# Investigation of High-Temperature Wear Behaviour of AA 2618-Nano Si3N4 Composites Using Statistical Techniques

Authors: Santhi M. George; Amel Gacem; A. Kistan; R. Mohammed Ashick; L. Malleswara Rao; Vinod Singh Rajput; N. Nagabooshanam; Moamen S. Refat; Amnah Mohammed Alsuhaibani; David Christopher
Journal: Journal of Nanomaterials
 (2022)

---

## Abstract

The wear behaviour of hot pressed AA 2618 aluminium alloy matrix composites reinforced through nano Si3N4 elements (1 percent and 2 percent) has been investigated in this paper. Temperatures of 50°C, 150°C, and 250°C were used to examine the tribological characteristics of the models under a range of loads and pressures. The best wear performance was found in AA 2618/2wt percent Si3N4. Under a load of 30 N and temperature of 250°C, it was discovered that Si3N4-enriched AA 2618 alloy was 35.7% more wear-resistant than unreinforced AA 2618 alloy. Metal flow and plain delamination were the most common wear mechanisms at higher temperatures. Delamination is the most common wear mechanism at temperatures between 50 and 250 degrees Celsius. In the analysis of variance, the wear rate was influenced by temperature, load, and the presence of Si3N4 by 47.2%. In order to predict the wear rate, regression equations (linear and nonlinear) were developed by Taguchi method. Using a high determination coefficient, the nonlinear regression was the preeminent success rate (92.8 percent).


---

## Body

## 1. Introduction

Lightweight, inexpensive, and energy-efficient alloys are becoming increasingly popular. It is broadly used in the automotive industries for its maximum specific strength, corrosion resistance, and excellent low-temperature properties [1]. Although Al alloys have some drawbacks, the most significant one is their less amount of wear and mechanical properties at higher temperatures [2, 3]. Al metal matrix composites have been developed to address these shortcomings (AMMCs). Al MMCs are commonly reinforced with a variety of materials, including SiC, Al2O3,B4C, TiC, CNT, GNPs, GO, and Y2O3 [4]. Since Si3N4 has a high melting point and good thermal conductivity, it was a natural choice for Al MMC reinforcement. Research into MMCs’ wear and friction patterns is essential [5–7]. In the event that two surfaces are in close proximity to each other, material loss can occur. Consequently, wear has become a major cause of failure. MMCs wear more quickly when subjected to varying loads, sliding speeds, temperatures, and reinforcement content [8, 9]. Statistics and the Taguchi method have become increasingly popular in the field of materials science in recent years. The Taguchi method reduces the amount of time and money required to conduct experiments in order to optimise design parameters [10]. Wear rates and friction coefficients can be studied using the analysis of variance method. Wear rate is also predicted using a regression model. The wear behaviour of AA 2618 matrix composites has been studied by researchers. In addition to silicon carbide, aluminium oxide, and carbon nanotubes, a variety of reinforcements were used [11, 12]. While some research has been done on the tribological performance of stir cast AA 2618/Si3N4 composite (wt% of 3, 6, and 9 Si3N4 content), only a few studies have focused on the properties of 6 percent SI3N4. AA 2618/Si3N4 (4 wt%) composites were stir casted to investigate the dry sliding wear behaviour [13]. They found that composites have a higher wear resistance than AA 2618 alloy without reinforcement. It has dry sliding tribological properties at elevated temperatures between 30 and 300 degrees Celsius. The fabricated MMCs by stir casting were attributed to the increase in wear resistance [14–16]. The wear performance of AA 2618-SiC-hSi3N4 nanocomposites has been improved by the addition of SiC and hSi3N4 particles [17–20]. There have been numerous studies on AA 2618/Si3N4 composites based on the literature. A liquid metallurgy production method was used in these studies [21]. Additionally, statistical analysis was not used to examine the wear behaviour of the samples. Furthermore, no research has been done on the wear behaviour of monolithic AA 2618/Si3N4 composites made by powder metallurgy at elevated temperatures [22–26]. Fuselage structures below tension frequently use AA 2618 alloy because of its high specific strength, good machinability, and high fatigue strength and thermal conductivity [27]. The AA 2618 alloy’s tribological performance is known to be poor at high temperatures. Ceramic particles have been added to AA 2618 alloy in order to increase its usefulness in high-temperature applications [28–30]. It is revealed that AA 2618 wear behaviour at elevated temperatures must be studied and improved. An investigation into hot pressing AA 2618/Si3N4 composites armoured with Si3N4 (1% wt and 2% wt) was primary goal of this research [31]. Different parameters (such as load, Si3N4 content, and temperature) were examined for their effects on wear rate using analysis of variance and regression models. It was also used in the search for the best process parameter that had the lowest wear rate [32–34].


## 2. Experimental Studies

### 2.1. Production and Materials

AA 2618 alloy powder was used as a matrix material in this research. Because of its ability to work at higher temperatures, AA 2618 is frequently used as pistons and spinning aviation components, as well as in automotive racing. The AA 2618 alloy chemical composition was determined to be 4.7Cu, 1.6 Mg, 0.6Zn, 0.5Mn, and 0.2Si and a weight percentage of Al that was balanced. Reinforcement was provided by Si3N4 nanoparticles (100 nm). The composites were made using semi-powder metallurgy. To separate the agglomerated particles, Si3N4 particles were ultrasonically treated in ethanol for one hour. Next, AA 2618 alloy powder was added to the solution containing Si3N4 nanoparticles. In a vacuum distillation system, a magnetic stirrer was used to mix the powders (AA 2618 alloy and Si3N4). After three hours, all of the ethanol had been flushed from the system. Previous studies provided a schematic diagram and detailed explanation of semi-powder metallurgy. Si3N4 nanoparticles of 0, 1, and 2wt percent were used in the samples. One hour of hot pressing at 525°C under 50 MPa pressure produced the test specimens. The rate of heating was10°C/min. In an argon atmosphere, all the processes were carried out. Due to the size requirements of this study, the attained samples of 15 mm x 23 mm were maintained. Figure 1 shows SEM image of powder used.Figure 1
SEM image of powder used.


### 2.2. Mechanical and Wear Tests

A hardness test device was used to take the hardness measurements. It was necessary to conduct precise measurements using the metallographic preparation. A 1 kg load and a dwell time of 20 seconds were used to measure hardness. The average of five successful indentations was calculated. A universal testing machine with a 0.5 mm/min test speed was used for the compression tests. A pin-on-disc test device was used to conduct wear tests under dry sliding conditions. Temperatures ranging from 50 to 250 degrees Celsius were used for wear tests at a sliding speed of 120 millimetres per second with loads of 15 to 45 Newtons. The sliding distance was 200 metres. The AISI 52100 steel used for the counterface had a hardness rating of 63 HRC. Figure2 shows pin on disc wear setup.Figure 2
Pin on disc wear setup.


### 2.3. The Experiment Design

The Taguchi method was used with three factors and three levels. Si3N4 content, load, and temperature were all factors in the experiment. Table 1 provides the values of the parameters. The Taguchi design used the L27 array.Table 1
Test parameters for measuring wear.
LevelsApplied load,NTemperature°CWt percent of silicon nitrate11550023015013452502


## 2.1. Production and Materials

AA 2618 alloy powder was used as a matrix material in this research. Because of its ability to work at higher temperatures, AA 2618 is frequently used as pistons and spinning aviation components, as well as in automotive racing. The AA 2618 alloy chemical composition was determined to be 4.7Cu, 1.6 Mg, 0.6Zn, 0.5Mn, and 0.2Si and a weight percentage of Al that was balanced. Reinforcement was provided by Si3N4 nanoparticles (100 nm). The composites were made using semi-powder metallurgy. To separate the agglomerated particles, Si3N4 particles were ultrasonically treated in ethanol for one hour. Next, AA 2618 alloy powder was added to the solution containing Si3N4 nanoparticles. In a vacuum distillation system, a magnetic stirrer was used to mix the powders (AA 2618 alloy and Si3N4). After three hours, all of the ethanol had been flushed from the system. Previous studies provided a schematic diagram and detailed explanation of semi-powder metallurgy. Si3N4 nanoparticles of 0, 1, and 2wt percent were used in the samples. One hour of hot pressing at 525°C under 50 MPa pressure produced the test specimens. The rate of heating was10°C/min. In an argon atmosphere, all the processes were carried out. Due to the size requirements of this study, the attained samples of 15 mm x 23 mm were maintained. Figure 1 shows SEM image of powder used.Figure 1
SEM image of powder used.


## 2.2. Mechanical and Wear Tests

A hardness test device was used to take the hardness measurements. It was necessary to conduct precise measurements using the metallographic preparation. A 1 kg load and a dwell time of 20 seconds were used to measure hardness. The average of five successful indentations was calculated. A universal testing machine with a 0.5 mm/min test speed was used for the compression tests. A pin-on-disc test device was used to conduct wear tests under dry sliding conditions. Temperatures ranging from 50 to 250 degrees Celsius were used for wear tests at a sliding speed of 120 millimetres per second with loads of 15 to 45 Newtons. The sliding distance was 200 metres. The AISI 52100 steel used for the counterface had a hardness rating of 63 HRC. Figure2 shows pin on disc wear setup.Figure 2
Pin on disc wear setup.


## 2.3. The Experiment Design

The Taguchi method was used with three factors and three levels. Si3N4 content, load, and temperature were all factors in the experiment. Table 1 provides the values of the parameters. The Taguchi design used the L27 array.Table 1
Test parameters for measuring wear.
LevelsApplied load,NTemperature°CWt percent of silicon nitrate11550023015013452502


## 3. Results and Discussion

Table2 displays the specimens’ densities and hardness of the produced composites.Table 2
Specimens’ densities and hardness.
Materials(density)theoreticalg/cm3(density)Experimentalg/cm3(density)Relativeg/cm3Hardness, HV% of raise in hardnessAA 26183.713.7697.181.4 ± 3.692.4 ± 1.6AA 2618/1% silicon nitrate3.823.78098.498.194.8 ± 1.9AA 2618/2% silicon nitrate3.783.7989.416.210


### 3.1. Mechanical Properties

According to these results, increasing the amount of Si3N4 results in greater compressive yield strength (CYS) and ultimate compressive strength (UCS). Comparing the CYS and UCS of the AA 2618/2Si3N4 composite to those of the AA 2618 alloy, 17.2% and 28.9% increases were observed. Figure 3 shows the curves of the samples compression stress and strain. Table 3 summarises the mechanical properties and average grain size for each type of cereal grain. The dislocations are also slowed by the presence of reinforcing particles. Due to the occurrence of reinforcement elements and an increased grain boundary area as a result of grain refinement, dislocation movement is hindered As a result, the strongest material has the smallest grain size. Grain size decreases as the Si3N4 content increases. AA 2618/1Si3N4 and AA 2618/2Si3N4 reduced the grain size by 7.8 percent and 21.9 percent, respectively, when compared to the AA 2618 alloy. Sintering grain refinement may be attributed to the hard Si3N4 elements in the structure acting as a wall to grain limit movement. There is also evidence to suggest that nano-reinforcement slows down grain growth by causing pinning at grain boundaries. It is also crucial to have a mechanism for transferring the weight of the load. The transmission of loads from the soft matrix to hard fortification particles has been credited with increasing the strength of composite materials. The interfacial bonding between the matrix material and the reinforcement particles is responsible for the increased strength. Using Si3N4 particles, the researchers were able to transfer loads from a matrix to a reinforcement. Al matrix composites reinforced with nanoparticles can also benefit from the Orowan strengthening mechanism. Nanoparticles are used to form residual dislocation loops in the Orowan strengthening mechanism. The back stress created by the dislocation loops prevents the dislocation from moving forward. Composite materials become stronger as a result.Figure 3
Curves of the samples’ compression stress and strain.Table 3
Fabricated samples’ mechanical properties.
MaterialsGrain size,CYS, MpaUCSAA 261820.2276.2 ± 11.2516.7 ± 14.2AA 2618/1% silicon nitrate19.4302.6 ± 10.2591.6 ± 23.4AA 2618/2% silicon nitrate15.6324.6 ± 9.1665.8 ± 20.1


### 3.2. Wear Results

#### 3.2.1. The Effect of Load on the Rate of Wear

There was a clear correlation between increasing load and increasing wear rate at all temperatures. Aside from that, the AA 2618/2Si3N4 composite’s wear rate was the lowest. The lubricating effect of Si3N4 particles contributes to the improved wear resistance of composites. Due to the Si3N4 particles, composite materials have a lower rate of wear because the metallic contact between sliding surfaces is reduced. A 45 N load on AA 2618/2Si3N4 reduces wear rates by 45.8 percent, 42.1 percent, and 35.7 percent compared to an unreinforced alloy at 50°C, 150°C, and 250°C. As the temperature rises, the wear rates of the samples also increase significantly. At temperatures of 50°C, 150°C, and 250°C, AA 2618 has a wear rate of 0.0024 mm-3/Nm. for the alloy, according to research.Dislocation density increases when there is a mismatch in thermal expansion. This has a significant impact on the hardness of composite materials. Grains of AA 2618/Si3N4 composites have fewer grain boundaries, resulting in a higher wear resistance. The dislocation movement is hindered by the increased grain boundaries. The improvement in tribological performance was attributed to this. The improved wear performance of AA 2618/Si3N4 composites is due to the increased hardness and mechanical properties of the strengthening mechanisms.


#### 3.2.2. Temperature Effect on Wear Rate

With increasing Si3N4 content at temperatures between 50 and 250 degrees Celsius, the wear rate of AA 2618/Si3N4 composites is reduced. Al matrix thermal stability is said to improve as the amount of Si3N4 in the alloy increases. The wear rate of the models also increases the temperature of the test increases. As the test temperature rises, the softening trend becomes more pronounced.


#### 3.2.3. Effect of Wear Rate on Silicon Nitride

AA 2618 alloy wear rate is thought to be affected by silicon nitride content below 15 N, 30 N, and 45 N loads at temperatures of 50°C, 150°C, and 250°C. After adding Si3N4, the wear performance was noticeably improved in all conditions. Sample and counterface material are subjected to low shear stress because of Si3N4 particles in the structure. At a temperature of 250°C and a load of 45 N, the AA 2618 alloy showed severe wear.


#### 3.2.4. Coefficient of Friction

In this graph, friction coefficient (COF) is plotted against load and temperature. Under all wear conditions, it can be seen that friction coefficient reduces with increasing silicon nitride content. The COF of the samples rises in direct proportion to the increase in load and test temperature. The AA 2618/2Si3N4 sample had the lowest COF. This resulted in an average COF value of 0.333 for AA 2618/1Si3N4 and 0.210 for the AA 2618/2Si3N4 samples tested at a temperature of 50°C. At a temperature of 250 degrees Celsius and a load of 45 Newton, the average COF values of AA 2618/1Si3N4 were 0.668, 0.572, and 0.474, respectively, and all are shown in Figures 4 and Figure 5. Composites have low coefficients of friction because of a solid lubricant, Si3N4. Furthermore, it was discovered that the presence of hard reinforcement particles reduces the actual contact area between the counterface and the matrix. As a result, composite materials have a lower COF. It is well established that the matrix softens as the temperature rises. As the counterface and matrix become more adherent, so does the matrix’s adhesion to the counterface material. As a result, the samples’ COF rises.Figure 4
Temperature affects the rate of wear on the load: (a) 50°C, (b) 150°C, and (c)250°C.
(a)(b)(c)Figure 5
Temperature-dependent wear rates under a variety of load conditions: (a) 15 N, (b) 30 N, and (c) 45 N.
(a)(b)(c)Adding Si3N4 particles improves wear performance in this study, according to researchers. The COF is also found to be reduced when Si3N4 particles are added. The wear behaviour of Si3N4-reinforced aluminium composites was identified to be similar by several researchers. The Al matrix’s wear performance was reported to have improved, and the COF was reported to have decreased and it is shown in Figure 6. Composites with the addition of Si3N4 reinforcement were found to be more resistant to wear because of the material’s lubricant properties. Due to the matrix strengthening that occurred as dislocation density increased, wear resistance also increased.Figure 6
Si3N4 content’s wear rate under various loads: (a) 15 N, (b) 30 N, and (c) 45 N.
(a)(b)(c)Grain refinement and particle dispersion strengthening were both associated with an increase in composite strength and hardness. Composites have grain sizes that are smaller than those of the AA 2618 alloy, as shown in Table3. Counterface material and matrix are separated by a thin layer of oxide, according to this theory. In this study, the surface was found to be oxidised when heated to high temperatures.


## 3.1. Mechanical Properties

According to these results, increasing the amount of Si3N4 results in greater compressive yield strength (CYS) and ultimate compressive strength (UCS). Comparing the CYS and UCS of the AA 2618/2Si3N4 composite to those of the AA 2618 alloy, 17.2% and 28.9% increases were observed. Figure 3 shows the curves of the samples compression stress and strain. Table 3 summarises the mechanical properties and average grain size for each type of cereal grain. The dislocations are also slowed by the presence of reinforcing particles. Due to the occurrence of reinforcement elements and an increased grain boundary area as a result of grain refinement, dislocation movement is hindered As a result, the strongest material has the smallest grain size. Grain size decreases as the Si3N4 content increases. AA 2618/1Si3N4 and AA 2618/2Si3N4 reduced the grain size by 7.8 percent and 21.9 percent, respectively, when compared to the AA 2618 alloy. Sintering grain refinement may be attributed to the hard Si3N4 elements in the structure acting as a wall to grain limit movement. There is also evidence to suggest that nano-reinforcement slows down grain growth by causing pinning at grain boundaries. It is also crucial to have a mechanism for transferring the weight of the load. The transmission of loads from the soft matrix to hard fortification particles has been credited with increasing the strength of composite materials. The interfacial bonding between the matrix material and the reinforcement particles is responsible for the increased strength. Using Si3N4 particles, the researchers were able to transfer loads from a matrix to a reinforcement. Al matrix composites reinforced with nanoparticles can also benefit from the Orowan strengthening mechanism. Nanoparticles are used to form residual dislocation loops in the Orowan strengthening mechanism. The back stress created by the dislocation loops prevents the dislocation from moving forward. Composite materials become stronger as a result.Figure 3
Curves of the samples’ compression stress and strain.Table 3
Fabricated samples’ mechanical properties.
MaterialsGrain size,CYS, MpaUCSAA 261820.2276.2 ± 11.2516.7 ± 14.2AA 2618/1% silicon nitrate19.4302.6 ± 10.2591.6 ± 23.4AA 2618/2% silicon nitrate15.6324.6 ± 9.1665.8 ± 20.1


## 3.2. Wear Results

### 3.2.1. The Effect of Load on the Rate of Wear

There was a clear correlation between increasing load and increasing wear rate at all temperatures. Aside from that, the AA 2618/2Si3N4 composite’s wear rate was the lowest. The lubricating effect of Si3N4 particles contributes to the improved wear resistance of composites. Due to the Si3N4 particles, composite materials have a lower rate of wear because the metallic contact between sliding surfaces is reduced. A 45 N load on AA 2618/2Si3N4 reduces wear rates by 45.8 percent, 42.1 percent, and 35.7 percent compared to an unreinforced alloy at 50°C, 150°C, and 250°C. As the temperature rises, the wear rates of the samples also increase significantly. At temperatures of 50°C, 150°C, and 250°C, AA 2618 has a wear rate of 0.0024 mm-3/Nm. for the alloy, according to research.Dislocation density increases when there is a mismatch in thermal expansion. This has a significant impact on the hardness of composite materials. Grains of AA 2618/Si3N4 composites have fewer grain boundaries, resulting in a higher wear resistance. The dislocation movement is hindered by the increased grain boundaries. The improvement in tribological performance was attributed to this. The improved wear performance of AA 2618/Si3N4 composites is due to the increased hardness and mechanical properties of the strengthening mechanisms.


### 3.2.2. Temperature Effect on Wear Rate

With increasing Si3N4 content at temperatures between 50 and 250 degrees Celsius, the wear rate of AA 2618/Si3N4 composites is reduced. Al matrix thermal stability is said to improve as the amount of Si3N4 in the alloy increases. The wear rate of the models also increases the temperature of the test increases. As the test temperature rises, the softening trend becomes more pronounced.


### 3.2.3. Effect of Wear Rate on Silicon Nitride

AA 2618 alloy wear rate is thought to be affected by silicon nitride content below 15 N, 30 N, and 45 N loads at temperatures of 50°C, 150°C, and 250°C. After adding Si3N4, the wear performance was noticeably improved in all conditions. Sample and counterface material are subjected to low shear stress because of Si3N4 particles in the structure. At a temperature of 250°C and a load of 45 N, the AA 2618 alloy showed severe wear.


### 3.2.4. Coefficient of Friction

In this graph, friction coefficient (COF) is plotted against load and temperature. Under all wear conditions, it can be seen that friction coefficient reduces with increasing silicon nitride content. The COF of the samples rises in direct proportion to the increase in load and test temperature. The AA 2618/2Si3N4 sample had the lowest COF. This resulted in an average COF value of 0.333 for AA 2618/1Si3N4 and 0.210 for the AA 2618/2Si3N4 samples tested at a temperature of 50°C. At a temperature of 250 degrees Celsius and a load of 45 Newton, the average COF values of AA 2618/1Si3N4 were 0.668, 0.572, and 0.474, respectively, and all are shown in Figures 4 and Figure 5. Composites have low coefficients of friction because of a solid lubricant, Si3N4. Furthermore, it was discovered that the presence of hard reinforcement particles reduces the actual contact area between the counterface and the matrix. As a result, composite materials have a lower COF. It is well established that the matrix softens as the temperature rises. As the counterface and matrix become more adherent, so does the matrix’s adhesion to the counterface material. As a result, the samples’ COF rises.Figure 4
Temperature affects the rate of wear on the load: (a) 50°C, (b) 150°C, and (c)250°C.
(a)(b)(c)Figure 5
Temperature-dependent wear rates under a variety of load conditions: (a) 15 N, (b) 30 N, and (c) 45 N.
(a)(b)(c)Adding Si3N4 particles improves wear performance in this study, according to researchers. The COF is also found to be reduced when Si3N4 particles are added. The wear behaviour of Si3N4-reinforced aluminium composites was identified to be similar by several researchers. The Al matrix’s wear performance was reported to have improved, and the COF was reported to have decreased and it is shown in Figure 6. Composites with the addition of Si3N4 reinforcement were found to be more resistant to wear because of the material’s lubricant properties. Due to the matrix strengthening that occurred as dislocation density increased, wear resistance also increased.Figure 6
Si3N4 content’s wear rate under various loads: (a) 15 N, (b) 30 N, and (c) 45 N.
(a)(b)(c)Grain refinement and particle dispersion strengthening were both associated with an increase in composite strength and hardness. Composites have grain sizes that are smaller than those of the AA 2618 alloy, as shown in Table3. Counterface material and matrix are separated by a thin layer of oxide, according to this theory. In this study, the surface was found to be oxidised when heated to high temperatures.


## 3.2.1. The Effect of Load on the Rate of Wear

There was a clear correlation between increasing load and increasing wear rate at all temperatures. Aside from that, the AA 2618/2Si3N4 composite’s wear rate was the lowest. The lubricating effect of Si3N4 particles contributes to the improved wear resistance of composites. Due to the Si3N4 particles, composite materials have a lower rate of wear because the metallic contact between sliding surfaces is reduced. A 45 N load on AA 2618/2Si3N4 reduces wear rates by 45.8 percent, 42.1 percent, and 35.7 percent compared to an unreinforced alloy at 50°C, 150°C, and 250°C. As the temperature rises, the wear rates of the samples also increase significantly. At temperatures of 50°C, 150°C, and 250°C, AA 2618 has a wear rate of 0.0024 mm-3/Nm. for the alloy, according to research.Dislocation density increases when there is a mismatch in thermal expansion. This has a significant impact on the hardness of composite materials. Grains of AA 2618/Si3N4 composites have fewer grain boundaries, resulting in a higher wear resistance. The dislocation movement is hindered by the increased grain boundaries. The improvement in tribological performance was attributed to this. The improved wear performance of AA 2618/Si3N4 composites is due to the increased hardness and mechanical properties of the strengthening mechanisms.


## 3.2.2. Temperature Effect on Wear Rate

With increasing Si3N4 content at temperatures between 50 and 250 degrees Celsius, the wear rate of AA 2618/Si3N4 composites is reduced. Al matrix thermal stability is said to improve as the amount of Si3N4 in the alloy increases. The wear rate of the models also increases the temperature of the test increases. As the test temperature rises, the softening trend becomes more pronounced.


## 3.2.3. Effect of Wear Rate on Silicon Nitride

AA 2618 alloy wear rate is thought to be affected by silicon nitride content below 15 N, 30 N, and 45 N loads at temperatures of 50°C, 150°C, and 250°C. After adding Si3N4, the wear performance was noticeably improved in all conditions. Sample and counterface material are subjected to low shear stress because of Si3N4 particles in the structure. At a temperature of 250°C and a load of 45 N, the AA 2618 alloy showed severe wear.


## 3.2.4. Coefficient of Friction

In this graph, friction coefficient (COF) is plotted against load and temperature. Under all wear conditions, it can be seen that friction coefficient reduces with increasing silicon nitride content. The COF of the samples rises in direct proportion to the increase in load and test temperature. The AA 2618/2Si3N4 sample had the lowest COF. This resulted in an average COF value of 0.333 for AA 2618/1Si3N4 and 0.210 for the AA 2618/2Si3N4 samples tested at a temperature of 50°C. At a temperature of 250 degrees Celsius and a load of 45 Newton, the average COF values of AA 2618/1Si3N4 were 0.668, 0.572, and 0.474, respectively, and all are shown in Figures 4 and Figure 5. Composites have low coefficients of friction because of a solid lubricant, Si3N4. Furthermore, it was discovered that the presence of hard reinforcement particles reduces the actual contact area between the counterface and the matrix. As a result, composite materials have a lower COF. It is well established that the matrix softens as the temperature rises. As the counterface and matrix become more adherent, so does the matrix’s adhesion to the counterface material. As a result, the samples’ COF rises.Figure 4
Temperature affects the rate of wear on the load: (a) 50°C, (b) 150°C, and (c)250°C.
(a)(b)(c)Figure 5
Temperature-dependent wear rates under a variety of load conditions: (a) 15 N, (b) 30 N, and (c) 45 N.
(a)(b)(c)Adding Si3N4 particles improves wear performance in this study, according to researchers. The COF is also found to be reduced when Si3N4 particles are added. The wear behaviour of Si3N4-reinforced aluminium composites was identified to be similar by several researchers. The Al matrix’s wear performance was reported to have improved, and the COF was reported to have decreased and it is shown in Figure 6. Composites with the addition of Si3N4 reinforcement were found to be more resistant to wear because of the material’s lubricant properties. Due to the matrix strengthening that occurred as dislocation density increased, wear resistance also increased.Figure 6
Si3N4 content’s wear rate under various loads: (a) 15 N, (b) 30 N, and (c) 45 N.
(a)(b)(c)Grain refinement and particle dispersion strengthening were both associated with an increase in composite strength and hardness. Composites have grain sizes that are smaller than those of the AA 2618 alloy, as shown in Table3. Counterface material and matrix are separated by a thin layer of oxide, according to this theory. In this study, the surface was found to be oxidised when heated to high temperatures.


## 4. Statistical Analysis

### 4.1. ANOVA Results

Table4 displays the Taguchi L27 orthogonal array results and response value values. The experimental data was analysed using ANOVA. ANOVA can be used to find which variables have greatest impact on the rate at which clothing wears out. The ANOVA studies were conducted with a 95% level of confidence. The ANOVA results are shown in Table 5. Temperature is widely believed to be the most significant factor in the rate of wear (46.21 percent). Load and Si3N4 content were found to be responsible for 23.97 percent and 12.92 percent of the total. Interactions appear to have a smaller impact than individual parameters. There is a 13.67 percent correlation between load and temperature, followed by a correlation between temperature and Si3N4 (2.43 percent).Table 4
Taguchi L27 orthogonal array results and response value values.
Ex.NoLoadTempSi3N4 wt%Wear rateRatio of S/N1155000.0028546652155010.00165708813155020.000962848241515000.004249284651515010.002354112861515020.001856108671525000.005946212781525010.004149121891525020.002655134210305000.003848972011305010.002952153512305020.0019610012133015000.0071450182143015010.0056466710153015020.0049481312163025000.0099413421173025010.0069452817183025020.005947081619455000.003153012420455010.002455842821455020.0024581868224515000.0091421868234515010.0069461828244515020.0054472858254525000.0152381824264525010.0112391524274525020.0096411015Table 5
The results of the ANOVA test for each sample.
SourceDegrees of freedomSeq SS% of contributionAdj SSAdj MSF valueP valueLoad (L)20.00007123.40.0000710.000038168.940.0000Temperature (°C)20.00013846.40.0001340.000069316.180.0000Si3N4 wt%20.00003911.690.0000390.00002189.140.0000L x T40.00000214.240.0000410.00001517.860.0000L x Si3N440.0000090.310.0000010.00000.790.5960T x Si3N440.0000033.350.0000080.000038.960.00070Error80.0002890.610.0000030.00000Total26100Temperature and Si3N4 content were independent variables. It was determined that the wear rate was the dependent variable. The wear rate of samples was predicted using a linear and a nonlinear regression model and it is shown in Figures 7 and 8.Figure 7
(a) Wear rate and (b) S/N ratios for the samples are shown in the main effects plots.
(a)(b)Figure 8
Wear rate is plotted as a function of the interaction between wear parameters.


### 4.2. Analysis of S/N Ratios

This research made use of 27-row, 3-column full-factorial arrays. The constraints, wear rate, and signal-to-noise ratio are listed. Using the S/N ratio “Small is better” characteristic, this study was able to determine the wear rate. Each variable’s impact on output was evaluated using the S/N ratio. Given here is the S/N Ratio (S/N) in equation (1)
(1)S/N=−10log1n∑i=1nyi2.Signal-to-noise ratio (S/N) is the ratio of a signal’s strength to the background noise, andn is how many trials were conducted. This study looked at how different loads, temperatures, and levels of Si3N4 content affected wear rates. Table 6 summarises the relative importance of various wear test parameters and their respective means. The best results are achieved when the S/N ratio of the combination of wear rate-related parameters is the highest. There is a correlation between wear rate and temperature, which is more pronounced than the effects of load and Si3N4. The effect of Si3N4 content was overshadowed by the effect of load. Plots’ wear rates are the primary goal of the analysis. Alloy AA 2618 must have a low wear rate and high Si3N4 content in order to function properly. For the interaction plots, the non-parallelism effect is well-known. If the interaction plot’s lines are not parallel, a finding of low interaction is valid. The wear parameters interact strongly at the intersection of lines. The relationship between load and temperature can be clearly seen. Temperature Si3N4 content and load Si3N4 content had a low interaction. The signal-to-noise ratio compares the strength of a signal to the background noise.Table 6
Responses to the sample S/N ratios.
LevelsApplied loadTemperature of control factors °CWt% of Si3N4 content154.854.846.4248.648.449.8347.145.253.3Delta8.113.17.4Rank213


### 4.3. Evaluation Parameters

Regression models were assessed against two criteria in this study.R2 and root mean square error were used to calculate the determination coefficient, R2 (RMSE). The following equations were used to calculate the RMSE in equation (2) and R2 in equation (3).
(2)RMSE=1−1n∑i=1nei−pi21/2.There are two values:ei is the actual and pi is the predicted one, respectively.
(3)R2=1−∑i=1nei−pi2∑i=1nei−pi¯21/2.For the regression models,R2 is a measure of how well they perform based on the mean of the actual values. High R2 and low RMSSE values are what we are looking for in the model we are building here. Linear and nonlinear regressions have RMSE values of 0.0013 and 0.0009, respectively, for the two methods as in Figures 7 and 8. For linear and nonlinear regressions, the R2 (percent) was 84.8 and 91.5, respectively. Regression models with low RMSE values are more likely to be successful. Nonlinear regression models outperformed linear regression models by 1.4 times as in tables.Prediction accuracy is higher in models that use nonlinear regression than in linear regression models. Lower prediction values were obtained by using a linear regression model. For AA 2618/Si3N4 composites, nonlinear regression can be used to accurately predict the wear rate. Tribological studies can save money and time by using the nonlinear regression model as shown in Figures 9 and 10.Figure 9
The dataset’s regression model results.Figure 10
Comparison of experiment and predicted values.


## 4.1. ANOVA Results

Table4 displays the Taguchi L27 orthogonal array results and response value values. The experimental data was analysed using ANOVA. ANOVA can be used to find which variables have greatest impact on the rate at which clothing wears out. The ANOVA studies were conducted with a 95% level of confidence. The ANOVA results are shown in Table 5. Temperature is widely believed to be the most significant factor in the rate of wear (46.21 percent). Load and Si3N4 content were found to be responsible for 23.97 percent and 12.92 percent of the total. Interactions appear to have a smaller impact than individual parameters. There is a 13.67 percent correlation between load and temperature, followed by a correlation between temperature and Si3N4 (2.43 percent).Table 4
Taguchi L27 orthogonal array results and response value values.
Ex.NoLoadTempSi3N4 wt%Wear rateRatio of S/N1155000.0028546652155010.00165708813155020.000962848241515000.004249284651515010.002354112861515020.001856108671525000.005946212781525010.004149121891525020.002655134210305000.003848972011305010.002952153512305020.0019610012133015000.0071450182143015010.0056466710153015020.0049481312163025000.0099413421173025010.0069452817183025020.005947081619455000.003153012420455010.002455842821455020.0024581868224515000.0091421868234515010.0069461828244515020.0054472858254525000.0152381824264525010.0112391524274525020.0096411015Table 5
The results of the ANOVA test for each sample.
SourceDegrees of freedomSeq SS% of contributionAdj SSAdj MSF valueP valueLoad (L)20.00007123.40.0000710.000038168.940.0000Temperature (°C)20.00013846.40.0001340.000069316.180.0000Si3N4 wt%20.00003911.690.0000390.00002189.140.0000L x T40.00000214.240.0000410.00001517.860.0000L x Si3N440.0000090.310.0000010.00000.790.5960T x Si3N440.0000033.350.0000080.000038.960.00070Error80.0002890.610.0000030.00000Total26100Temperature and Si3N4 content were independent variables. It was determined that the wear rate was the dependent variable. The wear rate of samples was predicted using a linear and a nonlinear regression model and it is shown in Figures 7 and 8.Figure 7
(a) Wear rate and (b) S/N ratios for the samples are shown in the main effects plots.
(a)(b)Figure 8
Wear rate is plotted as a function of the interaction between wear parameters.


## 4.2. Analysis of S/N Ratios

This research made use of 27-row, 3-column full-factorial arrays. The constraints, wear rate, and signal-to-noise ratio are listed. Using the S/N ratio “Small is better” characteristic, this study was able to determine the wear rate. Each variable’s impact on output was evaluated using the S/N ratio. Given here is the S/N Ratio (S/N) in equation (1)
(1)S/N=−10log1n∑i=1nyi2.Signal-to-noise ratio (S/N) is the ratio of a signal’s strength to the background noise, andn is how many trials were conducted. This study looked at how different loads, temperatures, and levels of Si3N4 content affected wear rates. Table 6 summarises the relative importance of various wear test parameters and their respective means. The best results are achieved when the S/N ratio of the combination of wear rate-related parameters is the highest. There is a correlation between wear rate and temperature, which is more pronounced than the effects of load and Si3N4. The effect of Si3N4 content was overshadowed by the effect of load. Plots’ wear rates are the primary goal of the analysis. Alloy AA 2618 must have a low wear rate and high Si3N4 content in order to function properly. For the interaction plots, the non-parallelism effect is well-known. If the interaction plot’s lines are not parallel, a finding of low interaction is valid. The wear parameters interact strongly at the intersection of lines. The relationship between load and temperature can be clearly seen. Temperature Si3N4 content and load Si3N4 content had a low interaction. The signal-to-noise ratio compares the strength of a signal to the background noise.Table 6
Responses to the sample S/N ratios.
LevelsApplied loadTemperature of control factors °CWt% of Si3N4 content154.854.846.4248.648.449.8347.145.253.3Delta8.113.17.4Rank213


## 4.3. Evaluation Parameters

Regression models were assessed against two criteria in this study.R2 and root mean square error were used to calculate the determination coefficient, R2 (RMSE). The following equations were used to calculate the RMSE in equation (2) and R2 in equation (3).
(2)RMSE=1−1n∑i=1nei−pi21/2.There are two values:ei is the actual and pi is the predicted one, respectively.
(3)R2=1−∑i=1nei−pi2∑i=1nei−pi¯21/2.For the regression models,R2 is a measure of how well they perform based on the mean of the actual values. High R2 and low RMSSE values are what we are looking for in the model we are building here. Linear and nonlinear regressions have RMSE values of 0.0013 and 0.0009, respectively, for the two methods as in Figures 7 and 8. For linear and nonlinear regressions, the R2 (percent) was 84.8 and 91.5, respectively. Regression models with low RMSE values are more likely to be successful. Nonlinear regression models outperformed linear regression models by 1.4 times as in tables.Prediction accuracy is higher in models that use nonlinear regression than in linear regression models. Lower prediction values were obtained by using a linear regression model. For AA 2618/Si3N4 composites, nonlinear regression can be used to accurately predict the wear rate. Tribological studies can save money and time by using the nonlinear regression model as shown in Figures 9 and 10.Figure 9
The dataset’s regression model results.Figure 10
Comparison of experiment and predicted values.


## 5. Conclusions

This work used experimental and statistical approaches to investigate the wear behaviour of AA 2618/Si3N4 (1 and 2wt percent) composites. The following findings were obtained from this investigation. In all test settings, composites containing AA 2618/2 wt percent Si3N4 demonstrated the best wear resistance. Delamination was most noticeable at 50 degrees Celsius, with substantial delamination and metal flow occurring at 250 degrees Celsius.(i)
There was a 47.32 percent temperature, 24.96 percent load, and 12.51 percent Si3N4 content contribution to wear rate, respectively(ii)
Linear regression had anR2 of 83.4%, while nonlinear regression had an R2 of 92.8%. By using nonlinear regression to predict wear rate, examining time and the number of examinations can be reduced(iii)
Because of their superior elevated temperature tribological performance, AA 2618 with Si3N4 composites are the best choice for wear applications at high temperatures



---

*Source: 3449903-2022-09-16.xml*