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Abstract

This study investigates the effects of abrasive water jet machining parameters on plain HDPE specimens and zirconia-coated multiwall carbon nanotube (ZrO₂-MWNT) HDPE composite specimens. In this experiment, 1–4 wt.% of zirconia-coated multiwall carbon nanotubes were added to the HDPE matrix. The results indicate that water pressure significantly impacts surface roughness in neat HDPE, while stand-off distance shows increased influence in reinforced HDPE composites, likely due to altered jet-material interaction from hard-phase reinforcements. This sequence of influences is observed in both cases. Furthermore, in each trial of machining, the surface roughness of neat HDPE is less than the roughness of zirconia-coated multiwall carbon nanotube reinforced HDPE. The experimentation results confirmed that as the percentage of the zirconia-coated multiwall carbon nanotubes increases, there is an increment in the hardness of the material and in roughness values, which signifies that the addition of reinforcement significantly affects surface roughness.

Keywords

Hydrothermal process injection moulding melt mixing polymer composites surface roughness water jet machining

Introduction

Abrasive Water Jet Machining (AWJM) is an advanced, non-conventional machining process that uses a high-velocity stream of water mixed with abrasive particles to cut, shape, or drill materials.¹ This technique is particularly useful for machining polymer nanocomposites, especially High-Density Polyethylene (HDPE) reinforced with zirconia (ZrO₂)-coated Multi-Walled Carbon Nanotubes (MWCNTs).^2–4 AWJM offers several advantages, including precise cuts without generating significant heat. This helps to effectively address the challenges posed by traditional machining techniques when working with these materials.⁵

HDPE, a widely used thermoplastic, is valued for its excellent mechanical properties, chemical resistance, and ease of processing.⁶ When reinforced with ZrO₂-coated MWCNTs,³ the resulting nanocomposite exhibits significant improvements in mechanical strength and thermal stability.⁷ MWCNTs, due to their high aspect ratio and remarkable mechanical properties, facilitate superior load transfer within the composite, creating a material that is not only lightweight but also exceptionally strong.⁸ However, machining this composite using conventional methods presents several challenges. The combination of the soft HDPE matrix and the hard, abrasive ZrO₂-coated MWCNT reinforcement makes it difficult to achieve optimal results without causing issues such as thermal deformation, surface irregularities, or accelerated tool wear.⁶

One of the main concerns in conventional machining is the thermal sensitivity of HDPE (high-density polyethylene). As a thermoplastic, HDPE is susceptible to melting or warping when exposed to high temperatures. The excessive heat generated during traditional machining processes can cause deformation, compromising the structural integrity of the composite.¹ Additionally, achieving a smooth surface finish is challenging due to the significant difference in hardness between the HDPE matrix and the ZrO2-coated MWCNTs (multi-walled carbon nanotubes). Furthermore, the abrasive nature of the ZrO2-coated MWCNTs accelerates the wear of conventional cutting tools, making it difficult to effectively machine these materials using traditional methods.

AWJM provides an effective solution to these challenges. The process involves cold cutting, where water and abrasives act as the cutting tools without generating significant heat.⁹ This makes it ideal for machining heat-sensitive materials like HDPE, as the cold cutting process prevents the material from melting or softening, thereby preserving its mechanical properties.¹⁰ Furthermore, since there is no direct contact between the tool and the workpiece, AWJM induces minimal mechanical stresses, which is crucial for maintaining the structural integrity of the HDPE/ZrO₂-MWCNT nanocomposite. This non-contact nature of AWJM ensures that the material’s enhanced properties are not compromised during machining.¹¹

AWJM is highly precise, making it well-suited for cutting complex geometries and intricate details.¹² This level of precision is particularly important in applications where the exact shape and dimensions of HDPE/ZrO₂-MWCNT components are critical. By carefully adjusting process parameters such as water pressure, abrasive type, and nozzle distance, AWJM can achieve a good surface finish, which is vital in ensuring that the performance of the final product is not adversely affected by surface irregularities.¹³ The process is also environmentally friendly, as it does not produce hazardous fumes or dust, and the water used can be filtered and recycled, reducing waste and minimizing environmental impact.⁵

The effectiveness of AWJM in machining HDPE/ZrO₂-MWCNT nanocomposites depends largely on the optimization of various process parameters, including water pressure, abrasive flow rate, stand-off distance, nozzle diameter, and traverse speed.¹⁴ Among these parameters, water pressure, traverse speed, and stand-off distance are considered the most critical, as they have the greater impact on machining quality.¹⁵ Optimization of these parameters is essential to achieve the desired balance between precision, surface quality, and material integrity during the machining process.

This study explores the potential of Abrasive Water Jet Machining (AWJM) as an effective and efficient technique for machining HDPE/ZrO2-MWCNT nanocomposites. We examine and optimize the effects of selected parameters within a specified range to achieve minimal surface roughness. This research is expected to make a significant contribution to the field.

Experimentation

Materials

High-density polyethylene (HDPE) 002DP48, obtained in pellet form from Indian Oil Propel in Mumbai, India, is classified as a PE100 grade material. The multi-walled carbon nanotubes (MWCNTs) used in this project have a purity of 99% and were sourced from AD-Nano Technologies Pvt. Ltd. Additionally, essential chemicals—including 98% ethanol, 70% nitric acid (HNO₃), and polytetrafluoroethylene (PTFE) filter membranes with a pore size of 0.5 micrometres (μm)—were acquired from Raman Lab Equipment Pvt. Ltd. in Karnataka, India. Furthermore, zirconium oxychloride octahydrate (ZrOCl₂•8H₂O) was procured from Loba Chemie in India. Other necessary materials were sourced locally.

Preparation of zirconia-coated MWCNT

Acid functionalization of MWCNT

To minimize the agglomeration of multi-walled carbon nanotubes (MWCNTs) and achieve a superior coating with zirconia (ZrO₂), MWCNTs undergo acid treatment using different acid combinations. A study by Suhas et al.³ indicates that MWCNTs treated with 8M HNO3 at 60°C for 3 h show stability with minimal agglomeration and improved ZrO₂ coating. Based on these findings, the MWCNTs were subjected to acid treatment using a reflux setup to prevent acid loss during evaporation. An 8 M HNO₃ solution was prepared with 100 mL of deionized water. One gram of MWCNTs was added to this solution. Since MWCNTs tend to agglomerate when dispersed in a solution due to strong van der Waals forces, a two-step mixing procedure was implemented to ensure their uniform distribution within the acid solution. First, the solution was stirred using a magnetic stirrer, followed by ultrasonication for 1 hour each. A water bath was employed to prevent overheating of the solution during ultrasonication.

The multi-walled carbon nanotubes (MWCNTs) were dispersed in a solution and then refluxed for 3 h at 60°C. After the refluxing process, the solution was filtered using a PTFE filter membrane and thoroughly washed several times with deionized water to eliminate any residual acid. The pH of the solution was monitored and adjusted to a range of 6 to 7 through repeated washings. Finally, the MWCNTs were dried overnight in a hot air oven at 60°C to ensure that all moisture was removed. The resulting functionalized MWCNTs (f-MWCNTs), which initially formed a cake-like structure, were then crushed into a fine powder using a mortar.

Coating of ZrO₂ on MWCNT using hydrothermal setup

The coating of ZrO₂ on the surface of functionalized multi-walled carbon nanotubes (f-MWCNTs) is achieved using zirconium oxychloride octahydrate (ZrOCl₂·8H₂O) as a precursor. First, a 0.3 M aqueous solution is prepared by dissolving a measured quantity of ZrOCl₂·8H₂O in 100 mL of deionized (DI) water. Next, 100 mg of f-MWCNTs are added to this solution and mixed using a two-step method, which includes stirring with a magnetic stirrer followed by ultrasonication, with each step lasting 60 min. The resulting solution is subjected to a hydrothermal reaction by heating in an autoclave at a temperature of 200°C for 18 h. After the reaction, the solution is thoroughly dried in a hot air oven at 60°C to remove any entrapped moisture, resulting in uniformly coated ZrO₂ on the MWCNTs. Figure 1 shows the resultant powder of ZrO₂-coated MWCNTs.

Figure 1.

ZrO2-coated MWCNT.

Melt blending of HDPE and ZrO₂-coated MWCNT

Grinding of HDPE pellets

The HDPE pellets were pulverized into a powdered form using an ROTO Pulveriser. The primary objective of grinding the pellets was to achieve better mixing with zirconia-coated MWCNT. When HDPE was combined with the reinforcing materials in pellet form, the reinforcing materials tended to separate and settle at the bottom due to their powdered form, leaving the HDPE pellets on top. To address this issue, the HDPE pellets were subjected to fine grinding to ensure the uniform blending of HDPE and zirconia-coated MWCNT.

A pulveriser operate by powdering and grinding the HDPE through the interaction of stationary serrated plates and oscillating rotary beaters. The HDPE pellets to be pulverized is introduced at the centre, where it encounters both static and swiftly rotating pulverizing disks. These disks exert a centrifugal force, facilitating the passage of the material through the processing region before expelling it pneumatically from the apparatus and as a result we obtain a finely powdered HDPE.

Blending using twin screw extruder

The HDPE/ZrO₂ coated MWCNT blends were obtained via a twin-screw extruder (O-Micron 10 Twin Screw extruder, Steer Engineering, India). Twin screw extruders are designed to provide excellent mixing capabilities. They utilize co-rotating or counter-rotating screws that intermesh closely, creating a highly efficient mixing zone. This ensures thorough dispersion of ZrO₂-coated MWCNT and uniform distribution of materials, resulting in consistent blending of HDPE with ZrO₂-coated MWCNT. In the current work, we have considered 4 wt.% of zirconia-coated MWCNT. Later, powdered HDPE and ZrO₂ coated MWCNT were manually mixed for 10 min in a plastic container to obtain a uniform distribution of HDPE and ZrO₂-coated MWCNT.

Then the mixture is fed into the barrel of the extruder from a hopper. The barrel temperature is maintained at 200°C and the rotational speed of the screw is maintained at 60 r/min. The mechanical energy produced due to the turning of screws and heaters arranged in the barrel melts the HDPE and ZrO₂ coated MWCNT will be uniformly distributed. The main advantage of twin screw extruder is eliminating the agglomeration of ZrO₂-coated MWCNT. Then the molten ZrO₂-coated MWCNT reinforced HDPE is forced into a die and exits in the form of small tubes. Later, on cooling they can be studded into small pellets as shown in Figure 2.

Figure 2.

Zirconia-coated MWCNT reinforced HDPE pellets.

Injection moulding

Injection moulding is an important industrial method for die casting of polymer materials. Injection moulding is primarily used for glasses, elastomers, and especially thermoplastic and thermosetting polymers. However, it is most commonly employed for fabricating thermoplastic materials. This technique involves heating the raw material and injecting it into the mould cavity under pressure at a specific temperature, without altering the material’s chemical composition. The injection moulding setup is shown in Figure 3.

Figure 3.

Injection moulding set up.

In this process, HDPE-reinforced ZrO₂-coated MWCNT pellets were poured into the hopper using a feeding device. The moulding material then descends under the influence of gravity into the cylinder (barrel). A circumferential heater located on the barrel maintains the temperature at 280°C to melt the material. As the powdered moulding material moves from the hopper into the barrel, it begins to melt. A hydraulic ram or a rotating screw then propels the molten material forward under applied pressure with a holding pressure of 80 bar, holding time of 15 s, and a cooling time of 30 s. The molten plastic is injected into a closed mould attached to the opposite end of the barrel. The rotating screw continuously moves the moulding material forward, while pressure is applied to the mould by the hydraulic system. Izod moulds were used to prepare the sample, which consisted of 1, 2, 3, and 4 wt.% HDPE-reinforced ZrO₂-coated MWCNT. The 4 wt.% specimen is shown in Figure 4.

Figure 4.

Test specimen as per ASTM standards.

Abrasive water jet machining (AWJM)

The composite samples were machined using a Maxiem 1515 (injection-type) AWJ machine from OMAX Corporation. The machine was equipped with a 30 HP direct drive pump for the cutting experiments, as shown in Figure 5. The machining process was conducted on injection moulded samples with a thickness of 3.2 mm and a machining distance of 10 mm for all samples.

Figure 5.

Abrasive waterjet machining schematic representation.

AWJM process involves several input parameters that need to be adjusted to achieve both good quality and efficiency. However, considering all these parameters can be time-consuming and may create confusion when evaluating cutting performance. The shape and quality of the cut depend greatly on selecting the right process settings. In this study, three important input parameters—waterjet pressure (P), traverse speed (V), and standoff distance (SOD) were chosen, each tested at three different levels. These parameters were selected based on a review of previous studies and the machine’s specifications, with both dynamic and constant parameters some of the constants considered in this study are Garnet size, Mass flow rate (Mf), Orifice diameter, and Angle of target. Selected parameters and their levels are shown in Tables 1 and 2. There are several types of abrasives used in the AWJM process, including garnet, olive sand, steel grit, silicon carbide (SiC), silica sand, aluminium oxide (Al₂O₃), and glass beads. Among these, garnet has shown the best performance due to its sharp edges and good flow properties, which make it more effective at cutting and it is also affordable. Garnet abrasive (mesh #60) with an average particle size of approximately 250 µm and hardness of 7.5–8 Mohs was used.

Table 1.

Variable and constant machining parameters.

	Units	Level 1	Level 2	Level 3
Dynamic parameters
Waterjet pressure (p)	MPa	100	150	200
Traverse speed (V)	mm min⁻¹	100	150	200
Standoff distance (SOD)	mm	2	3	4
Constant parameters
Garnet size	MESH	60
Mass flow rate (Mf)	g min⁻¹	100
Orifice diameter	mm	0.35
Angle of target		90°

Table 2.

Taguchi L9 experimental design table.

Experiment no	Process parameters
	Waterjet pressure (P) MPa	Traverse speed (TS) mm min⁻¹	Standoff distance (SOD) mm	Ra (µm)	Ra (µm)	Ra (µm)	Ra (µm)	Ra (µm)
	Waterjet pressure (P) MPa	Traverse speed (TS) mm min⁻¹	Standoff distance (SOD) mm	0 wt.%	1 wt.%	2 wt.%	3 wt.%	4 wt.%
1	100	100	2	4.021	4.077	4.184	4.286	4.369
2	100	150	3	4.381	4.208	4.366	4.388	4.392
3	100	200	4	4.409	4.284	4.435	4.451	4.493
4	150	100	3	4.012	4.376	4.455	4.468	4.386
5	150	150	4	4.271	4.359	4.317	4.382	4.399
6	150	200	2	4.350	4.499	4.380	4.411	4.359
7	200	100	4	4.164	4.290	4.308	4.341	4.314
8	200	150	2	3.899	4.146	4.297	4.312	4.349
9	200	200	3	3.929	4.202	4.375	4.395	4.403
Average Ra				4.159	4.271	4.346	4.381	4.445

Design of experiments

The Taguchi method’s design of experiments (DOE) was used to select a suitable mixed-level L₉ orthogonal array to design the AWJM experiments, based on the selected input levels. The Taguchi technique was applied to determine the effect of each input parameter on surface roughness. Taguchi’s method offers a clearer understanding of the process while minimizing the interaction between process parameters. It effectively analyses the impact of each parameter. The table provides details of the nine cutting trials conducted according to the selected AWJM input parameters, and the corresponding surface roughness values were calculated based on these experiments. The mean surface roughness values were evaluated using the signal-to-noise (S/N) ratio with the small-is-better criterion, to determine the optimal parameters for achieving a smoother surface. Additionally, the analysis identified the significance ranking of each parameter in influencing surface quality. The contribution of each input parameter to surface roughness was quantified through analysis of variance (ANOVA), with all statistical computations performed using Minitab software.

Measurement of surface roughness

Surface roughness was measured using a Taylor Hobson surface tester (Figure 6) with a tip radius of 2.5 µm and an evaluation length of 10 mm was used. Roughness is evaluated on both the Cutting Wear Zone (CWZ) and the Deformation Wear Zone (DWZ), as depicted in Figure 7. Precisely, roughness measurements were conducted in two regions of the CWZ and one region of the DWZ. The CWZ is a striation-free zone, characterized by its smooth surface and superior surface finish. In contrast, the DWZ exhibits striations, which are formed as a result of the waterjet impingement on the material’s surface. Each abrasive particle in the water jet acts as a cutting edge upon impact with the top surface. As the high-velocity water jet removes material from the top surface, the water jet velocity decreases when it penetrates the material. Consequently, the interaction between the eroded material and the abrasive particles leads to the formation of striation marks at the lower end of the specimen. The average value of three Ra measured were considered for further analysis.

Figure 6.

Taylor Hobson surface tester.

Figure 7.

Surface roughness measuring regions.

Results and discussions

Analysis of the effect of reinforcement percentage

SEM images were taken and examined to confirm the zirconia coating on MWCNT. Figure 8 demonstrates the adhesion of the zirconia coating along the length of the tube, which appears in white against the MWCNT. SEM investigations were conducted to obtain a more detailed microstructural characterization of the ZrO₂-coated MWCNTs. The images reveal that ZrO₂ nanoparticles are uniformly distributed along the sidewalls of the MWCNTs. The nanoparticle layer can be identified in the SEM image as a bright, discrete structure surrounding the nanotube, as highlighted in Figure 8. The observed diameter of the MWCNTs is approximately 6–8 nm, while the ZrO₂ nanospheres coating the surface range from 2 to 4 nm in size. This spherical nanostructure appears consistently along the entire length of the nanotube, indicating a uniform and cohesive deposition of ZrO₂ nanoparticles on the MWCNT surface.

Figure 8.

SEM image of zirconia-coated MWCNT.

To analyze the effect of reinforcement percentage on the hardness of the specimens, Vickers hardness tests were conducted. The reinforcement of zirconia-coated MWCNT enhances the hardness of the specimens. It was observed that as the percentage of reinforcement increases, the hardness of the material also increases. This phenomenon can be attributed to the hard properties of zirconia-coated MWCNT. The variation of hardness with percentage of reinforcement is presented in Figure 9. The sharp increase in hardness at 3–4 wt.% may be attributed to agglomeration of ZrO₂-coated MWCNTs, leading to localized hard zones which enhance resistance to deformation, as similarly observed in prior nanocomposite studies.

Figure 9.

Hardness versus weight percentage of reinforcement.

Figure 10 illustrates the variation of machined surface roughness in relation to the percentage of reinforcement. The figure shows that an increase in reinforcement corresponds to an increase in average machined surface roughness. This is attributed to the hard surface of zirconia-coated multiwall carbon nanotubes, which results in higher roughness due to abrasion during the machining process.

Figure 10.

Variation of surface roughness with hardness.

Analysis of neat HDPE machining

Regression equation

A regression equation is generated for the surface roughness of 0% reinforcement and presented in equation (1).

Ra 0 = 4.26 - 0.00390 P + 0.00147 TS + 0.114 SOD

(1)

Surface roughness is negatively impacted by parameter P in the regression equation, indicating that roughness decreases as P increases. Roughness is favourably affected by parameters V and SOD, suggesting that raising these values may result in increased roughness.

Main effect plot

Figure 11 shows the main effect plot for the data means. The greater deviation indicates that water pressure significantly affects the created surface roughness. The parameter stand-off distance has a considerable deviation next to pressure with moderate deviation. The parameter traverse speed has the least deviation, indicating the least significant effect.

Figure 11.

Main effect plot for plain HDPE.

Figure 12 illustrates the Pareto chart of the 0 wt.% reinforcement. The figure proves the significant effect of parameter P on surface roughness. Next to parameter P, parameter TS significantly influences surface roughness. The least contributing parameter is SOD.

Figure 12.

Pareto chart for plain HDPE.

Results of ANOVA for surface roughness of neat HDPE

Table 3 displays the ANOVA table for the current experiment. R2 = 97.99% of the surface roughness variability is displayed by the model. This implies that the majority of the variation is explained by the variables and interactions that are part of the model. It also suggests that surface roughness is influenced by the parameter pressure and traverse speed strongly. The standoff distance have a negligible impact on the machined surface’s roughness. The table also makes it evident that the combined impact of standoff distance and traverse speed is greater than the sum of the effects of the other components. This demonstrates that while the effects of traverse speed and standoff distance alone may be small, their combined influence is significant.

Table 3.

ANOVA table for neat HDPE.

Source	DF	Adj SS	Adj MS	F value	p value
P	1	0.123609	0.123609	29.89	0.032
TS	1	0.020949	0.020949	5.07	0.153
SOD	1	0.008729	0.008729	2.11	0.283
P*V	1	0.006739	0.006739	1.63	0.33
P*SOD	1	0.000262	0.000262	0.06	0.825
V*SOD	1	0.030241	0.030241	7.31	0.114
Error	2	0.008271	0.004135
Total	8	0.321984
Model summary
S	R-sq	R-sq (adj)
0.064307	97.43%	89.73%

Optimization by Taguchi method and RSM method

The Taguchi analysis of the current experiment is shown in Table 4. By giving the water pressure parameter a ranking of 1, it further validates the substantial influence of this parameter over surface roughness. Simultaneous standoff distance and traverse rank second and third, respectively. Predicting which level of the factors under consideration results in the least amount of surface roughness during machining is also feasible from the table. The table highlights the level of the parameters that provide the least amount of roughness. The chart makes it clear that the best combination of parameters to attain the least amount of surface roughness can be found by choosing and combining these levels of parameters. Therefore, the best combination for the current experimental work is P3 V1 SOD1 (the pressure should be 200 MPa, the stand-off distance should be 2 mm and the traverse speed should be 100 mm/min).

Table 4.

Taguchi table for HDPE.

Level	p	V	SOD
1	4.27	4.066	4.09
2	4.211	4.184	4.107
3	3.997	4.229	4.281
Delta	0.273	0.164	0.191
Rank	1	3	2

The contour plot for 0 wt.% reinforcement is shown in Figure 13. It illustrates the combination of parameters that results in lower surface roughness. The key information from the figure is that when the parameter P is set to its highest value, while the parameters TS and SOD are kept at their lowest levels, the machined surface will exhibit the lowest surface roughness.

Figure 13.

Surface roughness contour plot of pure HDPE.

Figure 14 is the RSM optimization chart for the composite with 0 wt.% reinforcement. The chart is providing the optimum combination of levels of the parameters to be maintained in order to achieve the minimum surface roughness. According to the chart the Parameter P should be 200 MPa, the parameter TS should be 100 mm/min and the parameter SOD should be 2 mm.

Figure 14.

RSM optimization plot for 0 wt.% reinforcement.

Analysis of ZCMWCNT (zirconia-coated multiwall carbon nano tubes) machining

Regression equations

The regression equations for 1 wt.%, 2 wt.%, 3 wt.%, and 4 wt.% reinforcement is presented in equations (2)–(5).

Ra 1 = 4.01 - 0.00166 P + 0.00212 TS + 0.0868 SOD

(2)

Ra 2 = 4.04 - 0.00125 P + 0.00232 TS + 0.0698 SOD

(3)

Ra 3 = 4.11 - 0.00138 P + 0.00208 TS + 0.0741 SOD

(4)

Ra 4 = 4.42 - 0.00110 P + 0.000830 TS + 0.0244 SOD

(5)

In all the regression equations, the parameter P negatively affects surface roughness, suggesting that increasing P reduces roughness. Meanwhile, parameters TS and SOD positively impact roughness, indicating that increases in these parameters could lead to higher roughness. The surface roughness of any intermediate value of P, TS and SOD can be calculated mathematically by putting the values within the considered range.

Main effect plot

The main effect plots for data means of 1 wt.%, 2 wt.%, 3 wt.%, and 4 wt.% reinforcement are illustrated in Figures 15(a)–(d) simultaneously. From the figures, it is confirmed that as the reinforcement percentage changes, different parameters are dominating the effect on machined surface roughness. The same trend is also shown in the Pareto charts in Figures 16(a)–(d).

Figure 15.

Main effect plots for data means (a = 1 wt.%), (b = 2 wt.%), (c = 3 wt.%), and (d = 4 wt.%).

Figure 16.

Pareto charts for various reinforcement (a = 1 wt.%), (b = 2 wt.%), (c = 3 wt.%), and (d = 4 wt.%).

Results of ANOVA for surface roughness of ZMH4

The ANOVA tables for 1 wt.% reinforcement is shown in Table 5. The model shows R² = 90.17% of the variability in the surface roughness. This suggests that the factors and interactions included in the model account for most of the variation. It also indicates that the parameter TS has a relatively strong influence on surface roughness. Parameters P and SOD show a low magnitude influence on the roughness of the machined surface. It is also clear from the table that the combined effect of TS and SOD is higher than the combined effects of other parameters.

Table 5.

Analysis of variance for 1 wt.%.

Source	DF	Seq SS	Adj SS	Adj MS	F	p
P	1	0.04135	0.00895	0.00895	0.73	0.482
TS	1	0.06747	0.07436	0.07436	6.10	0.132
SOD	1	0.04516	0.01637	0.01637	1.34	0.366
P*TS	1	0.04659	0.00314	0.00314	0.26	0.662
P*SOD	1	0.00079	0.00621	0.00621	0.51	0.550
TS*SOD	1	0.02239	0.02239	0.02239	1.84	0.308
Error	2	0.02440	0.02440	0.01220
Total	8	0.24815

S = 0.110446 R-Sq = 90.17% R-Sq(adj) = 80.67%.

The ANOVA tables for 2 wt.% reinforcement is shown in Table 6. The model shows R² = 96.29% of the variability in the surface roughness. This recommends that the factors and interactions included in the model account for most of the variation. It also indicates that the parameter TS has a relatively strong influence on surface roughness. Parameters P stands next to TS and the parameter SOD influences the roughness of the machined surface. It is also clear from the table that the combined effect of P and SOD is higher than the combined effects of other parameters. It is clear from the table that, though the individual effect of parameter P and TS is minimal, the combined effect of these two parameters has a significant influence on the surface roughness produced.

Table 6.

Analysis of variance for 2 wt.%.

Source	DF	Seq SS	Adj SS	Adj MS	F	p
P	1	0.023563	0.014183	0.014183	4.42	0.170
TS	1	0.080736	0.025536	0.025536	7.96	0.106
SOD	1	0.029260	0.008876	0.008876	2.77	0.238
P*TS	1	0.023912	0.001548	0.001548	0.48	0.559
P*SOD	1	0.004316	0.009152	0.009152	2.85	0.233
TS*SOD	1	0.004908	0.004908	0.004908	1.53	0.342
Error	2	0.006418	0.006418	0.003209
Total	8	0.173112

S = 0.0566472 R-Sq = 96.29% R-Sq(adj) = 85.17%.

The ANOVA table for 3 wt.% reinforcement is presented in Table 7. The model demonstrates an R² value of 93.16%, indicating that the factors and interactions included account for most of the variability in surface roughness. This suggests that parameter P has a relatively strong influence on surface roughness, followed by parameter TS. Parameter SOD has the least influence on the roughness of the machined surface. It is evident from the table that the combined effect of parameters P and TS is greater than the combined effects of the other parameters.

Table 7.

Analysis of variance for 3 wt.%.

Source	DF	Seq SS	Adj SS	Adj MS	F	p
P	1	0.02875	0.02350	0.02350	1.43	0.354
TS	1	0.06497	0.01376	0.01376	0.84	0.457
SOD	1	0.03295	0.00049	0.00049	0.03	0.879
P*TS	1	0.02547	0.01648	0.01648	1.00	0.422
P*SOD	1	0.00611	0.00024	0.00024	0.01	0.914
TS*SOD	1	0.00409	0.00409	0.00409	0.25	0.667
Error	2	0.03287	0.03287	0.01644
Total	8	0.19521

S = 0.128201 R-Sq = 93.16% R-Sq(adj) = 72.64%.

The ANOVA tables for the 4 wt.% reinforcement is shown in Table 8. The model demonstrates an R² value of 93.45%, indicating that it accounts for most of the variability in surface roughness. This suggests that the included factors and interactions significantly influence the variation observed. Notably, parameter P has a strong impact on surface roughness, followed by the parameter SOD. In contrast, the parameter TS has the least influence on the roughness of the machined surface. The table clearly shows that the combined effect of parameters P and TS is greater than the combined effects of the other parameters.

Table 8.

Analysis of variance for 4 wt.%.

Source	DF	Adj SS	Adj MS	F	p	%
P	1	0.079997	0.079997	17.3	0.043	56.55
TS	1	0.005349	0.005349	1.16	0.395	3.82
SOD	1	0.009005	0.009005	1.95	0.298	6.87
P*TS	1	0.0117	0.0117	2.53	0.253	7.32
P*SOD	1	0.00416	0.00416	0.9	0.443	3.14
TS*SOD	1	0.030295	0.030295	6.55	0.125	21.81
Error	2	0.009251	0.004625

S = 0.05820 R-Sq = 93.45% R-Sq(adj) = 72.63%.

Analysis through the contour plots

The contour plots of the experimentations with various percentages of reinforcement are illustrated in Figure 17. According to the plots, the surface roughness decreases as pressure increases from 100 to 200 MPa, suggesting that higher pressures result in smoother surfaces. An explanation could be that increased pressure improves material removal efficiency, reducing roughness.

Figure 17.

Contour plot for (a = 1 wt.%), (b = 2 wt.%), (c = 3 wt.%), and (d = 4 wt.%). reinforcements.

The surface roughness increases as the traverse speed rises from 100 mm/min to 200 mm/min. This trend indicates that slower traverse speeds allow for more effective material removal, producing a smoother surface. Faster speeds are likely to reduce the cutting time at any point, leading to a rougher finish.

The surface roughness increases as the stand-off distance increases from 2 mm to 4 mm. Larger SOD may reduce the cutting intensity of the abrasive particles, leading to less effective material removal, and thus a rougher surface. This effect could be attributed to the dispersion of the water jet energy at greater distances.

Incorporating 4 wt.% ZrO₂-coated MWCNTs into HDPE shows that controlling the AWJM parameters can significantly affect surface roughness. To minimize Ra, lower traverse speeds (100 mm/min), higher pressures (200 MPa), and shorter stand-off distances (2 mm) seem favourable based on this plot.

Optimization by Taguchi method

Tables 9–12 represent the Taguchi analysis of the present experiment with various wt.% reinforcements. Tables confirm the significant effect of the various parameters at different reinforcement percentages. From the table, it is possible to predict which level of the considered parameters yields the minimum surface roughness during machining. The level of the parameters that generate minimal roughness are highlighted in the table. It is evident from the tables that by selecting and combining these levels of parameters, we can obtain the optimum combination of parameters to achieve the minimum surface roughness for that reinforcement. Hence, for the present experimental work, the optimum combination is Pressure should be at 200 MPa, Traverse speed should be 100 mm/min, and stand-off distance should be 2 mm (P3 TS1 SOD1) for percentage reinforcement. It is evident from the tables that, though the significant parameters are changed according to the reinforcement percentages, the optimum combination of parameters to achieve the low surface finish remains the same for all reinforcement percentages.

Table 9.

Taguchi optimization table for 1 wt.%.

Level	P	TS	SOD
1	4.393	4.248	4.214
2	4.398	4.311	4.363
3	4.227	4.460	4.414
Delta	0.170	0.212	0.174
Rank	3	1	2

Table 10.

Taguchi optimization table for 2 wt.%.

Level	P	TS	SOD
1	4.462	4.296	4.338
2	4.440	4.416	4.423
3	4.337	4.528	4.478
Delta	0.125	0.232	0.140
Rank	3	1	2

Table 11.

Taguchi optimization table for 3 wt.%.

Level	P	TS	SOD
1	4.496	4.365	4.378
2	4.466	4.383	4.417
3	4.358	4.573	4.526
Delta	0.138	0.208	0.148
Rank	3	1	2

Table 12.

Taguchi optimization table for 4 wt.%.

Level	P	TS	SOD
1	4.500	4.423	4.423
2	4.480	4.441	4.476
3	4.390	4.506	4.472
Delta	0.138	0.083	0.053
Rank	1	2	3

Optimization by response surface methodology (RSM)

The RSM optimization plots for the present work with various percentages of reinforcements are illustrated in Figures 18–21. Each table shows the optimum level of each parameter to obtain the minimum roughness for that percentage of reinforcement. These RSM optimum plots also confirm that the optimum combinations of parameters for all percentages of reinforcements are the same. It is also noticed that the optimum combination of parameters obtained by the Taguchi method and RSM method are the same.

Figure 18.

RSM optimization plot for 1 wt.%.

Figure 19.

RSM optimization plot for 2 wt.%.

Figure 20.

RSM optimization plot for 3 wt.%.

Figure 21.

RSM optimization plot for 4 wt.%.

Comparison of surface roughness with and without reinforcement

The comparison of surface roughness produced with various percentages of reinforcements is depicted in Figure 22. In this figure, Series 1 represents the specimen without reinforcement, Series 2 represents the specimen with 1 wt.% reinforcement, Series 3 represents the specimen with 2 wt.% reinforcement, Series 4 represents the specimen with 3 wt.% reinforcement, and Series 5 represents the HDPE specimen reinforced with 5 wt.% zirconia-coated multiwall carbon nanotubes.

Figure 22.

Roughness developed without and with reinforcements.

The results show that the surface roughness is directly proportional to the percentage of reinforcement. This means that as the percentage of reinforcement increases, the surface roughness also increases. Notably, the sample with 4 wt.% zirconia-coated multiwall carbon nanotubes exhibits greater surface roughness than all other percentages of reinforced HDPE composites. This increase in roughness is attributed to the rise in hardness of the composite due to the higher concentration of hard zirconia.

Analysis of the process parameter effect and reinforcement on the roughness

In this experiment, it was observed that as water pressure increases, the roughness of the surface decreased in all cases. This phenomenon may be attributed to the fact that the abrasives start at a high-velocity during water jet machining, effectively cutting the material in the initial zone. However, as the cutting depth increases, the velocity of the abrasive particles decreases, resulting in a rough surface in the transition zone. By increasing the water pressure, both the velocity of the cutting abrasives and the cutting depth are enhanced without causing deviations in the jet. Consequently, a smoother surface is achievable throughout the entire depth.¹⁶

When examining a specimen at various traverse speeds it both cases, it is observed that as the traverse speed increases, the surface roughness also increases. At lower speeds, the roughness in the initial damage region (IDR), the smooth surface region (SSR), and the rough surface region (RSR) tend to be similar. This occurs because, at lower speeds, there are enough abrasive particles available to cut the material, resulting in less roughness effectively. However, as the speed increases, the abrasive particles and cutting time become insufficient for the jet to cut through the entire depth of the material effectively. Consequently, at higher speeds, a rougher surface is generated in the rough surface region (RSR), leading to an overall increase in average roughness.¹⁷

In water jet machining of considered cases, it is observed that the standoff distance and surface roughness are directly proportional. As the standoff distance increases, the surface roughness also increases. Therefore, to achieve lower roughness values, it is essential to maintain a shorter standoff distance. When the standoff distance is greater, the water jet diverges before reaching the cutting surface, which reduces the cutting energy of the abrasive particles and results in a rougher surface.¹⁸

Compared to the plain HDPE the average roughness developed while machining the zirconia-coated multiwall carbon reinforced HDPE is higher. This could be because high strength carbon particles coated with hard zirconia are not easily cut by the abrasive particles by generating a smooth surface. Also, it could be because of peeling of zirconia-coated carbon particles without undergoing cutting because of the high hardness.

Conclusions

The following conclusions can be drawn from the present research work:

• In this experiment, it is observed that water pressure significantly affects surface roughness. Surface roughness is inversely proportional to water pressure, so it is better fixed at higher levels.

• The traverse speed and standoff distance parameters are directly proportional to surface roughness and have a moderate effect on it.

• The reinforcement of zirconia-coated multiwall carbon nanotubes enhances the mechanical properties of the composite material such as hardness and as well as the roughness of the machined surfaces.

• To achieve the minimum surface roughness in this experiment, the pressure should be set at a higher level, while the traverse speed and standoff should remain at lower levels.

Footnotes

Author contributions

Suhas K: Conceptualization, Data curation, Formal analysis, Writing – original draft. Murthy BRN: Conceptualization, Investigation, Validation, Visualization, Writing – review and editing. Harisha SR: Resources, Supervision, Project administration. Gajanan Anne: Investigation, Validation, Pavan Hiremath: Writing – Review and editing, Methodology. Prasanna A A: Writing – Review and editing, Formal analysis.

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

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Murthy BRN

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Effects of reinforcement percentages in AWJM of zirconia coated MWCNTs reinforced HDPE composites

Abstract

Keywords

Introduction

Experimentation

Materials

Preparation of zirconia-coated MWCNT

Acid functionalization of MWCNT

Coating of ZrO2 on MWCNT using hydrothermal setup

Melt blending of HDPE and ZrO2-coated MWCNT

Grinding of HDPE pellets

Blending using twin screw extruder

Injection moulding

Abrasive water jet machining (AWJM)

Design of experiments

Measurement of surface roughness

Results and discussions

Analysis of the effect of reinforcement percentage

Analysis of neat HDPE machining

Regression equation

Main effect plot

Results of ANOVA for surface roughness of neat HDPE

Optimization by Taguchi method and RSM method

Analysis of ZCMWCNT (zirconia-coated multiwall carbon nano tubes) machining

Regression equations

Main effect plot

Results of ANOVA for surface roughness of ZMH4

Analysis through the contour plots

Optimization by Taguchi method

Optimization by response surface methodology (RSM)

Comparison of surface roughness with and without reinforcement

Analysis of the process parameter effect and reinforcement on the roughness

Conclusions

Footnotes

Author contributions

Declaration of conflicting interests

Funding

ORCID iD

Data Availability Statement

References

Coating of ZrO₂ on MWCNT using hydrothermal setup

Melt blending of HDPE and ZrO₂-coated MWCNT