1. Introduction
Over recent years, the use of smart materials, such as shape-memory materials, has attracted special attention from researchers. In that sense, numerous studies have been performed in laboratories and real conditions to facilitate the application of smart alloys in the maritime industry as well.
Since being discovered in 1932 [
1], various families of alloys based on Cu, Al, Ni, Ti, and Fe have been used in different industries, such as medicine, transport, robotics, aviation, traffic, etc. [
2,
3,
4]. Among the many different alloy families, NiTi alloys are the most attractive. William Buehler and Frederick Wang [
5] recognised and described the shape memory effect on a nickel-titanium alloy (a NiTi alloy called nitinol) in 1962 [
2,
6].
Although the cost of NiTi alloys is high and they have very low fabrication, the main advantages of these alloys are in their application at temperatures from −100 °C to 100 °C, hysteresis up to 30 °C, and maximum recovery strain up to 8% [
7]. In order to reach the better thermo-mechanical characteristics of the NiTi alloy, different production processes can be used such as: casting processes, vacuum induction melting, vacuum arc remelting, electron beam melting, plasma arc melting, and electron beam melting as the most commonly used processes [
8].
During the corrosion process, metals are transformed into compounds that are most often found in nature. This process takes place as the result of the oxidation of metals and is reversed from the process of obtaining metals. During corrosion processes, the chemical composition of the material changes and the metal is decomposed completely or incompletely, or a layer of corrosion products is formed on its surface.
Due to the negative influence of the external, complex environmental factors in which metallic materials are usually used, there is a physical change in the metal that is normally detected as a decrease in the thickness and weight of the metal. External factors affect changes in the chemical structure of metals, which result in the occurrence of oxidation and a decrease in the percentage of less precious metals. By extending the time of exposure to negative environmental influences, corrosion processes intensify and changes in the physical form and chemical composition of the material become even more pronounced, manifesting as a reduction in volume and damage that destroys the material in depth.
Corrosion processes depend directly on the environment and operating conditions in which they are located and perform their functions. The more corrosive the environment and the more dynamic the processes, the faster the corrosion process will take place. In coastal and marine conditions, on the surface of the sea where the wet-dry cycles of seawater and atmosphere alternate, the effects of waves contribute to the acceleration of corrosive processes. Different chemical, physical, and biological parameters can accelerate the corrosion process in seawater and a coastal environment. Furthermore, humidity and air temperatures, wind and sea temperature, salinity, conductivity, and pressure are the most dominant influences that determine the course of corrosion processes [
4].
Corrosion is an electrochemical process that occurs on metallic materials in different environments. As a consequence of the corrosion process, different types of corrosion can occur such as general, inter-granular, pitting, galvanic, crevice, stress, cavitation corrosion, etc. [
5,
9]. Each of these forms of the corrosion process takes place by the spatial separation of the anode and cathode sites, the presence of oxidants (dissolved oxygen and negative ions), and the salt concentration. In that sense, degradation on the surface of materials caused by electrochemical corrosion results primarily in the separation of anode oxidation processes of metals from cathode sites (the reduction of dissolved oxygen or other corrosive agents).
Due to the increase in the time of exposure to the environment, the corrosion rate for a longer period of time usually also increases and the weight of the metal decreases. In previous research, corrosion processes have been viewed mainly as linear models but also as nonlinear models, in which corrosive processes depend on various factors, in which the time of exposure to the environment prevails. A linear model was developed in Guedas, Soares, and Garbatov [
10], while non-linear models are presented in Yamamoto and Ikegami [
11], Paik et al. [
12,
13,
14], Melchers [
15,
16], and in the studies of other researchers. As corrosive processes are very complex and are affected by many different factors and parameters of the environment in which the metal structures are located, some more modern corrosive models consider salinity, pH, temperature and seawater flow, dissolved oxygen content, sulphur pollution, and fouling as model parameters [
15,
17].
In that sense, this research relies on an experiment in the real conditions of the marine environment. The paper analyses the influences of different types of marine environment (the atmosphere, tide, and sea) and the time of exposure on the changes in the chemical composition of two different NiTi alloy surfaces. In relation to the type of environment, the research investigates the dependence of the corrosion depth (expressed in nm of alloy wear) on the change in the percentage of oxygen in the alloy during 6, 12, and 18 months of exposure.
3. Results
Statistical analysis was performed with the aim of establishing the dependence between the corrosion depth of the NiTi1 and NiTi2 alloys and the observed percentage of oxygen formed on the surfaces of the metal samples. Multivariate linear regression was used as a tool to build a statistical model that will predict the corrosion depth of the NiTi1 and NiTi2 alloys (measured in nm) accurately in three types of the marine environment as a function of the environmental exposure time and oxygen percentage formed on the surfaces of the samples. Oxygen amounts expressed as a percentage, together with the exposure time expressed in months were considered explanatory variables in each of the six formed regression models, while the corrosion depth played the role of the response variable.
The basic descriptive statistics, i.e., Mean, Standard Deviation (StD), minimum (Min), first quartile (Q1), median, third quartile (Q3), and maximum (Max), related to the formed empirical database for the NiTi1 samples are shown in
Table 2.
Table 2 shows the number of measured points on the samples (
n), the basic statistical values related to the percentage of oxygen, and the corresponding corrosion depth (expressed in nm) statistical values observed on the NiTi1 alloy for each considered environment.
Table 3 has an identical structure but shows the descriptive statistics of the formed empirical databases for NiTi2 alloy corrosive processes.
The Mean values of the measured oxygen concentration for the NiTi1 alloy show that the lowest percentage of developed oxygen was in the case of air environments, while approximately the same percentage of oxygen was present on the alloy surface in both the tidal and sea environments. The scattering of the percentage of oxygen was approximately the same in the air and sea environments while slightly less in the tidal environment. The developed corrosive processes had similar values in the air and tidal environments, while in the marine environment there was a significantly higher value of the measured depth of corrosion. The scatter values of corrosion depth were similar for air and tide, but the values for the sea environment were significantly higher.
From
Table 3, in the case of the NiTi2 alloy, it can be seen that the average values of the percentage of oxygen were the lowest for the air and that they showed growth when the tidal and sea environments were observed. The increasing trend of the measured corrosion depth average value was the same; it was the smallest for the air and then increased in the tidal and sea environments, but the average depth of corrosion was three times higher in the tidal environment, and five times higher in the marine environment. The values of the Standard Deviation followed these growth trends from the air environment and tidal environment to the highest values for the sea environment, both from the point of view of the observed oxygen values and the observed corrosion depth values. The largest scatter values were observed for the depth of corrosion in the tidal environment and the sea environment.
When comparing the mean oxygen percentage of the two alloys, it was noticed that the measured values for the NiTi2 alloy were higher in all three environments than the measured values of oxygen for the NiTi1 alloy. Compared to the detected mean corrosion depth, NiTi1 and NiTi2 alloys showed similar properties in the air and sea environments but differed significantly in the tidal environment. The average value of the corrosion depth was four times higher for the NiTi2 alloy compared to the NiTi1 alloy for the tidal environment. The variations in the values of the average percentage of oxygen, as well as the average measured depth of corrosion in the air environment, were similar for both alloys, but differences were observed for the tidal environment as well as the sea environment. In both environments and for both measured Mean values of the observed values, the NiTi2 alloy showed significantly higher scatter values.
Figure 4 shows the dependence of the corrosion depth of the NiTi1 and NiTi2 alloys on oxygen percentage and the time of exposure to the environment influence graphically. More precisely,
Figure 4 visualises the empirical databases formed for the alloys NiTi1 and NiTi2. The data, as well as the belonging graphical representations, are grouped with respect to the three types of marine environment. The corrosion depth values are shown on a colour scale, whereby the lowest values are blue, while the highest values are shown in red. In all images, the corrosion rate is shown as a function of two independent variables (oxygen percentage and exposure time expressed in months) that are the basis for the following regression models.
Figure 5 shows the results of a correlation analysis between the corrosion depth and independent variables. The correlation analysis is presented in the form of a correlation matrix for both observed alloys in each of the three seawater environments. Pearson’s correlation coefficient [
27] is used in this paper as one of the most common correlation coefficients.
According to
Figure 5a–f, in the three types of the environment examined and for both alloys, only positive correlation effects occurred. In the correlation matrices shown in
Figure 5, the degrees of correlation are further highlighted by the intensity of the blue colour assigned to each cell of the matrix. As expected, both alloys showed tendencies to increase the depth of corrosion in all observed types of the marine environment (air, tide, sea), when taking into account the increase in the duration of exposure of the alloy to the environment and the percentage of oxygen formed.
Using a generalised regression model with the form shown in Formula (1), concrete models were formed by applying it to the empirical databases. Completely new and undamaged samples were used in the experiment so the development of the model started from the initial assumption that in the zero month the corrosion depth, as well as the percentage of oxygen, was equal to zero for each sample subjected to the experiment. Therefore, the value of the intercept parameter
in the regression model was also set to zero. In the modelling process, the corrosion depth was observed as a dependent quantity. The notations
,
, and
, were introduced in order to differentiate the regression models associated with each observed alloy and the seawater environment for the NiTi1 alloy. The same notation was used for the NiTi2 alloy, and the obtained regression models were noted as notations
,
, and
. The independent variables were denoted as x
1, x
2, and x
3, respectively, in each regression model formed. These three variables are explanatory variables which, in the presented regression models, are respectively associated with the time elapsed since the beginning of the experiment, the percentage of oxygen in the alloy, and the simultaneous interaction of oxygen and elapsed time (obtained by multiplying these two values). The coefficients of the explanatory variables in the regression models are denoted by
,
, and
, respectively. The results of regression analysis for the NiTi1 alloy are shown in
Table 4, while the results of the regression analysis obtained for the NiTi2 alloy are presented in
Table 5.
The significance level in this regression analysis was set to 95%, or, more precisely, a value of 0.05 was taken as the
parameter. Comparing the
parameters and the corresponding
p-values in
Table 4 and
Table 5, it can be concluded that all three observed independent model variables are significant for the air and tidal environments for both observed NiTi alloys. The values of the coefficients of the independent variables are shown in the third column of
Table 4 and
Table 5.
For both NiTi alloys in the sea environment, it is notable that oxygen does not have a statistically significant effect on the corrosive processes. This indicates the need for further study of the corrosive regression model associated with NiTi alloys in a marine environment. It is evident that complex corrosive processes in seawater are influenced by many additional factors and it is necessary to perform additional analyses to determine which environmental parameters need to be included in regression models.
The resulting linear regression models describing the depth of corrosion for the NiTi1 alloy with the coefficients listed in
Table 4 are shown in Formulas (2)–(4). Regression models for the corrosion depth of the NiTi1 alloy in air, tide, and sea environments, are presented respectively.
The regression models for the corrosion depth of the NiTi2 alloy influenced by air, tide, and sea environments are presented in Formulas (5)–(7). These models were obtained by applying the coefficient values from
Table 5 in the general regression model shown in (1).
The coefficient of determination, i.e., R2, was used to estimate the goodness of fit for the six regression models represented by expressions (2)–(7). The corresponding values of R2 for the three formed regression models (, , and ) equal 0.9595, 0.9619 and 0.9626, respectively. The calculated coefficients of determination for the NiTi2 alloy and corresponding linear regression models in the three observed seawater environments (, , and ) are 0.9782, 0.9544 and 0.9556, respectively.
As a statistical tool for hypothesis testing, analysis of variance (ANOVA) was used to check if there was a significant linear relationship [
28] between the corrosion depth and the chosen explanatory variables. The obtained linear regression models (2)–(7) were verified by an ANOVA test with a 95% confidence level. The ANOVA results for regression models (2)–(4) are summarised in
Table 6. The results of hypothesis testing by ANOVA in the case of the NiTi2 alloy are shown in
Table 7.
Table 6 and
Table 7 show the Degrees of Freedom (DF), Adjusted Sums of Squares (AdjSS), and Adjusted Mean Square (AdjMS) for the regression model, error, and the total.
To determine whether models that do not contain explanatory variables describe empirical data better than the formed regression models [
29], a statistical test known as the F-test was used. From
Table 6 and
Table 7 it is evident that, for both observed NiTi alloys and each observed seawater environment, the formed linear regression models describe the behaviour of the alloys well from the point of view of corrosion depth. More precisely, the models represented by Formulas (2)–(7) can be used to predict future corrosion depth values of NiTi1 and NiTi2 alloys as well as to estimate the corrosion depth of these alloys based on the known values of the explanatory variables. This conclusion is in full agreement with the conclusion obtained when observing the previously calculated values of R
2.
One of the techniques for examining the quality of a regression model is a graphical representation of the normal residual probability [
30]. A normal residual plot is shown in
Figure 6 for both observed NiTi alloys and all three marine environment types. It is noticeable that the analysed regression model describes corrosion depth adequately as the independent variable. Namely, the residual plot indicates the fact that the residuals followed the normal distribution. Therefore, it can be concluded that there were no deviations, unexpected behaviour, or evident existence of unidentified explanatory variables in the formed regression models.
A general conclusion can be drawn based on the previously described results of statistical analysis. The functional dependence of the corrosion depth as a response variable described by models (2)–(7) was determined adequately. As a consequence of this result, the formed regression models (2)–(7) can predict future values of corrosion depth as a function of the three observed explanatory variables adequately. By applying these models, it is possible to estimate the depth of corrosion if the values of all three independent variables are known. A graphical representation of the estimated values of corrosion depth depending on the type of seawater environment is shown in
Figure 7,
Figure 8 and
Figure 9. Each figure shows one of the three observed seawater environments. In these figures, the corrosion depth values are shown using a scale where blue shades illustrate the lowest values while red shades illustrate the highest corrosion depth values. In each of the figures, the illustration on the left refers to the NiTi1 alloy, while the illustration on the right is associated with the NiTi2 alloy. Based on the graphical representation of the estimated values of corrosion depth, there are differences between the corrosive behaviour of the observed NiTi alloys depending on their exposure to the seawater environment but also the different behaviours of the NiTi1 and NiTi2 alloys when considering the influence of the explanatory variables in the observed environment.
Since the statistical analysis indicated the fact that, under the influence of the sea environment, oxygen cannot be considered a statistically significant explanatory variable, the regression analysis for corrosion depth was repeated and, in this case, oxygen was eliminated from consideration. The results of the obtained regression analysis can be represented by the following formulas:
The corresponding R2 values are 0.9638 and 0.9552 for the NiTi1 and NiTi2 alloy, respectively, showing that these linear models explain 96.38% and 95.52%, respectively, of the corrosion depth variable variation for both alloys.
4. Discussion
The definition of the marine environment represents the interconnected body of saltwater and the coastal area. In this environment, the movement of seawater due to the gravitational force of the moon as tides, the action of winds as waves, and the changes in air pressure and temperature differences that cause sea currents, etc., are crucial. Thus, corrosion tests were performed in a real environment where the samples were affected by influences arising from the marine environment and not from simulations in the laboratory where it is not possible to create conditions that could ensure validity. The research in this paper is based on the examination of the corrosive behaviour of two different NiTi alloys (different technological production processes were applied) whose selected samples were subjected to the influence of three different seawater environments. Samples that were not treated with corrosive protective coatings and which initially did not have corrosive damage were exposed to the air, tide, and sea for 6, 12, and 18 months. Afterwards, the testing samples were subjected to SEM/FIB and EDX analysis which provided data on the resulting corrosion depth (expressed in nm) and the percentage of oxygen that developed on the surface of the samples. In a continuation of the research, statistical analysis based on multivariate linear regression was applied in order to examine the functional dependence of the corrosion depth of the observed samples’ surfaces on the determined percentage of oxygen, exposure time of the sample, and the simultaneous influence of these two variables. More precisely, the depth of corrosion was taken as the response variable, while the percentage of oxygen, the time of exposure to the environment (expressed in months), and the joint impact of these two variables were considered as explanatory variables. Regression analysis confirmed the assumption that the corrosion depth of both NiTi alloys can be represented adequately by the formed linear model and that the selected model parameters were statistically significant. Corrosion depth regression models of both NiTi alloys in all three seawater environments can be used to predict and estimate the future values of corrosion depth.
In the case of the NiTi1 alloy, the lowest percentage of oxygen was determined under the influence of air, slightly higher under the influence of the sea, and the highest was under the influence of the tide environment; however, the values in the tide and sea environments were approximately the same. In the case of the NiTi2 alloy, the percentage of oxygen increased progressively when the influence of air, tide, and sea was observed. The percentage of oxygen in all environments was higher on the NiTi2 alloy’s surface compared to NiTi1.
From the point of view of corrosion depth, the NiTi1 alloy surface behaved similarly in both air and tide environments, but in a sea environment, the corrosion depth was 6.49 times greater than that caused by air and 7.25 times greater than the depth of corrosion caused by the tide. In the case of the NiTi2 alloy’s surface, the depth of corrosion followed the trend of increasing the percentage of oxygen and was the lowest for air, then for the tide, and the highest for the sea. However, the values of corrosion depth were three times higher in the tide environment and five times higher in the sea environment compared to the air environment. Both alloys behaved similarly from the point of view of corrosion depth in both air and sea environments, while they had significantly different behaviours in tide environments which are considered the most complex due to their constant dynamics and changes in wet and dry periods. The average corrosion depth value was four times higher for the NiTi2 alloy compared to the NiTi1 alloy’s surface for the tidal environment. The resulting difference in the corrosion behaviour of the two NiTi alloys can be attributed to their different microstructures resulting from different production processes. Similar conclusions were reached by other authors in comparative studies [
31,
32] when they determined the functional properties of NiTi alloys depending on the production technique.
In this paper, we intended to monitor the corrosion behaviour of NiTi alloy in the marine environment over a long time period to get a more realistic picture of the impact of different marine environments on the degradation of the tested NiTi alloy. We were mostly interested in the differences in the chemical composition of the corrosion products which were precipitated on the surfaces. In this study, we wanted to determine separately in three places, the influence of all conditions of the marine environment on the corrosion process, and thus, on the resistance of a NiTi alloy. Monitoring the corrosion potential always gives very important information about corrosion processes. Therefore, the samples’ connections (resulting in the galvanic cells) will be used in our future studies. Extending the defined databases with additional measurements after the prolonged influence of the seawater environment as well as adding new explanatory variables to the regression models (such as the microstructure and phase composition of NiTi alloy) represent the future directions of this research. In order to avoid possible problems caused by highly collinearly dependent model variables, an additional application of principal component analysis, or partial least squares regression, is planned.