Metal microstructure device corrosion rate prediction method based on characteristic grain boundary and application

Abstract

The invention discloses a metal microstructure device corrosion rate prediction method based on a characteristic grain boundary and application. The method comprises the following steps: establishing a theoretical calculation model of characteristic grain boundary proportion and strain size and a theoretical calculation model of corrosion rate and strain size by acquiring characteristic grain boundary proportion data and corrosion rate data of different pre-deformed samples; according to the characteristic grain boundary proportion of different areas in the plastic forming metal microstructure device, the strain size is reversely solved, and therefore corrosion resistance prediction of the plastic forming metal microstructure device in the target working condition environment is achieved. According to the method, a theoretical calculation model of the internal corrosion rate of the formed metal microstructure device is established on the basis of the evolution rule of the internal characteristic grain boundary proportion of the metal material in the plastic forming process, and the corrosion resistance of the plastic forming metal microstructure device under the specific target working condition can be accurately predicted; the method is widely applied to the fields of new energy, biomedicine, petrochemical engineering, marine equipment, transportation, aerospace and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of corrosion test and analysis of metal materials, and particularly relates to a method for predicting the corrosion rate of metal microstructure devices based on characteristic grain boundaries and its application. Background Art

[0002] Metal devices in the fields of new energy, biomedicine, petrochemical industry, marine equipment, transportation, and aerospace all face varying degrees of corrosion in their application environments. Accurately calculating and evaluating the corrosion rate of plastically formed metal microstructure devices is crucial for assessing their service life under specific working conditions and ensuring the safety of equipment and personnel. For plastically formed metal microstructure devices, their corrosion rate is not only affected by the external environment and working conditions, but also depends on their internal microstructure and its changes, especially the crystal defects inside the metal under deformation conditions, such as dislocation density, large-angle grain boundaries, small-angle grain boundaries, and low coincidence site lattice grain boundaries.

[0003] For plastically formed metal microstructure devices, not only external environmental factors need to be considered, but also the changes in the internal organizational structure caused by the plastic forming process. Existing corrosion test and analysis technologies generally have difficulty accurately measuring the corrosion rates of different regions inside plastically formed metal microstructure devices with small sizes and relatively complex structures. At the same time, due to the non-linear relationship between the changes in the metal microstructure and the corrosion rate during the plastic deformation process, it further increases the difficulty of analyzing and predicting the corrosion rate of metal microstructure devices.

[0004] Therefore, how to overcome the deficiencies of the existing corrosion performance test and evaluation methods for metal microstructure devices, propose an innovative corrosion rate calculation method based on the change laws of characteristic grain boundaries such as large-angle grain boundaries, small-angle grain boundaries, and low coincidence site lattice grain boundaries in the forming process of metal microstructure devices, achieve accurate prediction and analysis of the corrosion rates of different regions inside metal microstructure devices, and ultimately provide a basic theoretical basis for their material selection, design, and application has become a difficult problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0005] Aiming at the technical problems described in the background art, the present invention aims to provide a method for predicting the corrosion rate of metal microstructures based on characteristic grain boundaries and its application.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for predicting the corrosion rate of metal microstructure devices based on characteristic grain boundaries, comprising the following steps:

[0008] S1. Prepare pre-deformed specimens with different strain magnitudes from the blank of the metal microstructure device;

[0009] When preparing the specimens, the types of mechanical property specimens shall be determined according to the plastic forming method and characteristics of the metal microstructure device, and the sampling direction shall be selected according to the microscopic organizational structure characteristics of the blank of the metal microstructure device to ensure that the stress direction of the specimen during the mechanical property test conforms to the deformation law of the organizational structure during the forming process.

[0010] Specifically, the following methods are included:

[0011] Prepare one or more of tensile, compression or bending specimens according to the microscopic structure of the metal blank and the deformation characteristics of the formed metal microstructure device; measure the tensile, compression or bending and other mechanical properties corresponding to the metal blank before forming the metal microstructure device; prepare pre-deformed specimens of metal blanks with different strain magnitudes according to the test results of the tensile, compression or bending and other mechanical properties of the metal blank before forming.

[0012] More specifically, for the mechanical property tests such as tensile, compression or bending, one of them can be used alone, or two or three of them can be used comprehensively. The specific parameter selection for the mechanical property test refers to the ISO or ASTM standards.

[0013] S2. Obtain the characteristic grain boundary proportion data of different pre-deformed specimens;

[0014] In a specific embodiment of the present invention, the backscattered electron diffraction technique is used to detect and analyze the proportion of characteristic grain boundaries such as large-angle grain boundaries, small-angle grain boundaries or low-Σ (1≤Σ≤29) coincidence site lattice grain boundaries in different pre-deformed specimens.

[0015] S3. Establish a theoretical calculation model between the characteristic grain boundary proportion and the strain magnitude;

[0016] Preferably, the relationship between the characteristic grain boundary proportion and the strain magnitude is described by the Boltzmann model, specifically as shown in Equation (1):

[0017] f (ε) =(A1 - A2) / (1 + exp((ε - ε0) / dε)) + A2 (1)

[0018] In the formula, f (ε) is the proportion of characteristic grain boundaries such as large-angle grain boundaries, small-angle grain boundaries or coincidence site lattice grain boundaries inside the metal, ε is the true strain or engineering strain of the deformed metal, and A1, A2, ε0 and dε are fitting constants.

[0019] S4. Measure the corrosion rate data of different pre-deformed specimens in the target working condition environment;

[0020] In the corrosion test, it is more beneficial to use a corrosion system close to the target working condition environment to predict the corrosion situation of the metal microstructure device under actual working conditions.

[0021] In a specific embodiment of the present invention, an electrochemical test and analysis technique is adopted. In a three-electrode test system, the solution system and working conditions used during the corrosion test must be consistent with the actual working conditions of the metal microstructure device, and the corrosion rates of different pre-deformed specimens in the target working environment are measured.

[0022] S5. Establish a theoretical calculation model between the corrosion rate and the strain magnitude;

[0023] Preferably, the relationship between the corrosion rate and the strain magnitude is described by the Polynomial model, specifically as shown in Equation (2):

[0024] I (ε) = B0 + B1·ε + B2·ε 2 + B3·ε 3 +… (2)

[0025] In the formula, I (ε) is the corrosion rate inside the metal, ε is the true strain or engineering strain of the deformed metal, and B0, B1, B2, and B3 are fitting constants.

[0026] S6. According to the theoretical calculation model established in steps S3 and S5, establish a theoretical calculation model between the corrosion rate and the proportion of characteristic grain boundaries.

[0027] Preferably, the relationship between the corrosion rate and the proportion of characteristic grain boundaries is jointly described by the Polynomial model and the Boltzmann model.

[0028] Specifically, a theoretical calculation model between the proportion of characteristic grain boundaries and the corrosion rate inside the deformed metal can be established based on one or a combination of the proportion of large-angle grain boundaries, or small-angle grain boundaries, or low coincidence site lattice grain boundaries in different pre-deformed specimens. Generally, using multiple characteristic grain boundaries for modeling, the expected results are relatively more accurate, but the experimental and analysis costs are relatively higher.

[0029] If a theoretical calculation model is established based on one characteristic grain boundary, then:

[0030] According to Equation (1), Equation (3) is calculated:

[0031] ε = dε·ln((A1 - A2) / (f (ε) - A2)-1) + ε0 (3)

[0032] Substitute the solution result of ε into Equation (2) to obtain:

[0033] I (ε) = B0 + B1×[dε·ln((A1 - A2) / (f(ε) -A2)-1)+ε0]+B2×[dε·ln((A1 - A2) / (f (ε) -A2)-1)

[0034] +ε0] 2 +B3×[dε·ln((A1 - A2) / (f (ε) -A2)-1)+ε0] 3 (4)

[0035] In the above formula, ε is the true strain or engineering strain of the deformed metal, and f (ε) is the proportion of characteristic grain boundaries such as large-angle grain boundaries, small-angle grain boundaries or coincidence site lattice grain boundaries inside the metal, and I (ε) is the corrosion rate inside the metal, and A1, A2, ε0 and dε are fitting constants. B0, B1, B2 and B3 are fitting constants.

[0036] If a theoretical calculation model is established based on multiple characteristic grain boundaries, the corrosion rates of each characteristic grain boundary are calculated separately, and then the average value is taken to obtain the theoretical calculation model between the corrosion rate and the proportion of characteristic grain boundaries.

[0037] In a specific embodiment of the present invention, a theoretical calculation model between the proportion of small-angle grain boundaries and the strain magnitude in different pre-deformed specimens is established; in a specific embodiment of the present invention, a theoretical calculation model between the proportion of coincidence site lattice grain boundaries (Σ3 + Σ9 + Σ27) and the strain magnitude in different pre-deformed specimens is established; in a specific embodiment of the present invention, a theoretical calculation model between the proportion of small-angle grain boundaries + coincidence site lattice grain boundaries (Σ3 + Σ9 + Σ27) and the strain magnitude in different pre-deformed specimens is established.

[0038] S7. Detect and analyze the proportion of characteristic grain boundaries in different regions of the plastic formed metal microstructure device; use the theoretical calculation model obtained in step S6 to analyze and predict the corrosion resistance of the plastic formed metal microstructure device in the target working condition environment.

[0039] To reduce systematic errors, the same detection technique as that of the pre-deformed specimen is used for the proportion of characteristic grain boundaries of the plastic formed metal microstructure device, such as using backscattered electron diffraction technology.

[0040] Further, step S7 takes the proportion of characteristic grain boundaries such as large-angle grain boundaries, small-angle grain boundaries, or coincidence site lattice grain boundaries obtained by detection and analysis as input. First, it calculates the strain in the corresponding area of the metal microstructure according to the Boltzmann model, and then calculates the corrosion rate of this area according to the Polynomial model. According to the theoretical models adopted, the input value can be based on any one of the characteristic grain boundaries such as large-angle grain boundaries, small-angle grain boundaries, and coincidence site lattice grain boundaries, or it can be based on two or more of these characteristic grain boundaries.

[0041] As described in the background art of the present invention, the prior art cannot directly obtain the degree of deformation (strain magnitude) of a plastically formed metal microstructure device. The changes in the metal microstructure and corrosion rate during the plastic deformation process are non-linear. In addition, in addition to plastic deformation, other factors (such as heat treatment) can also cause changes in the internal microstructure such as characteristic grain boundaries. Without calibrating the degree of deformation (strain magnitude) - characteristic grain boundaries, it is easy to cause serious deviations in the prediction and judgment of the corrosion resistance of plastically formed metal microstructure devices. The present invention establishes two theoretical models: degree of deformation (strain magnitude) - characteristic grain boundaries, and degree of deformation (strain magnitude) - corrosion resistance. Then, the corrosion rate is calculated and predicted according to the above two models. That is, by establishing the relationship between the degree of deformation (strain magnitude) of the basic specimen and its corrosion resistance, and based on the changes in the characteristic grain boundaries, the reverse solution of the degree of deformation (strain magnitude) generated in different regions inside the plastically formed metal microstructure device is realized, so as to accurately predict the corrosion rate of different regions inside the metal microstructure device, and finally provide a basic theoretical basis for its material selection, design, and application, etc. Brief Description of the Drawings

[0042] The present invention will be further described below with reference to the drawings and embodiments.

[0043] Figure 1 is a schematic diagram of the principle and operation method for predicting the corrosion rate of the metal microstructure in the embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of the shape and size of the pre-deformed specimen of the present invention;

[0045] Figure 3 is the backscattered electron diffraction pattern of the pre-tensioned specimens with different strains of the present invention; among them, the strains of the specimens in Figures (a), (b), (c), (d), and (e) are 0, 0.1, 0.2, 0.3, and 0.4 respectively;

[0046] Figure 4 is a graph showing the variation law of the proportion of small-angle grain boundaries with strain of the present invention;

[0047] Figure 5It is the variation diagram of the coincidence site lattice grain boundary ratio of the present invention with respect to strain;

[0048] Figure 6 It is the variation diagram of the corrosion rate of the pre-stretched stainless steel thin sheet of the present invention with respect to strain;

[0049] Figure 7 It is the cross-sectional view of the stamped stainless steel bipolar plate of the present invention; wherein, a, b, c, d, and e are the corrosion calculation and prediction regions of the stainless steel bipolar plate;

[0050] Figure 8 It is the small-angle grain boundary ratio diagram of different regions of the stamped stainless steel bipolar plate of the present invention;

[0051] Figure 9 It is the coincidence site lattice grain boundary ratio diagram of different regions of the stamped stainless steel bipolar plate of the present invention. Detailed implementation manners

[0052] Now, the present invention will be further described in detail with reference to the accompanying drawings. The specific embodiments are only used to explain the present invention and are not used to limit the present invention. The present invention can also be implemented or applied through other different specific implementation manners. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0053] The metal material in the specific embodiment is ultra-thin stainless steel with a thickness of 0.1 mm, the formed microstructural device is a stainless steel bipolar plate for fuel cells, and the forming process is stamping. However, it should be understood that the materials applicable to the metal microstructural devices of the present invention include ferrous and non-ferrous metal materials that can be plastically formed; the forming processes of the present invention are cold and hot plastic forming manufacturing processes, such as cold stamping and hot stamping, cold rolling and hot rolling, cold extrusion and hot extrusion, etc.; the application fields of the formed metal microstructural devices include not only new energy technology fields such as fuel cells, but also fields such as biomedicine, petrochemical industry, marine equipment, transportation, and aerospace.

[0054] The operation flow of the corrosion rate prediction method for the metal microstructural device in the following specific embodiment is shown in Figure 1 .

[0055] Example 1:

[0056] A corrosion rate prediction method for a stamped stainless steel bipolar plate based on small-angle grain boundaries, comprising the following steps:

[0057] S1. Adopt wire cutting to prepare tensile specimens along the rolling direction of the ultra-thin stainless steel blank, and the shape and size of the specimens are as Figure 2 shown;

[0058] S2. Conduct tensile property tests on ultra-thin stainless steel specimens on a universal testing machine. The specimen preparation and testing processes are both implemented according to the ISO 6892-1:2019 standard. Tensile strain rate: 0.001 / s; tensile test temperature: 25 °C;

[0059] S3. According to the tensile property test results of the ultra-thin stainless steel billets before forming, prepare pre-tensioned deformed stainless steel specimens with strain magnitudes of 0, 0.1, 0.2, 0.3, and 0.4 respectively, for detecting the small-angle grain boundary ratio and corrosion resistance under different strain conditions;

[0060] S4. Select the cross-section parallel to the tensile direction as the observation surface. Adopt the electron backscatter diffraction technique, and select parameters such as a scanning area size of approximately 90 μm × 90 μm, a scanning step of 0.25 μm, an accelerating voltage of 20 kV, a working distance of 13.1 mm, and a diffraction angle of 70° to conduct electron backscatter diffraction tests. As Figure 3 shown, detect and analyze the small-angle grain boundary ratio in different regions of the pre-tensioned stainless steel bipolar plates with different strains. The results are as Figure 4 shown;

[0061] S5. According to the small-angle grain boundary ratios in different pre-deformed specimens, establish a theoretical calculation model between the small-angle grain boundary ratio and the strain magnitude. Among them, f (ε) is the small-angle grain boundary ratio inside the metal, ε is the engineering strain of the deformed metal, A1 = 0, A2 = 0.66, ε0 = 0.2, dε = 0.06, specifically as shown in Equation (5):

[0062] f (ε) = -0.66 / (1 + exp((ε - 0.20) / 0.06)) + 0.66 (5)

[0063] S6. Adopt the three-electrode electrochemical test and analysis technique to measure the corrosion rate magnitudes of the pre-tensioned deformed stainless steel specimens with strain magnitudes of 0, 0.1, 0.2, 0.3, and 0.4 in a fuel cell simulated environment (reductive atmosphere, test solution: pH = 3 H2SO4 + 2 ppm HF; test temperature: 70 °C). The results are as Figure 6 shown;

[0064] S7. According to the test results of the corrosion rates of the stainless steel specimens with different strains in the fuel cell simulated anode environment, establish a theoretical calculation model between the corrosion rate of the deformed stainless steel specimens and the strain magnitude. Among them, I (ε) is the corrosion rate inside the metal, ε is the engineering strain, B0 = 5.60 × 10 -6 、B1 = 7.10 × 10 -6 、B2 = 3.08 × 10 -5 . As shown in Equation (6):

[0065] I (ε) = 3.08×10 -5 ·ε 2 + 7.10×10 -6 ·ε + 5.60×10 -6 (6)

[0066] S8. Based on the relationship between the corrosion rate, the proportion of small-angle grain boundaries, and the strain magnitude of stainless steel specimens with different strains, establish a theoretical calculation model for the change of the corrosion rate of stainless steel specimens with the proportion of small-angle grain boundaries, as shown in Equation (7):

[0067] I (ε) = 3.08×10 -5 × [0.06·ln((-0.66) / (f (ε) - 0.66) - 1) + 0.20] 2

[0068] + 7.10×10 -6 × [0.06·ln((-0.66) / (f (ε) - 0.66) - 1) + 0.20] + 5.60×10 -6 (7)

[0069] S9. Adopt the electron backscatter diffraction technique, and select parameters such as a scanning area size of approximately 90μm × 90μm, a scanning step of 0.25μm, an accelerating voltage of 20kV, a working distance of 13.1mm, and a diffraction angle of 70° to conduct electron backscatter diffraction tests, and detect and analyze the proportion of small-angle grain boundaries in different regions within the stamping-formed stainless steel bipolar plate as shown Figure 7 ; the results are as shown Figure 8 ;

[0070] S10. According to the proportion of small-angle grain boundaries in different regions within the stamping-formed stainless steel bipolar plate measured in Step S9, use the theoretical calculation model for the change of the corrosion rate of stainless steel specimens with the proportion of small-angle grain boundaries shown in Equation (7) to calculate that the corrosion rates in regions a, b, c, d, and e within the stamping-formed stainless steel bipolar plate shown Figure 7 are 6.47×10 -6 A·cm -2 , 1.29×10 -5 A·cm -2 , 1.27×10 -5 A·cm -2 , 1.28×10 -5 A·cm -2 and 6.17×10 -6 A·cm -2 respectively.

[0071] Example 2:

[0072] A method for predicting the corrosion rate of a stamping-formed stainless steel bipolar plate based on the proportion of coincidence site lattice grain boundaries (Σ3+Σ9+Σ27) includes the following steps:

[0073] Prepare pre-stretched deformed stainless steel specimens with strain magnitudes of 0, 0.1, 0.2, 0.3, and 0.4 according to steps S1 to S3 of Example 1 for detecting the proportion of coincidence site lattice grain boundaries (Σ3+Σ9+Σ27) and corrosion resistance under different strain conditions;

[0074] S4. Select a cross-section parallel to the tensile direction as the observation surface. Using the electron backscatter diffraction technique, select parameters such as a scanning area size of approximately 90μm×90μm, a scanning step of 0.25μm, an accelerating voltage of 20kV, a working distance of 13.1mm, and a diffraction angle of 70° to perform electron backscatter diffraction testing. As Figure 3 shown, detect and analyze the proportion of coincidence site lattice grain boundaries (Σ3+Σ9+Σ27) in different regions within the pre-strained stainless steel bipolar plates with different strains. The results are as Figure 5 shown;

[0075] S5. Based on the proportion of coincidence site lattice grain boundaries (Σ3+Σ9+Σ27) in different pre-deformed specimens, establish a theoretical calculation model between the proportion of coincidence site lattice grain boundaries (Σ3+Σ9+Σ27) and the strain magnitude, as shown in Equation (8):

[0076] f (ε) = 0.43 / (1 + exp((ε - 0.14) / 0.06)) + 0.03 (8)

[0077] S6 and S7 are the same as in Example 1, and establish a theoretical calculation model between the deformed stainless steel specimen and the strain magnitude, as shown in Equation (6).

[0078] S8. Based on the relationship between the corrosion rate, the proportion of coincidence site lattice grain boundaries (Σ3+Σ9+Σ27), and the strain magnitude of stainless steel specimens with different strains, establish a theoretical calculation model for the change in the corrosion rate of stainless steel specimens with the proportion of coincidence site lattice grain boundaries (Σ3+Σ9+Σ27), as shown in Equation (9):

[0079] I (ε) = 3.08×10 -5 × [0.06·ln((0.43) / (f (ε) - 0.03) - 1) + 0.14] 2

[0080] + 7.10×10 -6 × [0.06·ln((0.43) / (f(ε) -0.03)-1)+0.14]+5.60×10 -6 (9)

[0081] S9. Select a scanning area with a size of approximately 90 μm × 90 μm, a scanning step of 0.25 μm, an accelerating voltage of 20 kV, a working distance of 13.1 mm, and a diffraction angle of 70° and other parameters to conduct electron backscatter diffraction tests, and detect and analyze the coincidence site lattice boundary (Σ3+Σ9+Σ27) ratio in different regions within the stamping-formed stainless steel bipolar plate as shown in Figure 6 ; the results are as shown in Figure 9 ;

[0082] S10. According to the coincidence site lattice boundary (Σ3+Σ9+Σ27) ratio in different regions within the stamping-formed stainless steel bipolar plate measured in step S9, adopt the theoretical calculation model of the corrosion rate of the stainless steel specimen varying with the coincidence site lattice boundary (Σ3+Σ9+Σ27) ratio shown in formula (9) to calculate and obtain the corrosion rates in regions a, b, c, d, and e within the stamping-formed stainless steel bipolar plate as shown in Figure 7 are 6.86×10 -6 A·cm -2 、1.17×10 -5 A·cm -2 、1.11×10 -5 A·cm -2 、1.14×10 - 5 A·cm -2 and 6.74×10 -6 A·cm -2 respectively.

[0083] Example 3:

[0084] A method for predicting the corrosion rate of a stamping-formed stainless steel bipolar plate based on the small-angle grain boundary and the coincidence site lattice boundary (Σ3+Σ9+Σ27) ratio, comprising the following steps:

[0085] Prepare pre-stretched deformed stainless steel specimens with strain magnitudes of 0, 0.1, 0.2, 0.3, and 0.4 according to steps S1 to S3 of Example 1 for detecting the small-angle grain boundary, the coincidence site lattice boundary (Σ3+Σ9+Σ27) ratio, and the corrosion resistance under different strain conditions;

[0086] S4. Select a cross-section parallel to the tensile direction as the observation surface, and adopt electron backscatter diffraction technology to conduct electron backscatter diffraction tests with a scanning area size of approximately 90 μm × 90 μm, a scanning step of 0.25 μm, an accelerating voltage of 20 kV, a working distance of 13.1 mm, and a diffraction angle of 70° and other parameters, as shown inFigure 2 As shown, the proportions of low-angle grain boundaries and coincidence site lattice boundaries (Σ3+Σ9+Σ27) in different regions within the strain pre-stretched stainless steel bipolar plates were detected and analyzed, and the results are respectively as Figure 4 and Figure 5 shown;

[0087] S5. According to the proportions of low-angle grain boundaries and coincidence site lattice boundaries (Σ3+Σ9+Σ27) in different pre-deformed specimens, theoretical calculation models between the proportions of low-angle grain boundaries and coincidence site lattice boundaries (Σ3+Σ9+Σ27) and the strain magnitude were respectively established, as shown in Equations (5) and (8):

[0088] f 1(ε) = -0.66 / (1 + exp((ε - 0.20) / 0.06)) + 0.66 (5)

[0089] f 2(ε) = 0.43 / (1 + exp((ε - 0.14) / 0.06)) + 0.03 (8)

[0090] S6 and S7 are the same as in Example 1. A theoretical calculation model between the deformed stainless steel specimen and the strain magnitude was established, as shown in Equation (6):

[0091] I (ε) = 3.08×10 -5 ·ε 2 + 7.10×10 -6 ·ε + 5.60×10 -6 (6)

[0092] S8. According to the relationships between the corrosion rates, proportions of low-angle grain boundaries and coincidence site lattice boundaries (Σ3+Σ9+Σ27) and the strain magnitude of stainless steel specimens with different strains, a theoretical calculation model for the change of the corrosion rate of stainless steel specimens with the proportion of coincidence site lattice boundaries (Σ3+Σ9+Σ27) was established, as shown in Equation (10):

[0093] I (ε) = 0.5×{3.08×10 -5 ×[0.06·ln((-0.66) / (f 1(ε) - 0.66) - 1) + 0.20] 2

[0094] + 7.10×10 -6 ×[0.06·ln((-0.66) / (f 1(ε) - 0.66) - 1) + 0.20] + 5.60×10 -6}

[0095] + 0.5×{3.08×10-5 × [0.06 · ln((0.43) / (f 2(ε) -0.03)-1) + 0.14] 2

[0096] +7.10×10 -6 × [0.06 · ln((0.43) / (f 2(ε) -0.03)-1) + 0.14] + 5.60×10 -6}(10)

[0097] S9. The backscattered electron diffraction technique was adopted, and parameters such as a scanning area size of approximately 90 μm × 90 μm, a scanning step of 0.25 μm, an accelerating voltage of 20 kV, a working distance of 13.1 mm, and a diffraction angle of 70° were selected for the backscattered electron diffraction test to detect and analyze the proportion of low-angle grain boundaries and coincidence site lattice boundaries (Σ3 + Σ9 + Σ27) in the stamping-formed stainless steel bipolar plate as shown Figure 6 respectively, and the results are as shown Figure 8 and Figure 9 shown;

[0098] S10. According to the proportion of coincidence site lattice boundaries (Σ3 + Σ9 + Σ27) in different regions of the stamping-formed stainless steel bipolar plate measured in step S9, the theoretical calculation model of the corrosion rate of the stainless steel sample varying with the proportion of low-angle grain boundaries and coincidence site lattice boundaries (Σ3 + Σ9 + Σ27) shown in formula (10) was adopted to calculate the corrosion rates in regions a, b, c, d, and e within the stamping-formed stainless steel bipolar plate as shown Figure 7 respectively, which are 6.66×10 -6 A·cm -2 、1.23×10 -5 A·cm -2 、1.19×10 - 5 A·cm -2 、1.21×10 -5 A·cm -2 and 6.45×10 -6 A·cm -2 .

[0099] The present invention also adopted the micro-area scanning electrochemical test method to detect the corrosion rates of each region of the metal microstructure device, and the results are shown in Table 1

[0100] Table 1 Prediction results of the corrosion performance of the metal microstructure device by establishing prediction models for different characteristic grain boundaries

[0101]

[0102] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for predicting the corrosion rate of a metal microstructure device, characterized in that, It includes the following steps: S1. Prepare the blank of the metal microstructure device into pre-deformed specimens with different strain magnitudes; S2. Obtain the characteristic grain boundary ratio data of the pre-deformed specimens with different strain magnitudes; S3. Establish a theoretical calculation model between the characteristic grain boundary ratio and the strain magnitude; S4. Measure the corrosion rate data of different pre-deformed specimens in the target working condition environment; S5. Establish a theoretical calculation model between the corrosion rate and the strain magnitude; S6. According to the theoretical calculation models established in steps S3 and S5, establish a theoretical calculation model of the change in the corrosion rate with the characteristic grain boundary ratio; S7. Obtain the characteristic grain boundary ratio data of different regions within the metal microstructure device; use the theoretical calculation model obtained in step S6 to analyze and predict the corrosion resistance of the metal microstructure device in the target working condition environment.

2. The method for predicting the corrosion rate of the metal microstructure device according to claim 1, wherein The metal microstructure device is a plastically formed metal microstructure device.

3. The method for predicting the corrosion rate of the metal microstructure device according to claim 1, characterized in that For the pre-deformed specimen, the specimen type needs to be determined according to the plastic forming method and characteristics of the metal microstructure device, and the sampling direction is selected according to the microscopic tissue structure characteristics of the blank of the metal microstructure device to ensure that the stress direction during the mechanical property test of the specimen conforms to the deformation law of the tissue structure during the forming process.

4. The method for predicting the corrosion rate of the metal microstructure device according to claim 1, wherein The characteristic grain boundaries include large-angle grain boundaries, small-angle grain boundaries, and low-Σ coincidence site lattice grain boundaries, where 1 ≤ Σ ≤ 29.

5. The method for predicting the corrosion rate of the metal microstructure device according to claim 1, wherein The theoretical calculation model between the characteristic grain boundary ratio and the strain magnitude established in step S3 is described by the Boltzmann model, specifically as shown in Equation (1): f (ε) = (A1 - A2) / (1 + exp((ε - ε0) / dε)) + A2 (1) where f (ε) is the characteristic grain boundary ratio, ε is the true strain or engineering strain, and A1, A2, ε0 and dε are fitting constants.

6. The method for predicting the corrosion rate of a metal microstructure device according to claim 1, wherein The theoretical calculation model between the corrosion rate and the strain magnitude established in step S5 is described by the Polynomial model, specifically as shown in Equation (2): I (ε) = B0 + B1·ε + B2·ε 2 + B3·ε 3 + … (2) where I (ε) is the corrosion rate, ε is the true strain or engineering strain, and B0, B1, B2, and B3 are fitting constants.

7. The method for predicting the corrosion rate of the metal microstructure device according to claim 1, wherein, For the theoretical calculation model of the change in the corrosion rate with the characteristic grain boundary ratio in step S6, the characteristic grain boundaries include one or more of large-angle grain boundaries, small-angle grain boundaries, and low-Σ coincidence site lattice grain boundaries.

8. The method for predicting the corrosion rate of the metal microstructure device according to claim 7, wherein For the theoretical calculation model of the change in the corrosion rate with the characteristic grain boundary ratio in step S6, if it is based on one characteristic grain boundary, the calculation formula is as shown in Equation (4): I (ε) = B0 + B1 × [dε · ln((A1 - A2) / (f (ε) - A2) - 1) + ε0] + B2 × [dε · ln((A1 - A2) / (f (ε) - A2) - 1) +ε0] 2 +B3×[dε·ln((A1-A2) / (f (ε) -A2)-1)+ε0] 3 (4) In the formula, f (ε) is the ratio of characteristic grain boundaries, I (ε) is the corrosion rate inside the metal, A1, A2, ε0 and dε, B0, B1, B2 and B3 are fitting constants; If a theoretical calculation model is established based on multiple characteristic grain boundaries, the corrosion rates of each characteristic grain boundary are calculated separately, and then the average value is taken to obtain the theoretical calculation model between the corrosion rate and the characteristic grain boundary ratio.

9. The method for predicting the corrosion rate of the metal micro-structure device according to claim 1, characterized in that In step S7, taking the obtained characteristic grain boundary ratio data of different regions within the metal microstructure device as the input, first calculate the strain magnitude according to the Boltzmann model, and then calculate the corrosion rate of the corresponding region of the metal microstructure device according to the Polynomial model.

10. Application of the method according to any one of claims 1-9 in predicting the corrosion performance of metal microstructure devices.

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