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Abstract

The current research work involves the fabrication and tribological properties analysis of constant basalt filler reinforced (30 wt %) bio-based polypropylene (PP) and high density polyethylene (HDPE) thermoplastic composites. Compression molding technique is used after an internal mixing process in order to produce composite samples. The physical and hardness properties have been evaluated for both neat polymers and composite samples. In order to study the coefficient of friction (COF) and specific wear rate (SWR) of PP and HDPE composite samples, the Taguchi and Analysis of Variance (ANOVA) methodologies were applied. For PP samples, the optimum parameters in response to COF are found to be 0 wt% basalt (rank 3), 9 N load (rank 1), 200 r/min speed (rank 4), and 100 m distance (rank 2); for the SWR output, the optimum parameters are 30 wt% basalt (rank 1), 6 N load (rank 4), 100 r/min speed (rank 2), and 200 m distance (rank 3). For HDPE samples, the optimum parameters in response to COF are 0 wt% basalt (rank 1), 6 N load (rank 3), 100 r/min speed (rank 4), and 100 m distance (rank 2); for the SWR output, the optimum parameters are 30 wt% basalt (rank 1), 6 N load (rank 3), 100 r/min speed (rank 4), and 150 m distance (rank 2). Consistently, it has been shown that incorporating basalt fillers to PP and HDPE composites has more dramatically decreased SWR than COF. The depth of wear constantly rises according to increasing load, irrespective of the processing variables, as shown in 2D depth profiles. It is discovered that the confirmation tests carried out for the optimum parameters are within statistically acceptable bounds. The depth profile plots revealed that the worn track edges are found with polymer bumps because of deep grooves and softened polymer debris, which commonly observed more with HDPE samples due to low softening temperature. Moreover, the worn surfaces of the composites have plowed lines and cracks that are brought about by the micro-cutting and micro-plowing activity of the erosive asperities counterface. In addition to surface characteristics, the transfer films created during sliding also significantly influenced the mode of sample wear.

Keywords

Tribology polymer composites basalt wear friction Taguchi analysis of variance

Introduction

The remarkable friction and wear response of polymer-based materials, including their self-lubricating capacity, viscoelasticity, temperature, and corrosion resistance qualities, etc., make them crucial for a variety of applications in industry. In this context, many researchers are reported the works related to the enhancement of tribological properties by reinforcing the filler materials or short fibers with the polymer matrices.^1,2 The first crucial requirement for lowering plastic waste volume is to reuse those waste kinds for which it is feasible, according to the concept of a sustainable future and a secure waste management plan. In the identical time, the current global economic model of the sustainable development concept, which aims to cut down on waste production overall and extend the useful lives of natural assets and commodities, is closely tied to cyclical development.^3,4 In this circumstances, it is feasible to use naturally available and sustainable mineral resources for products development. Moreover, it is quiet advantageous in the reduction of polymer ratio within the product by replacing with the mineral materials. Among many mineral materials, the basalt emerged with an attractive material as a reinforcement with polymer matrices. Basalt rock, an exuberant volcanic rock that is overground and filled with 45%–52% silicon dioxide, serves as the foundation for basalt fiber. Because of the conditions that led to its genesis, basalt has many remarkable qualities. The fibers formed of it have a great capacity for wear, heat, and acoustic resistance and are effective vibration isolators in addition to having an elevated elasticity and exceptional heat resistance.^5–7

Thermoplastics and thermosets are the two common matrices used with polymer composites. The most popular material nowadays is thermoplastic composite, which has an array of advantages over thermoset composites. In terms of the mechanisms creating friction and wear, polymer tribology is less understood and more complex than that of metal. The tribological characteristics of thermoplastic composites are influenced by operational parameters, such as the force, speed, distance, heat, and time, to produce adequate resistance to wear and the possible friction coefficient.^8–10 Surfaces that come in mechanical touch with one another and glide against one another experience a variety of intricate microscopic interactions that cause wear and friction. The substances, geometrical, and topology features of the surfaces as well as the general circumstances in which they are forced to slide across one another affect these interactions in various ways. Examples of these circumstances include loading, temperature, the environment, kind of contact, and the conditions of loading.^11–14 In this context, many research works were reported with the study of tribological properties of polymer composites reinforced with various fillers. Pan and team¹⁵ worked on the study of tribology on the rare earth oxide (La₂O₃) reinforced polyimide composites. According to friction properties, adding 1.5 wt% La₂O₃ microparticles to PI at elevated preloads of 3–5 N resulted in a 70% decrease in frictional forces and COF. Studies on the impact of sliding velocity revealed that as velocity increased, friction forces drastically decreased. Both of neat PI and La-PI-Cs film experienced linear increases in friction forces when preload was raised from 0.2 N to 5.0 N. In contrast to thermosets, Nayak and Satapathy¹⁶ reported the decrement in wear rate of polyester polymer reinforced with waste marble dust filler. The most important variables discovered to have a substantial impact on the wear rate of these composites are the filler ratio and the velocity, according to the order. An innovative review work on the tribological study and the effect of solid lubricants is reported by Padhan and group¹⁷ reveal the significant potential of nanoparticles in the reduction of wear rate within the polymer matrices. These area of expertise polymers appear to possess a tremendous opportunity to be used in a variety of tribo-applications, including dry bearings, gears, slides, etc., which call for exceptionally low friction and wear under difficult sliding circumstances. Prakash and team¹⁸ also reported the positive response on the wear resistance of epoxy composites reinforced with activated carbon fillers. It has been shown that adding 1% of permeable nano activated carbon to epoxy composites as reinforcement increased the materials’ durability against wear by 106%. Regarding composites with 2% filler, which have nearly three times greater resilience than epoxy polymer, the lowest wear rate was observed. On the P800 abrasion surface, sliding wear study at different forces on various abrasion surfaces shows that 2% wt filler loaded composites had greater abrasive wear resistance with the least amount of the substance loss. Myalski and group¹⁹ presented the influence of carbon fillers on the tribological response of thermoplastic PA6 polymer. According to the acquired results, adding fillers to the PA6 matrix leads to advantageous wear behavior, such as the development of roller-like debris that reduces wear and increases friction. In comparison with the remaining samples, those with filler had a nearly two-fold reduced coefficient of friction. Abdellah and team²⁰ worked on strengthening of ABS polymer with the reinforcement of short basalt fiber (SBF) and studied the wear properties. SBF is an inorganic substance that has been discovered to have poor interaction with the ABS when utilized as a reinforcing material in an ABS matrix. The surface toughness and wear strength of the ABS matrix were improved by increasing the weight percentage of the SBF component. Another comparison of the wear behavior of basalt fiber/epoxy (BE) and basalt fiber/polyester (BP) composites demonstrates that BE composites exhibit higher wear resistance than BP composites due to BE composites’ minimal shrinkage on cure and strong bonding capabilities. The three main wear processes in BP composites are fiber matrix breakdown, fiber deletion, and fiber shattering.²¹ Pelto and team²² work demonstrated the 80% reduction of specific wear rate with the addition of graphene based nanofillers to the HDPE matrix. The uniform mixing and proper adhesion of fillers with the HDPE prevent the polymer detachment during sliding action. In a regular action, the pristine HDPE shows stable COF values during the testing process, however the changes are observed at the end of the testing due to polymer degradation and mechanical fracture at the transfer layers. Suresha and team²³ developed a bearing material and studied the tribological properties by reinforcing the nanoclay and graphite filler materials with PP/PA66 polymer blend. The minimum wear rate and mechanical stability was achieved with nanoclay fillers at the contact interface. Moreover, the nanoclay addition was the main root cause for the both plastic and elastic deformations at the surface.

In most of the literature works, the authors reported the positive influence of filler material on the wear performance of thermoplastic polymer matrices. However, the works related to the polypropylene and high density polyethylene are found to be less on bio-based concept. Moreover, the utilization of mineral basalt micro-filler is not yet explored completely to fulfill the sustainability concept. In this context, an attempt was made in this research work to study the effect of basalt micro-fillers on the wear and frictional performance of both bio-based polypropylene and high density polyethylene concept. In addition, the Taguchi optimization method and the ANOVA method are used in this work to examine the ideal parameters and determine the importance of each parameter, respectively. Additionally, the depth pattern of the worn surface is going to be studied using a 2D depth profile graph acquired from a profilometer, and the wear surface characteristics of all polymer samples with different conditions will be examined using scanning electron microscopy (SEM) photographs.

Materials and fabrication process

Materials

The current research work is executed on the two kinds of thermoplastic polymers reinforced with constant basalt filler reinforcement (30 wt%). Chinese company Huabin General Machinery & Equipment Import & Export Co. Ltd supplied the basalt fillers as fiber powder. These filler possesses a higher density of 2.6–2.8 g/cc which is significantly more than the thermoplastic polymers. The derived properties of polypropylene and high density polyethylene thermoplastics are provided in Table 1. The images of reinforcement and matrix materials are presented in Figure 1. Polimaxx, IRPC Public Co. Ltd in Thailand provided the bio-based polypropylene thermoplastic matrix of grade PP B1441C in the pellet form. Braskem (Sao Paulo, Brazil) provided the high density polyethylene (SHA7260) that is generated from sugar cane at a concentration of 94%.

Table 1.

Properties of thermoplastic polymers.

Matrix	Melt flow index (MFI)	Density (g/cc)	Glass transition temperature (°C)	Elongation (%)	Tensile strength (MPa)	Modulus of elasticity (GPa)
Polypropylene (PP)	7 g/10 min	0.97	− (18–20)	>10	33	1.4–2.1
High density polyethylene (HDPE)	20 g/10 min	0.955	− (80–100)	7.2	29	0.8–1.2

Figure 1.

Presentation of reinforcement and matrix materials.

Fabrication process

The fabrication process flowchart for PP and HDPE composites fabrication is illustrated in Figure 2. The process has mainly five steps beginning from sieving to compression molding process. In the step 1, the fillers are sieved since it contains the basalt microfibers of maximum particle size. The lowest available mesh size of the siever is < 63 μm, and the sieving process is done for 15 min. The purchased PP and HDPE pellet polymers are dried at 50°C for overnight in the hot air oven to ensure the moisture free materials in the step 2.²⁴ Once the materials are ready for processig, they were subjected to internal mixing process (step 3) with their threshold temperature conditions. The PP and HDPE-based composites were internally mixed at 170°C and 150°C respectively at a speed of 50 r/min and a duration of 8 min. Before that the 200*200*3 mm mold is prepard with the application of PVC and silicone spray to ensure smooth and uniform laminate surface.⁷ Once the composite granules were obtained from the internal mixing process, it was subjected compression molding process in the final step. The PP-based composites were pre-heated at 170°C (5 min), compressed at 170°C with 2500 lbf/in² pressure (8–10 min), and cooled to room temperature. Whereas the HDPE-based composites were pre-heated at 150°C (5 min), compressed at 150°C with 2500 lbf/in² pressure (8–10 min), and cooled to room temperature.²⁵ Further, the compressed laminates are subjected to hacksaw process for cutting the samples as per the ASTM dimensions.

Figure 2.

Fabrication process flow chart.

Experimental design

Density and void fraction analysis

It is quite obvious to calculate the void % of fabricated neat and composite samples due to defects raised during the fabrication process. Since the composite has two phases, the theoretical density of the composite is calculated with the consideration of both basalt and polymer densities. It is anticipated that the composite density may increase due to the reinforcement of high density basalt filler with low-density polymers. The theoretical density (ρ_t) and void % (V_f) in the samples are calculated using the equation (1)²⁶ and (2)²⁷ respectively. The experimental density (ρ_e) was measured using the Pycnometer method which obeys Archimedes principle, and the setup is shown in Figure 3. By weighing the sample across a liquid and a dry media, the experimental test was carried out. The densities were determined by doing five trials of each combination to obtain precise findings.

\frac{1}{ρ_{t}} = \frac{W_{b}}{ρ_{b}} + \frac{W_{m}}{ρ_{m}}

(1)

V_{f} = \frac{ρ_{t} - ρ_{e}}{ρ_{e}}

(2)

where W_b is the basalt’s weight fraction, W_m is the matrix weight fraction, ρ_b is the basalt’s density, ρ_m is the matrix density, ρ_t is the theoretical density, and ρ_e is the experimental density.

Figure 3.

Experimental density measurement setup: (a) weight measurement in air, and (b) weight measurement in liquid.

Hardness

The assessment of the relative hardness of all the neat and composite samples is done using a Shore-D hardness durometer according to the ASTM D2240 standard.²⁸ The load applied was 5 kg on the hardness tester (Rex Durometer, OS-1) to ensure the proper indentation. The durometer is designed for analysis of hardness in soft materials like rubber, plastics, elastomers, and roller materials. The Shore D hardness number has been recorded according to the resistance exhibited by the materials against the indentation force. The device included with sensor element with a dial accuracy of 0.5 units. To get the mean value, each sample is employed with 10 trails at the various locations on the sample surface.

Design of experiment

This study’s main goal is to determine the ideal processing variables that will lower the specific wear rate (SWR) and coefficient of friction (COF) for PP and HDPE samples. This research also showcase the impact of mineral filler on both the polymers with the reinforcement of constant 30 wt%. L₁₈, a pre-designed orthogonal array (OA), is employed in the current inquiry to examine the effects of design elements and interactions while controlling for the effects of the primary variable. The MINITAB-19 program is used to complete the DOE procedure before starting the experimentation. The design was done with the combination of both two and three levels as tabulated in Table 2. The examples were made with 0 wt% and 30 wt% reinforcement, and the two-level design was assigned to basalt filler. Three levels each exist for the load parameter (3 N, 6 N, and 9 N), the speed parameter (100 r/min, 150 r/min, and 200 r/min), and the ultimate parameter distance (100 m, 150 m, and 200 m).²⁹ All the experimental runs are executed at a room temperature.

Table 2.

DOE factors and respective levels.

Design factors		Factor levels
Design factors	I	II	III
Basalt (wt %)	0	30	—
Load (N)	3	6	9
Speed (rpm)	100	150	200
Distance (m)	100	150	200

Tribology experiment

The Anton Paar TRB3 Pin-on-disk tribometer is used to conduct the tribology test on the PP and HDPE polymer samples in accordance with the G99 standard. To keep the samples’ surfaces uniformly rough throughout the experiment, they are first rubbed with 1500 grit paper, then with 2000 grit paper. The load, speed, and distance are the variables that change while performing the tribology testing. The COF results are directly obtained from the system, and the weight loss was recorded for each sample. In accordance with the orthogonal array design, the experiments are carried out in a comfortable environment. Figure 4 depicts the tribology setup for the experiment. For the particular wear rate calculation according to equation (3),²⁷ the sample weight loss is tracked after each experiment.

W_{s} = \frac{∆ W}{ρ \times S \times F_{n}}

(3)

where W_s indicates the specific wear rate (mm³/N-m), ΔW indicates the weight loss (g), ρ indicates the density (g/mm³), S is assigned for distance (m), and F_n is the normal force (N).

Figure 4.

Tribology experimentation setup.

Taguchi and analysis of variance techniques

An effective method for analyzing the impact of control variables on the COF and SWR of samples is Taguchi optimization. To examine the quality characteristics, Taguchi uses the S/N ratio results from all the experimental runs. In this method, the terms “signal” and “noise” symbolize the output response’s intended value (mean) and undesirable value (standard variance), respectively. Equation (4)’s²⁷ signal-to-noise ratio is used in this method to discover quality parameters that differ from the desired value. In this study, the Taguchi method is used to reduce the COF and SWR values by choosing the best basalt weight percentage, load, speed, and distance variables. Since both replies must be reduced, the performance feature that is smaller is preferred. ANOVA may also be used to support the results of the Taguchi technique since it highlights the importance of the factors' order in influencing the answer.²⁸

\frac{S}{N} = - 10 \log (\frac{1}{n} \sum_{i = 1}^{n} \frac{1}{y_{i}^{2}})

(4)

where n denotes the total experimental runs, and y_i indicates the response for the ith results.

Results and discussions

Density, void fraction, and hardness analysis

Table 3 presents the theoretical density, experimental density, and void % of both neat thermoplastic polymers and their composites with 30 wt% basalt reinforcement. As per the literature, polymer composites that have voids of less than 5% are designated as good load-bearing materials. The composite strength tends to decrease when the voids ratio exceeds the 5% limit.³⁰ As per the tabulated values, all the thermoplastic sample voids fall below the 5% acceptable limit. In all the cases, the theoretical density is found to be lesser than the experimental density. It is found that the addition of 30 wt% basalt filler significantly improved the density of all the thermoplastic polymers due to the higher density of basalt particles. Nambiraj and colleagues’ work³¹ claims that a composite with particles smaller than 50 μm has a lesser experimental density than composites generated with bigger particles because larger particles tend to generate more voids, which in turn cause the composite to become airtight. Moreover, the reduced particle size improved the packing density where the particles occupy industrial sites inside the polymers. In some scenarios, the presence of the void also depends on the fabrication techniques where there is an application of heat. Because of more efficient internal mixing and compression molding techniques under various heating stages throughout the manufacturing process, the thermoplastics exhibit fewer voids both in neat and composite samples.

Table 3.

Experimental and theoretical densities of thermoplastic samples with voids %.

Polymer samples	Theoretical density ρ_t (g/cc)	Experimental density ρ_e (g/cc)	Voids (%)	Shore D hardness
Neat PP	0.989	0.981	0.81	72.1
PP + B30	1.154	1.138	1.40	74.3
Neat HDPE	0.949	0.940	0.95	62.6
HDPE + B30	1.111	1.098	1.18	64.2

The Shore D hardness measurements of neat thermoplastic polymers and their basalt filler-loaded composites are provided in Table 3. These are the mean values of 10 average trails on the different locations of the surface. The material’s hardness dictates the extent to which it can withstand little local deformations. The kind of reinforcing element and the stiffness of that material have a significant role in determining the hardness values in the research of polymer composites. The maximum hardness is recorded for neat PP of 72.1 SD, and the filler addition to this PP improves the hardness value by 3.05%. The lowest hardness of 62.6 SD is found in neat HDPE polymer because of more deformation of the polymer under the application load. A significant increment in hardness value by 2.55% is observed with HDPE polymer with filler reinforcement. This increase may be explained by the fact that the basalt filler has substantially greater hardness levels than neat polymers, which increases the load-bearing capability. Additionally, the filler’s inclusion in the microscopic makeup of the matrices restricts the movement of dislocations under loading, increasing the strength of the composites.³² The quantity of reinforcing distribution in the matrix also determines the hardness of the composites as indentation on thermoplastics causes plastic deformation in the indented zone. Fillers are made accessible for indentation by the dispersion, which also provides resilience to the indentation.³³ The homogeneous dispersion of the basalt micro-filler within the polymer matrices and subsequent tangling of the polymer chains could be the reason for the lower deviations in error bars.³⁴

Friction and wear analysis

Polypropylene samples

The coefficient of friction and specific wear rate of basalt filler-loaded polypropylene composites are obtained under different processing and experimental conditions. The experimental values of COF and SWR are tabulated in Table 4. The COF values are directly obtained by the tribology setup with the calculation of frictional force to the applied force/load. The SWR is calculated by taking the weight loss after the individual experimental run relating to their specific densities. Among all the experiments, run nine shows the lowest COF value (0.1633) at 9 N load, 200 r/min speed, and 100 m distance. Whereas the maximum COF is found in the experimental run 2 (0.3789) at 3 N load, 150 r/min speed, and 150 m distance. The interesting fact is that both minimum and maximum COF are found in the unfilled PP samples. The COF obtained for basalt filler-loaded PP composites seems to be optimum under L₁₈ designed array. In the case of SWR, the lowest value is observed in run 13 which is 30 wt% basalt filler reinforcement at conditions of 6 N load, 100 r/min speed, and 150 m distance. This reveals that the basalt fillers act as an abrasive resister by minimizing weight loss. Whereas the highest SWR is found in the same lowest COF obtained in experimental run 9. Since there are no erosion inhibitors in the PP sample, the material exhibited more weight loss. Additionally, the use of basalt filler increased the load-bearing capacity of PP under various conditions, which is explained by the material’s strong abrasive resistance to fracture initiation and dissemination.³⁵ Another important factor that strongly affects the wear process and is closely related to the voids in the sample is density. The SWR value for all the filler-loaded samples is comparably lower than the neat PP samples.

Table 4.

Taguchi design of experiments using L₁₈ orthogonal array for PP samples.

Exp. Runs	Basalt (wt%)	Load (N)	Speed (rpm)	Distance (m)	COF	S/N ratio	SWR (mm³/N-m)	S/N ratio
1.	0	3	100	100	0.2835	10.9489	0.000511	65.8316
2.	0	3	150	150	0.3789	8.4295	0.000696	63.1478
3.	0	3	200	200	0.2853	10.8940	0.000488	66.2316
4.	0	6	100	100	0.2372	12.4977	0.000876	61.1499
5.	0	6	150	150	0.2313	12.7165	0.000651	63.7284
6.	0	6	200	200	0.3154	10.0228	0.000662	63.5828
7.	0	9	100	150	0.3003	10.4489	0.000509	65.8656
8.	0	9	150	200	0.2201	13.1476	0.000584	64.6717
9.	0	9	200	100	0.1633	15.7403	0.001426	56.9176
10.	30	3	100	200	0.3266	9.7197	0.000115	78.7860
11.	30	3	150	100	0.2730	11.2767	0.000259	71.7340
12.	30	3	200	150	0.3202	9.8916	0.000211	73.5144
13.	30	6	100	150	0.2813	11.0166	0.000077	82.2702
14.	30	6	150	200	0.2710	11.3406	0.000129	77.7882
15.	30	6	200	100	0.2922	10.6864	0.000202	73.8930
16.	30	9	100	200	0.2722	11.3022	0.000168	75.4938
17.	30	9	150	100	0.2564	11.8216	0.000173	75.2391
18.	30	9	200	150	0.2563	11.8250	0.000301	70.4287

The effect of each parameter can be analyzed using the Taguchi technique with the main effect plots as illustrated in Figure 5(a). The influence of the response is shown by the slope of each variable. The difference between the various levels hints about the impact of the variable concerning other variables as well. About these criteria, the load parameter shows more slope and distance among the different levels. Hence, this parameter has more effect on the COF response. The second highest slope can be observed with the distance parameter where the difference between the first and second levels is significantly high compared to other parameters levels. The third and fourth impacting parameters are basalt wt% and speed respectively as appeared with lowest slope values. The optimum parameters in response to COF based on the Taguchi analysis are 0 wt% basalt, 9 N load, 200 r/min speed, and 100 m distance. It is crucial to consider any potential correlations between the control parameters before seeking to predict performance using this simple model. The correlations among the parameters are illustrated in Figure 5(b). The basalt wt% has strong interaction with all other variables and the unfilled samples exhibited minimized COF value as shown in the plot. Whereas the load parameter showed a correlation only with basalt wt% and speed with the optimum load value of 9 N. The establishment of this correlation is based on the interactions exhibited among the parameters with their significance level. Table 5 records the responses obtained by the Taguchi analysis by utilizing S/N ratios. As per the response data, the maximum delta value is indicated with the most significant factor in response to COF. The maximum delta is achieved by the load parameter of 2.19 with the rank 1. These values are obtained with smaller the better S/N ratios. This can be explained as the load parameter has given its maximum contribution in achieving the lowest COF value. The second significant parameter is distance having a delta value of 1.44 and achieving a rank of 2. The 3^rd and 4^th ranks are allotted to basalt wt% and speed according to their impact level.

Figure 5.

(a) Main effect plot, and (b) Interaction plots for PP samples in response to COF.

Table 5.

Taguchi response based on S/N ratio for PP samples in response to COF.

Level	Basalt (wt%)	Load (N)	Speed (rpm)	Distance (m)
1	11.65	10.19	10.99	12.16
2	10.99	11.38	11.46	10.72
3		12.38	11.51	11.07
Delta	0.66	2.19	0.52	1.44
Rank	3	1	4	2

^aSmaller is better.

To distinguish the various impacts from each of the control values, the ANOVA statistical design approach is utilized. In this investigation, a significance level of 5% was employed, which is equivalent to a 95% confidence interval. The response values obtained from the ANOVA concerning COF are tabulated in Table 6. A contributing parameter with p-values has been taken into consideration to assess the statistical significance of the effects. The load parameter which is having a p-value of .053 signifies the most impact parameter and meets the designed significance level. The remaining decreased order of significant parameters is distance, basalt wt%, and speed according to the p-Values of .214, .459, and .855 respectively. Moreover, the significance can also be measured based on the F-value where the maximum value is identified as the most significant one. Figure 6(a) reveals the probability of data values concerning the mean value. The measured data values marked with tiny circles are approximately close to the COF mean line. This distribution shows that the model is appropriate and exists with the typical error patterns. However, owing to the nature of the analyzed samples, there are not numerous severe areas on the right side of the test, and the right tests are not bigger than the left tests, suggesting that the trend of error may be slightly skewed. The residual plots in Figure 6(b) and (c) clearly show that the residuals are distributed within the certain range of −2 < r < +2 in both coordinates. The lack of a pattern in the residual versus run plot provides additional proof that not all residues are associated because of time-related factors.

Table 6.

ANOVA response for PP samples in response to COF.

Source	DF	Adj SS	Adj MS	F-Value	p-Value
Basalt (wt%)	1	0.000996	0.000996	0.59	.459
Load (N)	2	0.013435	0.006717	4.00	.053
Speed (rpm)	2	0.000535	0.000268	0.16	.855
Distance (m)	2	0.006071	0.003035	1.81	.214
Error	10	0.016791	0.001679
Total	17

Figure 6.

COF model plots for PP samples: (a) Probability, (b) Residuals versus fits, and (c) Residuals versus order.

Using 2D contour plots, as shown in Figure 7, the interaction between processing factors that affect the COF of PP samples is examined. Figure 7(a) reveals the strong interaction among load and basalt wt% regarding COF response. In this interaction, the minimum COF can be found at lower basalt filler contents and maximum loading conditions. Both the parameters vary inversely to each other in achieving COF values in vice versa. The maximum conditions for both the parameters are anticipated to be the lowest COF response, and it is indicated with a light green color region. The relation among speed and basalt wt% in response to COF is illustrated in Figure 7(b). In this plot, the lowest COF is shown to be at 0–5 wt% basalt reinforcement and 180–200 r/min speed indicated with a light green color region. Whereas the maximum COF is observed with higher basalt reinforcement and the lowest rotational speed. Since there is more interfacial contact at a lower speed, the sample experienced more friction. Similarly, the correlation between distance and basalt wt% is shown in plot Figure 7(c). The minimum distance travel and basalt wt% are indicated with the lowest COF value that is observed in the light green color region. Similarly, the maximum distance and basalt wt% records the highest COF of more than 0.38 as indicated in the dark green color region. Since it is a thermoplastic polymer, the travel distance is anticipated to be the minimum to get the lowest COF due to lower dissipation temperature.

Figure 7.

Contour plots for PP samples in response to COF: (a) COF versus load, basalt wt%, (b) COF versus speed, basalt wt%, and (c) COF versus distance, basalt wt%.

The main effects plots and interaction plots in response to SWR for PP samples are illustrated in Figure 8(a) and (b) respectively. The plots based on the S/N ratio utilized smaller is a better condition to predict the lowest SWR response. The major effect parameter is basalt wt% with a huge influential impact mainly responsible for SWR values. The slop of this plot among the two levels of reinforcement is found to be large as compared to other parameters. This basalt filler interfacial adhesion with the PP matrix reduced the material removal rate irrespective of the working parameters. The second and third highest impact parameters on the SWR are speed and distance respectively. Generally, the increase in speed coins the raise of temperature due to more frictional force development. This mechanism is more severe in polymers than in metals and alloys. The least impact parameter is a load on the SWR response as observed with low slope value and distance among their levels. The optimum parameters obtained are 30 wt% basalt, 6 N load, 100 r/min speed, and 200 m distance. In the interaction plots, the interacting lines are not aligned in a parallel manner which means there is strong interaction among them. The responses obtained from the Taguchi technique are tabulated in Table 7. These responses are recorded based on the maximum and minimum S/N ratios with respect to the individual parameters. The basalt wt% parameter is given the top ranking since it has a greater influence and a maximum delta value of 12.00. The second rank is allotted to the speed parameter which is having the second-highest delta value of 4.14. The difference in delta values among the variables clearly illustrates the amount of impact and significance level. The third and fourth ranks are allotted to the distance and load parameters which are having a delta value of 3.63 and 2.30 respectively. In comparison to all the parameters, the delta value of basalt wt% is nearly more than four times greater than the other parameters. Hence, both the effect plot and response table conclude that basalt is the major contributing factor in reducing the SWR of PP samples.

Figure 8.

(a) Main effect plot, and (b) Interaction plot for PP samples in response to SWR.

Table 7.

Taguchi response based on S/N ratio for PP samples in response to SWR.

Level	Basalt (wt%)	Load (N)	Speed (rpm)	Distance (m)
1	63.46	69.87	71.57	67.46
2	75.46	70.40	69.38	69.83
3		68.10	67.43	71.09
Delta	12.00	2.30	4.14	3.63
Rank	1	4	2	3

^aSmaller is better.

Utilizing statistical MINITAB-19 software, analysis of variance (ANOVA) was performed to look at the many levels of the selected parameters of the PP samples that had statistically significant results. The ANOVA response about SWR concerning four parameters is tabulated in Table 8. The 95% confidence level and 5% significance level are the two main criteria fitted for the analysis. The tabulated values reveal that only basalt wt% has a major impact and others are negligible significance. Hence, the p-Value obtained is less than .05 and it is almost zero. But the other three parameter’s p-Values are greater than .05 and the values are not even nearer to the significance level. If it takes the F-Value into account, the basalt wt% has 30.64 and the remaining parameters obtained less than 2. Hence, it confirmed that basalt wt% is the only parameter that has a huge impact on the SWR values of PP samples. The model accuracy for the data sets for SWR response is illustrated through a probability plot as shown in Figure 9(a). The majority of the residuals fit the normal distribution, proving that the p-Values and intervals of confidence were calculated adequately. The probability chart also showed that the errors were broadly distributed in a straight line. The concept that the residuals are unconnected to one another is supported by the residuals versus order chart. Standalone residuals do not exhibit any trends or patterns when studied chronologically (Figure 9(b) and (c)).

Table 8.

ANOVA results of PP samples in response to SWR.

Source	DF	Adj SS	Adj MS	F-Value	p-Value
Basalt (wt%)	1	0.000001	0.000001	30.64	.000
Load (N)	2	0.000000	0.000000	0.81	.474
Speed (rpm)	2	0.000000	0.000000	1.19	.345
Distance (m)	2	0.000000	0.000000	1.88	.203
Error	10	0.000000	0.000000
Total	17

Figure 9.

SWR model plots for PP samples: (a) Probability, (b) Residuals versus fits, and (c) Residuals versus order.

The contour plots provided in Figure 10 reveal the detailed distribution of SWR values over the interactions among the variables. Figure 10(a) illustrates the 2D contour plot of SWR versus load, basalt wt%, and revealed the appearance of parallel straight lines with different color illustrations in response to SWR. As per the illustration, the increase of basalt wt% from 0 to 30 wt% and decrement of load values gives the lowest SWR values as indicated in the dark blue region. To get a lower SWR response with a higher load, the basalt wt% has to be increased beyond 30 wt% as per the prediction. These similar observations are also found in the contour plot of SWR versus speed, basalt wt% in Figure 10(b). In both plots, the basalt wt% played a major role in determining the SWR values. In a quiet different case (Figure 10(c)), the maximum basalt reinforcement (30 wt%) and the intermediate distance values (140–200 m) are given the lowest SWR value indicated in the light green color region. The examination of intermediate SWR value is quiet easier with the 2D contour plots when there is a need for an intermediate response for different parametric values. The confirmation test, the last stage of the Taguchi method, compares experimental and predicted results using the best parameters. Equation (5) is used to determine the error percent with the difference between the experimental and anticipated values. Effective statistical analysis requires error levels of less than 20%.³⁶ The literature³⁷ states that when the optimal set of parameters and their corresponding levels agree with one among the experimental runs in the designed array, the confirmation test is not necessary. For the PLA samples, the optimum parameters obtained for both COF and SWR responses are within the designed array. Whereas in the case of PP samples, the optimum parameters obtained for COF response are within the bound of an array, and the optimum parameters recorded for SWR response are not within the limit. Hence, the confirmation experiment was conducted for the optimum parameters in response to SWR. The experimental value obtained for SWR response is 0.0002710 after executing for optimum parameters. Whereas the model predicted an SWR value of 0.0002563 and obtained an error of 5.42% as tabulated in Table 9.

E r r o r (%) = \frac{Exp . v a l u e - P r e d . v a l u e}{Exp . v a l u e} \times 100

(5)

Figure 10.

Contour plots in response to SWR: (a) SWR versus load, basalt wt%, (b) SWR versus speed, basalt wt%, and (c) SWR versus distance, basalt wt%.

Table 9.

Confirmation test for SWR response for PP samples.

Response	Optimum parameters	Experimental value	Predicted value	Error (%)
SWR	30 wt%, 6 N, 100 r/min, 200 m	0.0002710	0.0002563	5.42

Table 10 clearly illustrates the COF plot, the worn surface morphology, and the 2D depth profile plots of PP samples. All the plots and morphologies are provided to distinguish among the 18 experimental runs. All COF graphs have the common finding that the COF value first seems to be lower due to a smoothened polymer surface, increases throughout an experimental run, and then stabilizes with very minimal variations. It is possible to observe more variations/disturbances due to the noise factor. The common factor in the depth profile is that the depth of wear is gradually increased with increasing load irrespective of the processing parameters. The morphology of experimental run nine which is having a maximum SWR and minimum COF reveals that the surface has appeared smooth and has less debris over the wear track. The depth of profile reached the maximum value of nearly 100 μm which is higher than other runs due to maximum load. The depth of profile in the lowest SWR obtained run 13 is found to be less than 15 μm and the morphology revealed with low width track with groove appearance. The common factor in all the depth profiles is that there is an irregular wear depths beside the major wear track between the 0 and 5 μm range. The repeated sliding over the same track, especially on the debris resulted in more variations of the COF values as indicated in COF plots. The majority of morphologies exhibit ductile-yielding together with micro-deformation and the ability to peel off the soft PP matrix. It suggests that a major part of the wear process is sticky wear. It is understood that adherence in the contact region and erosion by the asperities on the pin are the two main wear processes for polymer matrix composites when they rotate across the pin surface.³⁸ In pristine PP samples, the micro-cracks caused by the matrix’s separation and ripping under applied load may eventually cause substantial wear. Compared to higher applied loads, lower loads show less debris production because repeated abrasion over the same track causes the generated debris to soften and become more pliable. This is because frictional temperature generation is higher with higher loads. Seizures were followed by an abrupt rise in wear rate, loud noise, and vibration whenever the applied load was raised. The term “galling seizure” has been used to describe this sort of seizure. Additionally, because of the polymer’s crucial surface energy, wear, and friction will rise under higher loads.³⁹ The maximum load effect is more with unfilled PP samples with the appearance of deep grooves and multiple tracks over the same worn surface. However, basalt filler particles in composites were able to limit this effect by preventing polymer detachment and shielding the surface from polymer peeling.

Table 10.

COF graph, SEM morphology, and 2D profile of L₁₈ OA designed for PP samples.

High density polyethylene samples

This sub-section reports the influence of basalt wt%, load, speed, and distance parameters on the tribological properties of neat HDPE and basalt filler-loaded HDPE composites. The experimental values of COF and SWR are provided in Table 11. Here also 18 experimental runs were carried out with different parametric combinations as per the design produced in MINITAB-19 software using an orthogonal array. The first nine experimental runs are owing to neat HDPE samples and the runs 9–18 are belongs to 30 wt% basalt fillers reinforced samples with different combinations of parameters. According to the experimental data, the lowest COF (0.0116) is obtained in the experimental run nine at 9 N load, 200 r/min speed, and 100 m distance. Same to other thermoplastics, the lowest COF is recorded for unfilled HDPE samples instead of the composite sample. This is owing to the basalt fillers agglomerations at the sample surface and higher surface roughness which resulted in increased COF values.⁴⁰ This is correlated to the maximum COF (0.0826) recorded in run 18 at 30 wt% basalt filler reinforcement, 9 N load, 200 r/min speed, and 150 m distance parameters. The lowest SWR (0.000,033 mm³/N-m) is found at an experimental run of 18 in the composite sample with parameters of 9 N load, 200 r/min speed, and 150 m distance. The tabulated figures make it obvious that the SWR of filler-loaded composite samples is lower than those of the plain HDPE samples. Whereas the maximum SWR is recorded concerning run 11 also with filler loaded composite at 3 N load, 150 r/min speed, and 100 m distance. This might be accounted for the agglomerated micro-fillers that were extracted from the matrix and had an abrasive function at the surfaces of the rubbing contact zone as well as removing the transfer film that had been formed.⁴¹ The detailed analysis of individual parameters’ impact with their statistical significance is discussed in the following paragraphs.

Table 11.

Taguchi design of experiments using L₁₈ orthogonal array for HDPE samples.

Exp. Runs	Basalt (wt%)	Load (N)	Speed (rpm)	Distance (m)	COF	S/N ratio	SWR (mm³/N-m)	S/N ratio
1.	0	3	100	100	0.0200	33.9794	0.000141	77.0156
2.	0	3	150	150	0.0414	27.6600	0.000137	77.2656
3.	0	3	200	200	0.0525	25.5968	0.000184	74.7036
4.	0	6	100	100	0.0121	38.3443	0.000088	81.1103
5.	0	6	150	150	0.0201	33.9361	0.000051	85.8486
6.	0	6	200	200	0.0516	25.7470	0.000232	72.6902
7.	0	9	100	150	0.0419	27.5557	0.000329	69.6561
8.	0	9	150	200	0.0329	29.6561	0.000317	69.9788
9.	0	9	200	100	0.0116	38.7108	0.000217	73.2708
10.	30	3	100	200	0.0563	24.9898	0.000075	82.4988
11.	30	3	150	100	0.0716	22.9017	0.000361	68.8499
12.	30	3	200	150	0.0686	23.2735	0.000080	81.9382
13.	30	6	100	150	0.0441	27.1112	0.000070	83.0980
14.	30	6	150	200	0.0678	23.3754	0.000060	84.4370
15.	30	6	200	100	0.0421	27.5144	0.000136	77.3292
16.	30	9	100	200	0.0755	22.4411	0.000055	85.1927
17.	30	9	150	100	0.0519	25.6967	0.000050	86.0206
18.	30	9	200	150	0.0826	21.6604	0.000033	89.6297

The parameter effect on the COF response and their illustration in provided in Figure 11(a). The influence on COF is found to be in the order of basalt wt% > distance > load > speed. The optimum parameters for COF response are neat HDPE sample, 6 N load, 100 r/min speed, and 100 m distance. Except for load, the remaining parameter’s low level is much contributed to a reduction in COF value. Since there is more slope and distance among the levels, the basalt wt% is pointed as a major impact parameter than the other variables. The least impact parameter is the speed where the level difference is quiet low among themselves. As seen in Figure 11(b), each of the parametric lines is exhibited with exceptional alignment, which implies that the parameters interact significantly with each other. Table 12 provides the Taguchi response based on the S/N ratio to confirm the significant factor. In this response, the significance is decided based on the calculation of the delta value by using the S/N ratio. According to the tabulated values, the basalt wt% gained the maximum delta value of 6.91 and was given rank one based on the impact. In the case of basalt-filled composites, heat is produced during sliding, which wears down the matrix, exposes the hard components to the worn surface, and scrapes the counterface, producing friction. This is correlated to the optimum level of basalt wt% obtained above the mean line in the main effect plot. The second highest delta value is found with a distance parameter of 5.89 which has a nearer value to that of basalt wt% and is allotted with the rank 2. The third highest impact parameter is a load which is having a delta value of 2.94 is given with rank 3. Finally, the least significant parameter is speed which is having a delta value of 1.99 and it is nearly three times less than the basalt wt% parameter.

Figure 11.

(a) Main effect plot, and (b) Interaction plots for HDPE samples in response to COF.

Table 12.

Taguchi response based on S/N ratio for HDPE samples in response to COF.

Level	Basalt (wt%)	Load (N)	Speed (rpm)	Distance (m)
1	31.24	26.40	29.07	31.19
2	24.33	29.34	27.20	26.87
3		27.62	27.08	25.30
Delta	6.91	2.94	1.99	5.89
Rank	1	3	4	2

^aSmaller is better.

Table 13 lists the statistically significant variables that impact COF response together with their p values (the probability of significance). The statistical calculations were performed based on the significance level of 5% and 95% confidence level. Based on these criteria, two parameters such as basalt wt% and distance have p-Values of less than .05. Furthermore, the load and speed parameters possess p-Values of .241 and .407 which are above the critical values and projected as less significant factors. These outcomes can also be validated with the F-Values based on the highest is the most significant one. When using the ANOVA, it’s crucial to verify the model’s underlying premises. The following are the underlying presumptions: (1) the output data has regular distributions, independent, and non-normal residuals; (2) the output data have a constant variability for all sets of the input parameters; and the errors are impartial.⁴⁰ The data for the typical likelihood of COF response is plotted and can be seen in Figure 12(a), which helps to confirm the initial premise. The majority of the data are near the linear fit, supporting the assumption of normality. To confirm the second premise, the residuals were shown about the fitted values (Figure 12(b) and (c)) and the input parameters. The findings of an irregular distribution of residuals close to the zero line and a lack of a consistent trend in the residuals support the second hypothesis of a steady variance.⁴² The absence of outliers in the data sets was also shown by the residual plots.

Table 13.

ANOVA results for COF for HDPE samples.

Source	DF	Adj SS	Adj MS	F-Value	p-Value
Basalt (wt%)	1	0.004244	0.004244	28.30	.000
Load (N)	2	0.000494	0.000247	1.65	.241
Speed (rpm)	2	0.000295	0.000148	0.98	.407
Distance (m)	2	0.001424	0.000712	4.75	.036
Error	10	0.001500	0.000150
Total	17

Figure 12.

COF model plots for HDPE samples: (a) Probability, (b) Residuals versus fits, and (c) Residuals versus order.

The COF response contour maps (Figure 13) offered a workable approach for estimating the impact of various factors on the COF, which was extremely helpful for the designer to choose the best parameters as per the requirements. As illustrated in plot Figure 13(a), the COF values are increased with decreasing basalt fillers ratio and with the increment of the applied load. Probably, the scratching impact on the composite materials and increased COF of the basalt-reinforced HDPE composites were caused by the hard basalt filler particles that might be separated from the matrix during the wear test.⁴³ The contour diagram offers the flexibility to pick the proper operating parameters for the composite's ideal sliding circumstances and the potential to regulate the COF of this composite by choosing the proper filler ratio, applied load, and distance following the sliding speeds. Similarly in the plot Figure 13(b), the COF value is found to be reduced at the lowest filler contents and lowest speed parameters. The hard basalt particles are the sole reason for the rise in COF value along with the elevated temperature at the interface. Similar variations are also observed in the contour plot of distance versus basalt wt% in response to COF (Figure 13(c)). In all three cases, the maximum basalt filler reinforcement promotes the higher COF value irrespective of the other parametric values.

Figure 13.

Contour plots in response to COF: (a) COF versus load, basalt wt%, (b) COF versus speed, basalt wt%, and (c) COF versus distance, basalt wt%.

It was observed from Table 11 that the SWR values of HDPE samples got decreased with the addition of 30 wt% basalt fillers reinforcement even with the different processing variables. The effects plots of each parameter on SWR obtained from Taguchi analysis are provided in Figure 14(a). The maximum S/N ratio implies the lowest possible SWR values. The optimum parameters obtained are 30 wt% basalt, 6 N load, 100 r/min speed, and 150 m distance. Among these parameters, the basalt wt% is found to be with most impacting one by showing the maximum slope and distance among the levels. The least impact parameter on the SWR response is speed which exhibits a lower slope among the levels. The order of impact level as per the effect plot is basalt wt% > distance > load > speed. Figure 14(b) displays the plot of the interaction effect of the control parameters for the SN ratio. In interaction plots, interactions don’t happen while the lines exist in parallel, but when they cross, there are significant interactions among the control factors.⁴⁴ But in this case, most of the lines are cross-linked which indicates the strong interaction among the parameters. The response obtained from the Taguchi technique in determining the significant parameter is tabulated in Table 14. The basalt wt% parameter achieved a delta value of 6.38, it is a maximum one among the other parameters and given a rank of 1. Hence, it confirms that the filler particles paid a significant contribution to reducing the SWR of HDPE samples. The second highest delta value of 3.97 is found with the distance parameter indicating the second impact parameter and allotted to rank 2. The third and fourth impact parameters are load (rank 3) and speed (rank 4) which possess a delta value of 3.71 and 1.50 respectively.

Figure 14.

(a) Main effect plot, and (b) Interaction plot for HDPE samples in response to SWR.

Table 14.

Taguchi response for HDPE samples based on S/N ratio for SWR response.

Level	Basalt (wt%)	Load (N)	Speed (rpm)	Distance (m)
1	75.73	77.05	79.76	77.27
2	82.11	80.75	78.73	81.24
3		78.96	78.26	78.25
Delta	6.38	3.71	1.50	3.97
Rank	1	3	4	2

^aSmaller is better.

The analysis of variance results and the probability values for each parameter are tabulated in Table 15. To understand the impact of numerous control variables and interactions on the results of experimental data, it is important to build the ANOVA. As per the fixed significance level threshold of 5%, the p-Values less than .05 is noted as the most significant parameters. Unfortunately, no parameters are having a p-Value of less than .05. This lack of values shows the less significance of each parameter. Based on the results, it can be said that the significance of order follows the Taguchi technique response. Contrarily, a larger F-Value denotes the most important variable while a lower value denotes less influence. The experimental runs change somewhat when they are followed in a straight line, showing a little variation in the distribution of the errors as indicated in the probability plot (Figure 15(a)). Some experimental runs are found with a little far from the predicted line indicating the error present in the developed model. Each of the experimental results was reviewed using residual plots to confirm that the model has a normal distribution, which is necessary to make sure the model for SWR analysis is not violated. The residuals versus fits and residuals versus order plots for SWR response are shown in Figure 15(b) and (c) respectively. In the plots, it is discovered that the residuals have an erratic distribution around the zero line and lack a clear pattern. Moreover, the independent residuals are examined historically, but no trends or patterns are found. Additionally, the residuals are spread along a predetermined gradient between positive and negative residue positions.

Table 15.

ANOVA results of HDPE samples in response to SWR.

Source	DF	Adj SS	Adj MS	F-Value	p-Value
Basalt (wt%)	1	0.000000	0.000000	2.59	.139
Load (N)	2	0.000000	0.000000	0.54	.601
Speed (rpm)	2	0.000000	0.000000	0.15	.859
Distance (m)	2	0.000000	0.000000	0.30	.746
Error	10	0.000000	0.000000
Total	17

Figure 15.

SWR model plots for HDPE samples: (a) Probability, (b) Residuals versus fits, and (c) Residuals versus order.

Figure 16 illustrates the 2D contour plots of basalt wt% concerning other parameters in response to SWR. Figure 16(a) reveals that the load parameter seems to be independent of the basalt wt% irrespective of the SWR values. There are two possibilities, either the load parameter is not significant or it is an independent variable. The minimum SWR is found with 30 wt% basalt filler reinforcement with all the levels of load parameter. The vertical light green color region is indicated with the lowest SWR. Whereas there is a strong interaction among the basalt wt% and speed parameters as indicated in Figure 16(b). The lowest COF is recorded above the 25 wt% filler reinforcement, and it is continued to be low with an increase of both speed and basalt wt%. As per the plot in Figure 16(c), the lowest SWR is also found with 30 wt% basalt and the lowest travel distance. With a decrease in basalt weight percentage and an increase in distance traveled it is observed that the vertical green color zone indicating lower SWR value becomes wider. In all three plots, the common observation is that the lowest and highest SWR value begins with the 30 wt% and 0 wt% basalt reinforcement respectively. The confirmation experiment is executed for SWR response because the optimum parameters are found in the designed array. The experimental and predicted values with the error% are tabulated in Table 16. The error of 7.19% is found with the difference between experimental and predicted values which is in the statistical limit.

Figure 16.

Contour plots in response to SWR: (a) SWR versus load, basalt wt%, (b) SWR versus speed, basalt wt%, and (c) SWR versus distance, basalt wt%.

Table 16.

Confirmation test for SWR response to HDPE samples.

Response	Optimum parameters	Experimental value	Predicted value	Error (%)
SWR	30 wt%, 6 N, 200 r/min, 150 m	0.0000389	0.0000361	7.19

The SEM images, COF plots, and the 2D depth profiles of neat HDPE and basalt-reinforced HDPE composites are shown in Table 17. Since the HDPE polymer has less softening temperature and sensitive to heat variations, the morphologies are exist with smooth surfaces without much debris formation. Particularly, bigger lumps of polymer wear debris can be found on the filler-loaded composite surfaces. These lumps are likely made up of worn material that has gathered into patches and migrated into the point of contact during the pin-on-disc test.²² Because of the moving contact, the worn surfaces showed a highly deformed surface layer with characteristics including lips, grooves, bumps, and fibrils that may break off and travel across the surfaces (runs 1–9). The depth profile plots revealed that the worn track edges are found with polymer bumps because of deep grooves and softened polymer debris. The depth of wear is varied with the increase of load and the range is between 0 and −60 μm for neat HDPE samples shown between 1 and 9 runs. Whereas the depth of profile is significantly reduced for basalt filler-loaded polymer composites and it falls below the −30 μm in most of the runs between 11 and 18. The adhesive nature of HDPE with higher parametric values resulted in more COF variation as observed in the COF plots. The noise disturbance is less during the analysis of COF as revealed with fewer variations in plots as compared to PLA and PP polymers.

Table 17.

COF graph, SEM morphology, and 2D profile of L₁₈ OA designed for HDPE samples.

According to the experimental data, the lowest COF is observed in run nine and the morphology of this run appeared with a smooth surface without any deeper grooves and the debris formation on the wear track. Whereas the morphologies with maximum COF in run 18 are observed with adhesive wear mechanism with moderate grooves and abrasive scratch appearance. Also, the SWR is found with this run due to basalt fillers resistance and the depth of profile is nearly −55 μm. Even though the depth of profile is less in run 11 (maximum SWR), the wear track width is found to be higher than in run 18, and also the pulled-out basalt particles are observed in the wear track edges. Moreover, particle aggregation can also be observed on the wear edges due to the higher speed and the erosive behavior on the polymer surface. In runs 2, 3, 5, 6, 7, and 8, the more polymeric transfer thin layers are observed with the appearance of sharp edges because of repeated softening action and multiple grooves over the same track. In contrast to pristine HDPE samples, the wear process for filler-loaded composites involves less micro-plowing and material loss.⁴⁵ As discussed in the density and void fraction analysis, the presence of the void is more in the composite samples and is observed with the morphologies in runs 9, 10, 13, 16, 17, and 18. The worn surfaces of the composites have plowed lines and cracks that are brought about by the micro-cutting and micro-plowing activity of the erosive asperities counterface. In addition to surface characteristics, the transfer films created during sliding also significantly influence the mode of sample wear. The basalt fillers present in the HDPE polymer were conveniently transported to the counterface and contacting zone of the composite during the sliding process and quickly disengaged from the composite surface. As a result, the fillers act as a solid lubricant between the meeting interfaces, preventing direct contact between them, which lowers the coefficient of friction and increases wear resistance.⁴⁶

Conclusion

The presented work describes the evaluation of the coefficient of friction and specific wear rate of two thermoplastic polymers (PP, HDPE) and their composites strengthened with 30 wt% consistent basalt filler reinforcement. The produced composites have higher densities and larger voids as a result of the basalt filler’s enhanced weight ratio. Basalt’s rigidity as well as stiffness have significantly aided in raising the hardness of composites by providing a barrier against indentation. The tribology test is executed on the pin-on-disc tribometer as per L₁₈ configuration for both neat and composite samples. MINITAB-19 is helpful in designing, optimizing, and analyzing the obtained results. The optimum values were achieved for the PP samples at 0 wt% basalt, 9 N load, 200 r/min speed, and 100 m distance in reaction to COF. Whereas the optimum response for SWR is found at 30 wt% basalt, 6 N load, 100 r/min speed, and 200 m distance. For COF and SWR responses in PP samples, the most important variables are load and basalt wt%, respectively. For the SWR response, the optimal conditions are 30 wt% basalt, 6 N load, 200 r/min speed, and 150 m distance, whereas the COF response for the HDPE sample is optimum at 0 wt% basalt, 6 N load, 100 r/min speed, and 100 r/min distance. The parameter with the greatest influence for both the COF and SWR responses is basalt wt%. The applied load had a significant impact on the profile’s depth, as greater load values resulted in deeper wear tracks. The optimum variables that are not seen in the proposed orthogonal array were subjected to confirmation tests, which produced error rates that were lower than 10% and within the limits of statistical acceptability. The majority of PP morphologies show micro-deformation, ductile-yielding, and the capacity to peele off the soft PP matrix. It implies that sticky wear makes up a significant portion of the wear process. When polymer matrix composites spin over the pin surface, it is known that the two primary wear processes are adhesion in the contact zone and erosion by the asperities on the pin. The HDPE morphologies are present with smooth surfaces with little debris development because the HDPE polymer has a lower softening temperature and is sensitive to heat fluctuations. On the composite surfaces filled with filler, larger lumps of polymer wear debris may be seen in particular. These lumps most likely consist of worn material that has migrated into the pin-on-disc test’s contact site in patches.

Footnotes

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the King Mongkut’s University of Technology North Bangkok (KMUTNB), Thailand under Grant No. KMUTNB-PHD-65–02. This research budget was allocated by National Science, Research and Innovation Fund (NSRF), and King Mongkut’s University of Technology North Bangkok (Project no. KMUTNB-FF-67-A-01).

Ethical statement

ORCID iDs

Praveenkumara Jagadeesh

Sanjay Mavinkere Rangappa

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Tribological performance analysis of sustainable basalt micro-filler loaded bio-based polypropylene and high density polyethylene composites

Abstract

Keywords

Introduction

Materials and fabrication process

Materials

Fabrication process

Experimental design

Density and void fraction analysis

Hardness

Design of experiment

Tribology experiment

Taguchi and analysis of variance techniques

Results and discussions

Density, void fraction, and hardness analysis

Friction and wear analysis

Polypropylene samples

High density polyethylene samples

Conclusion

Footnotes

Declaration of conflicting interests

Funding

Ethical statement

ORCID iDs

References