behnken design of experiments. Polym Polym Compos 2011; 19: 697–710.

25.

Hamdi

Merghache

Aliouane

. Effect of cutting variables on bearing area curve parameters (BAC-P) during hard turning process. Arch Mech Eng 2020; 67: 73–95.

26.

Jogi

Tarekar

Dhajekar

, et al. Multi objective optimization using taguchi grey relational analysis (GRA) for cnc turning of poly-ether-ether-ketone (PEEK) polymer. Polym Polym Compos 2016; 24: 523–528.

27.

Wang

Melly

, et al. Helical milling response of glass fiber-reinforced polymer composite with carbon nanotube buckypaper interlayer. Polym Polym Compos 2020; 28: 378–387.

28.

Jenarthanan

Jeyapaul

. Optimisation of machining parameters on Milling of GFRP composites by desirability function analysis using Taguchi method. Int J Eng Sci Technol 2018; 5: 22.

29.

Ali

Mia

Khan

, et al. Multi-response optimization of face milling performance considering tool path strategies in machining of Al-2024. Materials (Basel) 2013; 12: 1–19.

30.

Tsao

. Grey – Taguchi method to optimize the milling parameters of aluminum alloy. Int J Adv Manuf Technol 2009; 41–48.

31.

Ribeiro

César

Lopes

. Optimization of machining parameters to improve the surface quality. Procedia Struct Integr 2017; 5: 355–362.