Optimization of machining parameters during end milling of carbon fiber-reinforced polypropylene textile composite

Abstract

Carbon fiber-reinforced polypropylene (CFRPP) composites are being used at an increasing rate in lightweight mechanical applications. The reasons are their excellent strength-to-weight ratio, corrosion resistance, and recyclability. However, their anisotropic and heterogeneous structure holds considerable challenges during machining processes, such as end milling, often resulting in surface defects and dimensional inaccuracies. The present study focuses on the optimization of milling parameters for CFRPP composites using the VIKOR technique and systematically investigates the influence of critical process variables, including feed rate, depth of cut, and spindle speed, on key output responses. These responses included machining temperature, machining force, delamination factor, and surface roughness. To develop and understand these models, a Box-Behnken experimental design of Response Surface Methodology was employed. Spindle speed had the highest influence on machining temperature (43.36%), followed by feed rate (32.77%) and depth of cut (21.74%). Machining force was mainly affected by feed rate (55.02%), depth of cut (31.02%), and spindle speed (12.83%). For delamination factor, feed rate contributed the most (54.53%), followed by depth of cut (27.21%) and spindle speed (17.09%). Surface roughness was also primarily influenced by feed rate (43.49%), followed by depth of cut (30.86%) and spindle speed (24.45%). The SEM analysis helped in understanding the defects such as fiber fracture, fiber pullout, voids, and matrix smearing.

Keywords

End milling CFRP thermoplastic polypropylene optimization VIKOR

Highlights

(1) Investigates the end milling performance of carbon fiber-reinforced polypropylene (CFRPP) textile composite.

(2) Evaluates critical responses such as machining force, machining temperature, delamination factor, and surface roughness.

(3) Studies the influence and contribution of feed rate, depth of cut, and spindle speed on machining responses.

(4) Utilizes VIKOR optimization techniques to identify optimal machining parameters.

(5) Microscopic characterization of the machined surface.

Introduction

Thermoplastic composites are a class of lightweight, high-performance, and recyclable materials that have found application in a variety of fields, including aerospace, automotive, and sports.¹ Carbon fiber-reinforced polypropylene (CFRPP) composites are a combination of carbon fiber, which possesses high specific strength, and a thermoplastic matrix, which offers advantages such as recyclability.² Textile architecture offers superior in-plane properties and adaptability for intricate shaping.³ Composites require milling for dimensional accuracy and assembly. These materials are difficult to machine due to their inherent anisotropy and inhomogeneity.⁴ Textile composites have been shown to possess favorable in-plane properties; however, they are susceptible to delamination when subjected to loads applied perpendicular to the plane. Improper machining parameters may lead to defects like fiber pullout, uncut fiber, delamination, thermal degradation, and severe tool wear.⁵

Milling parameters strongly influence the machining responses.⁶ Thrust force decreased with the increase of speed, while cutting force increased with the increase of speed. Cutting temperature increased with an increase in spindle speed.⁷ The cutting force increased with feed rate and radial depth.⁸ Machining with small depth of cut reduced the burr formation during milling of CFRP composite.⁹ Force is increased by increasing depth of cut and decreased by increasing spindle speed during milling of CFRP composite using a micro-textured milling tool.¹⁰ Temperature analysis during slot cutting in CFRP revealed that increased feed and spindle speed increased the machining temperature. Effect of spindle speed is more than feed rate.

Optimization of the parameters increases productivity along with better quality of parts.¹¹ MWCNT amount, feed rate, and spindle speed were optimized by applying TOPSIS during milling of CFRP composite. Addition of 1.5% MWCNT with the lowest feed rate and spindle speed produced slot with the lowest delamination factor and surface roughness.¹² Response Surface Methodology (RSM) was used to optimize the machining parameters to obtain a lower delamination factor and surface roughness. Slots were made using a solid carbide end mill of diameter 8 mm. Highest spindle speed, lowest feed, and lowest depth of cut provided the least delamination factor and surface roughness.¹³

Tool characteristics influence the machinability of the composite materials. Micro-textured tools were employed during milling of CFRP composite. Surface quality improved by the milling with the micro-textured tools.¹⁴ Three-facet and four-facet end mill tools were employed for making slots on the CFRP composite. A lower delamination factor was observed for the slots made by a four-facet end mill.¹⁵ Atomized vegetable oil reduced the friction during milling of CFRP composite. The lubricant application decreased the tool wear, delamination, and machining force. The force was proportional to the feed rate.¹⁶

Several studies are available for the milling of thermosetting composites; however, thermoplastic-based, especially textile composites, remain largely unexplored, which motivates the current study. Composite laminates were fabricated, and slots were made on the fabricated composite with a four-facet end mill having a diameter of 8 mm. Experiments were designed on the basis of RSM. The aim is to optimize milling parameters to improve the machining outcomes, such as milling temperature, milling force, delamination factor, and surface roughness. This study offers significant insights into enhancing the machinability of CFRPP composites and contributes to the development of efficient, high-quality manufacturing strategies for thermoplastic composite components.

Materials and methods

Sample preparation

Bi-directional woven carbon fabric (CF) and polypropylene (PP) film were taken as reinforcement and matrix. CF has a thickness of 0.2 mm, and PP has a thickness of 0.14 mm.

The fabrication of the carbon fiber-reinforced polypropylene (CFRPP) composites was done using compression molding technique. CF and PP were cut into mold dimensions of 220 mm * 140 mm. Both reinforcement and matrix were arranged alternately in the mold. The mold, carrying stacked CF and PP, was placed between the platens of the compression molding machine. The mold was preheated at 100°C for 30 min without applying pressure. The mold was pressed by applying 10 bar pressure at 200°C for 1 h. The combination of temperature and pressure allowed the impregnation of the matrix into fabrics and the removal of entrapped air. The mold was cooled under pressure to room temperature, then the composite laminate was demolded. The achieved average thickness of the composite laminates was 4 mm. The composite laminates were cut by an abrasive water jet machine to a size of 120 mm * 100 mm * 4 mm. The fabrication process is shown in Figure 1.

Figure 1.

Materials and fabrication process (a) Reinforcement and matrix, (b) mold dies, (c) compression molding machine, (d) fabricated CFRPP laminate, (e) CFRPP laminates used for machinability study.

The characterization of composite laminates has been conducted in accordance with their respective ASTM specifications. The tensile strength of the fabricated composite laminate was measured to be 281.57 MPa (ASTM D3039), while its fiber volume fraction was determined to be 64.78% (ASTM D2584), and its density was found to be 1331.24 kg/m³ (ASTM D792).

Experimental work

The milling experiments were performed on a Bridgeport Interact 3-axis CNC vertical milling machine. The composite laminate was fixed on a customized fixture. Slots have been made using a TiAlN-coated carbide four-facet end mill (YG1 G9A69080). The tool has a helix angle of 30°, a total length of 64 mm, a cut length of 21 mm, and a diameter of 8 mm.

The machining forces (MF) have been recorded using a Kistler 9257B dynamometer. The dynamometer is connected to the Kistler 5167A charge amplifier. The force data has been analyzed using Kistler DynoWare software. The machining temperature (MT) has been recorded using a FLIR A400 infrared thermal camera. Machining setup used for the experiment is shown in Figure 2. The surface roughness (SR) of the machined surface has been recorded using TESA Rugosurf 90G. Nova Nano FESEM 450 was used to study the microstructure of the machined surface.

Figure 2.

Machining setup.

The delamination factor (DF) quantitatively indicates the delamination, which is the ratio of W_max to W_nom. Where W_max is the maximum damage length, and W_nom is the nominal diameter of the cut boundary. The schematic for the DF is shown in Figure 3. DF was measured with the help of ImageJ software.¹⁷ ImageJ software was chosen because it is widely used, freely available, and capable of providing precise quantitative image analysis with high reproducibility. High-resolution images of the machined edges were captured under consistent lighting and magnification conditions to minimize distortion and pixelation errors. To ensure accuracy, the scale was calibrated using a known distance. Each measurement was repeated three times, and the average was considered for further analysis. In addition, measurements were independently verified by two operators, and discrepancies greater than 5% were re-evaluated to minimize operator error.

Figure 3.

Schematic for the delamination factor calculation.

Three machining parameters were varied at three levels. The machining parameters and their levels are provided in the Table 1. Levels were identified from previous studies, like¹⁸ on thermoplastic composite milling, ensuring that the values fell within conditions known to avoid excessive tool wear or severe thermal degradation. These ranges were then refined through preliminary trials to identify settings that produced measurable differences in machinability indicators without causing catastrophic defects.

Table 1.

Machining parameters.

Machining parameters	Unit	Levels
Feed rate (FR)	mm/min	80	160	240
Depth of cut (DOC)	mm	0.7	1.4	2.1
Spindle speed (SS)	rpm	800	1600	2400

Results and discussion

The experiments were performed according to the Response Surface Methodology (RSM) designed in the Minitab software. A total of 15 experiments have been generated. Each experiment was repeated three times, and the average was considered for the analysis. Table 2 contains the experiment number along with machining parameters and outcomes.

Table 2.

Experiment runs, machining parameters, and outcomes.

Experiment number	FR (mm/min)	DOC (mm)	SS (rpm)	MT (°C)	MF (N)	DF	SR (µm)
1	80	0.7	1600	50.25	40.28	1.107	3.1
2	240	0.7	1600	70.49	66.19	1.125	4.4
3	80	2.1	1600	67.82	59.47	1.121	4.3
4	240	2.1	1600	93.42	83.48	1.136	5.5
5	80	1.4	800	44.82	52.17	1.109	4.1
6	240	1.4	800	69.14	79.24	1.126	5.6
7	80	1.4	2400	71.28	41.28	1.118	3.1
8	240	1.4	2400	95.73	66.38	1.136	4.6
9	160	0.7	800	49.86	55.19	1.112	4.3
10	160	2.1	800	65.79	74.84	1.123	5.3
11	160	0.7	2400	75.49	41.99	1.121	3.1
12	160	2.1	2400	96.61	62.76	1.133	4.4
13	160	1.4	1600	67.44	59.42	1.122	4.3
14	160	1.4	1600	73.58	62.07	1.123	4.6
15	160	1.4	1600	77.97	55.44	1.119	4.5

Standard uncertainty

Each experiment has been repeated three times. So, it is important to calculate the standard uncertainty (SU) associated with the experimental values. Standard uncertainty is defined as the uncertainty of a measurement result expressed as a standard deviation, representing the dispersion of values reasonably attributed to the measurand. Serving as a consistent measure of reliability, standard uncertainty provides the foundation for calculating combined and expanded uncertainties in experimental analysis. Equations (1)–(3) have been used to calculate the SU.¹⁹ Table 3 provides the SU associated with each machining outcome.

\bar{X} = \frac{\sum_{i = 1}^{n} X_{i}}{n}

(1)

Where n is the number of repetitions. Here, n = 3.

SD = \sqrt{\frac{\sum_{i = 1}^{n} {(X_{i} - \bar{X})}^{2}}{n - 1}}

(2)

SU = \frac{SD}{\sqrt{n}}

(3)

Table 3.

Standard uncertainty (SU) associated with machining outcomes.

Experiment number	MT (°C)	SU	MF (N)	SU	DF	SU	SR (µm)	SU
1	50.25	2.84	40.28	1.84	1.107	0.003	3.1	0.1
2	70.49	1.65	66.19	2.52	1.125	0.003	4.4	0.2
3	67.82	1.91	59.47	2.37	1.121	0.002	4.3	0.1
4	93.42	2.74	83.48	1.85	1.136	0.003	5.5	0.2
5	44.82	2.47	52.17	1.20	1.109	0.002	4.1	0.1
6	69.14	1.52	79.24	2.11	1.126	0.002	5.6	0.2
7	71.28	3.03	41.28	1.97	1.118	0.002	3.1	0.1
8	95.73	2.69	66.38	1.26	1.136	0.003	4.6	0.0
9	49.86	1.61	55.19	2.27	1.112	0.002	4.3	0.1
10	65.79	2.95	74.84	1.61	1.123	0.002	5.3	0.1
11	75.49	2.72	41.99	1.88	1.121	0.002	3.1	0.1
12	96.61	2.19	62.76	2.27	1.133	0.002	4.4	0.2
13	67.44	2.06	59.42	1.65	1.122	0.002	4.3	0.2
14	73.58	2.60	62.07	1.31	1.123	0.002	4.6	0.2
15	77.97	2.68	55.44	1.43	1.119	0.002	4.5	0.1

Machining temperature analysis

Temperature analysis during milling of thermoplastic-based composites is necessary because the heat generated during machining affects the surface integrity and dimensional accuracy, along with defects like matrix smearing and fiber pullout. The generation of heat during the machining process has been observed to soften the thermoplastic matrix. At elevated temperatures, the matrix capacity of holding the fiber is reduced.²⁰

In the contour plot, shown in Figure 4, machining temperature (MT) increases continuously with FR, DOC, and SS. The increase in MT with FR, DOC, and SS for the CFRPP is primarily attributed to the higher material removal rate and increased friction at the tool–workpiece interface. As the FR increases, more material is engaged per unit time, resulting in higher machining forces and greater frictional heat generation. Additionally, a higher FR reduces the time available for heat dissipation, leading to the accumulation of heat in the milling tool. Similarly, increasing DOC enlarges the tool–workpiece contact area, which elevates the machining forces and further contributes to friction-induced heat generation. The problem is intensified in CFRPP composites due to the low thermal conductivity of the polypropylene matrix, which restricts heat dissipation and causes localized temperature rise near the cutting edges.²¹ Furthermore, increasing the SS raises the cutting speed, leading to higher sliding action between the tool and workpiece, which in turn increases friction and thermal energy generation. In the case of CFRPP, the thermoplastic matrix softens at elevated temperatures, which increases adhesion and friction at the interface, further intensifying the temperature rise.

Figure 4.

Contour plots of machining temperature versus machining parameters.

Analysis of variance (ANOVA) was done to determine the significance and contributions of FR, DOC, and SS. ANOVA for the MT is provided in Table 4. All machining parameters are significant since the P-values are less than 0.05. SS affected MT the most, followed by FR, with contributions of 43.36% and 32.77%, respectively. Contribution of DOC in MT is 21.74%.

Table 4.

ANOVA for machining temperature.

Source	DF	Seq SoS	Contribution (%)	Adj SoS	Adj MoS	F-value	P-value
FR	2	1134.16	32.77	1135.14	567.569	61.46	0.000
DOC	2	752.21	21.74	752.35	376.175	40.74	0.000
SS	2	1500.37	43.36	1500.37	750.185	81.24	0.000
Error	8	73.88	2.13	73.88	9.235
Total	14	3460.62	100.00

The model achieved an R² of 98.27%, adjusted R² of 95.15%, and predicted R² of 94.15%, indicating excellent fit, robustness, and strong predictive capability. The regression model exhibits a high degree of compatibility with the experimental data, as evidenced by its lack of fit value of 0.12%, a measure of how closely the model aligns with the observed data. The regression equation for the MT is given in equation (4).

MT = 0.6 + 0.218 * FR + 8.6 * DOC + 0.0171 * SS - 0.000328 * FR * FR - 0.82 * DOC * DOC - 0.000001 * SS * SS + 0.0239 FR * DOC + 0.000001 FR * SS + 0.00232 DOC * SS

(4)

Table 5 compares the predicted and experimental results for different machining parameters, showing a very close agreement between the two. The prediction errors ranged from 0.14% to 1.18%, with an overall average error of 0.82%. This indicates that the developed model is highly accurate and reliable in estimating the machining response.

Table 5.

Comparison of predicted and experimental values for machining temperature.

S. No.	Machining parameters			Predicted	Experimental	Error (%)
S. No.	FR	DOC	SS	Predicted	Experimental	Error (%)
1	80	0.7	1600	50.32	50.25	0.14
2	240	0.7	1600	71.29	70.49	1.13
3	80	2.1	1600	67.02	67.82	1.18
Average						0.82

Machining force analysis

Machining force (MF) is governed by the tool-workpiece interaction during machining. It is a critical parameter that controls the machinability factors such as tool life, fiber pullout, and delamination during milling of polymeric textile composite materials. MF during milling is considered the vector sum of forces in three mutually perpendicular axes.²² Controlling MF is critical to avoid fiber-matrix debonding, tool deflection, and other damage mechanisms associated with milling CFRPP composites.

The effect of machining parameters such as FR, DOC, and SS on MF was thoroughly investigated. The results showed (Figure 5) that increasing FR resulted in a significant increase in MF. This is due to increased material removal per revolution, which increases the mechanical load on the cutting tool. Similarly, DOC was found to have a direct relationship with MF. Increasing the DOC engaged a larger volume of material with the tool, resulting in significantly higher MF. On the other hand, the SS had an inverse relationship with the MF. MF was reduced at higher SS. This reduction is due to matrix softening at higher SS.

Figure 5.

Contour plots of machining force versus machining parameters.

ANOVA for the MF is provided in Table 6. All machining parameters are significant since the P-values are less than 0.05. FR affected MF the most, followed by DOC, with contributions of 55.02% and 31.02%, respectively. The contribution of SS in MF is 12.83%.

Table 6.

ANOVA for machining force.

Source	DF	Seq SoS	Contribution (%)	Adj SoS	Adj MoS	F-value	P-value
FR	2	1321.62	55.02	1321.08	660.541	193.38	0.000
DOC	2	745.13	31.02	744.11	372.055	108.93	0.000
SS	2	308.09	12.83	308.09	154.046	45.10	0.000
Error	8	27.33	1.13	27.33	3.416
Total	14	2402.17	100.00

The model achieved an R² of 98.95%, adjusted R² of 97.07%, and predicted R² of 96%, indicating excellent fit, robustness, and strong predictive capability. A 0.12% lack of fit value shows how well the regression model fits the experimental data. The regression equation for the MF is given in (5).

MF = 31.5 + 0.0724 * FR + 7.70 * DOC + 0.00004 * SS + 0.000348 * FR * FR + 2.35 * DOC * DOC - 0.000002 * SS * SS - 0.0085 * FR * DOC - 0.000008 * FR * SS + 0.00050 * DOC * SS

(5)

There is a high degree of agreement between the experimental and predicted results for various machining parameters, as shown in Table 7. The overall average prediction error was 0.86%, with a range of 0.32% to 1.91%. This suggests that the developed model for estimating the machining response is very accurate and dependable.

Table 7.

Comparison of predicted and experimental values for machining force.

S. No.	Machining parameters			Predicted	Experimental	Error (%)
S. No.	FR	DOC	SS	Predicted	Experimental	Error (%)
1	80	0.7	1600	39.51	40.28	1.91
2	240	0.7	1600	65.98	66.19	0.32
3	80	2.1	1600	59.68	59.47	0.35
Average						0.86

Delamination factor analysis

During the machining of the fiber-reinforced plastic composite, delamination is a critical damage phenomenon that affects the structural integrity.²³ Machining forces cause fiber-matrix debonding near the machined edges and exit surface, which initiates the delamination.

The delamination factor (DF) evidently demonstrates the dependence on machining parameters (Figure 6). It has been demonstrated that the magnitude of the FM is directly proportional to the increase in the FR. The component of the MF that is perpendicular to the plane of the composite exerts an effort to dislodge the laminates.²⁴ In instances where the value of this component surpasses the interlaminar strength of the composite, delamination becomes a probable outcome. Therefore, an increase in the FR value corresponds to an increase in the DF value. As the SS increases, the MT also increases. The elevated temperature reduces the fiber-holding capacity of the matrix. Therefore, DF escalates in proportion to the rise in SS. The increment of DF with an increase in DOC is associated with high MF and increased material engagement. As the DOC increases, the tool must remove additional material, thereby increasing the MF. This increased MF increases the possibility of delamination.

Figure 6.

Contour plots of delamination factor versus machining parameters.

ANOVA for the DF is provided in Table 8. All machining parameters are significant since the P-values are less than 0.05. FR affected DF the most, followed by DOC, with contributions of 54.53% and 27.21%, respectively. The contribution of SS in MF is 17.09%.

Table 8.

ANOVA for delamination factor.

Source	DF	Seq SoS	Contribution (%)	Adj SoS	Adj MoS	F-value	P-value
FR	2	0.000579	54.53	0.000579	0.000289	186.45	0.000
DOC	2	0.000289	27.21	0.000289	0.000144	93.03	0.000
SS	2	0.000181	17.09	0.000181	0.000091	58.40	0.000
Error	8	0.000012	1.17	0.000012	0.000002
Total	14	0.001061	100.00

The model achieved an R² of 99.09%, adjusted R² of 97.45%, and predicted R² of 96.65%, indicating excellent fit, robustness, and strong predictive capability. The regression model fits the experimental data extremely well, as indicated by a lack of fit value of 0.09%. The regression equation for the DF is given in equation (6).

DF = 1.08733 + 0.000096 * FR + 0.00738 * DOC + 0.000002 * SS + 0.000000 * FR * FR + 0.00094 * DOC * DOC - 0.000013 * FR * DOC

(6)

As shown in Table 9, there is a high degree of agreement between the experimental and predicted results for various machining parameters. The overall average prediction error was 0.44%, ranging from 0.27% to 0.71%. These results suggest that the model developed to estimate the machining response is highly accurate and reliable.

Table 9.

Comparison of predicted and experimental values for delamination factor.

S. No.	Machining parameters			Predicted	Experimental	Error (%)
S. No.	FR	DOC	SS	Predicted	Experimental	Error (%)
1	80	0.7	1600	1.107	1.104	0.27
2	240	0.7	1600	1.125	1.129	0.35
3	80	2.1	1600	1.121	1.129	0.71
Average						0.44

Machined surface analysis

The quality of the machined surface is a critical aspect in the machinability study. Heterogeneity and anisotropy lead to surface defects such as fiber pullout, uncut fibers, edge chipping, and matrix smearing. Surface quality controls dimensional accuracy, structural integrity, and mechanical performance.

An analysis of surface roughness (SR) revealed the impact of machining parameters on surface quality.²⁵ SR has been observed to increase with the increase in FR and DOC, while it has been shown to decrease with the increase in SS, as evident from Figure 7. At increased FR and DOC, SR has been found to increase due to increased MF, defects, such as fiber pullout, uncut fibers, and fiber fracture. The presence of increased SR is attributable to two factors: firstly, continuous rubbing, and secondly, stable tool-material interaction. It is important to note that an associated phenomenon occurs as the SS increases. This is due to an increase in the MT, which results in an increase in the matrix smearing. The phenomenon of matrix smearing, which involves the covering of protruded fibers, has been observed to result in a decrease in SR.

Figure 7.

Contour plots of surface roughness versus machining parameters.

ANOVA for the SR is provided in Table 10. All machining parameters are significant since the P-values are less than 0.05. FR affected SR the most, followed by DOC, with contributions of 43.49% and 30.86%, respectively. The contribution of SS in MF is 24.45%.

Table 10.

ANOVA for surface roughness.

Source	DF	Seq SoS	Contribution	Adj SoS	Adj MoS	F-value	P-value
FR	2	3.78269	43.49%	3.78535	1.89268	145.36	0.000
DOC	2	2.68359	30.86%	2.68833	1.34417	103.23	0.000
SS	2	2.12689	24.45%	2.12689	1.06345	81.67	0.000
Error	8	0.10417	1.20%	0.10417	0.01302
Total	14	8.69733	100.00%

The model achieved an R² of 99.09%, adjusted R² of 97.45%, and predicted R² of 92.81%, indicating excellent fit, robustness, and strong predictive capability. A lack of fit value of 0.37% indicates that the regression model fits the experimental data extremely well. The regression equation for the SR is given in equation (7).

SR = 2.267 + 0.01089 * FR + 1.298 * DOC - 0.000411 * SS - 0.000005 * FR * FR - 0.221 * DOC * DOC - 0.00045 * FR * DOC + 0.000134 * DOC * SS

(7)

A substantial degree of concordance has been observed between the experimental outcomes and the predictions derived from various machining parameters, as illustrated in Table 11. The mean prediction error was determined to be 2.61%, with a range of 2.23% to 2.33%. This finding indicates that the developed model for estimating the machining response is highly accurate and reliable.

Table 11.

Comparison of predicted and experimental values for surface roughness.

S. No.	Machining parameters			Predicted	Experimental	Error (%)
S. No.	FR	DOC	SS	Predicted	Experimental	Error (%)
1	80	0.7	1600	3.0	3.1	3.23
2	240	0.7	1600	4.5	4.4	2.27
3	80	2.1	1600	4.2	4.3	2.33
Average						2.61

Scanning electron microscope (SEM) images reveal various types of surface damage that aid in understanding the underlying material removal mechanisms and process-induced defects. Detailed SEM analysis of these features is crucial for evaluating surface integrity and optimizing machining parameters to reduce defects and improve the quality of thermoplastic composite components. The SEM image shows loose matrix in Figure 8(a) caused by thermal softening or mechanical detachment. Localized heat can also weaken the matrix material, particularly in thermoplastics, contributing to detachment. Increased FR, DOC, and SS increase the loose matrix phenomenon. Matrix smearing occurs when the thermoplastic resin softens as a result of high MT. As the matrix softens, it flows over the machined surface instead of being removed cleanly, resulting in an irregular surface finish. Matrix smearing sometimes reduces the SR. The matrix smearing phenomenon is shown in Figure 8(b). Factors that increase MT increase the matrix smearing phenomenon, such as higher FR, DOC, and SS. Fiber fracture, shown in Figure 8(c), is frequently visible in SEM images as a result of excessive MF, causing brittle carbon fibers to break rather than shear cleanly. Machining parameters that increase MF, such as increased FR and DOC, significantly increase the risk of fiber fracture.

Figure 8.

SEM images of the machined surface.

Another common defect is fiber pull-out, which occurs when fibers become partially or completely dislodged from the matrix. This is caused by weak fiber-matrix interfacial bonding or excessive mechanical stresses during cutting, which result in cavities and contribute to poor surface quality. Fiber pullout can be seen in Figure 8(a). At higher feed rates and larger depths of cut, the cutting forces acting on the fiber–matrix interface increase, promoting debonding and subsequent fiber pull-out rather than clean shearing. Conversely, higher spindle speeds generally reduce cutting forces per fiber and improve the shearing action, thereby minimizing fiber pull-out. However, excessively high speeds can cause thermal softening of the thermoplastic matrix, which may also weaken the interfacial bond and increase pull-out.

Voids are small gaps within the matrix or at the fiber-matrix interface. These voids can be caused by manufacturing flaws in the composite or by milling under improper conditions, thermal degradation, or material separation. Voids are seen in Figure 8(c). At elevated feed rates and increased depths of cut, the resultant elevated cutting forces and aggressive material removal can induce fiber–matrix debonding, leaving behind voids on the machined surface. Conversely, elevated spindle speeds have been shown to facilitate smoother cutting by decreasing the chip load per fiber and augmenting the shearing action, consequently minimizing void formation. However, it has been demonstrated that excessively elevated spindle speeds may induce localized thermal softening of the thermoplastic matrix, which has the potential to weaken interfacial bonding and increase the probability of void initiation.

Fiber imprints, shown in Figure 8(d), appear as distinct patterns or shallow depressions left on the machined surface where fibers were previously embedded. These imprints are typically formed when the carbon fibers are either pulled out or fractured during machining, leaving behind impressions or outlines of their original position within the matrix. Fiber imprints are generally a result of inadequate fiber-matrix bonding, where the matrix fails to hold the fibers firmly during machining, or due to improper cutting parameters that lead to aggressive tool-material interaction. At higher feed rates and depths of cut, increased cutting forces press fibers into the thermoplastic matrix, causing imprint marks. Higher spindle speeds enhance shearing and reduce fiber imprint, but excessively high speeds can soften the matrix, worsening the effect. Thus, controlled cutting parameters are essential to minimize fiber imprint.

Optimization of the machining parameters

VIKOR optimization technique was used to find the best set of machining parameters for the optimal outcomes. VIKOR optimization is a multi-criteria decision-making (MCDM) method developed to rank and select from a set of alternatives when multiple, often conflicting, criteria are involved.²⁶ VIKOR, an acronym for VIseKriterijumska Optimizacija I Kompromisno Resenje, is a Serbian term denoting Multi-Criteria Optimization and Compromise Solution. Its application in various fields, including engineering, management, and materials selection, underscores its versatility. This approach is particularly useful in scenarios where no single option meets all the criteria. VIKOR was selected because it focuses on identifying a compromise solution closest to the ideal while considering both group utility and individual regret, making it particularly suited for balancing conflicting responses such as temperature, cutting force, delamination, and surface roughness.²⁷ The technique involves the following steps to optimize the process parameters.

Step I: Construction of the decision matrix

Decision matrix (Table 12) is formed by the alternatives and criteria. Alternatives are the set of machining parameters, and criteria are the output responses. Fifteen experiments that have been designed with the help of RSM will be alternatives, and four output responses (MT, MF, DF, and SR) will be criteria. Let X = [x_ij], where i ≤ m and j ≤ n. For the 15 alternatives and four output responses, m = 15 and n = 4. The criteria can be benefit type or cost type. In this study, all four responses have to be minimized; hence, the criteria will be cost type.

Table 12.

Decision matrix.

Experiment number	MT (°C)	MF (N)	DF	SR (µm)
1	50.25	40.28	1.107	3.1
2	70.49	66.19	1.125	4.4
3	67.82	59.47	1.121	4.3
4	93.42	83.48	1.136	5.5
5	44.82	52.17	1.109	4.1
6	69.14	79.24	1.126	5.6
7	71.28	41.28	1.118	3.1
8	95.73	66.38	1.136	4.6
9	49.86	55.19	1.112	4.3
10	65.79	74.84	1.123	5.3
11	75.49	41.99	1.121	3.1
12	96.61	62.76	1.133	4.4
13	67.44	59.42	1.122	4.3
14	73.58	62.07	1.123	4.6
15	77.97	55.44	1.119	4.5

Step II: Determination of projection values. Equation (8) has been used for the calculation of the projection values.

P_{ij} = \frac{x_{ij}}{\sum_{i = 1}^{m} x_{ij}}

(8)

Step III: Calculation of entropy. Entropies were calculated using Equations (9) and (10).

E_{j} = - ξ \sum_{i = 1}^{m} P_{ij} \ln (P_{ij})

(9)

ξ = \frac{1}{\ln m} = 0.369

(10)

Step IV: Calculation of dispersion value. Dispersion values have been calculated using equation (11).

Ψ_{j} = 1 - E_{j}

(11)

Step V: Calculation of weight. Weights for the criteria have been calculated using the equation (12). The entropy method was chosen because it is an objective weighting approach that determines criterion weights directly from the variability of the data, without relying on subjective judgments.²⁸ The entropy method quantifies the amount of information each criterion contributes, assigning higher weights to those with greater variability and thus stronger discriminating power in the decision matrix.

W_{j} = \frac{Ψ_{j}}{\sum_{j = 1}^{n} Ψ_{j}}

(12)

Step VI: Calculation of Utility measure (U_i) and Regret measure (R_i). Values of both measures are provided in Table 13. The Utility Measure (Ui) represents the overall performance of an alternative across all considered criteria. It reflects the collective satisfaction level or the aggregated distance of an alternative from the ideal solution. The Regret Measure (Ri) captures the maximum individual regret of an alternative with respect to any single criterion. It reflects the worst deviation from the ideal for that alternative, emphasizing caution or risk minimization. U_i and R_i were calculated using Equations (15) and (16), respectively.

Table 13.

Utility measure (U_i), Regret measure (R_i), VIKOR Index (V_i), and Rank.

Experiment number	U_i	R_i	V_i	Rank
1	0.029021627	0.029021627	0	1
2	0.549908045	0.224519983	0.56709843	10
3	0.452093802	0.166288633	0.429447269	7
4	0.945970878	0.374344394	1	15
5	0.208030466	0.104966812	0.207573703	2
6	0.75994594	0.337603185	0.845364763	13
7	0.211994376	0.203151388	0.351898655	4
8	0.746855199	0.362770337	0.8746666	14
9	0.269752527	0.129200808	0.276318813	3
10	0.675768975	0.299475515	0.744258361	11
11	0.232706048	0.217662202	0.384202957	5
12	0.679933911	0.348259523	0.817164821	12
13	0.451676679	0.165855364	0.428592478	6
14	0.527912344	0.188818619	0.503411728	8
15	0.488921239	0.210406795	0.513408375	9

If j is a benefit criterion, equation (13) will be used.

U_{ij} = \frac{W_{j} | {(x_{ij})}_{\max} - x_{ij} |}{| {(x_{ij})}_{\max} - {(x_{ij})}_{\min} |}

(13)

If j is a cost criterion, equation (14) will be used.

U_{ij} = \frac{W_{j} | x_{ij} - {(x_{ij})}_{\min} |}{| {(x_{ij})}_{\max} - {(x_{ij})}_{\min} |}

(14)

U_{i} = \sum_{j = 1}^{n} U_{ij}

(15)

R_{i} = Maximum of U_{ij}

(16)

Step VII: Calculation of VIKOR Index (V_i). Values are provided in Table 13. It is the final compromise ranking in the VIKOR method for multi-criteria decision-making (MCDM). It combines the overall performance (Utility Measure, Ui) and the worst-case performance (Regret Measure, Ri) to determine the best alternative based on a balance between group utility and individual regret. Equation (17) has been used for the calculation of VIKOR index.

V_{i} = µ [\frac{U_{i} - U_{i}^{-}}{U_{i}^{+} - U_{i}^{-}}] + (1 - µ) [\frac{R_{i} - R_{i}^{-}}{R_{i}^{+} - R_{i}^{-}}]

(17)

Where µ = 0.5.

Step VIII: Calculation of Rank. Ranks of all the alternatives are provided in Table 8. The alternative having the lowest V_i will be the best.

The first experiment gets the first rank due to the lowest VIKOR index. The optimum set of parameters for the best machinability is FR: 80 mm/min, DOC: 0.7 mm, and SS: 1600 r/min. Machining with the lowest feed rate, lowest depth of cut, and medium spindle speed provided the best machinability.

Conclusions and future scopes

This study comprehensively evaluated the end milling performance of CFRPP composites with a specific focus on the influence of machining parameters and optimization strategies on machining quality and process stability. Based on the experimental results and analysis, the following conclusions are drawn.

• FR, DOC, and SS exhibit a direct correlation with MT. SS had the highest influence on MT (43.36%), followed by FR (32.77%) and DOC (21.74%).

• MF increases with FR and DOC, while it decreases with an increase in SS. MF was mostly affected by FR (55.02%), followed by DOC (31.02%) and SS (12.83%).

• DF increases with an increase in FR, DOC, and SS. For DF, FR contributed the most (54.53%), followed by DOC (27.21%) and SS (17.09%).

• SR increases with increase in FR and DOC, while decreases with increase in SS. SR was primarily influenced by FR (43.49%), followed by DOC (30.86%) and SS (24.45%).

• Optimization of machining parameters through the VIKOR technique revealed that maintaining the lowest FR (80 mm/min), the lowest DOC (0.7 mm), and a moderate SS (1600 rpm) improves machining forces, temperature rise, delamination, and improves surface finish.

The machinability of CFRPP composites is governed by the fiber–matrix interaction under varying cutting conditions, highlighting the need for careful selection of parameters to preserve structural integrity and achieve superior surface finish. The outcomes of this work provide a foundation for process optimization in the milling of thermoplastic textile composites, which is critical for extending their applicability in lightweight structural and industrial applications. Future research should focus on exploring advanced tool geometries and coatings, as well as hybrid machining approaches such as cryogenic cooling or MQL, to further minimize damage. Additionally, predictive modeling through FEA or AI-based optimization may help establish more robust machining parameter selection.

Footnotes

Author contributions

The authors confirm their contribution to the paper as follows: SRP: Study, conception and design, data collection, experimentation, draft manuscript preparation. AM: Analysis and interpretation of results, draft manuscript preparation, supervision. HSM: Resources, supervision. All authors reviewed the results and approved the final version of the manuscript.

Declaration of conflicting interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is financially supported by the DRDO-ARMREB, Government of India, in accordance with Grant No. ARMREB/MAA/2019/213.

ORCID iDs

Shashi Ranjan Pathak

Anup Malik

References

Yao

Jin

Yop

, et al. Recent advances in carbon-fiber-reinforced thermoplastic composites: a review. Composites Part B 2018; 142(November 2017): 241–250.

Böhland

Tutunea Fatan

Schulze

. Formation and occurrence of subsurface damage mechanism with the analysis of optimal machining strategy when milling different fibre matrix combination of unidirectional FRPs. J Thermoplast Compos Mater 2025; 38(8): 1–25.

Pastore

. Opportunities and challenges for textile reinforced composites. Mech Compos Mater 2000; 36(2): 97–116.

Slamani

Chatelain

. Kriging versus Bezier and regression methods for modeling and prediction of cutting force and surface roughness during high speed edge trimming of carbon fiber reinforced polymers. Measurement 2020; 152: 107370.

Catalanotti

Falzon

, et al. Process characteristics, damage mechanisms and challenges in machining of fibre reinforced thermoplastic polymer (FRTP) composites: a review. Composites Part B 2024; 273(January): 111247.

Slamani

Chatelain

Hamedanianpour

. Influence of machining parameters on surface quality during high speed edge trimming of carbon fiber reinforced polymers. Int J Material Form 2019; 12(3): 331–353.

Liu

Hong

, et al. Research on multiscale modeling and experiment of CFRP milling. Materials 2023; 16(20): 6748.

Zhang

Wang

Qiao

, et al. High-speed milling of CFRP composites: a progressive damage model of cutting force. Int J Adv Manuf Technol 2020; 106(3–4): 1005–1015.

Wang

Yan

Zhao

, et al. Surface damage reduction of dry milling carbon fiber reinforced plastic/polymer using left–right edge milling tool. J Reinforc Plast Compos 2020; 39(11–12): 409–421.

10.

Song

Zhang

Gao

, et al. Cutting force prediction model for machining CFRP with a micro-textured tool considering derivative cutting. Proc Inst Mech Eng Part B J. Eng. Manuf 2025; 333: 09544054251325275.

11.

Pathak

Sharma

Mali

, et al. Optimization of machining parameters during milling on glass fiber-reinforced textile composite. Multiscale Multidiscip. Model. Exp. Des 2023; 7: 249–261.

12.

Thakur

Singh

. Impact of MWCNT in CFRP composite during end milling process. Mater Manuf Process 2022; 37(7): 816–824.

13.

Khairusshima

MKN

Aqella

AKN

Sharifah

ISS

. Optimization of milling carbon fibre reinforced plastic using RSM. Procedia Eng 2017; 184: 518–528.

14.

Chen

Guo

Zhang

, et al. Study on the surface quality of CFRP machined by micro-textured milling tools. J Manuf Process 2019; 37: 114–123.

15.

Kiliçkap

Yardimeden

Çelik

. Investigation of experimental study of end milling of CFRP composite. Sci Eng Compos Mater 2015; 22(1): 89–95.

16.

Elgnemi

TSM

Jun

MBG

Songmene

, et al. Milling performance of CFRP composite and atomised vegetable oil as a function of fiber orientation. Materials 2021; 14(8): 2062.

17.

Sayin

Danisman

Ersoy

, et al. Experimental and statistical damage analysis in milling of S2-glass fiber/epoxy and basalt fiber/epoxy composites. Polym Compos 2024; 45: 15140–15158.

18.

Pathak

Malik

Mali

. Machinability study on carbon fiber-reinforced polypropylene textile composite. Interactions 2025; 246(1): 24–27.

19.

Al-Hasani

Freegah

Al-Taee

AAH

. Numerical and experimental study of the hydrothermal response enhancement in mini channel heat sink with double outlet: influence of convergent channels. Int J Heat Fluid Flow 2025; 115: 109895.

20.

Meng

Liu

, et al. A review on the cutting of carbon fiber reinforced thermoplastic composites. Polym Compos 2024; 45: 13459–13479.

21.

Slamani

Chatelain

Hamedanianpour

. Comparison of two models for predicting tool wear and cutting force components during high speed trimming of CFRP. Int J Material Form 2015; 8(2): 305–316.

22.

Kumar

Verma

Mondal

, et al. A hybrid optimization technique to control the machining performance of graphene/carbon/polymer (epoxy) nanocomposites. Polym Polym Compos 2021; 29(9_suppl): S1168–S1180.

23.

Slamani

Chatelain

. A review on the machining of polymer composites reinforced with carbon (CFRP), glass (GFRP), and natural fibers (NFRP), Discover Mechanical Engineering. Discov Mech Eng 2023; 2(1): 4.

24.

Ozkan

Sabri Gok

Oge

, et al. Milling behavior analysis of carbon fiber-reinforced polymer (CFRP) composites. Mater Today Proc 2019; 11: 526–533.

25.

Baraheni

Shabgard

Amini

, et al. Experimental evaluation and optimization of parameters affecting delamination, geometrical tolerance and surface roughness in ultrasonic drilling of 3D-Printed PLA thermoplastic. J Thermoplast Compos Mater. 2024; 08927057241264803.

26.

Özcan

Vahapoglu

Uysal

, et al. Evaluation of slot milling processes on carbon fiber reinforced polyetheretherketone thermoplastic composite utilizing nanofluid-enhanced minimum quantity lubrication technique. J Thermoplast Compos Mater 2025; 0(0): 1–32.

27.

Luo

Yang

. Design and evaluation of a sustainable entropy-weighted and VIKOR-based method for offshore oil collecting. Heliyon 2023; 9(11): e21256.

28.

Fajdek-Bieda

. Using Entropy-VIKOR method in chemical processes optimization. Procedia Comput Sci 2021; 192: 4208–4217.