Optimization of cold spray process parameters to maximize adhesion and deposition efficiency of Ni+Al₂O₃ coatings

In the current research, a commercially available metal-matrix composite powder mixture based on nickel Ni+Al₂O₃ was used for coating spraying ((Dycomet Europe B.V., the Netherlands) (figure 1). The mixture is obtained by mechanical mixing in certain proportions by the manufacturer and is supplied in a ready-to-spray state.

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VT3-1 titanium alloy (GOST 19807-91) was used as the substrate material, the chemical composition of the alloy and some physical and mechanical properties are presented in tables 1 and 2 [45].

Samples of a flat plate shape (figure 2(a)) with a size of 25×50×1.5 mm and a cylindrical shape (figure 2(b)) with a diameter of 25 mm and a height of 40 mm were prepared for coating deposition.

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The preliminary surface preparation of the samples before spraying consisted of 60 Grit sandblasting with aluminum oxide to remove the surface oxide layer and increase the surface roughness, followed by degreasing with a solvent (acetone). The sample surface roughness after sandblasting ranged from 120 μm to 160 μm. The same grit blasting conditions are recommended for the practical application of cold spraying of protective and restorative coatings on real parts. The time after completion of surface preparation and coating spraying was no more than twenty minutes. Low-pressure cold spraying machine DYMET-405 was used for spraying coatings (at National Aerospace University 'Kharkiv Aviation Institute', Kharkiv, Ukraine). A photo of the DYMET-405 machine is shown in figure 3.

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The first step in experiment design is to determine the process parameters (independent variables or factors) that have the greatest impact on the coating characteristics. It is well known that the coating quality and deposition efficiency depend on a large number of operating parameters and their complex impact. According to the results of the literature analysis and previous investigations conducted in our laboratory, the main parameters of cold spraying, which largely depend on the adhesion strength and deposition efficiency (response variables), there is the initial gas temperature at the nozzle inlet, powder feed rate, and stand-off distance. These parameters affect the heating and acceleration of particles, their flattening upon contact with the substrate, and, as a result, ensure their adhesion strength to the latter with the formation of a certain set of physical and mechanical characteristics.

The solution of the optimization problem begins with the selection of the experimental area. This area is selected based on the analysis of predetermined information. The main levels and intervals of their variation are determined in the experimental area. The main or zero level of a factor is its value taken as the initial value in the experiment design. The main levels are selected so that their combination corresponds to the value of the optimization parameter, which is as close to optimal as possible. Each combination of factor levels is a multidimensional point in the factor space. A factor variation interval—symmetrical to the main level—was selected to determine the upper and lower levels for each factor.

Table 3 shows the investigated parameters of the cold spraying and their levels.

To reduce the time and cost of conducting experiments, the of experimental design methodology was used to obtain the necessary information about the effect of the investigated process parameters on the adhesion strength and deposition efficiency of coatings. An effective experiment requires a scientific approach to its design. In the practice of optimizing the spraying parameters of thermal spray coatings, the response surface methodology (RSM) is widely used with the use of a corresponding design of experiment (DoE). RSM is a tool for studying the influence between several independent variables and one or more response variables, followed by the development of mathematical models, optimization of initial factors, and construction of graphical models of the results obtained for a visual representation of the dependences of the objective function on input parameters [46].

In this research, the responses are functions of the initial gas temperature at the nozzle inlet T₀ (X₁), powder feed rate G_p (X₂), and stand-off distance SoD (X₃), and can be represented as follows

$$\begin{eqnarray}&&{Responses}=f\left({T}_{0},{G}_{p},{SoD}\right).\end{eqnarray} \tag{ 1 }$$

Three levels were used for the studied parameters: low (–1), main (0), and high (+1). A second-order polynomial is used to describe dependencies in the response surface methodology and predict the optimization parameter from the input parameters. For an experiment with three input parameters (X₁, X₂, X₃), the regression can be determined by the equation (2)

$$\begin{eqnarray}&&y\left({x}_{1}{x}_{2}{x}_{3}\right)={\beta }_{0}+{\beta }_{1}{x}_{1}+{\beta }_{2}{x}_{2}+{\beta }_{3}{x}_{3}{+\beta }_{12}{x}_{1}{x}_{2}+{\beta }_{13}{x}_{1}{x}_{3}+{\beta }_{23}{x}_{2}{x}_{3}+{\beta }_{11}{x}_{1}^{2}+{\beta }_{22}{x}_{2}^{2}+{\beta }_{33}{x}_{3}^{2},\end{eqnarray} \tag{ 2 }$$

where β₀ is constant; β₁, β₂, β₃ are coefficients of variables X₁, X₂, and X₃, respectively; β₁₁, β₁₂, β₁₃ are coefficients of squares of variables X₁, X₂, and X₃, respectively; β₂₁, β₂₂, β₂₃ are interaction coefficients of variables X₁, X₂, and X₃, respectively.

Taking into account the investigated parameters, variables X₁, X₂, and X₃ can be replaced and the equation (2) can be written as follows

$$\begin{eqnarray*}&&y={\beta }_{0}+{\beta }_{1}\left({T}_{0}\right)+{\beta }_{2}\left({G}_{p}\right)+{\beta }_{3}\left({SoD}\right){+\beta }_{12}\left({T}_{0}{G}_{p}\right)+{\beta }_{13}\left({T}_{0}{SoD}\right)\end{eqnarray*}$$

$$\begin{eqnarray}&&+\,{\beta }_{23}\left({SoD}{G}_{p}\right)+{\beta }_{11}\left({T}_{0}^{2}\right)+{\beta }_{22}\left({G}_{p}^{2}\right)+{\beta }_{33}\left({{SoD}}^{2}\right).\end{eqnarray} \tag{ 3 }$$

As can be seen from the equation, the model includes the influence of the main factors and their interaction. In the research, the central composite design (CCD) was used in the experiment design. In this design, 6 'star' points with coordinates (+α; 0; 0), (–α; 0; 0), (0; +α; 0), (0; –α; 0), (0; 0; +α), and (0; 0; –α), and a number of n₀ points in the design center are added to the full factorial experiment 2³ for three factors. The value of the 'star' arm is α = 1 (face centered-CCD), and the number of parallel runs in the design center is n = 6 (figure 4) [47]. Thus, the total number of runs is 20, which is sufficient to estimate the linear, quadratic, and two-factor effects of variable parameters on the coating adhesion strength and deposition efficiency.

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Statistical planning of the experiment will allow for obtaining the necessary data, applying statistical methods for their analysis, and drawing correct and objective conclusions. Table 4 shows the design matrix consisting of 20 experiments with encoded and initial values.

An experiment was conducted after selecting the experiment design, main levels, and intervals for varying factors. Each matrix row (see table 4) is the spraying mode for a separate run. In accordance with the experimental design, a coating of three samples was sprayed for each combination of spraying modes, followed by determining the average value of the response parameter. Experiments were performed in any order to prevent the occurrence of systematic error in the experiment (the order of runs is shown in the second column of table 4). The parameters of the cold spraying, which did not change during all runs, are as follows: nozzle traverse speed—20 mm s⁻¹, gas inlet pressure—0.9 MPa, and number of passes—2.

The adhesion strength of the obtained coatings was measured in accordance with ASTM 633 guidelines [48]. According to the experimental design, coatings with a thickness of approximately 350 μm were sprayed on the prepared cylindrical samples (figure 1(a)). For bonding the coated sample and the uncoated counter-sample, it was used VK-9 OST V 84-2081-83 adhesive, the bonding strength of which was determined before the tests on the uncoated samples and averaged 38.8 MPa. After adhering to the tensile machine, a tensile force was applied to one of the samples, the maximum value of which was recorded at the moment of the beginning of the bond failure. Based on the known values of the maximum applied load F and the contact area S, the bonding force was calculated as shown in equation (4)

$$\begin{eqnarray}&&\sigma =\frac{F}{S}.\end{eqnarray} \tag{ 4 }$$

The deposition efficiency (DE) was determined using the following method [39]. The sample was weighed before coating deposition (m₀) and coated sample (m₁). Next, the mass of powder consumed for coating deposition (m_p), as the difference between the initial mass of the powder loaded into the powder feeder (m_p0), and the mass of powder that remains therein and the powder feeder pipe after the spraying process is complete (m_p1). That means

$$\begin{eqnarray}&&{DE}=\frac{{m}_{1}-{m}_{0}}{{m}_{p}}100 \% =\frac{{m}_{1}-{m}_{0}}{{m}_{p0}-{m}_{p1}}100 \% .\end{eqnarray} \tag{ 5 }$$

Loading of a certain mass of powder into the powder feeder hopper before each new experiment was performed after cleaning it and the powder feeder pipes to the nozzle. Spraying was carried out in two passes at a powder feed rate of G_p of 0.2, 0.5, and 0.8 g s⁻¹, the value of which was set on the spraying machine. The powder was fed manually after reaching the set operating gas temperature.

The beginning of the spray gun movement was the moment when the beginning of coating growth on the substrate was visually fixed. After two passes, the powder supply was completed, the electric gas heater was powered off, and then the gas supply was turned off. Powder residues in the powder feeder hopper and feeder pipe to the nozzle were collected for further weighing.

Ni+Al₂O₃ powder in accordance with the developed experiment design was deposited on prepared samples made of VT3-1 titanium alloy using a DYMET-405 low-pressure cold spraying machine. Sample of the microstructure of the deposited coating is shown in figure 5, and the results of calculations of adhesion strength and deposition efficiency are shown in table 4.

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Regression coefficients are calculated in the statistical data processing software Stat-Ease 360. The significance of each of the coefficients is estimated using the Student's t-test and p-values given in tables 5 and 6.

The Model F-value of 242.39 implies the model is significant. There is only a 0,01% chance that an F-value this large could occur due to noise. P-values less than 0,05 indicate model terms are significant. For coating adhesion, it can be seen that A, B, C, A², B², C² are significant model terms. Values greater than 0.1000 indicate the model terms are not significant and they were not included in the model. The Lack of Fit F-value of 1,47 implies the Lack of Fit is not significant relative to the pure error. There is a 34,84% chance that a Lack of Fit F-value this large could occur due to noise. Non-significant lack of fit is good—we want the model to fit.

In table 6 the Model F-value of 126,99 implies the model is significant. There is only a 0,01% chance that an F-value this large could occur due to noise. The same as for coating adhesion results, P-values less than 0,05 indicate model terms are significant. In this case, A, B, C, AB, AC, BC, B², C² are significant model terms. Values greater than 0.1 indicate the model terms are not significant. The Lack of Fit F-value of 0,40 implies the Lack of Fit is not significant relative to the pure error. There is an 84,98% chance that a Lack of Fit F-value this large could occur due to noise. Non-significant lack of fit is good—we want the model to fit.

After determining significant values (with a confidence level of 95%), empirical dependences were obtained to predict the adhesion strength and deposition efficiency.

For the adhesion strength of coatings:

$$\begin{eqnarray*}&&{Adhesio}{n}^{1.75}\left({MPa}\right)=-9884.692+36.49\left({T}_{0}\right)+510.285\left({G}_{p}\right)+73.265\left({SoD}\right)\end{eqnarray*}$$

$$\begin{eqnarray}&&\,-\,0.034{\left({T}_{0}\right)}^{2}-437.295{\left({G}_{p}\right)}^{2}-3.285{\left({SoD}\right)}^{2}.\end{eqnarray} \tag{ 6 }$$

For the deposition efficiency (DE):

$$\begin{eqnarray*}&&\frac{1}{\sqrt{\left({DE}+1.9\right)}}\left( \% \right)=0.4302-0.0002\left({T}_{0}\right)-0.2251\left({G}_{p}\right)-0.0096\left({SoD}\right)\end{eqnarray*}$$

$$\begin{eqnarray*}&&\,+\,0.0001\left({T}_{0}{G}_{p}\right)-9.3992\cdot {10}^{-6}\left({T}_{0}{SoD}\right)+0.0029\left({T}_{p}{SoD}\right)\end{eqnarray*}$$

$$\begin{eqnarray}&&\,+\,0.0796{{(G}_{p})}^{2}+0.0006{\left({SoD}\right)}^{2}.\end{eqnarray} \tag{ 7 }$$

The equations (6) and (7) in terms of actual factors can be used to make predictions about the responses for given levels of each factor. Here, the levels should be specified in the original units for each factor. These equations should not be used to determine the relative impact of each factor because the coefficients are scaled to accommodate the units of each factor and the intercept is not at the center of the design space.

The value of the R² determination coefficient for adhesion strength and deposition efficiency amounted to 0.9911 and 0.9893, respectively. This means that 99.11% and 98.93% of experimental results are described by the above empirical equations (6) and (7), respectively. R² value tends to be 1.0, which means high accuracy of the obtained models. The normal probability plot for the responses under study is shown in figure 6. The graphs show that the regression residuals for coating adhesion strength and deposition efficiency fall on a straight line, which means a normal error distribution.

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Scatter plots of experimental and calculated data are shown in figure 7.

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The graph shows that the actual and calculated results for both adhesion strength and deposition efficiency are closely related. Based on the results obtained, it can be concluded that the developed empirical dependences can be used to predict the adhesion strength and coating deposition efficiency from the gas temperature at the nozzle inlet, powder feed rate, and stand-off distance in the investigated ranges of values.

Using the obtained equations (6) and (7), three-dimensional graphs of the dependence of the adhesion strength and deposition efficiency of nickel coatings from a Ni+Al₂O₃ powder mixture were developed as a function of the gas temperature at the nozzle inlet, powder flow rate, and stand-off distance in the investigated ranges of values (see table 3), which are shown in figures 8 and 9. Contour diagrams are provided to facilitate visual search for optimal response values. Using the obtained response contours, it can be determined the value of the objective function (adhesion strength and deposition efficiency) at any point within the specified input parameters.

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As can be seen from figure 8, the gas temperature at the nozzle inlet has the greatest effect on the adhesion strength of coatings. From the analysis of figure 8 and table 5, a significant increase in the adhesion strength of the Ni+Al₂O₃ coating is observed when the gas temperature increases from 450 °C to 550 °C.

As the temperature of the gas at the nozzle inlet increases, the gas flow rate in the channel of this nozzle increases, which directly affects the particle velocity in the flow. The relationship between the gas flow velocity and its temperature can be seen from equation (8) [49]

$$\begin{eqnarray}&&v=\sqrt{\gamma {RT}},\end{eqnarray} \tag{ 8 }$$

where γ is the ratio of the heat capacity of the gas, $\gamma =\frac{{C}_{p}}{{C}_{v}};$ C_v is heat capacity at constant volume; C_p is heat capacity at constant pressure, v is the gas velocity in the nozzle; R is the gas constant; T is the gas temperature at the nozzle inlet.

In addition, it can be observed an increase in the velocity of particles and in their temperature. Both factors—an increase in the velocity and temperature of the particle–substrate contact—have a significant impact on the growth of the coating adhesion strength. The effect of particle temperature on adhesion can be explained by their higher intensity of deformation in contact with the substrate due to temperature softening. It is known that there is a certain relationship between the temperature of particles at the moment of collision with the substrate and the critical velocity—a decrease in the latter with an increase in the temperature of particles [13]:

$$\begin{eqnarray}&&{v}_{{crit}}=667-14\rho +0.08{T}_{m}+0.1{\sigma }_{{UTS}}-0.4{T}_{{P}_{i}},\end{eqnarray} \tag{ 8a }$$

where ${{\rm{v}}}_{{\rm{crit}}}$ is critical velocity; ${{\rm{T}}}_{{\rm{m}}}$ is the melting point of the powder material; ${\rho }$ is powder material density; ${\sigma }_{{UTS}}$ is the tensile strength of powder material; ${T}_{{P}_{i}}$ is the temperature of the powder particle in contact with the substrate.

The dependence of the critical velocity on the particle temperature was shown in [49]. Reducing the required critical velocity leads to higher values of the ratio of the particle velocity to the critical velocity, which, as shown in [50], positively affects the flattening ratio, improving the properties of the coating (reducing porosity, increasing microhardness, adhesion and cohesion strength, etc), increasing the deposition efficiency.

The tendency to increase the adhesion and cohesion strength with an increase in the temperature of the gas at the nozzle inlet during cold spraying is also confirmed in the works of many researchers [51–54].

The maximum adhesion strength of the coating was obtained at a stand-off distance of approximately 10 mm (see figure 8). Figure 8 shows that as the stand-off distance increases from 5 mm to 10 mm, the adhesion of coatings increases. However, a further increase in the distance to 15 mm leads to its reduction.

The stand-off distance affects the gas flow between the nozzle outlet section and the substrate surface. When the flow hits an obstacle, an area of increased pressure is formed on its surface, i.e. a bow shock. The thickness of this layer depends on the stand-off distance, i.e. the greater the distance from the nozzle outlet to the substrate, the smaller the thickness of the bow shock. Based on the results of computer simulations and experimental studies, various authors found that when particles pass through a bow shock, their inhibition is observed [31, 55]. For powder particles with a diameter of 5 μm, this is generally significant, which can even affect the change in their trajectory [56]. As the distance from the nozzle exit to the substrate increases from 5 mm to 10 mm, the time spent by particles in this flow increases, the speed and temperature of which continue to increase. This reduces the effect of the bow shock on the particle velocity in contact with the substrate. However, with a further increase in the stand-off distance and reaching a certain value, inhibition of particles in the flow and a drop in their temperature are observed, which affects the decrease in the adhesion strength of coatings.

The effect of powder feed rate on the adhesion strength of coatings is shown in figure 8. An increase in powder feed rate slightly affects the increase in coating adhesion. An increase in adhesion strength is observed with an increase in powder feed rate to a certain value, which equals 0.5 g s⁻¹ in this research. A further increase in powder feed rate has almost no effect on the adhesion strength of coatings to the substrate. An increase in the concentration of powder particles in the gas flow accordingly increases their interaction with each other, which in turn affects the process of their acceleration. Reducing the distance between particles increases the number of their collisions with each other and with the inner walls of the nozzle, reducing their velocity. Experimental studies on determining the distance between particles and the concentration of particles per unit volume are impossible; the results of numerical calculations are provided by the authors in [36, 57, 58].

Figure 9 shows the effect of gas temperature at the nozzle inlet, stand-off distance, and powder flow rate on the deposition efficiency of coatings made from the Ni+Al₂O₃ powder mixture. It can be seen from the figure that the gas temperature and powder feed rate have the greatest effect on this characteristic of low-pressure cold spraying. As the gas temperature increases, the percentage of adhered particles in the coating increases, i.e. an increase in the powder deposition efficiency is observed. Similar to the case of adhesion strength, the main reason for this is the increase in particle velocity in collision with the substrate as the gas velocity in the nozzle increases (see equation (1)). In addition, as the gas temperature increases, the temperature of the particles in this flow increases, which ensures their better plastic deformation when they collide with the substrate.

Figure 9 also shows the effect of the stand-off distance on deposition efficiency, namely the growth of the latter with an increase in the distance from 5 mm to 10 mm. The maximum deposition efficiency of the Ni+ Al₂O₃ powder mixture in this study was achieved at 28.1% at a stand-off distance of 10 mm. Further increase in the distance to 15 mm leads to a decrease in the deposition efficiency. This trend, as well as for the effect of the stand-off distance on the adhesion strength, is due to the inhibition of the flow and powder particles, respectively. When the distance is increased from 5 mm to 10 mm, the thickness of the bow shock on the substrate surface decreases when the flow hits it and its effect on the particle velocity.

Ni+ Al₂O₃ coatings demonstrated the best results in terms of ensuring maximum deposition efficiency at a powder feed rate of 0.8 g s⁻¹. Figure 9 shows the dependence of the powder deposition efficiency on its consumption. In the beginning, with an increase in powder feed rate, an increase in deposition efficiency is observed. This can be explained by an increase in the concentration of powder particles in the gas flow, which reduces the activation time of the surface before spraying. This study did not measure the surface activation time from the beginning of the interaction of the gas flow with powder particles and the surface to the beginning of the formation of the first coating layer. However, it was visually noticeable that with an increase in powder feed rate, the coating growth is faster. The relationship between powder feed rate and activation time is shown in [30, 37]

Increasing the powder feed rate to a certain value during low-pressure cold spraying affects the thickness of the coating layer in one pass of the nozzle, and therefore the deposition efficiency. In addition, it is believed that an increase in the concentration of particles in the flow leads to an increase in their concentration in the normal shock wave that occurs when a two-phase flow hits an obstacle (substrate surface) [57]. At too high a powder feed rate, powder particles that do not adhere and reflect off the surface greatly reduce the kinetic energy (velocity) of the flying particles, which negatively affects their deformation during collision and adhesion during coating formation. Conclusions about the trend of reducing the deposition efficiency with increasing powder feed rate during cold spraying due to a decrease in the velocity of incoming particles are made in the works of other researchers [37, 59]. And even if an increase in the deposition efficiency can be observed at a high powder feed rate, other parameters of the coating quality shall be carefully monitored, such as porosity, and microhardness, the value of which may deteriorate [39].

In order to find the optimal values of the nozzle inlet temperature, stand-off distance, and powder feed rate, which would ensure the maximum values of the adhesion strength and deposition efficiency of coatings from the Ni+ Al₂O₃ powder mixture, the multi-criteria optimization method was used. The optimization module in Stat-Ease 360 searches for the optimal combination of factors that will ensure simultaneous best results for each of the target functions (response). The program implements two methods for finding optimal values, i.e. numerical and graphical. In multi-criteria optimization, areas are found on the contour graph that simultaneously meets the critical properties of each individual response. Graphical optimization displays the range of acceptable response values in the factor plane. In numerical optimization, threshold values for each of the responses (upper and/or lower limit) that will be taken into account for this procedure can be specified, or a desired value of the response can be specified.

The purpose of the optimization process of this research is to ensure the maximum values of the target functions, i.e., adhesion strength and deposition efficiency. In addition, the minimum permissible lower limit of adhesion strength was specified at 15 MPA, which is a criterion for the practical use of the coating for part restoration. Figure 10 shows the results of numerical optimization of the spraying modes of the NI+ Al₂O₃ powder mixture in the Stat-Ease 360 software, taking into account the established criteria.

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As can be seen from figure 10, the calculated maximum values of coating adhesion strength of 34.78 MPa and deposition efficiency of 29.46% from the Ni+ Al₂O₃ powder mixture with 95% reliability can be obtained by setting the gas temperature at the nozzle inlet to 537 °C, the powder feed rate of 0.6 g s⁻¹, and the stand-off distance of 11 mm.

An important stage of the optimization process is to check the obtained empirical dependencies for the possibility of using them to predict the objective function, i.e., the degree of correspondence between the values of the predicted characteristic and the actual one. For this purpose, three additional experiments were performed with randomly selected spraying modes, which are shown in table 7.

Table 7 shows the results of experimental studies of the adhesion strength and deposition efficiency of coatings, as well as their calculated values obtained using empirical dependencies. From the analysis of the table, it can be concluded that the obtained dependences can be used to predict the adhesion strength and deposition efficiency in the studied operating mode ranges.