# Mathematical Modeling and Analysis of Wear Behavior of AlTiN Coating on Titanium Alloy (Ti-6Al-4V)

Authors: P. Sivaprakasam; A. Kirubel; G. Elias; P. Maheandera Prabu; P. Balasubramani
Journal: Advances in Materials Science and Engineering
 (2021)

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## Abstract

In this study, the wear behavior of a physical vapor deposition (PVD) AlTiN coated on the titanium alloy was investigated. Response surface methodology (RSM) was used to analyze input factors, such as load (A), sliding speed (B), and sliding distance (C), while wear mass loss (WML) and coefficient of driction (COF) were considered as the response parameters. The statistical analysis shows that main factors, that is, interaction of AC and pure quadratic terms B2 and C2, have maximum influences on WML. However, COF was highly affected by load, sliding speed, and interaction of AB and quadratic term A2. The present work attempts to carry out empirical modeling to predict output response on WML and COF. Desirability-based optimization technique was employed to obtain minimum WML and COF. Microscopy images of the wear tracks reveal visible grooves and scratches that confirm abrasive wear to be the primary wear mechanism accompanied by adhesive wear. The investigation concluded that AlTiN has better wear resistance properties and can be used to coat titanium implants for biomedical application. The result shows that the minimum WML and COF have been found at applied load 15 N, sliding speed at 0.5 m/s, and sliding distance 500 m.


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## Body

## 1. Introduction

Surface engineering is enabling technology that meets the expectations of modern science, energy, material properties, and environmental friendliness. The technology allows for the altering of materials surfaces to have enhanced properties [1]. PVD technique is a vaporization coating method that involves transforming the coating material into the gaseous phase by using resistive heating, electron beam, etc., to deposit on the surface of the substrate [2, 3]. Many studies have considered different coating techniques for clinical purposes. PVD, CVD, sol-gel coating, and self-assembly are some of the deposition methods for biomedical applications [2–5]. However, PVD has become widely used on various medical devices, including surgical tools, orthopedic implants, pacemakers, and orthodontic appliances, to deposit wear-resistant single or multilayer coatings. PVD’s importance is focused on its ability to change a device’s surface features unaffected the underlying material’s properties or biomechanical functionality. Chen and Thouas [6] highlighted that lower modulus and high corrosion resistance characteristics of Ti-alloy make the material have superior biocompatibility property than stainless steel cobalt alloys. The advantage in elemental composition similarity is taken between Ti-6Al-4V substrate and TiN coating, where diffusion or adhesion property is expected to occur between materials to yield improved sliding wear resistance [7]. AlTiN films are suitable as ideal wear-resistant coatings because of their higher strength, low coefficient of friction, and chemical inertness [8]. Szala et al. [9] investigated the sliding wear mechanism of AlTiN (PVD) coated stainless steel substrate (grade AISI 304). The results showed that AlTiN-coated samples exhibited 24 times sliding wear resistance and two times decrease in friction coefficient than the uncoated substrates. Furthermore, it was observed that grooving, microscratching, microplowing, and smearing were the main types of sliding wear mechanisms.In the aircraft sector, titanium alloys are utilized for landing systems, fasteners, engine components, airframes, and other applications. The Ti-6Al-4V alloy stands out among these alloys because of its high strength-to-weight ratio, corrosion resistance, and low density [10]. Ti and Ti alloys are lightweight and have improved biocompatibility and corrosion resistance, making them popular in medical and dental applications [11]. Protecting Ti-6Al-4V substrates with PVD coatings has been shown to improve mechanical characteristics, biocompatibility, osseointegration, wear, and corrosion resistance [12].Biomedical areas use metallic biomaterials such as stainless steels (SS 316L), cobalt-based alloys, commercially pure titanium and its alloy, Sn alloys, and Au-based materials for different purposes [4]. Titanium alloys are the most attractive biomedical metallic material under static conditions; it is more corrosion-resistant. These materials are subject to intrinsic limitations when dynamic conditions such as fretting, wear, fatigue, and chemical corrosion [7]. Generally, these alloys have a reputation for poor tribo-characteristics, and they are restricted for further uses in their tribological applications. In general, as stated in the literature mentioned above, the significance of an AlTiN coating on titanium alloys is rarely mentioned. The applied force, temperature, and oxidation, all associated with the sliding contact mechanism, regulate coated surfaces’ friction and wear behavior. Particularly, load, sliding velocity, and distance have a significant impact on wear behavior. Therefore, the research concentrated on the wear properties of titanium alloys coated with AlTiN.


## 2. Materials and Methods

### 2.1. Materials

Titanium alloy (Ti-6Al-4V) is used for this investigation. The cylindrical bar is cut into 20 specimens using WEDM. The specimens were mechanically grained and polished according to required standards (Ø 50 mm and 10 mm thickness). AlTiN is deposited using an arc evaporation technique (Oerlikon Balzers coating system, India) with an approximate deposition thickness of 2μm. The EN31 steel pin (6 mm diameter and length 35 mm) materials were used against the AlTiN-coated titanium alloy disc.


### 2.2. Response Surface Methodology (RSM)

RSM is the most popular method for designing an experiment for engineering problems and predicting and optimizing a given model on surface roughness [13, 14]. RSM [15] is the best tool for analyzing the impact of factors and their interaction on the response. Table 1 shows the input variables with levels taken into account for wear studies. The input factors and levels selected based on thr tribometer range and relevant literature [14]. The wear tests were carried out using face center cubic design (RSM) at random fashion as shown in Table 2.Table 1
Wear test factors and their levels.
FactorsLevelsLevel 1Level 2Level 3A: load (N)153045B: sliding speed (m/s)0.511.5C: sliding distance (m)50010001500Table 2
Experimental results of wear test.
S. No.Input factorsResponsesLoadSliding speedSliding distanceWMLCOFNm/smmg13011000550.40412150.5500240.224733011500560.39254300.51000490.41985450.5500350.38646451.515001000.25237301.51000630.422383011000490.42059151.51500400.4414103011000460.4131114511000680.310512450.51500960.387913150.51500330.2483143011000520.423915451.5500390.2463161511000260.330417151.5500260.449183011000480.421219301500260.4132203011000500.4115


### 2.3. Sliding Wear Test

For the sliding wear test, an American Society for Testing Materials (ASTM) G99 guideline is followed. A pin on disc (DUCOM) tribometer is used to test the coated substrate material’s sliding wear and friction behavior (Figure1). The experiment is conducted at ambient temperature and humidity under dry sliding conditions. The pin on the disc tribometer consists of a stationary pin prepared of EN31 steel, which is harder than the coating material, held vertically and pressed with a specific load against a rotary sample. Figures 2(a)–2(c) show the test samples for uncoated, coated with AlTiN and wear test, respectively. The test was performed based on the design of experiments using RSM. The wear mass loss was calculated by analytical balance (0.0001 g least count) before and after wear tests. Acetone is used for cleaning the surface of the disc and pin. COF is calculated by frictional force/applied force.Figure 1
Pin-on-disc tribometer.Figure 2
Titanium alloy test samples. (a) Uncoated. (b) Coated. (c) Wear test.


## 2.1. Materials

Titanium alloy (Ti-6Al-4V) is used for this investigation. The cylindrical bar is cut into 20 specimens using WEDM. The specimens were mechanically grained and polished according to required standards (Ø 50 mm and 10 mm thickness). AlTiN is deposited using an arc evaporation technique (Oerlikon Balzers coating system, India) with an approximate deposition thickness of 2μm. The EN31 steel pin (6 mm diameter and length 35 mm) materials were used against the AlTiN-coated titanium alloy disc.


## 2.2. Response Surface Methodology (RSM)

RSM is the most popular method for designing an experiment for engineering problems and predicting and optimizing a given model on surface roughness [13, 14]. RSM [15] is the best tool for analyzing the impact of factors and their interaction on the response. Table 1 shows the input variables with levels taken into account for wear studies. The input factors and levels selected based on thr tribometer range and relevant literature [14]. The wear tests were carried out using face center cubic design (RSM) at random fashion as shown in Table 2.Table 1
Wear test factors and their levels.
FactorsLevelsLevel 1Level 2Level 3A: load (N)153045B: sliding speed (m/s)0.511.5C: sliding distance (m)50010001500Table 2
Experimental results of wear test.
S. No.Input factorsResponsesLoadSliding speedSliding distanceWMLCOFNm/smmg13011000550.40412150.5500240.224733011500560.39254300.51000490.41985450.5500350.38646451.515001000.25237301.51000630.422383011000490.42059151.51500400.4414103011000460.4131114511000680.310512450.51500960.387913150.51500330.2483143011000520.423915451.5500390.2463161511000260.330417151.5500260.449183011000480.421219301500260.4132203011000500.4115


## 2.3. Sliding Wear Test

For the sliding wear test, an American Society for Testing Materials (ASTM) G99 guideline is followed. A pin on disc (DUCOM) tribometer is used to test the coated substrate material’s sliding wear and friction behavior (Figure1). The experiment is conducted at ambient temperature and humidity under dry sliding conditions. The pin on the disc tribometer consists of a stationary pin prepared of EN31 steel, which is harder than the coating material, held vertically and pressed with a specific load against a rotary sample. Figures 2(a)–2(c) show the test samples for uncoated, coated with AlTiN and wear test, respectively. The test was performed based on the design of experiments using RSM. The wear mass loss was calculated by analytical balance (0.0001 g least count) before and after wear tests. Acetone is used for cleaning the surface of the disc and pin. COF is calculated by frictional force/applied force.Figure 1
Pin-on-disc tribometer.Figure 2
Titanium alloy test samples. (a) Uncoated. (b) Coated. (c) Wear test.


## 3. Results and Discussion

### 3.1. Statistical Analysis

The statistical analysis is applied to analyzing the responses as per input factor combinations, coefficients estimation, and model adequacy checking and predicting the responses [14]. The factors considered for the study were load, sliding speed, and sliding distance. The WML and COF were taken as response parameters. The face-centered cubic design was employed using Design Expert software. The experimental results of the wear test coated on titanium alloy were presented in Table [2].


#### 3.1.1. Mathematical Models for WML and COF

The input trials developed the mathematical models for WML and COF. Wear mass loss (YWML) and COF (YCOF) are represented using quadratic equations, as shown in (1) and (2), respectively. WML and COF predictions are based on the mathematical models with a coding unit as given in equation [1, 2].Model equation of WML:(1)YWML=49.11+18.90×A+3.10×B+17.50×C+12.37×AC+7.44×B2−7.56×C2.Model equation of COF:(2)YCOF=0.41−0.011×A+0.014×B−0.087×AB−0.086×A2.The applied load (A), sliding speed (B), sliding distance (C), the interaction of A and C, and pure quadratic terms B2 and C2 have the greatest influence on WML; according to the quadratic equation for WML (equation (1)), applied load (A), sliding speed (B), the interaction of A and C, and pure quadratic term A2 have foremost influences on COF (equation (2)).


#### 3.1.2. Model Adequacy Testing

The following graphical analysis was used to validate the adequacy of the model. Figures3 and 4 show the residual plot for WML and COF. The residuals fall in a straight line in the normal probability plot for the WML and COF (Figures 3(a) and 4(a)), which clearly shows the errors are normally distributed, and no outliers are presented. The residuals are dispersed, suggesting that they are independent, as shown in Figures 3(b) and 4(b). There is no peculiar structure in Figures 3(c) and 4(c), indicating that the model is correct. The model adequacy test results confirm that the experimental and predicted values well fit each other. The R2 and R2 (adj) values for WML are 0.9836 and 0.9761 and those for COF are 0.9829 and 0.9783, respectively. The R2 values agree with R2 (adj) values, indicating that model data fit with experimental values. In both models, both R2 and R2 (adj) values are above 95%, which allows the model to fit the prediction of the solution.Figure 3
Residual plot for WML. (a) Normal probability plot. (b) Residuals versus run number. (c) Residuals versus predicted values.
(a)(b)(c)Figure 4
Residual plot for COF (a) Normal probability plot. (b) Residual versus run number. (c) Residuals versus predicted values.
(a)(b)(c)


### 3.2. Analysis of Wear Mass Loss

ANOVA analysis is used to evaluate input factors on WML. The ANOVA for WML simplified model is shown in Table3. The P values indicate the confidence level, which is less than 0.05. It shows that the model has more than a 95% confidence interval. The coefficient of determination (R2) value of the WML model is 0.9836. Hence, the developed model is acceptable, and the predicted values fit the measured results. Insignificant factors are eliminated from the full quadratic model by the backward elimination process. Minimum mass loss is desirable since it indicates less wear amount. The minimum WML was found at 15 N, 0.5 m/sec, and 500 m.Table 3
ANOVA for WML.
SourceSum of squareDegree of freedomMean squareF valuep value prob > FModel8180.88861363.481130.2729<0.0001SignificantA3572.113572.1341.2939<0.0001B96.1196.19.181810.0097C3062.513062.5292.6045<0.0001AC1225.12511225.125117.0537<0.0001B2177.01251177.012516.912540.0012C2183.01251183.012517.485810.0011Residual136.06251310.46635Lack of fit86.0625810.757811.0757810.4905Pure error50510Cor total8316.9519WML has been increased while the load and sliding distance has been increased. The higher wear mass loss occurred at a high level of load and sliding distance. From Figures5 and 6, AlTiN-coated surface are deforms marginally when subjected to a 15 N load. As the load and sliding distance increase, the abrasive wear increases, resulting in greater wear mass loss. Asperities compress between the sliding surfaces, resulting in a blunt surface and increased contact area, leads to higher wear mass loss as the sliding distance increases.Figure 5
3D surface plot of WML.Figure 6
Contour plot-WML.The sliding speed has less influence on wear mass loss. The experimental observation shows that the maximum wear mass loss recorded was 100 mg. Fan et al. [16] can also support these results, where the adhesive strength of PVD (arc ion implant) coated AlTiN was found to be 39.7 N, which is lower than the load applied in this study. This clearly explains the significant effect of the applied load on WML. Figures 5 and 6 show the interactive effect of load and sliding distance on WML. From the figures, when applied load increases, mass loss tends to increase. Similarly, mass loss also increases as sliding distance increases.


### 3.3. Analysis of Coefficient of Friction

ANOVA Table4 for COF shows that load and sliding speed and its interaction influence the response. It is evident that an increase in sliding speed results in a linear increase in COF, while an increase in applied load results in a parabolic (progressive) increment in COF (Figures 7 and 8). This phenomenon was also observed by Yousfi [17], where it was assumed that it is the cause of COF instability. In addition, a recent study by Bahi et al. [12] showed that the COF for TiN/TiO2 multilayer coating of Ti-6Al-4V substrate to be unstable COF as sliding distance increases. From Figure 7, it can be understood that there is a tendency for COF to decrease as load increases. The COF decreases as load increases, and related findings were observed by Dejun et al. [18]. Slight variation in COF was observed, which attributes to the galling effect [17] and an increase in the sliding distance [12]. This also confirms that AlTiN was an excellent choice of material for a substrate subjected to increased load under dry conditions.Table 4
ANOVA for COF.
SourceSum of squareDegree of freedomMean squareF valuep value prob > FModel0.10074940.025187215.370<0.0001SignificantA0.00121910.00121910.42170.0056B0.00207910.00207917.78010.0007AB0.06004810.060048513.4588<0.0001A20.03740310.037403319.82<0.0001Residual0.001754150.000117Lack of fit0.001475100.0001472.63820.1479Pure error0.00027955.59 × 10−05Cor total0.10250319Figure 7
3D surface plot of COF.Figure 8
Contour plot of COF.The coefficient of friction is expected to increase as load increases between sliding surfaces, particularly between materials of high hardness. Lattice constraint is minimized due to the solid solution hardening of aluminum which attributes to the property of improved hardness of AlTiN coatings [3]. Adhesion strength is the energy required to separate a coating from its substrate. The addition of Al in TiN hard-facing improved resistance to elastic deformation. Furthermore, according to the literature [19, 20], the hardness to elastic modulus (H/E) values are also used to indicate wear initiation. Resistance to abrasion and higher hardness and resistance to elastic deformation are exhibited when H/E values are high. With a higher load, the rate of forming the tribo-layer will be faster as the temperature in the contact area rises, causing plastic deformation on the surface; thus, the coefficient of friction will decrease [21, 22].The main wear mechanism of AlTiN coating for abrasion wear is seen when microscopy pictures have been analyzed. Abrasive and adhesive wear mechanisms were observed in Figure9, based on the direction of wear. It might be inferred that the primary wear mechanism of AlTiN coating is abrasive wear accompanied by adhesive wear. Also, the wear track contains more groves and scratches.Figure 9
Microscope images of wear tracks.The desirability-based optimization technique was employed for finding the optimal combination for the wear test [14]. The objective function for the wear test is minimum wear mass loss and minimum COF. The optimal conditions for the wear test were load at 15 N, sliding speed at 0.5 m/s, and sliding distance at 500 m.The test results are validated based on the optimal processing parameters through a confirmation test. With a desirability value of 0.971, the optimal combinations for lower wear mass loss and COF are 15 N applied load, 0.5 m/s sliding speed, and 500 m sliding distance (Table5). Table 5 shows that, at 95% confidence intervals, the predicted and test values are in good agreement.Table 5
Optimum conditions for sliding parameter of AlTiN-coated titanium alloy.
ResponseGoalDesirabilityABCPredicted valueExperimental valueWMLMinimum0.971150.550021.93423.00COFMinimum0.971150.55000.23770.2320


## 3.1. Statistical Analysis

The statistical analysis is applied to analyzing the responses as per input factor combinations, coefficients estimation, and model adequacy checking and predicting the responses [14]. The factors considered for the study were load, sliding speed, and sliding distance. The WML and COF were taken as response parameters. The face-centered cubic design was employed using Design Expert software. The experimental results of the wear test coated on titanium alloy were presented in Table [2].


### 3.1.1. Mathematical Models for WML and COF

The input trials developed the mathematical models for WML and COF. Wear mass loss (YWML) and COF (YCOF) are represented using quadratic equations, as shown in (1) and (2), respectively. WML and COF predictions are based on the mathematical models with a coding unit as given in equation [1, 2].Model equation of WML:(1)YWML=49.11+18.90×A+3.10×B+17.50×C+12.37×AC+7.44×B2−7.56×C2.Model equation of COF:(2)YCOF=0.41−0.011×A+0.014×B−0.087×AB−0.086×A2.The applied load (A), sliding speed (B), sliding distance (C), the interaction of A and C, and pure quadratic terms B2 and C2 have the greatest influence on WML; according to the quadratic equation for WML (equation (1)), applied load (A), sliding speed (B), the interaction of A and C, and pure quadratic term A2 have foremost influences on COF (equation (2)).


### 3.1.2. Model Adequacy Testing

The following graphical analysis was used to validate the adequacy of the model. Figures3 and 4 show the residual plot for WML and COF. The residuals fall in a straight line in the normal probability plot for the WML and COF (Figures 3(a) and 4(a)), which clearly shows the errors are normally distributed, and no outliers are presented. The residuals are dispersed, suggesting that they are independent, as shown in Figures 3(b) and 4(b). There is no peculiar structure in Figures 3(c) and 4(c), indicating that the model is correct. The model adequacy test results confirm that the experimental and predicted values well fit each other. The R2 and R2 (adj) values for WML are 0.9836 and 0.9761 and those for COF are 0.9829 and 0.9783, respectively. The R2 values agree with R2 (adj) values, indicating that model data fit with experimental values. In both models, both R2 and R2 (adj) values are above 95%, which allows the model to fit the prediction of the solution.Figure 3
Residual plot for WML. (a) Normal probability plot. (b) Residuals versus run number. (c) Residuals versus predicted values.
(a)(b)(c)Figure 4
Residual plot for COF (a) Normal probability plot. (b) Residual versus run number. (c) Residuals versus predicted values.
(a)(b)(c)


## 3.1.1. Mathematical Models for WML and COF

The input trials developed the mathematical models for WML and COF. Wear mass loss (YWML) and COF (YCOF) are represented using quadratic equations, as shown in (1) and (2), respectively. WML and COF predictions are based on the mathematical models with a coding unit as given in equation [1, 2].Model equation of WML:(1)YWML=49.11+18.90×A+3.10×B+17.50×C+12.37×AC+7.44×B2−7.56×C2.Model equation of COF:(2)YCOF=0.41−0.011×A+0.014×B−0.087×AB−0.086×A2.The applied load (A), sliding speed (B), sliding distance (C), the interaction of A and C, and pure quadratic terms B2 and C2 have the greatest influence on WML; according to the quadratic equation for WML (equation (1)), applied load (A), sliding speed (B), the interaction of A and C, and pure quadratic term A2 have foremost influences on COF (equation (2)).


## 3.1.2. Model Adequacy Testing

The following graphical analysis was used to validate the adequacy of the model. Figures3 and 4 show the residual plot for WML and COF. The residuals fall in a straight line in the normal probability plot for the WML and COF (Figures 3(a) and 4(a)), which clearly shows the errors are normally distributed, and no outliers are presented. The residuals are dispersed, suggesting that they are independent, as shown in Figures 3(b) and 4(b). There is no peculiar structure in Figures 3(c) and 4(c), indicating that the model is correct. The model adequacy test results confirm that the experimental and predicted values well fit each other. The R2 and R2 (adj) values for WML are 0.9836 and 0.9761 and those for COF are 0.9829 and 0.9783, respectively. The R2 values agree with R2 (adj) values, indicating that model data fit with experimental values. In both models, both R2 and R2 (adj) values are above 95%, which allows the model to fit the prediction of the solution.Figure 3
Residual plot for WML. (a) Normal probability plot. (b) Residuals versus run number. (c) Residuals versus predicted values.
(a)(b)(c)Figure 4
Residual plot for COF (a) Normal probability plot. (b) Residual versus run number. (c) Residuals versus predicted values.
(a)(b)(c)


## 3.2. Analysis of Wear Mass Loss

ANOVA analysis is used to evaluate input factors on WML. The ANOVA for WML simplified model is shown in Table3. The P values indicate the confidence level, which is less than 0.05. It shows that the model has more than a 95% confidence interval. The coefficient of determination (R2) value of the WML model is 0.9836. Hence, the developed model is acceptable, and the predicted values fit the measured results. Insignificant factors are eliminated from the full quadratic model by the backward elimination process. Minimum mass loss is desirable since it indicates less wear amount. The minimum WML was found at 15 N, 0.5 m/sec, and 500 m.Table 3
ANOVA for WML.
SourceSum of squareDegree of freedomMean squareF valuep value prob > FModel8180.88861363.481130.2729<0.0001SignificantA3572.113572.1341.2939<0.0001B96.1196.19.181810.0097C3062.513062.5292.6045<0.0001AC1225.12511225.125117.0537<0.0001B2177.01251177.012516.912540.0012C2183.01251183.012517.485810.0011Residual136.06251310.46635Lack of fit86.0625810.757811.0757810.4905Pure error50510Cor total8316.9519WML has been increased while the load and sliding distance has been increased. The higher wear mass loss occurred at a high level of load and sliding distance. From Figures5 and 6, AlTiN-coated surface are deforms marginally when subjected to a 15 N load. As the load and sliding distance increase, the abrasive wear increases, resulting in greater wear mass loss. Asperities compress between the sliding surfaces, resulting in a blunt surface and increased contact area, leads to higher wear mass loss as the sliding distance increases.Figure 5
3D surface plot of WML.Figure 6
Contour plot-WML.The sliding speed has less influence on wear mass loss. The experimental observation shows that the maximum wear mass loss recorded was 100 mg. Fan et al. [16] can also support these results, where the adhesive strength of PVD (arc ion implant) coated AlTiN was found to be 39.7 N, which is lower than the load applied in this study. This clearly explains the significant effect of the applied load on WML. Figures 5 and 6 show the interactive effect of load and sliding distance on WML. From the figures, when applied load increases, mass loss tends to increase. Similarly, mass loss also increases as sliding distance increases.


## 3.3. Analysis of Coefficient of Friction

ANOVA Table4 for COF shows that load and sliding speed and its interaction influence the response. It is evident that an increase in sliding speed results in a linear increase in COF, while an increase in applied load results in a parabolic (progressive) increment in COF (Figures 7 and 8). This phenomenon was also observed by Yousfi [17], where it was assumed that it is the cause of COF instability. In addition, a recent study by Bahi et al. [12] showed that the COF for TiN/TiO2 multilayer coating of Ti-6Al-4V substrate to be unstable COF as sliding distance increases. From Figure 7, it can be understood that there is a tendency for COF to decrease as load increases. The COF decreases as load increases, and related findings were observed by Dejun et al. [18]. Slight variation in COF was observed, which attributes to the galling effect [17] and an increase in the sliding distance [12]. This also confirms that AlTiN was an excellent choice of material for a substrate subjected to increased load under dry conditions.Table 4
ANOVA for COF.
SourceSum of squareDegree of freedomMean squareF valuep value prob > FModel0.10074940.025187215.370<0.0001SignificantA0.00121910.00121910.42170.0056B0.00207910.00207917.78010.0007AB0.06004810.060048513.4588<0.0001A20.03740310.037403319.82<0.0001Residual0.001754150.000117Lack of fit0.001475100.0001472.63820.1479Pure error0.00027955.59 × 10−05Cor total0.10250319Figure 7
3D surface plot of COF.Figure 8
Contour plot of COF.The coefficient of friction is expected to increase as load increases between sliding surfaces, particularly between materials of high hardness. Lattice constraint is minimized due to the solid solution hardening of aluminum which attributes to the property of improved hardness of AlTiN coatings [3]. Adhesion strength is the energy required to separate a coating from its substrate. The addition of Al in TiN hard-facing improved resistance to elastic deformation. Furthermore, according to the literature [19, 20], the hardness to elastic modulus (H/E) values are also used to indicate wear initiation. Resistance to abrasion and higher hardness and resistance to elastic deformation are exhibited when H/E values are high. With a higher load, the rate of forming the tribo-layer will be faster as the temperature in the contact area rises, causing plastic deformation on the surface; thus, the coefficient of friction will decrease [21, 22].The main wear mechanism of AlTiN coating for abrasion wear is seen when microscopy pictures have been analyzed. Abrasive and adhesive wear mechanisms were observed in Figure9, based on the direction of wear. It might be inferred that the primary wear mechanism of AlTiN coating is abrasive wear accompanied by adhesive wear. Also, the wear track contains more groves and scratches.Figure 9
Microscope images of wear tracks.The desirability-based optimization technique was employed for finding the optimal combination for the wear test [14]. The objective function for the wear test is minimum wear mass loss and minimum COF. The optimal conditions for the wear test were load at 15 N, sliding speed at 0.5 m/s, and sliding distance at 500 m.The test results are validated based on the optimal processing parameters through a confirmation test. With a desirability value of 0.971, the optimal combinations for lower wear mass loss and COF are 15 N applied load, 0.5 m/s sliding speed, and 500 m sliding distance (Table5). Table 5 shows that, at 95% confidence intervals, the predicted and test values are in good agreement.Table 5
Optimum conditions for sliding parameter of AlTiN-coated titanium alloy.
ResponseGoalDesirabilityABCPredicted valueExperimental valueWMLMinimum0.971150.550021.93423.00COFMinimum0.971150.55000.23770.2320


## 4. Conclusion

The tribological behavior of AlTiN-coated titanium alloy against EN-31 was examined under room temperature and dry sliding conditions. The scientific results are summarized as follows.ANOVA was performed to determine the significance of AlTiN-coated titanium alloy tribology behavior. The statistical analysis shows that applied load (A), sliding speed (B), sliding distance (C), the interaction of A and C, and pure quadratic terms B2 and C2 have maximum influences on WML.Applied load (A), sliding speed (B), the interaction of A and B, and pure quadratic term A2 have maximum influences on COF.The higher wear mass loss 100 g could be found at 45 N applied load, 1.5 m/s sliding speed, and 1500 m sliding distance.Desirability-based optimization techniques were employed for the sliding wear behavior of AlTiN-coated titanium alloy. The result shows that the minimum WML and COF have been found at applied load 15 N, sliding speed at 0.5 m/s, and sliding distance 500 m.



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