Optimizing the Effect of Solid Lubricants on the Sliding Wear Behavior of SiC_p Reinforced Cast Aluminum Alloy

Abstract

The present study's objective is to investigate the impact of adding solid lubricants (graphite, molybdenum disulfide, titanium dioxide) to SAE20-40 oil on the sliding wear performance of as-cast SiC dispersed aluminum alloy. Matrix alloy and conventionally used Gunmetal were also tested under identical conditions to understand the scope of latter material as a possible substitute in various engineering applications. The Taguchi's L₂₇ orthogonal array is considered for conducting experiments through three input parameters, i.e., Test Materials, Test Environment, and Applied Load. Based on the results, the analyses are carried out using analysis of variance. From the main effects plot, the optimum condition is obtained LM13S10 composite under MoS₂ lubrication at 50 N load.

1 Introduction

In the last few decades, many researchers' attention to Metal Matrix Composites (MMCs) has significantly increased due to the increase in demand for lightweight and rigid materials for the aerospace, aviation, and automotive industries. They are considered as potential engineering materials owing to having outstanding mechanical and tribological properties [1]. Among a diverse range of accessible matrix material, aluminum-based alloys have several benefits, including higher strength, low density, excellent thermal conductivity, and a lower friction coefficient [2]. Despite having mentioned properties, their weak wear resistance property restricts their use as promising material [3]. However, to enhance their mechanical and tribological properties, reinforcements, either hard or soft type, were added to the aluminum matrix to cope with stringent material requirements in different applications. In this regard, a variety of aluminum-based alloys such as AA2024 [4,5,6,7], AA3003 [8], AA4043 [9], AA5052 [10, 11], AA5083 [12], AA6061 [13,14,15,16,17], AA6082 [18], AA6360 [19], AA7075 [20, 21], A356 [2, 22, 23], A383 [3, 24], LM4 [25], LM6 [26], and LM13 [27] have been used as the matrix alloy. Various kinds of reinforcements such as SiC [2, 15, 16, 21, 24, 27,28,29,30,31,32], B₄C [17, 19, 22, 23], TiC [10, 19, 33], WC [34], Al₂O₃ [7, 15, 35,36,37,38], TiO₂ [8], ZrO₂ [39, 40], TiB₂ [41], ZrB₂ [11], CNT [42], Graphene [13], Red Mud [18, 43], Graphite [44] have been commonly used with an objective to realize property improvement of the alloy system. However, silicon carbide (SiC) is the most commonly employed reinforcement particulates for AMCs than other reinforcing materials [45].

The SiC being slightly denser than that of Aluminum alloy offered a challenge. SiC's chemical compatibility and adequate bonding with the matrix material tackled the said challenge without developing an intermetallic phase. Compared to other reinforcement materials, low-cost, good thermal conductivity and workability make it a good alternative [15]. Kumar et al. [16] studied the influence of SiC on an Al6061–SiC composite's hardness. They found that the relative increase in hardness up to 67% with 6 wt.% SiC reinforcement was achieved. This improvement can be attributed to the higher hardness of SiC. The presence of SiC in the composite provides an improvement in its hardness.

In the literature, a variety of processing methods for the synthesis of AMCs are available, such as stir casting [17], squeeze casting [6], rheocasting [46], spray deposition [29], powder metallurgy [37], and mechanical alloying [47]. Besides the type of application, the selection of synthesis process primarily depends on the reinforcement's shape, size, and volume fraction. The alloy is then prepared by melting the Al matrix completely, followed by the careful addition of SiC ceramic [24, 30] with a mechanical stirrer [7].

Several studies have been reported on the sliding wear behavior of different ceramic particle-reinforced aluminum composites. They indicate that the wear behavior is elaborate, dependent on the synergistic effects of operational and material-related conditions [48]. Also, Experimental factors (such as velocity, distance, conditions, load) control the wear behavior.

Mixed lubrication condition involving metal-to-metal contact in part is experienced in sliding applications in the events of delayed lubrication and starting and stopping operations [49, 50].

In such applications, liquid and semi-solid lubricants are used [51]. Additives help to improve lubricant performance [5, 49,50,51,52]. It is important to note that solid lubricants in the mixture of liquid/semi-solid lubricants are beneficial to vary degrees depending on the nature and content of the matrix of additives and lubricants [52].

Solid lubricant particles' role in controlling wear behavior when present within the material system depends on pressure, sliding speed, environment, the shape and size of the lubricant particles, and the nature of interfacial bonding between particles/matrix.

Studies have revealed that specific material under specific test conditions yield positive effects in solid lubricants [53, 54]. For example, by generating appropriate microstructural features such as spherical micro constituents and sound particle/matrix interfacial bonding in the material, a solid lubricant's advantages can be realized so that the lubricant film formation becomes effective [53, 54]. Another way to understand the advantages of solid lubricants could be by adding them to a base lubricant such as oil/grease [49,50,51,52]. Solid lubricant particles added externally together with a base (oil) lubricant on the sliding surfaces produce better results [52] as the particles enjoy a more efficient interaction with the contacting surfaces and a greater ability to smear and adhere to them, thus creating more stable films/layers of lubrication and improving wear response [52, 54]. This practice also nullifies the possible adverse effects of material parameters such as dispersoid/matrix interfacial features and the nature, morphology, and mode of distribution of different microconstituents.

Available solid lubricants are mostly lamellar solids [55], which allow them to easily smear along the contacting surfaces producing a lubricating effect and improving wear performance. Molybdenum disulfide (MoS₂) is a fine example of solids that are stacked. Fullerene systems can accommodate huge loads and speeds. However, they often oxidize and become inactive above 450 °C. Graphite is a very well-known solid lubricant used in all commercial lubrication applications with lamellar structure. For good lubricity, it needs moisture. Graphite blends and pure graphite dry film are commonly used for applications such as hot and cold shaping, wire drawing and billet grinding, high-speed cutting tools, as a mold release for die casting operations, plastic and rubber molding applications, cylinder head and exhaust bolts, weapons and armament applications, automobile engines and many other industrial applications. Titanium dioxide is an environmentally friendly lubricant that, like other solid lubricants described above, does not have lamellar morphology [56].

Based on the literature survey, the effects of SiC reinforcement appear to have been observed to a limited extent on the lubricated sliding wear behavior of an aluminum alloy system. In contrast, the optimization of sliding wear characteristics under solid lubricants' influence is not fully explored. Therefore, an attempt was made in this analysis to examine the sliding wear behavior of an aluminum alloy and its composite reinforced with 10 wt. % of SiC particles under the influence of solid lubricants (MoS₂, Graphite, and TiO₂). Further, the wear characteristics of SiC reinforced composite have been compared with conventionally used Gunmetal. A correlation has been established between wear, friction, and temperature with the applied load by selecting the performance characteristics using an L27 array orthogonal design.

2 Taguchi Method

The Taguchi method, a statistical method developed by G. Taguchi, is a measure of robustness used to identify variability in a process/product to determine the best control parameters and minimize noise factors(uncontrollable factors) [57]. The S/N ratio measures how the target/nominal value response varies under different noise conditions. Depending on the experiment's goal, there are three S/N ratios; Larger is better, Nominal is best, Smaller is better. The procedure can be summarized as

• Identify and evaluate quality characteristics and process parameters

• Identify the objective function

• Identify the number of levels and possible interactions for the process parameters

• Select an appropriate standard orthogonal array (OA)

• Conduct experiments based on the run order of the orthogonal array

• Analyze data using S/N ratio and ANOVA and predict the optimum conditions

• Perform a verification experiment.

3 Materials and Methods

3.1 Material Selection

In the work presented, LM13 has been chosen as the matrix material for preparing the metal matrix composite. The advantage of good resistance to wear, good bearing properties, and a low thermal expansion coefficient makes it suitable to be used for pulley sheaves, pistons for all types of diesel and petrol engines, and other engine parts operating at higher temperatures. Furthermore, SiC has been selected as a reinforcing material, which is very hard compared to aluminum alloy. The density of the matrix and reinforcement is approximately the same. The advantages over other reinforcement materials include its high thermal conductivity, good tensile and flexural strength, excellent resistance to erosion, corrosion, and chemical attacks in reducing environments [31].

3.2 Synthesis of the Test Materials

The test materials (alloy and composite) were synthesized by melting and casting techniques. Melting was performed using an electric furnace in graphite crucibles. A mechanical stirrer was used to create the molten alloy vortex, in which 10 wt. % of the preheated SiC particles (particle size ~ 50–100 μm) were dispersed. The melts were solidified in 14 mm diameter, 170 mm long cylindrical casting in preheated cast-iron molds. The melting process's schematic is shown in Fig. 1, whereas Table 1 shows the casted materials' chemical compositions.

Fig. 1

A schematic representation of the composite preparation

Table 1 Elemental weight composition of the cast material

3.3 Microstructural Observation

Scanning Electron Microscope (JSM-7100F JEOL, Germany) was used for Microstructural observations. The test specimens were polished using conventional metallographic methods, including polishing the sample with various grades of emery papers and eventually using a polishing cloth with fine alumina paste. After polishing, aluminum samples were etched using Keller's reagent and (10 g FeCl₃ + 50 ml HCl + 200 ml distilled water) was used for Gunmetal to observe grain structures and metal matrix interfaces.

3.4 Hardness Measurement

The hardness of the test materials has been evaluated with the Vickers' hardness tester. The samples were polished and appropriately cleaned before measurement. The load of 30 kg was applied to all the samples when measuring hardness. An average of five readings has been reported in this study.

3.5 Sliding Wear Tests

Ducom (India) made pin-on-disk machine was used to perform sliding wear tests as per ASTM G99-05 standard [58]. Figure 2 shows the schematic representation of wear test analysis. Cylindrical test pins (10 mm diameter and 30 mm length) were held against a rotating EN31 steel disk (1.02% C, 1.05% Cr, 0.3% Ni, 0.35% Mn and remainder Fe). The steel disk was mechanically polished to a roughness (Ra) level of 1–2 μm before each test. 5 wt. % of commercially available solid lubricants (graphite, molybdenum disulfide, titanium dioxide) with an average particle size of 7–10 μm were mixed in SAE 20W-40 oil to create the lubricating condition. Sliding wear tests were conducted at a sliding speed of 2.1 m/s over an applied load range of 50, 100 and 150 N. The test method involved inserting and permitting the disk to rotate in a lubricant mixture for 5 s at a speed of 3.35 m/s. The lubricated disk has been rotated so that the surplus lubricant is spun off, and uniform thickness of lubricant is obtained. Thus, the conditions were maintained similar to mixed lubrication. The sample was fixed in the specimen holder, enabling the disk to rotate at the specified sliding speed up to a specified distance of 2500 m or until the specimen seizer. Frictional heating was measured using a chromel–alumel thermocouple implanted into a 1.5 mm diameter hole on the 1.5 mm sample pin from the sliding surface.

Fig. 2

A schematic representation of the wear test assembly

The thermocouple output is fed to a computer-based information logging system where the sample's friction heating is continually recorded during each experiment. The loads were vertically applied to the pin sample against the disk. The strain gauge result is also transferred to a computer-based data logging system that records every experiment's tangential load on the sample pin. The friction coefficient has been calculated by dividing the tangential load with the applied normal load. The specimens had been thoroughly cleaned and measured before and after each test. A Denver make microbalance has been used to weigh the samples. Weight loss was then transformed into volume loss per unit sliding distance for wear rate measurement.

3.6 Design of Experiment

Design of experiment (DOE) is a methodology for identifying and examining all potential situations, including the various factors and variables that control an investigation. The Taguchi method is used based on DOE, which incorporates experimental and theoretical approaches to refine the response's most critical parameter. Here, three factors (Test Material, Test Environment, and Applied Load) with three design levels were adopted, as illustrated in Table 2, to study the effect of control factors on Wear Rate, Temperature, and Coefficient of Friction. The degree of freedom for each control parameter is equal to one less than number of levels of that parameter. According to the rule, the minimum number of experimental runs should be one greater than the sum of degrees of freedom of all control parameters and their interactions. L27 Orthogonal array was employed for three 3-level factors, as shown in Table 3. Twenty Seven experiments based on the run order generated by the Taguchi model were carried out. The responses to the model were the Wear Rate, Temperature, and Coefficient of Friction. In the Orthogonal array, the columns assigned followed the order material, test environment, and applied load for the factors and responses (Wear Rate, Temperature, and Coefficient of Friction) were set to the other columns. The objective of the model was to minimize the Wear Rate, Temperature, and Coefficient of Friction. S/N ratio and Mean was calculated, and results were subjected to Analysis of Variance (ANOVA).

Table 2 Control parameters and their levels

Table 3 L27 Orthogonal array of experimental layout

4 Results

4.1 Microstructure

Figure 3 elicits the micrograph of the test materials. Matrix alloy comprises of dendrites of Aluminum and eutectic Si (sharp-edge plate-shaped) traces in the inter-dendritic areas. Since the eutectic structure includes both elements, the solid solubility of Si in Al is negligible. The micrograph of SiC reinforced composites delineates the uniform distribution of SiC along with the traditional characteristics of Al alloys. The SiC particles wrapped within the primary dendrites of aluminum rather than settling down in the inter-dendritic region. Strong interfacial bonding and uniform SiC distribution in Al matrix can enhance mechanical and wear properties (explained in the following section). However, agglomeration of dispersoids leads to secure removal of dispersoids from the matrix and causing higher wear rates than the matrix alloy [27]. The Gunmetal microstructure includes primary α dendrites, intermetallic Cu–Al complex, and small iron particles.

Fig. 3

Microstructural features of a matrix alloy, b gunmetal, c, d composite

4.2 Hardness and Density

Table 4 shows the hardness and tensile strength of the sample. The Gunmetal has significantly high hardness and tensile strength than the alloy and composite. Additionally, the inclusion of SiC particulates in the matrix alloy enhances their hardness due to a harder phase in the matrix alloy. Concerning tensile strength, the dispersion of SiC particles within the matrix lowers the tensile strength; this can result from the typical ceramic nature of SiC particles, and the bonding between Si and Al is fragile like Van der Waals bonding.

Table 4 Mechanical properties of the test material

Aluminum and SiC do not react together at the processing temperature of composite materials, and therefore, there is no possibility to form interface products. There is de-cohesion at the particle–matrix interface during tensile stress, resulting in the creation of voids. In the case of coarse particles (50–100 μm) in the aluminum matrix, the likelihood of void formation is high, thus decreasing the tensile strength in composite materials [59].

4.3 Signal-to-Noise Ratio Analysis

"Smaller is better" strategy, Eq. (1) is employed to analyze Signal-to-Noise Ratio.

$$\left( \frac{S}{N} \right)_{{\text{Smaller is better}}} = - 10{\text{log}}_{10} \left( {\frac{1}{n}\mathop \sum \limits_{{{\text{i}} = 1}}^{n} Y_{{\text{i}}}^{2} } \right)$$

(1)

where n is the number of observations and Y_i represents the response to the wear rate.

The delta value in Table 5 indicates the effect of a factor. The delta value is the difference between the highest and lowest characteristic average for a factor. The higher the variation, the higher will be the delta value, and the greater will be the significance of that parameter on the responses. The significance of the parameter determines its rank. From the rank, it is evident that load has significant importance on Wear Rate, Temperature, and Coefficient of Friction followed by the test environment and test material.

Table 5 Response table for signal-to-noise ratio (smaller is better)

The sliding wear rates, friction coefficient, and frictional heating for different combinations of the variables (Material, Test Environments, and Applied Load) according to Taguchi's L27 orthogonal array (OA) are given in Table 6. MINITAB software is implemented for the DOE, and after that, subsequent analysis and plots are obtained for main effects and their interactions. S/N ratio plots determine the optimum level of each control parameter. From the main effects plot, the optimum condition for the minimum wear rate was obtained as (LM13S10, MoS₂@50 N Load). Similarly, the optimum level of processing variables for Temperature (LM13, Graphite @50 N load) and COF (LM13S10, MoS₂ @ 50 N load) are obtained from the main effects plot. The graph of wear rate shows the transition to wear for different parameter levels. The wear rate is directly proportional to the applied load and inversely to the amount of reinforcement for the composite. Compared to aluminum alloy, the composite shows a lower wear rate due to hard reinforcement particles. The uniformly distributed SiC particles induce hardness in the composite, which resists deformation of the composite and decreases wear rate compared to unreinforced alloy.

Table 6 L27 Orthogonal array experimental layout and results

4.4 Effects of Process Parameters on Sliding Wear

The response table (Table 7) for a signal-to-noise ratio depicts that the applied load has the highest delta value; hence the highest significance followed by the test material and test environment (Fig. 4).

Table 7 Response table for signal-to-noise ratios for wear rate (smaller is better)

Fig. 4

Main effects plots for S/N ratio for wear rate

The wear rate increased with applied load irrespective of the test material and lubricating environment. Applied load directly influences the wear rate, i.e., the higher the load greater the wear rate. As far as environmental impacts are concerned, much complexity exists [24, 60]. In all the test conditions, LM13 alloy delineates the maximum wear rate followed by the Gunmetal and composite. Moreover, MoS₂ has shown a reduced wear rate compared to graphite and TiO₂. It is about 50% less than others with a few exceptions.

Usually, the sliding wear characteristics of material improve by the use of lubricants. The amalgamation of a solid lubricant into a liquid lubricant further improves the wear behavior by increasing the film stability. The reason being that the lubricant amalgam should not only adhere to the contacting surface but also should have enough lubricating characteristics. Normal probability plots for the residual (Fig. 5) indicate a data trend similar to the central one.

Fig. 5

Normal probability plot for residuals for wear rate

4.5 Effects of Process Parameter on Frictional heating

From the Response Table for Signal-to-Noise Ratios (Table 8), it can be seen that the applied load has the highest delta value, hence the highest significance followed by the test material and test environment (Fig. 6). The temperature increases linearly irrespective of the Load and test material. In all the test environments, maximum frictional heating is obtained in composite and minimum for the matrix alloy, while Gunmetal shows an intermediate response. The frictional heat initially increased with load; however, further increment in the load reduces the frictional heating severity.

Table 8 Response table for signal-to-noise ratios for frictional heating (smaller is better)

Fig. 6

Main effects plots for S/N ratio for frictional heating

Moreover, frictional heating of all the test materials behaved identically. The sticking tendency of the specimen material to counterface disk tends to increase the temperature at the end of tests. More severe wear conditions due to increasing applied load caused the temperature to increase rapidly. Due to the matrix alloy's excellent lubricating tendency, it exhibited the least frictional heat generation. Unlike matrix alloy, more extensive frictional heating was observed due to the abrasion caused by the fragmented dispersoid SiC particles [61]. The Gunmetal suffered maximum temperature increase irrespective of the test condition due to the fragmented cementite particles' abrasion through their entrapment between the contacting surfaces. The addition of graphite decreased frictional heating by forming a still more stable lubricating film [62]. A normal probability plot (Fig. 7) indicates that the data is normally distributed, and the residuals follow an approximately straight line.

Fig. 7

Normal probability plot for residuals for frictional heating

4.6 Effects of Process Parameter on the Coefficient of Friction

The response table (Table 9) for the signal-to-noise ratio depicts that the test material has the highest delta value; hence the highest significance, followed by the applied load and test environment (Fig. 8).The test material's friction coefficient as a function of load applied in the various lubricated test environments has been plotted. The friction coefficient continuously increased with an increasing load applied for all the test materials. The maximum friction coefficient was obtained in the LM13 alloy case, followed by Gunmetal and composite in all the lubricated test environments. Moreover, the MoS₂ lubricated environment delineates the minimum friction coefficient, while the maximum friction coefficient is obtained in TiO₂ lubricated environment. The lubricating and less cracking nature caused the friction coefficient of the matrix alloy to become the least. The abrasion caused by the entrapped dispersoid phase after fragmentation during wear led to the composite's higher friction coefficient than matrix alloy [38, 62, 63]. A normal probability plot (Fig. 9) indicates that the data is normally distributed.

Table 9 Response table for signal-to-noise ratios (smaller is better)

Fig. 8

Main effects plots for S/N ratio for the friction coefficient

Fig. 9

Normal probability plot of residuals for friction coefficient

4.7 Analysis of Variance (ANOVA)

The ANOVA was performed for the 5% significance level and 95% confidence level to identify the significance of parameters and their interactions on all the three responses (Tables 10, 11, 12, 13, 14, 15). Significance level, denoted by $\alpha$ is a measure of the strength of the evidence that must be present in a sample before rejecting the null hypothesis and concluding that the effect is statistically significant. P-value, the probability that measures the evidence against the null hypothesis, if smaller than the significance level (as observed in the present study), advocates the rejection of the null hypothesis; otherwise, it accepts the null hypothesis. Each parameter's percentage significance is found out and indicated in the last column of Tables 10, 12, and 14 for wear rate, frictional heating, and friction coefficient, respectively. From Table 10, it is observed that applied load has higher significance (51.21%) on wear rate, followed by test material (35.99%), and test environment (6.61%). Moreover, Table 12 delineates that the load has higher significance (68.22%) on wear rate, followed by test material (24.28%), and test environment (3.36%).Furthermore, it has been observed that the test material has higher significance (28.79%) on wear rate, followed by test environment (27.41%) and load (22.28%), as depicted in Table 14.

Table 10 Analysis of variance for wear rates

Table 11 Model summary for wear rate

Table 12 Analysis of variance for temperature

Table 13 Model summary for temperature

Table 14 Analysis of variance for the coefficient of friction

Table 15 Model summary for the coefficient of friction

4.8 Confirmation Experiments

After identifying the most influential parameters, the final phase is to verify the responses by conducting the confirmation experiments. The LM13S10 composite under MoS₂ lubrication at 50 N load is an optimal parameter combination during the wear test. Therefore, the condition of the optimal parameter combination was treated as a confirmation test. The confirmation test result gives the wear rate, temperature, and friction coefficient similar to those shown in Table 6.

4.9 Worn Surface Analysis

The scanning electron microscope (SEM) analysis is used to understand the test specimens' worn surface mechanisms. Figure 10 delineates the worn surface of the SiC reinforced composite at 150 N for MoS₂, TiO₂, and Graphite lubricated environment. In the MoS₂ lubricated environment, the material removal rate observed was minimal due to layers' delamination. In contrast, in the TiO₂ environment, the abraded burrs formed during the wear cause three-body abrasions. However, in a lubricated graphite environment, the presence of suspended graphite particles in the oil lubricant proved to be still more effective towards forming a stable lubricating film on the contacting surfaces. Formation of dark patches on the wear surfaces and orientation/flow of graphite present within the sample material in sliding direction.

Fig. 10

Schematic of the wear test with worn out surface images

5 Conclusions

The liquid metallurgy route was adopted to synthesize SiC reinforced aluminum matrix composite. The effects of applied load and adding solid lubricants (MoS₂, TiO₂, and graphite) to the oil during the developed composite's sliding behavior have been experimentally investigated using a pin-on-disk machine and compared with the matrix alloy as well as conventionally used Gunmetal. The ANOVA analysis based on the Taguchi method's response table was used to optimize the test parameters. Based on the results of the present study, the following conclusions are drawn:

• MoS₂ shows a 20–35% reduction in wear rate than other lubricants at all the applied loads.

• Wear rate, coefficient of friction, and frictional heating all increased with an increase in the applied load.

• MoS₂ and graphite show the lowest temperature profiles owing to outstanding lubricity and surface adhesion.

The results' anticipation establishes that SiC reinforced aluminum alloys are more favorable than the traditional Gunmetal under lubricated environments.