Frequency–Time Domain Analysis Based on Electrochemical Noise of Dual-Phase (DP) and Ferrite–Bainite (FB) Steels in Chloride Solutions for Automotive Applications

Abstract

The automotive industry uses high-strength (HS), low-alloy (HSLA) steels and advanced high-strength steels (AHSSs) to manufacture front and rear rails and safety posts, as well as the car body, suspension, and chassis components of cars. These steels can be exposed to corrosive environments, such as in countries where de-icing salts are used. This research aims to characterize the corrosion behavior of AHSSs based on electrochemical noise (EN) [dual-phase (DP) and ferrite–bainite (FB)]. At room temperature, the steels were immersed in NaCl, CaCl₂, and MgCl₂ solutions and were studied by frequency–time domain analysis using wavelet decomposition, Hilbert–Huang analysis, and recurrence plots (RPs) related to the corrosion process and noise impedance (Z_n). Optical microscopy (OM) was used to observe the microstructure of the tested samples. The results generally indicated that the main corrosion process is related to uniform corrosion. The corrosion behavior of AHSSs exposed to a NaCl solution could be related to the morphology of the phase constituents that are exposed to solutions with chlorides. The Zn results showed that DP780 presented a higher corrosion resistance with 918 Ω·cm²; meanwhile, FB780 presented 409 Ω·cm² when exposed to NaCl. Also, the corrosion mechanism of materials begins with a localized corrosion process spreading to all the surfaces, generating a uniform corrosion process after some exposition time.

1. Introduction

The use of advanced high-strength steels (AHSSs) continues to grow in the automotive sector, especially for body structures, suspension, and chassis components. This is the result of multiple characteristics, such as high formability that can be used to achieve external designs, lightness, sufficient rigidity for vehicle handling, high mechanical strength, the possibilities of production in large quantities, and the possibility of achieving all of this under strong economic limitations [1,2].

Advanced high-strength steels (AHSSs) are multi-phase metallic alloys that provide a higher balance of strength and ductility than conventional mild steels and high-strength, low-alloy (HSLA) steels [1]. Advanced steels have been used for decades in the automotive sector; some types of steel (see Figure 1) include complex-phase (CP), transformation-induced plasticity (TRIP), hot-formed (HF), ferrite–bainite (FB), and dual-phase (DP) steels, among others [2,3].

The Auto/Steel Association published a document called Advanced Applications of High Strength Steel: Design Guidelines and Stamping Process, and in this edition, 12 detailed cases of using these steels were addressed [4]. Most of the steel parts in this document were focused on DP steel. Details are given on the component geometry, part design, secondary metallurgical processes such as forming and stamping, and indicators of mechanical properties.

Dual-phase steels have a bainite or martensite microstructure in a ferrite matrix. The microstructure of DP steels can be improved by an intercritical heat treatment consisting of two steps: first, intercritical annealing is carried out, and second, rapid cooling is performed to promote the transformation of intercritical austenite into a hard phase (martensite) [5]. To increase the hardness of DP steel (228 to 317 HV), it is necessary to perform a tempering heat treatment between 740 and 820 °C and thus obtain a higher percentage of martensite phase [6].

FB steels have a bainite microstructure in a ferrite matrix, which is obtained by austenitizing FB steel at a temperature of 1100 °C for 20–60 min, followed by air-cooling and immersion in a salt bath at temperatures of 500–800 °C for a time of 1 h, and finishing the process in a type of rapid quenching called water quenching [7,8,9]. It has been reported that the phase content present in AHSSs influences their mechanical properties; the minimum elongation of ferritic–bainitic steels is approximately 22%, while for dual-phase steels, it is only 8% [10]. The mechanical strength of FB steels/AHSSs is obtained from the grain size refinement, the creation of precipitates, and the high dislocation density present in the bainite phase [11]. Therefore, DP steels will enable lighter automotive production; however, using a thinner gauge strip means that corrosion resistance becomes paramount [12].

In various investigations, it has been reported that the heat treatment of AHSSs increases their corrosion resistance when there is an increase in the austenitization temperature, and it will also depend on whether the heat treatment involves annealing or quenching, due to the formation of a martensitic phase in a ferrite matrix when using DP steels. Abedini et al. [13] mentioned that an increase does not improve the corrosion resistance of DP steels in the martensitic phase. On the other hand, biphase steels have a higher corrosion resistance than conventional steels due to the formation of galvanic pairs between martensite and ferrite or pearlite and ferrite, the latter being more susceptible. In FB steels, corrosion resistance is lower due to the creation of galvanic pairs, compared to DP steels. However, this resistance can be increased by grain size refinement in FB steel, which also improves its resistance to pitting and intergranular corrosion. However, the information in the literature is limited when addressing the corrosion mechanism and kinetics of AHSSs [14,15]. Some chemical elements, such as magnesium, do not help to improve corrosion resistance, whereas silicon does [15].

Figure 2 shows the banana diagrams for steel. The AHSS presents an elongation lower than 30%, supporting tensile stress until 1600 MPa, depending on the type of AHSS, compared with conventional steels that only support values lower than 700 MPa and present elongation until 65% [16].

Corrosion studies using conventional electrochemical techniques allow for determining the mechanism and kinetics of corrosion of metallic materials immersed in corrosive environments. Direct or alternating current electrochemical techniques usually disturb the system under study, such as potentiodynamic polarization (PP), linear polarization resistance (LPR), galvanodynamic polarization (GP), and electrochemical impedance spectroscopy (EIS). The alteration can be caused by external signals or experimentation conditions [17]. On the other hand, there is electrochemical noise (EN), whose measurements do not disturb the system under study, and current and voltage signal fluctuations occur on the electrode surface due to corrosion processes and not by an external signal, as in other corrosion techniques. Oxidation and reduction reactions are associated with transients as a response to stochastic processes (the breakdown and re-passivation of the passive layer) and deterministic processes (the nucleation and growth of pits) [18,19,20]

EN measurements allow for the obtaining of data that several methods can analyze. In the time domain (statistical analysis such as electrochemical noise resistance (R_n), Kurtosis, Skewness, Localization Index (L.I.)); in the frequency domain (spectral densities), and in the time–frequency domain (wavelet decomposition, Hilbert–Huang analysis, and recurrence plots (RP)) [21,22,23,24,25].

Several studies have indicated that the corrosion kinetics obtained from potentiodynamic tests in 3.5 wt. % NaCl solution at room temperature have shown that the corrosion rate decreases when there are ferrite–martensite phases (DP steels) compared to when there are ferrite–pearlite phases (carbon steel) due to the galvanic couple that is formed [13,26]. Montoyaand Nagiub found similar behavior for different dual-phase steels in an electrolyte with chloride ions [2,27]. Ikpeseni et al. indicated that dual-phase steel exhibits corrosion potentials between carbon and martensitic steel. The galvanic behavior between the martensite and ferrite phases favors selective corrosion in the ferrite phase when it is in a NaCl solution [28]. In AHSSs, galvanic corrosion is expected, due to each phase’s susceptibility [29]. On the other hand, a study of the corrosion rate in DP600 and DP800 steels immersed in a 3.5% NaCl solution at room temperature indicated that the corrosion resistance of DP800 steel was higher than DP600 steel, which could be corroborated in the Nyquist diagrams, and the potentiodynamic curves showed similar corrosion current densities of 12 and 17 µA/cm², respectively [30].

Other authors have found that DP steels with simple non-metallic inclusions, such as manganese sulfides, exhibit low resistance to pitting corrosion in chloride solutions [31,32,33].

This work aimed to study the corrosion behavior of AHSSs by electrochemical noise using a frequency–time domain analysis with noise impedance (Z_n), wavelet decomposition, Hilbert–Huang analysis, and recurrence plots (RP). The steels were tested in different chlorides, namely MgCl₂, NaCl, and CaCl₂. Microstructural characterization was performed by scanning electron microscopy. AHSSs are used in the automotive industry in environments with a lot of snow and de-icing salts.

2. Materials and Methods

2.1. Materials

The most common AHSS grades in commercial use today are ferritic–bainitic (FB) and dual-phase (DP) steels, which were given the designations FB780 (Yield Strength (YS) = 780 MPa) for a ferrite–bainite microstructure and DP780 (YS = 780 MPa) for a ferrite–martensite microstructure. The chemical compositions of the DP and FB AHSSs utilized in this study, as determined by X-ray fluorescence (Olympus DELTA XRF, Houston, TX, USA), are listed in

Table 1. Chemical composition of different AHSS (wt.%).

Element	DP780	FB780
Fe	Balance	Balance
C	0.10	0.09
Mn	2.61	1.73
Cr	0.420	0.640
Mo	-	0.006
Si	0.510	0.300
Ti	0.080	0.021

.

2.2. Microstructural Characterization

The samples of the AHSSs were prepared using the metallography technique [34], using different SiC grit papers until grades of 4000 were achieved. An alumina suspension of 0.1 µm was used to obtain the microstructure. A five-weight percent Nital solution was used to etch the samples chemically. Lastly, a secondary electron (SE) detector was used to investigate the samples’ microstructures by scanning electron microscopy (SEM, JEOL-JSM-5610LV, Tokyo, Japan) at a magnification of 2000×. In order to test for electrochemical corrosion, AHSS samples were ground to 800-grit SiC paper.

2.3. Electrochemical Technique

In order to assess the corrosion behavior of various AHSSs in 3.5 weight percent NaCl, 2 weight percent CaCl₂, and 2 weight percent MgCl₂ solutions, the EN measurements were conducted at room temperature using a standard three-electrode cell, as shown in Figure 3 [two nominally identical specimens were used as the working electrodes (WE1 and WE2) and a saturated calomel electrode as the reference electrode (RE), respectively]. The EN measurements were conducted following ASTM G199-09 [35,36,37,38]. A scanning rate of one data point per second was used to measure 1024 data points in each trial. A Gill-AC potentiostat/galvanostat/ZRA (Zero Resistance Ammeter) from ACM Instruments (Manchester, UK) was used to record the EN measurements simultaneously. The measurements were carried out twice.

A program developed in MATLAB 2018a software (Math Works, Natick, MA, USA) was used to handle data analysis obtained from EN measurements. Energy dispersion plots (EDPs) were used in the frequency–time domain analysis, where the original signal (with DC) was subjected to the orthogonal wavelet transform. In order to obtain the intrinsic functions (IMF) of the EN signal by an empirical decomposition method (EMD), EN analysis with the Hilbert–Huang transform (HHT) was required. Afterward, instantaneous frequencies were shown using a Hilbert spectrum.

3. Results and Discussion

3.1. Microstructure

As shown in Figure 4a, hard islands martensite, or bulk martensite, and a soft ferrite phase, or black grains, make up the microstructure of DP780 steel. The average size of the martensite islands ranges from 5 to 55%, and their relative volumes vary accordingly [38,39,40,41]. The microstructure of FB780 steel, as shown in Figure 4b, is made up of 30 and 45% fine bainite (bright grains) and soft ferrite matrix (dark grains), with an average grain size of approximately 4 and 2 µm, respectively [42,43,44,45].

3.1.1. Noise Impedance

The following formula expresses the noise impedance (Z_n), also known as spectral noise resistance [46,47]:

$${\mathrm{Z}}_{\mathrm{n}}=\sqrt{{\displaystyle \frac{{\psi }_V\left(f\right)}{{\psi }_I\left(f\right)}}}$$

(1)

The PSD division of potential and current square root calculates Z_n. The electrochemical noise impedance zero (Z_n0) is related to the corrosion resistance [47].

Figure 5 shows the noise impedance graphic (Z_n) of an DP780 alloy exposed in the different electrolytes. The alloy shows similar behavior in the three electrolytes. However, the alloy exposed to NaCl exhibited higher corrosion resistance with a Z_n0 value of 918 Ω·cm² (see

Table 2. Noise impedance parameters.

Alloy	Electrolyte	Zn0 (Ω·cm2)
DP780	NaCl	918 ± 24
	MgCl2	441 ± 16
	CaCl2	825 ± 28
FB780	NaCl	409 ± 21
	MgCl2	502 ± 14
	CaCl2	432 ± 17

). On the other hand, the alloy exposed to MgCl₂ presented the lowest Z_n0 value, with 441 Ω·cm², indicating that the alloy presents faster corrosion degradation in this medium. The DP780 obtained a value of 825 Ω·cm² exposed to CaCl₂, indicating that its corrosion resistance is like that of NaCl.

Figure 6 shows the noise impedance of the FB780 alloy in different electrolytes. In this graphic, the FB780 exposed to MgCl₂ presented a different behavior; at high frequencies, the Z_n values did not change; however, when exposed to NaCl and CaCl2, the Z_n values increased at high frequencies. That behavior can be related to a more uniform corrosion process that occurs on the surface. Furthermore, the FB780 exposed to MgCl₂ presented the highest Z_n0 values of FB780 samples with 502 Ω·cm². In contrast, when FB780 was exposed to NaCl, the Z_n0 value was 409 Ω·cm².

3.1.2. Time-Domain Analysis

Wavelets

A high–low filter is used with wavelets to separate a signal; high frequencies are called details and low frequencies are approximations [45]. An N number of the data’s total energy can be found using Equation (2) [47].

$$E=\sum _{n-1}^N{x}_n^2$$

(2)

Additionally, Equation (3) provides the energy fractions of the details and approximations:

$${ED}_j^d={\displaystyle \frac{1}{E}}\sum _{n=1}^N{d}_{j,n}^2 {ED}_j^s={\displaystyle \frac{1}{E}}\sum _{n=1}^N{s}_{j,n}^2$$

(3)

The total energy evaluated is equal to the energy of each wavelet transform component, according to Equation (4):

$$E={ED}_j^s\sum _{j=1}^j{ED}_j^d$$

(4)

The authors linked the initial crystal energy, D1 to D3, to metastable pitting. While D7 and D8 are connected to diffusion or regulated processes (uniform corrosion), the crystals D4 through D6 are linked to localized corrosion [48,49].

Figure 7 shows the energy dispersion plot for the DP780 alloy in the different electrolytes. DP780 showed energy accumulation from the D5 crystal, indicating that localized attacks were beginning. Moreover, the major amount of energy is accumulated in crystals D7 and D8, indicating that a uniform corrosion process is presented. This behavior is associated with a uniform pitting attack on the alloy surface.

Figure 8 shows the energy dispersion plot for the FB780 alloy in different electrolytes. FB780 exposed to NaCl and CaCl₂ presented a similar behavior to DP780 alloys, with energy accumulation at the D5 and D6 crystals, indicating that localized processes were occurring on metal surfaces. In addition, the energy accumulation at the D7 and D8 crystals is associated with uniform corrosion provoked by a uniform pitting distribution.

Recurrence Plots and Hilbert–Huang Transform

Recurrence plots are a useful tool for non-linear system analysis. Various methodologies must be used to assess the type and progress of corrosion on the surface because corrosion is a chaotic and non-linear system. An effective method for obtaining that analysis is RPs.

Recurrence plots are time-function-realized, two-dimensional graphs. The path of x_i in R_m, where m is the spatial dimension, is given to I and j at a time interval of ε. The times t_i and t_j from two dimensions are displayed as a two-dimensional square matrix by the RP. The matrix is displayed in Equation (5) [50,51,52,53]:

$$R_{ij}\left(\epsilon \right)=\mathsf{\Theta } \left(\epsilon -‖{\overrightarrow{x}}_i-{\overrightarrow{x}}_j‖\right), i,j=1,\dots , N$$

(5)

where ||.|| is the norm (Euclidean, maximum, or Manhattan), μ is the distance umbral, Θ(x) is the Heaviside function, and N is the number of data or points x_i. Equation (4) illustrates the recurrence rate, which determines the recurrence density:

$$RR\left(\epsilon \right)={\displaystyle \frac{1}{N^2}}\sum _{i, j=1}^N{R}_{i, j}$$

(6)

In an umbral zone, the recurrence rate (RR) indicates the likelihood of reconstructing a single track.

The diagonal lines of the RP show the system’s determinism and how the trajectory has changed over time in the phase space. The determinism (DET) is computed using the following equation:

$$DET={\displaystyle \frac{{\sum }_{l=l_{min}}^{N}l p\left(l\right)}{\sum _{l=1}^{N}l p\left(l\right)}}$$

(7)

Long diagonal lines on the RP are necessary for periodicity, and DET approaches unity; stochastic signals result in solitary dots, which restricts DET to values near zero. The average diagonal line length (L), another RQA metric associated with diagonal lines, is depicted as follows in Equation (8):

$$L={\displaystyle \frac{{\sum }_{l=l_{max}}^{N}l p\left(l\right)}{{\sum }_{l=lmin}^{N}p\left(l\right)}}$$

(8)

TT gives the average length of the vertical structures and indicates the average time the system spends in a given condition (as indicated by the number of recurrence points); see Equation (9).

$$TT={\displaystyle \frac{{\sum }_{v=v_{min}}^{N}v P\left(v\right)}{{\sum }_{v=vmin}^{N}P\left(v\right)}}$$

(9)

The electrochemical phenomenon that takes place throughout the corrosion process is described by the electrochemical noise (EN) technique. The Hilbert–Huang analysis is one methodology used to describe the EN method. This technique facilitates the frequency and timing of energy exchanges; the energy, known as instantaneous energy, is derived from the signal’s inherent function and is retrieved by signal decomposition [54,55,56]. Equation (10) explains the empirical method of decomposition (EMD):

$$x\left(t\right)=\sum _{i=1}^N{h}^{\left(i\right)}\left(t\right)+d\left(t\right)$$

(10)

where d(t) is the average of the trend at a low frequency of the time series x(t) and cannot be decomposed; h⁽ⁱ⁾(t) is the i-th term of IMF that is generated; these numbers must satisfy the conditions that the extreme and cross numbers are equal or differ by a maximum of 1 and that each point using the local maximum and minimum must be 0. The HHT is represented by Equation (11):

$$y_j\left(t\right)={\displaystyle \frac{1}{\pi }}p{\int }_{-\infty }^{\infty }{\displaystyle \frac{h_j\left(\tau \right)}{t-\tau }}d\tau$$

(11)

where p is connected to the Cauchy principle and is associated with an average of IMF; y_j(t) is the Hilbert transform, and IMF is represented by h_j.

Figure 6 shows the signal analysis results by RPs and HHT, comparing the results with optical microscopy. Figure 9a shows the graphics of DP780 exposed to NaCl, where the RP showed a high determinist behavior with a value of 0.9769 (see

Table 3. Recurrence plots parameters.

Alloy	Electrolyte	RR	DET	L	TT
DP780	NaCl	0.0562 ± 0.0002	0.9769 ± 0.001	5.06 ± 0.02	6.51 ± 0.05
	MgCl2	0.0592 ± 0.0001	0.9831 ± 0.0002	6.28 ± 0.09	8.16 ± 0.09
	CaCl2	0.0589 ± 0.0004	0.9844 ± 0.0004	5.04 ± 0.005	6.48 ± 0.07
FB780	NaCl	0.0593 ± 0.0002	0.987 ± 0.0008	6.26 ± 0.008	8.12 ± 0.05
	MgCl2	0.0823 ± 0.0001	0.9391 ± 0.0001	10.07 ± 0.03	13.92 ± 0.2
	CaCl2	0.06 ± 0.004	0.9142 ± 0.0001	4.17 ± 0.08	5.45 ± 0.03

) and RR of 0.0562. The RP presents some blue points in the graphics, indicating that a process occurs at that time. It can be related to the energy presented at middle frequencies in the Hilbert specter. That behavior is associated with localized corrosion; however, the high DET value (indicating a uniform process) can be related to the corrosion process in the material; a galvanic couple occurs in the superficies of dual-phase alloy. The dominance of uniform process that occurs in a surface is related to the corrosion process that consists of the diffusion of ions on the metal surface, that attacks in specific zones (the more susceptible phase), generating localized attacks (energy at middle frequencies and blue zones in RP) that generate a uniform pitting attack. Finally, the localized attack occurs in the ferrite phase. All the DP780 samples from Figure 9a–c show this behavior. Figure 9c shows a similar behavior with a high DET value (0.9844). It can be related to oxygen diffusion, so the microscopy shows a highly corroded area; however, when zoomed in to a microscopic level, it can be observed how the corrosion is occurring in preference zones (ferrite), being governed by the process described previously.

Figure 10 shows the results of FB780 alloy exposed to the different electrolytes. Figure 10a,c show similar behavior in DP780, indicating that a uniform process occurs on the surface. However, the uniform process from the surface will attack preferent zones, principally ferrite zones. For that reason, in the Hilbert specter, energy is accumulated at the beginning of the time period at low frequencies due to ion diffusion in the metal surface. However, after some exposure time, energy begins to appear at middle frequencies, indicating that localized processes are occurring, and due to the galvanic couple in ferrite zones, the attacks will be uniform after some time.

In contrast, Figure 10b shows a localized process; in this case, the value of RR increases to 0.0823 (in comparison with the other values between 0.05 and 0.06). Also, L and T’s values are higher than those obtained in the other samples (10.07 and 13.92); those values are related to the time of recurrence point occurrence. The recurrence points are related to the localized process, and a high value of these two parameters indicates the localization process.

4. Discussion

Two basic reactions dominate the corrosion process:

$$\mathrm{F}\mathrm{e}\to {\mathrm{F}\mathrm{e}}^{2+}+{2\mathrm{e}}^{-}$$

(12)

$${\mathrm{O}}_2+{2\mathrm{H}}_2\mathrm{O}+{4\mathrm{e}}^{-}\to {4\mathrm{O}\mathrm{H}}^{-}$$

(13)

Those reactions generate an active oxidation; when oxygen is diffused on the surface, the corrosion products generated are porous and do not give passivation to the surface. That facilitates the attack of Cl⁻ ions on the ferrite phase, generating a localized attack and avoiding the passivation of surface-generating pitting in the ferrite zone [57,58]. The following reactions govern the chemical reaction:

$${\mathrm{F}\mathrm{e}}^{2+}+{\mathrm{C}\mathrm{l}}^{-}\to {\mathrm{F}\mathrm{e}\mathrm{C}\mathrm{l}}_2$$

(14)

$${\mathrm{F}\mathrm{e}\mathrm{C}\mathrm{l}}_2+{\mathrm{H}}_2\mathrm{O}\to {\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_2+{2\mathrm{C}\mathrm{l}}^{-}$$

(15)

This alloy type’s corrosion process depends on the metal matrix’s metallurgical heterogeneities and the passive layer’s absence [58,59]. The presence of cathodic and anodic zones due to the difference in phases, grain variations, and impurities, and the lack of homogeneity is conducive to localized attacks. For these alloys, the phase difference generates galvanic corrosion [60].

The cathode role of bainite and martensite, due to the oxygen reception, made ferrite act as an anode due to the low presence of O₂, making it susceptible to OH⁻ and Cl⁻ attacks [61].

The dissolution process was generated in uniform form in ferrite, so the results of RPs and HHT presented high activity at low frequencies, which was conducive to initiating some localized attacks. Also, some authors consider that the corrosion process in this class of alloys is not only for the galvanic couple; it can be generated by an auto-corrosion of the only phase in double-phase steels [62,63,64].

The corrosion mechanism of DP780 and FB780 presented previously can be complemented with a uniform dissolution of the area when exposed to CaCl₂. This occurs due to the effect of the solution in ferrite, martensite, and bainite, generating a galvanic couple through an auto-corrosion process. The ferrite dissolution provoked active zones, and the uniformly distributed pitting was connected, generating a uniform dissolution of material, as shown in Figure 11 [64,65]. Therefore, the RPs and HHT showed high energy accumulation at middle and high frequencies due to localized processes (at middle and high frequencies) that provoke uniform attacks (at high frequencies).

The corrosion behavior of FB780 steel with a ferrite–bainite microstructure in CaCl₂ and MgCl₂ solutions presents localized corrosion, where the bainite phase dissolved faster than the ferrite phase because it acted as an anode. The iron carbides (green dots) acted as cathodes, as Figure 10a indicated. The disintegration process of ferrite–martensite DP780 steel in test solutions is depicted in Figure 10b. Because it functions as a cathode, martensite acts as a barrier to corrosion, and the continuous ferrite-phase matrix restricts the spread of corrosion.

In the case of ferrite–bainite DP, the microstructure influences the corrosion initiation of the Cr-Mo alloyed steel. Zones with low Cr-Mo content generates more crystal defects, decreasing the potential and increasing the susceptibility of microgalvanic corrosion. The ferrite phase works as a cathode for a higher Cr-Mo concentration, and the bainite is dissolved [65,66,67,68].

It is important to mention the aggression of Cl⁻ ions; in several research studies, different alloys have been exposed to this medium, demonstrating that Cl⁻ will attack the more susceptible zones, provoking localized corrosion in this part and generating a more aggressive attack. It can be observed with energy at the middle crystals in a wavelets analysis or at middle frequencies in the HHT studies [69,70,71,72,73]. For these alloys, the attacks began at the ferrite-phase or inclusion zones; authors such as Macdonald [73,74,75,76] describe this phenomenon with the point defect model, indicating that the passive layer generated on a material surface presents defects of cation and oxygen vacancies generating and annihilating the metal/film and film/solution interfaces, being a similar process to high-temperature oxidation. FB steel shows a higher corrosion kinetic due to the ferritic phase matrix; as it is more present in the material, the anode is higher, and corrosion resistance decreases.

Due to the non-linear signal characteristics, the analysis by methods such as wavelets, HHT, and RPs are crucial in these complex systems. This research showed that a non-conventional EN analysis is helpful to determine the attacks in galvanic couples, supporting wavelets with HHT and RPs methods. It is possible to analyze the changes in the corrosion process within the time, and not only with a scalar aspect, as in conventional methods. With the HHT and RPs methods, it was possible to determine the moment of pitting attack and nucleations. Also, when only localized corrosion was present, it could be identified with these methods [77,78,79,80,81,82,83,84,85,86,87,88,89].

However, with wavelets, HHT, and RPs, it is not possible to determine the corrosion resistance of a material; for that reason, it is important to complement with a noise impedance analysis (Z_n) due to the certainty of this analysis in determining the corrosion resistance. With this analysis, it was possible to determine the high corrosion resistance of DP780 in comparison to FB780 in the different electrolytes.

5. Conclusions

DP780 and FB780 presented a galvanic couple with an auto-corrosion process on the surface. The corrosion process is generated by an ion diffusion in the surface (a uniform process) that attacks specific zones such as ferrite, inclusions, or some defects (localized process) and mitigates the attack of Cl⁻ that dissolves the ferrite phase uniformly, connecting the pitting distributed in a uniform way and dissolving the surface.

Wavelets, RPs, and HHT help identify the corrosion process that occurs on the surface. The RPs help to find the recurrence zones where some localized attacks (galvanic coupling) occur; in this case, the blue zones of the RPs indicate the uniform attacks generated by the dissolution of the material.

The presence of inclusions influences the beginning of localized attacks.

The Z_n parameter showed that DP780 presented a higher corrosion resistance (918 Ω·cm² and 825 Ω·cm²) than FB780 exposed to NaCl and CaCl₂. However, DP780 presented a lower noise impedance when exposed to MgCl₂ (441 Ω·cm²).

Parameters such as L (diagonal line) and TT (the average length of the vertical structures) help determine the corrosion type on the material’s surface. A high value of these parameters is related in this research to localized corrosion, indicating the time associated with the change in one process, as presented with FB780 exposed to MgCl₂ (10.07 and 13.92). As the uniform process occurs constantly, the TT value is going to be lower (4.17–6.2).