Corrosion Resistance of Metallic Materials - Method for Constructing and Applying Mechanical Performance Analysis Models

Abstract

This application relates to a method for constructing and applying a corrosion resistance-mechanical performance analysis model for metallic materials. The method for constructing this analysis model includes the steps of taking a metallic material as the test subject, obtaining experimental data sets of corrosion performance tests at different equivalent corrosion time points under corrosion test conditions, and experimental data sets of mechanical performance tests at different corrosion degrees corresponding to different equivalent corrosion time points, and establishing empirical relationships between corrosion parameters and mechanical parameters that change with the equivalent corrosion time, respectively.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims priority to the Chinese patent application filed on November 27, 2023, with application number CN2023116080448, titled "Method for constructing a mechanical performance analysis model for corrosion resistance of metallic materials and its application," the entire contents of which are incorporated herein by reference.

[0002] Technical field This application relates to the field of corrosion resistance testing and analysis of metallic materials and the field of battery technology, and further to a method for constructing and applying a corrosion resistance degree-mechanical performance analysis model for metallic materials, a method for constructing a corrosion resistance degree-mechanical performance analysis model for metallic materials, a method and apparatus for analyzing the service life of metallic materials, a method and apparatus for analyzing the mechanical performance of metallic materials, computer equipment, computer-readable storage media, and power consumption devices. [Background technology]

[0003] The information presented herein is merely background information relating to this application and does not necessarily constitute prior art. [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] The design and development of battery case materials require extremely high structural strength and corrosion resistance, both of which are crucial for the safe operation of batteries and, consequently, the entire power consumption device. Taking fuel cell case materials as an example, current research on the corrosion behavior and mechanisms of fuel cell case materials often focuses only on the electrochemical corrosion behavior of fuel cell case materials or the corrosion rates in different corrosive media, making it difficult to provide effective guidelines for the design and development of fuel cell case materials. [Means for solving the problem]

[0005] Summary of the Invention According to various embodiments and various examples of the present application, the present application provides a method for constructing a corrosion resistance-mechanical property analysis model of a metal material, a method and an analysis apparatus for analyzing the service life of a metal material, a method and an analysis apparatus for analyzing the mechanical properties of a metal material, a computer device, a computer-readable storage medium, and a power consumption device. This corrosion resistance-mechanical property analysis model of the metal material can be effectively used for predicting the mechanical properties and evaluating the service life of the metal material. The related metal material may include, but is not limited to, an aluminum alloy material, and may include, but is not limited to, a battery case material.

[0006] According to a first aspect, the present application provides a method for constructing a corrosion resistance-mechanical property analysis model of a metal material.

[0007] In some embodiments, the method for constructing this analysis model uses the metal material as a test object, and obtains a corrosion performance test experimental data set at different equivalent corrosion time points under corrosion test conditions and a mechanical property test experimental data set at different corrosion degrees corresponding to different equivalent corrosion time points, and includes the step of establishing an empirical relational expression that changes with the equivalent corrosion time of the corrosion parameter and the mechanical parameter respectively. Further, the corrosion test conditions may be used to simulate the target actual use environment.

[0008] In some embodiments, a method for constructing a corrosion resistance-mechanical property analysis model of a metal material is provided, which uses the metal material as a test object, obtains the test values of the corrosion parameters at different equivalent corrosion time points under corrosion test conditions to obtain a corrosion performance test experimental data set, and further obtains the test values of the mechanical parameters at different corrosion degrees corresponding to different equivalent corrosion time points to obtain a mechanical property test experimental data set, wherein the corrosion test conditions are used to simulate the target actual use environment of the metal material, and the corrosion parameters include at least one of the corrosion degree and the corrosion rate. Establishing a first set of empirical relational expressions that change with the equivalent corrosion time of the corrosion parameters based on the corrosion performance test experimental dataset, and further establishing a second set of empirical relational expressions that change with the equivalent corrosion time of the mechanical parameters based on the mechanical performance test experimental dataset, including.

[0009] Using a metal material as the test object, adopting corrosion test conditions to simulate the target actual use environment of the metal material, obtaining a corrosion performance test experimental dataset at different equivalent corrosion time points and a mechanical performance test experimental dataset at different corrosion degrees corresponding to different equivalent corrosion time points, using the equivalent corrosion time under corrosion test conditions and the mechanical performance at different corrosion degrees as a link, establishing a first set of empirical relational expressions that change with the equivalent corrosion time of the corrosion parameters including at least one of the corrosion degree and the corrosion rate, and a second set of empirical relational expressions that change with the equivalent corrosion time of at least one mechanical parameter, constructing an analysis model between the corrosion resistance degree and the mechanical performance of the metal material, being able to assist in understanding the corrosion mechanism of the metal material, being able to predict and analyze the mechanical performance of the metal material under target actual use conditions based on the corrosion behavior of the metal material, and evaluating the service life of the metal material structural member in the target actual use environment.

[0010] Based on any suitable embodiment of the present application, further, in some embodiments, the mechanical parameter includes at least one of the tensile strength and the elongation at break.

[0011] When the mechanical parameter in this method may include at least one of the tensile strength and the elongation at break, the corrosion resistance degree - mechanical performance analysis model of the metal material constructed and obtained can be applied to metal material structural members that are likely to become invalid under tensile stress.

[0012] Based on any suitable embodiment of the present application, further, in some embodiments, the mechanical parameter includes the yield strength.

[0013] By selecting appropriate parameters and using them to fit the functional relationships, the fitting effect is advantageous in obtaining a more favorable second set of empirical relationships, and further advantageous in obtaining a more effective corrosion resistance degree-mechanical performance analysis model.

[0014] Based on any suitable embodiment of this application, and in some embodiments, the construction method described above is: The aforementioned metal material is an aluminum alloy material, The aforementioned metal material satisfies at least one of the following characteristics: it is a metal structural member, and optionally an aluminum alloy structural member.

[0015] Based on any suitable embodiment of this application, and in some embodiments, the construction method described above is: The aforementioned metal material is one of the battery case materials, and selectively, the battery case material includes a fuel cell case material, and selectively, the battery case material includes a lithium battery case material. The metal material comprises at least a portion of the structural members of the battery case, and optionally, the battery case structural members comprise at least a portion of the structural members of the fuel cell case, and optionally, the battery case structural members comprise at least a portion of the structural members of the lithium battery case, satisfying at least one of these characteristics.

[0016] The metallic material may be an aluminum alloy material or a metallic structural member, and may further be an aluminum alloy structural member. Pressure-cast aluminum alloys have advantages such as low density, high specific strength, excellent thermal stability, good machinability, and low cost, and may be used to manufacture various parts including, but not limited to, automobile cylinder blocks, generator cases, fuel cell cases, and various engine brackets. Here, since the aluminum alloy material is applicable to the battery technology field and can be used as the main material (including compositional material) of a battery case or an aluminum alloy structural member in a battery case, the metallic material may be a battery case material, and the battery case may include, but not limited to, a fuel cell case, and may further include, but not limited to, a lithium battery case, and accordingly, the metallic material may include at least some structural members of the battery case. When obtaining corrosion parameters and mechanical parameters based on the aluminum alloy material, the corrosion resistance degree-mechanical performance analysis model of the metallic material constructed can be applied to predicting the mechanical performance and evaluating the lifespan of the aluminum alloy material, and in this case, it is also advantageous for understanding the white rust corrosion mechanism of the aluminum alloy material. When aluminum alloy materials are used as the main material or component material of a battery case or structural member within a battery case, the corrosion resistance-mechanical performance analysis model for metallic materials can be applied to predicting the mechanical performance and evaluating the lifespan of the battery case or structural member within a battery case.

[0017] Using fuel cell cases as an example, this can further contribute to understanding the white rust corrosion mechanism of fuel cell case materials. When fuel cell case materials are used as test subjects, the corrosion behavior of fuel cell case materials can be analyzed, the corrosion rates of different fuel cell case materials can be determined, and a relationship between the degree of corrosion of fuel cell case materials and their mechanical performance can be established. This has value as a model for the safe operation of fuel cells and, by extension, power consumption devices including fuel cells.

[0018] Based on any suitable embodiment of this application, and in some embodiments further, the corrosion performance test experimental dataset includes at least a corrosion performance test experimental dataset under salt spray corrosion test conditions. Selectively, the salt spray corrosion test conditions include one or more of the following: NaCl aqueous solution salt spray conditions, acetic acid salt spray conditions, copper salt accelerated acetic acid salt spray conditions, and alternating salt spray corrosion conditions. More selectively, the salt spray corrosion test conditions include conditions for spraying a NaCl aqueous solution.

[0019] Based on any suitable embodiment of this application, and in some embodiments, the NaCl aqueous solution salt spray conditions include the parameter of simulated salt spray conditions of a 3 wt% to 6 wt% NaCl aqueous solution at 34 to 36°C.

[0020] When the corrosion performance test experimental dataset includes at least the corrosion performance test experimental dataset under salt spray corrosion test conditions, the resulting corrosion resistance-mechanical performance analysis model for metallic materials can be applied to metallic materials and their structural components or products in real-world usage environments in fields such as road transport, computer, electronic communications, and electrical equipment, and can further be applied to structural components or products in related real-world usage environments such as electroplating, coating, packaging boxes, and transportation equipment. Here, the road traffic field may include, but is not limited to, road vehicle electronic and electrical equipment, rail transport locomotive vehicle equipment and devices, automobile parts, and other equipment or their metal structural components; the computer field may include, but is not limited to, computers, displays, hosts, computer devices, precision instruments such as medical equipment, and other equipment and products or their metal structural components; the electronic communications field may include, but is not limited to, mobile phones, radio frequency devices, electronic communication devices, printed circuit boards (PCBs), printed circuit board assemblies (PCBAs), and other equipment and products or their metal structural components; and electrical equipment may include, but is not limited to, home appliances, lighting fixtures, substations, and other various home appliances and electrical equipment, instruments and meters, medical equipment, and other equipment and products or their metal structural components.

[0021] Models that obtain corrosion performance test experimental data sets based on NaCl aqueous solution salt spray conditions are applicable to determining the mass and uniformity of protective coatings and to comparing differences in salt spray corrosion resistance of samples with similar structures, but are not limited to these applications. Models that obtain corrosion performance test experimental data sets based on acetic acid salt spray conditions are applicable to coastal cities in southern China and relatively harsh salt spray environments. Models that obtain corrosion performance test experimental data sets based on copper salt accelerated acetic acid salt spray conditions are applicable to harsh salt spray environments. Models that obtain corrosion performance test experimental data sets based on alternating salt spray corrosion conditions are applicable to high-temperature and high-humidity environments.

[0022] The aforementioned test conditions for NaCl aqueous solution salt spraying are advantageous for determining the corrosion resistance of battery cases, automobile parts, and other materials.

[0023] Based on any suitable embodiment of this application, and in some embodiments further, establishing a first set of empirical relations that change with the equivalent corrosion time of the corrosion parameters based on the aforementioned corrosion performance test experimental dataset is possible. This method involves segmenting and fitting each corrosion parameter in the aforementioned corrosion parameters based on the corresponding corrosion performance test experiment dataset, fitting each segment's fitting interval with the corresponding corrosion parameter as the dependent variable and the equivalent corrosion time as the independent variable, in the form of a power function or a linear function, constructing an empirical relationship between the type of corrosion parameter and the equivalent corrosion time corresponding to the fitting interval of each segment, and obtaining the first set of empirical relationship equations.

[0024] The establishment of a first set of empirical relationships that change with the equivalent corrosion time of corrosion parameters can be obtained by segmenting and fitting each corrosion parameter. The fitting method for each segment may be a power function or a linear function, and in this case the agreement between the fitting curve and the experimental test dataset will be higher and the model will be more effective, but it is not limited to the function type described above.

[0025] Based on any suitable embodiment of this application, and further in some embodiments, in the first set of empirical relations, the fitting method for the power function is y1 = A·x B The fitting method for the linear function is y1 = a + b·x, where x is the equivalent corrosion time, y1 is the corrosion parameter, A is a positive number, B is a negative number, b is a positive number, and a is a real number. Selectively, a is a negative number.

[0026] Based on any suitable embodiment of this application, and further in some embodiments, A is a real number selected from 0.01 to 1.00, B is a real number selected from -0.3 to -0.8, b is a real number selected from 0.001 to 0.05, and a is a real number selected from -0.02 to -0.8. Selectively, a is a real number chosen from -0.001 to -0.8, and further selectively, a is a real number chosen from -0.001 to -0.5. Selectively, b is a real number chosen from 0.001 to 0.02, and further selectively, b is a real number chosen from 0.001 to 0.01.

[0027] By selecting an appropriate fitting function type, the fitting effect is advantageous in obtaining a more favorable first set of empirical relations, and furthermore, a more effective corrosion resistance degree-mechanical performance analysis model is obtained.

[0028] Based on any suitable embodiment of this application, in some embodiments, the corrosion performance test experimental dataset further includes at least one of the corrosion performance test experimental dataset under electrochemical corrosion test conditions and the corrosion performance test experimental dataset under immersion corrosion test conditions. Selectively, the corrosion parameters in the experimental data set for corrosion performance tests under the electrochemical corrosion test conditions include at least one of the self-corrosion potential and the self-corrosion current. Selectively, the corrosion parameters in the corrosion performance test experimental dataset under the immersion corrosion test conditions include at least one of the degree of corrosion and the corrosion rate.

[0029] When the corrosion performance test experimental dataset includes corrosion performance test experimental datasets under electrochemical corrosion test conditions, this corrosion resistance-mechanical performance analysis model for metallic materials can be applied to predicting the performance and lifespan of metallic materials in electrochemical environments, such as predicting the performance and lifespan of battery cases, but is not limited to that. It may also, but is not limited to, predicting the performance and lifespan of fuel cell cases.

[0030] When the corrosion performance test experimental dataset includes data from corrosion performance test experiments under immersion corrosion test conditions, this corrosion resistance-mechanical performance analysis model for metallic materials can be applied to predict the performance and life assessment of metallic materials in environments where they come into contact with corrosive liquids.

[0031] When the corrosion performance test experimental dataset includes both a corrosion performance test experimental dataset under electrochemical corrosion test conditions and a corrosion performance test experimental dataset under immersion corrosion test conditions, this corrosion resistance-mechanical performance analysis model for metal materials can be applied to predict the performance and lifespan of battery cases containing electrolytes, and may also include, but is not limited to, performance prediction and lifespan of fuel cell cases.

[0032] Based on any suitable embodiment of this application, and in some embodiments further, establishing a second set of empirical relations that change with the equivalent corrosion time of the mechanical parameters based on the aforementioned mechanical performance test experimental data set, This method involves segmenting and fitting each of the aforementioned mechanical parameters based on the corresponding mechanical performance test experiment dataset, fitting each segment's fitting interval with the corresponding mechanical parameter as the dependent variable and the equivalent corrosion time as the independent variable, in the form of a power function or a linear function, constructing an empirical relationship between the corresponding type of mechanical parameter and the equivalent corrosion time, and obtaining the second set of empirical relationships.

[0033] The establishment of a second set of empirical relationships that change with the equivalent corrosion time of the mechanical parameters can be obtained by segmenting and fitting each mechanical parameter. The fitting method for each segment may employ a power function or a linear function, and in this case, the agreement between the fitting curve and the experimental test dataset will be higher and the model will be more effective, but it is not limited to the function type described above.

[0034] Based on any suitable embodiment of this application, and further in some embodiments, the fitting method for the power function in the second set of empirical relations is y = M·x N The fitting method for the linear function is y² = m + n·x, where x is the equivalent corrosion time, y² is the mechanical parameter, M is a positive number, N is a negative number, n is a negative number, and m is a positive number.

[0035] Based on any suitable embodiment of this application, and in some embodiments, M is a real number selected from 1 to 250, N is a real number selected from -0.01 to -1, n is a real number selected from -0.1 to -5, and m is a real number selected from 1 to 300. Selectively, N is a real number chosen from -0.01 to -0.5, and further selectively, N is a real number chosen from -0.01 to -0.2. Selectively, n is a real number chosen from -1 to -3; selectively, n is a real number chosen from -0.5 to -1.5; and selectively, n is a real number chosen from -0.05 to -0.5.

[0036] Based on any suitable embodiment of this application, in some embodiments, the method for constructing a corrosion resistance-mechanical performance analysis model for the metallic material further includes the step of establishing a third set of empirical relationships between equivalent corrosion time under corrosion test conditions and actual use time in a target actual use environment.

[0037] By establishing an empirical relationship (which may be written as a third set of empirical relationships) between the equivalent corrosion time under corrosion test conditions and the actual usage time in the target actual usage environment, the equivalent corrosion time can be converted into the actual usage time in the target actual usage environment, thereby enabling a more direct prediction of the service life of metal materials and their structural components or products.

[0038] Based on any suitable embodiment of this application, in some embodiments, the method for constructing a corrosion resistance-mechanical performance analysis model for the metallic material includes the step of establishing a fourth set of empirical relations between the corrosion rate and the equivalent corrosion time under the corrosion test conditions, wherein the first set of empirical relations includes the fourth set of empirical relations.

[0039] By establishing an empirical relationship (which may be written as a fourth set of empirical relationships) between the corrosion rate and equivalent corrosion time under corrosion test conditions, the relationship between the corrosion behavior and mechanical properties of metallic materials in target real-world usage environments can be dynamically analyzed.

[0040] A second aspect of this application provides a method for analyzing the service life of a metallic material, wherein the metallic material is defined in the first aspect of this application.

[0041] In some embodiments, the method for analyzing the service life of the metal material is: The steps include determining mechanical parameters related to the ineffective behavior of the metal material according to the target actual usage environment of the metal material, determining corrosion test conditions that can simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metal material under the corrosion test conditions, A step of obtaining a service life parameter for the metal material under the corrosion test conditions, using the corrosion degree test value, the effective state threshold of the mechanical parameter, and the corrosion degree-mechanical performance analysis model of the metal material, the step of including a first set of empirical relational expressions that change with the equivalent corrosion time of the corrosion parameter characterizing the corrosion degree and a second set of empirical relational expressions that change with the equivalent corrosion time of the mechanical parameter, wherein the corrosion degree-mechanical performance analysis model of the metal material includes

[0042] This method for analyzing the service life of a metallic material involves selecting mechanical parameters related to the ineffective behavior of the metallic material based on the target actual usage environment of the metallic material, selecting possible corrosion test conditions that simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metallic material under the selected corrosion test conditions. The service life parameter of the metallic material under the selected corrosion test conditions can then be obtained from the corrosion degree test values, the effective state thresholds of the selected mechanical parameters, and the corrosion resistance degree-mechanical performance analysis model of the metallic material. This corrosion resistance degree-mechanical performance analysis model of the metallic material includes two sets of empirical relations: a first set of empirical relations that change with the equivalent corrosion time of a corrosion parameter including at least one of the corrosion degree and corrosion rate, and a second set of empirical relations that change with the equivalent corrosion time of the aforementioned mechanical parameters, with the mechanical performance at different corrosion degrees being linked to the equivalent corrosion time. This analysis method can be used to effectively and accurately analyze the service life of a metallic material in its target actual usage environment.

[0043] Based on any suitable embodiment of this application, in some embodiments, the service life parameter includes at least equivalent corrosion time.

[0044] The aforementioned analysis method allows us to obtain at least the equivalent corrosion time parameter of this metallic material, and reflect the service life status of this metallic material.

[0045] Based on any suitable embodiment of this application, and further in some embodiments, the service life parameter of the metal material under the corrosion test conditions is obtained by the above-mentioned corrosion degree test value, the effective state threshold of the mechanical parameter, and the corrosion degree-mechanical performance analysis model of the metal material. This method includes obtaining the surplus actual usage time of the metal material by using the test value of the degree of corrosion, the effective state threshold of the mechanical parameters, the corrosion resistance degree-mechanical performance analysis model of the metal material, and a third set of empirical relational expressions between the equivalent corrosion time under the corrosion test conditions and the actual usage time in the target actual usage environment.

[0046] By using test values ​​for the degree of corrosion of the metal material, effective state thresholds for mechanical parameters related to the ineffective behavior of the metal material, a corrosion resistance-mechanical performance analysis model for the metal material, and an empirical relationship between the equivalent corrosion time under selected corrosion test conditions and the actual usage time in the target actual usage environment, the surplus actual usage time of the metal material can be obtained, enabling predictive analysis of the surplus service life of the metal material.

[0047] Based on any suitable embodiment of this application, in some embodiments, the mechanical parameters include at least two, and the degree of ineffective response of the metallic material is ranked in descending order based on each mechanical parameter, and a corrosion resistance-mechanical performance analysis model of the metallic material is obtained based on the highest-ranking mechanical parameter.

[0048] By ranking the degree of ineffective response of metallic materials in descending order based on each mechanical parameter, and obtaining a corrosion resistance-mechanical performance analysis model for the metallic material based on the highest-ranking mechanical parameter, it is possible to more effectively reflect the relationship between the corrosion behavior of metallic materials and the ineffectiveness of mechanical performance in the target actual usage environment.

[0049] Based on any suitable embodiment of this application, and in some embodiments further, the corrosion resistance degree-mechanical performance analysis model of the metallic material is constructed by the method for constructing the corrosion resistance degree-mechanical performance analysis model of the metallic material in the first aspect of this application.

[0050] The corrosion resistance degree-mechanical performance analysis model for the aforementioned method of analyzing the service life of metallic materials can be constructed by employing the construction method in the first aspect of this application, and by constructing it, a relationship formula can be obtained between the degree of corrosion of the metallic material and the mechanical performance of a mechanical parameter including at least one of tensile strength and elongation at break.

[0051] A third aspect of this application provides a service life analyzer for a metallic material, wherein the metallic material is defined in the first aspect of this application.

[0052] In some embodiments, the metal material service life analyzer is A corrosion performance data acquisition module for determining mechanical parameters related to the ineffective behavior of the metal material according to the target actual usage environment of the metal material, determining corrosion test conditions that can simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metal material under the corrosion test conditions, A corrosion performance data processing module for obtaining a service life parameter for a metal material under the corrosion test conditions, based on the corrosion degree test value, the effective state threshold of the mechanical parameter, and the corrosion degree-mechanical performance analysis model of the metal material, wherein the corrosion degree-mechanical performance analysis model of the metal material is a corrosion performance data processing module as defined in the first or second aspect of this application.

[0053] A metal material service life analyzer according to a third aspect of this application may be used to implement the metal material service life analysis method described in a second aspect of this application.

[0054] According to a fourth aspect of this application, a method for analyzing the mechanical properties of a metallic material is provided, wherein the metallic material is defined in the first aspect of this application.

[0055] In some embodiments, the method for analyzing the mechanical properties of the metallic material is: The steps include determining mechanical parameters related to the ineffective behavior of the metal material according to the target actual usage environment of the metal material, determining corrosion test conditions that can simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metal material under the corrosion test conditions, A step of obtaining preliminary predicted values ​​of the mechanical parameters after the target actual usage time of the metal material under the corrosion test conditions, using the test values ​​of the degree of corrosion, the target actual usage time of the metal material, a third set of empirical relational expressions between the equivalent corrosion time under the corrosion test conditions and the actual usage time in the target actual usage environment, and a corrosion resistance degree-mechanical performance analysis model of the metal material, wherein the corrosion resistance degree-mechanical performance analysis model of the metal material is defined in the first or second aspect of this application, A step of comparing a preliminary predicted value of the mechanical parameter with an effective state threshold of the mechanical parameter to obtain a predicted result of the mechanical parameter after the target actual usage time of the metal material under the corrosion test conditions, wherein if the preliminary predicted value of the mechanical parameter is greater than or equal to the effective state threshold of the mechanical parameter, the preliminary predicted value is output as the effective predicted value of the mechanical parameter after the target actual usage time of the metal material under the corrosion test conditions, and if the preliminary predicted value of the mechanical parameter is less than the effective state threshold of the mechanical parameter, an invalid prediction result is output before the target actual usage time of the metal material is reached.

[0056] The aforementioned method for analyzing the mechanical performance of a metallic material involves selecting mechanical parameters related to the ineffective behavior of the metallic material based on the target actual usage environment of the metallic material, selecting possible corrosion test conditions that simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metallic material under the selected corrosion test conditions. By using the test values ​​for the degree of corrosion, the target actual usage time of the metallic material, an empirical relationship between the equivalent corrosion time under the selected corrosion test conditions and the actual usage time in the target actual usage environment, and a corrosion resistance degree-mechanical performance analysis model of the metallic material, preliminary predictions of the mechanical parameters after the target actual usage time under the selected corrosion test conditions can be obtained. By comparing these preliminary predictions with the effective state thresholds of the mechanical parameters, prediction results for the mechanical parameters after the target actual usage time under the selected corrosion test conditions can be obtained. If the preliminary predicted value of the mechanical parameters is greater than or equal to the effective state threshold for the mechanical parameters, then the effective predicted value of the mechanical parameters after the target actual use time under the selected corrosion test conditions for the metal material will be equal to the preliminary predicted value. If the preliminary predicted value of the mechanical parameters is less than the effective state threshold for the mechanical parameters, it means that the metal material will become invalid before reaching the target actual use time.

[0057] According to a fifth aspect of this application, a device for analyzing the mechanical properties of a metallic material is provided, wherein the metallic material is defined in the first aspect of this application.

[0058] In some embodiments, the mechanical performance analyzer for the metallic material, A test data acquisition module for determining mechanical parameters related to the ineffective behavior of the metal material according to the target actual usage environment of the metal material, determining corrosion test conditions that can simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metal material under the corrosion test conditions, A test data processing module for obtaining preliminary prediction values ​​of the mechanical parameters after the target actual usage time of the metal material under the corrosion test conditions, using the test values ​​of the degree of corrosion, the target actual usage time of the metal material, a third set of empirical relational expressions between the equivalent corrosion time under the corrosion test conditions and the actual usage time in the target actual usage environment, and a corrosion resistance degree-mechanical performance analysis model of the metal material, wherein the corrosion resistance degree-mechanical performance analysis model of the metal material is a test data processing module as defined in the first or second aspect of this application, A mechanical performance classification identification module for obtaining a prediction result of the mechanical parameters after the target actual usage time of the metal material under the corrosion test conditions, by comparing a preliminary prediction value of the mechanical parameters with an effective state threshold of the mechanical parameters, wherein if the preliminary prediction value of the mechanical parameters is greater than or equal to the effective state threshold of the mechanical parameters, the mechanical performance classification identification module determines that the effective prediction value of the mechanical parameters after the target actual usage time of the metal material under the corrosion test conditions is equal to the preliminary prediction value.

[0059] The apparatus for analyzing the mechanical properties of a metallic material according to the fifth aspect of this application may be used to implement the method for analyzing the mechanical properties of a metallic material described in the fourth aspect of this application.

[0060] According to a sixth aspect of this application, a computer device is provided which includes a memory and a processor on which a computer program is stored, the processor implementing the steps of the method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material as described in the first aspect of this application, or the method for analyzing the service life of a metallic material as described in the second aspect of this application, or the method for analyzing the mechanical performance of a metallic material as described in the fourth aspect of this application, when executing the computer program.

[0061] According to the seventh aspect of this application, a computer-readable storage medium is provided in which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material as described in the first aspect of this application, the method for analyzing the service life of a metallic material as described in the second aspect of this application, or the method for analyzing the mechanical performance of a metallic material as described in the fourth aspect of this application are realized.

[0062] According to another aspect of this application, a computer program product is provided which includes a computer program that, when executed by a processor, realizes the steps of the method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material as described in the first aspect of this application, or the method for analyzing the service life of a metallic material as described in the second aspect of this application, or the method for analyzing the mechanical performance of a metallic material as described in the fourth aspect of this application.

[0063] According to the eighth aspect of this application, a power consumption device is provided, which, A battery system comprising a battery case and battery cells located inside the battery case, wherein the battery case comprises a metal material as described in the first aspect of this application, The present application includes a service life analyzer for metallic materials as described in the third aspect of this application, a mechanical performance analyzer for metallic materials as described in the fifth aspect of this application, a computer device as described in the sixth aspect of this application, and at least one of a computer-readable storage medium as described in the seventh aspect of this application.

[0064] In some embodiments, the battery cell includes a fuel cell.

[0065] In some embodiments, the battery cell includes a lithium battery cell.

[0066] At least one of the above-mentioned metal material service life analyzer, metal material mechanical performance analyzer, computer equipment, computer-readable storage medium, and computer program product may be installed on the power consumption device, and this is used to realize the above-mentioned analysis model construction method, the above-mentioned metal material service life analysis method, or the above-mentioned metal material mechanical performance analysis method, which is advantageous for realizing better management and maintenance of the power consumption device, advantageous for realizing safe maintenance of the battery case in the power consumption device, and further advantageous for assisting in the design of battery cases with longer actual operating times.

[0067] Details of one or more embodiments and examples of this application are proposed in the following drawings and description. Other features, purposes and advantages of this application will become apparent from the specification, drawings and claims.

[0068] To better describe and illustrate the embodiments, examples, or cases of this application, one or more drawings may be referenced. Any additional details or examples used to describe the drawings should not be considered limitations to any one scope of the disclosed application, the embodiments, examples, or cases described, and the best mode of these applications as currently understood. In all drawings, the same reference numerals represent the same members. Furthermore, it should be noted that the drawings are all drawn in a simplified form and are merely intended to aid in the easy and clear explanation of this application. The various sizes of the members shown in the drawings are arbitrary and may be precise, and may not be drawn according to actual proportions. For example, to make the drawings clearer, the size of the members may be appropriately enlarged in some parts of the drawings. Unless otherwise specified, the members in the drawings are not drawn according to proportions. This application does not limit the various sizes of the members. [Brief explanation of the drawing]

[0069] [Figure 1] This is a flowchart of a method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material according to one embodiment of this application. [Figure 2] This is a flowchart of a method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material according to one embodiment of this application. [Figure 3] This is a flowchart of a method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material according to one embodiment of this application. [Figure 4] This is a flowchart of a method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material according to one embodiment of this application. [Figure 5] This is a flowchart of a method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material according to one embodiment of this application. [Figure 6] This is a flowchart of a method for analyzing the service life of a metallic material according to one embodiment of this application. [Figure 7] This is a schematic diagram of a metal material service life analyzer according to one embodiment of this application. [Figure 8] This is a flowchart of a method for analyzing the mechanical properties of a metallic material according to one embodiment of this application. [Figure 9] This is a flowchart of a method for analyzing the mechanical properties of a metallic material according to one embodiment of this application. [Figure 10] This is a schematic diagram of a mechanical performance analyzer for metallic materials according to one embodiment of this application. [Figure 11] This is an internal structure diagram of a computer device according to one embodiment of this application. [Figure 12] This is a schematic diagram of a power consumption device in one embodiment of the present application. [Figure 13]These are the results of salt spray tests in several embodiments of this application, showing macroscopic morphological diagrams of four types of aluminum alloy materials at different equivalent corrosion points from day 1 to day 24: (a) A356.2, (b) A380, (c) AlSi10MgMn, and (d) AlSi9MnMoZr. Each type of aluminum alloy material was sampled daily, with the first row from left to right corresponding to day 1 (1d) to day 6 (6d), the second row from left to right corresponding to day 7 (7d) to day 12 (12d), the third row from left to right corresponding to day 13 (13d) to day 18 (18d), and the fourth row from left to right corresponding to day 19 (19d) to day 24 (24d). [Figure 14] These are the results of salt spray tests in several embodiments of this application, showing the mass loss and corrosion rate at different equivalent corrosion points for four types of aluminum alloy materials. The four types of aluminum alloy materials are A356.2 (square, ■), A380 (dot, ●), AlSi10MgMn (upper triangle, ▲), and AlSi9MnMoZr (lower triangle, ▼). [Figure 15] One embodiment of this application is a fitting diagram showing the relationship between the corrosion rate and the equivalent corrosion time when performing a salt spray corrosion test on an A356.2 aluminum alloy. [Figure 16] One embodiment of this application is a fitting diagram showing the relationship between the corrosion rate and the equivalent corrosion time in a salt spray corrosion test of A380 aluminum alloy. [Figure 17] One embodiment of this application is a fitting diagram showing the relationship between the corrosion rate and the equivalent corrosion time in a salt spray corrosion test of an AlSi10MgMn aluminum alloy. [Figure 18] One embodiment of this application is a fitting diagram showing the relationship between the corrosion rate and the equivalent corrosion time in a salt spray corrosion test of an AlSi9MnMoZr aluminum alloy. [Figure 19]In some embodiments of this application, experimental and fitting results of mechanical parameters and equivalent corrosion time at different equivalent corrosion points in a salt spray corrosion test of an aluminum alloy material A356.2 are provided, where the mechanical parameters are tensile strength (UTS), elongation (El), and yield strength (YS), and where fit corresponds to the fitting curve. [Figure 20] In some embodiments of this application, experimental and fitting results of mechanical parameters and equivalent corrosion time at different equivalent corrosion points in salt spray corrosion tests of A380 aluminum alloy material are provided. [Figure 21] In some embodiments of this application, experimental and fitting results of mechanical parameters and equivalent corrosion time at different equivalent corrosion points are shown for salt spray corrosion tests of AlSi10MgMn aluminum alloy materials. [Figure 22] In some embodiments of this application, experimental and fitting results of mechanical parameters and equivalent corrosion time at different equivalent corrosion points are shown for salt spray corrosion tests of AlSi9MnMoZr aluminum alloy materials. [Figure 23] One embodiment of this application shows a schematic diagram of the size of a sample for tensile testing, with units in millimeters (mm), where R2.5 indicates a radius of 2.5 millimeters. [Figure 24] This is a schematic diagram of the structure of a three-electrode system in an electrochemical corrosion testing apparatus used in one embodiment of this application, where R, mA, and V represent a resistance meter, ammeter, and voltmeter, respectively. The combination of the three can be understood as an electrochemical workstation, and by adjusting the set voltage and current, the signal from the sample can be collected as measurement data for corrosion parameters. [Figure 25]The curves show the change in open-circuit voltage (OCP, chemical potential, unit V) over time for four types of aluminum alloy materials. A 3.5 wt% NaCl aqueous solution was used, and the four types of aluminum alloy materials are A356.2 (square, ■), A380 (dot, ●), AlSi10MgMn (upper triangle, ▲), and AlSi9MnMoZr (lower triangle, ▼). [Figure 26] The following are the electrochemical corrosion test and analysis results in some embodiments of this application, where (a) is the EIS Bode (AC impedance spectrum Bode plot), (b) is the Phase plot, where the horizontal axis corresponds to frequency (unit: Hz (Hertz)) and the vertical axis corresponds to the Phase angle (unit: ° (degrees)), (c) is the Nyquist plot, and (d) is the equivalent circuit diagram. [Figure 27] The dynamic potential polarization curves of electrochemical corrosion tests in some embodiments of this application are shown, where the horizontal axis represents the chemical potential (in units of V) and the vertical axis represents the current density (in units of A / cm²). [Figure 28] These are macroscopic images of the sample surface at different immersion corrosion times in immersion corrosion tests of several embodiments of this application. From left to right, the four aluminum alloy materials are (a) A356.2, (b) A380, (c) AlSi10MgMn, and (d) AlSi9MnMoZr. From top to bottom, the images correspond to the initial state before corrosion (0d, gauge 50mm), 10 days (10d), 20 days (20d), and 30 days (30d), respectively. [Figure 29] Figure 28 of this application is a summary diagram of corrosion rates at different immersion corrosion times for four pressure-cast aluminum alloy materials in an immersion corrosion test of an embodiment shown, where the four aluminum alloy materials are A356.2, A380, AlSi10MgMn, and AlSi9MnMoZr, respectively. [Figure 30]This diagram shows the relationship between the corrosion rate of different aluminum alloy materials N1 and A380 in a salt spray corrosion test in one embodiment of this application, where corrosion rates I, II, and III at different corrosion times represent the early, middle, and late stages of corrosion, respectively. [Figure 31] This diagram shows the relationship between mechanical parameters in a tensile test on an aluminum alloy material N1 in one embodiment of this application, where (a) the tensile stress-strain curves correspond to the initial state and the tensile stress-strain curves after corrosion at 5 days (5d), 15 days (15d), and 25 days (25d); and (b) the mechanical performance fitting results include the results for tensile strength, elongation, and yield strength, and their fitting lines. [Modes for carrying out the invention]

[0070] The following describes in detail several embodiments and examples of the method for constructing and applying the corrosion resistance-mechanical performance analysis model for metallic materials of this application, with appropriate reference to the drawings. However, unnecessary detailed explanations may be omitted. For example, detailed explanations of well-known matters and redundant explanations of structures that are actually the same may be omitted. This is to avoid making the following explanation unnecessarily long and to make it easily understandable to those skilled in the art. The drawings and the following explanation are provided to enable those skilled in the art to fully understand this application and do not limit the topics described in the claims.

[0071] The “range” disclosed in this application may be limited in the form of a lower limit and an upper limit, and a given range is limited by selecting one lower limit and one upper limit, the selected lower limit and upper limit define the boundary of a particular range. The range thus limited may include or exclude endpoints, and any single endpoint may be included or excluded independently, and any combination is possible, that is, any lower limit can be combined with any upper limit to form a range. For example, if the ranges 60-120 and 80-110 are listed for a particular parameter, it is understood that the ranges 60-110 and 80-120 can also be assumed. Furthermore, if the minimum range values ​​1 and 2 are listed, and the maximum range values ​​3, 4 and 5 are also listed, then the ranges 1-3, 1-4, 1-5, 2-3, 2-4 and 2-5 can all be assumed. In this application, unless otherwise specified, the numerical range "a~b" represents an abbreviated expression for any combination of real numbers a~b, where a and b are both real numbers. For example, the numerical range "0~5" means that all real numbers between "0~5" have already been listed in this specification, and "0~5" is merely an abbreviated expression for combinations of these numbers. Also, when a parameter is described as an integer ≥2, it is equivalent to disclosing that this parameter is, for example, an integer such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, etc. For example, when a parameter is described as an integer selected from "2~10", it is equivalent to listing the integers 2, 3, 4, 5, 6, 7, 8, 9 and 10.

[0072] In this application, terms such as "multiple," "multiple types," and "multiple claims" refer to two or more in number, unless otherwise specified. For example, "one or more types" means one or two or more types. To make it clear, when referring to "any or more" items, it refers to any combination of any multiple items, that is, a combination of "any or more" items that does not conflict and is in a manner that allows this application to be implemented.

[0073] Unless otherwise specified, all embodiments and optional embodiments of this application can be combined to form new technical inventions.

[0074] The “Examples” as used herein mean that certain features, structures, or characteristics described in conjunction with the Examples may be included in at least one Example or Embodiment of this Application. The appearance of this phrase at each location in the Specification does not necessarily refer to the same Example, nor does it mean that each Example is mutually exclusive or alternative to the others. Those skilled in the art will understand, both explicitly and implicitly, that the Examples described herein can be combined with other Examples. The “Embodiments” as used herein have a similar possible understanding.

[0075] As those skilled in the art will understand, in each embodiment or example of the method, the order in which each step is performed does not mean that there is any limitation on the execution process in a strict execution order, and the detailed execution order of each step should be determined by its function and possible inherent logic. Unless otherwise specified, all steps of this application may be performed sequentially, randomly, and preferably sequentially. For example, if method M includes steps (a) and (b), then method M may include steps (a) and (b) performed sequentially, or steps (b) and (a) performed sequentially. Also, if method M may further include step (c), then step (c) may be added to method M in any order, for example, method M may include steps (a), (b), and (c), or steps (a), (c), and (b), and so on.

[0076] In this application, open technical features or technical proposals described with terms such as “containing,” “incorporating,” or “including,” may be deemed to provide closed features or proposals consisting of listed members, without excluding additional members other than those listed, unless otherwise specified, and may further provide open features or proposals that include additional members other than those listed. For example, A includes a1, a2, and a3, and may further include other members, or not, unless otherwise specified, and may provide features or proposals where “A consists of a1, a2, and a3” or “A is selected from a1, a2, and a3,” and may further provide features or proposals where “A includes a1, a2, and a3, and further includes other members.”

[0077] In this application, unless otherwise specified, A (for example, B) may be understood to mean that B is a non-restrictive example of A, and that A is not limited to B.

[0078] In this application, "optionally," "selective," and "optional" refer to something that may or may not be present, that is, one of two parallel options, "present" or "absent." If multiple "optional" terms appear in a single technical proposal, each "optional" is considered independent unless otherwise specified, and there are no contradictions or mutual constraints. Unless otherwise explained, in this application, descriptions such as "optionally include" and "optionally encompass" use "optionally include" as an example to mean "may or may not include."

[0079] In this application, unless otherwise specified, a feature or solution corresponding to "and / or" includes any one of two or more related listed items, and also includes any and all combinations of related listed items, and "any and all combinations" includes any two related listed items, any more than two related listed items, or all combinations of related listed items. For example, "A and / or B" represents a combination consisting of A, B, and "a combination of A and B". Here, "encompassing A and / or B" can mean "encompassing A, encompassing B, and encompassing A and B", and can further mean "encompassing A, encompassing B, or encompassing A and B", and can be understood appropriately from the context in which it is placed.

[0080] As used herein, "the combination," "any combination," and "any combination scheme" include all appropriate combination schemes of any two or more of the listed items.

[0081] In this specification, "appropriate" in relation to "appropriate combination method," "appropriate method," "any appropriate method," etc., means that the technical proposal of this application can be implemented.

[0082] In this specification, terms such as “preferred,” “better,” “more suitable,” “good,” and “relatively good” are used to describe embodiments or examples that have a better effect and should be understood as not constituting a limitation on the scope of protection of this application. If multiple “preferred” terms appear in a single technical invention, each “preferred” is considered independent unless otherwise specified and there are no contradictions or mutual constraints between them.

[0083] In this application, words such as "furthermore," "in particular," "for example," "example," and "exemplify" are for descriptive purposes and indicate differences in content, but should not be understood as limitations on the scope of protection of this application.

[0084] In this application, terms such as "first aspect," "second aspect," "third aspect," and "fourth aspect," which are themselves terms, are used solely for descriptive purposes and should not be understood as indicating or suggesting relative importance or number, nor should they be understood as suggesting the importance or number of the technical features being referred to. Furthermore, terms such as "first," "second," "third," and "fourth" are merely for the purpose of non-exhaustive enumeration and should not be understood as constituting a closed limitation on number.

[0085] In this application, the term "room temperature" generally refers to 4°C to 35°C, and may also refer to 20°C ± 5°C. In some embodiments of this application, room temperature refers to 20°C to 30°C.

[0086] In this application, when referring to the units of a data range, if a unit is placed only after the rightmost endpoint, it indicates that the units of the leftmost and rightmost endpoints are the same. For example, 3~5h or 3~5h both indicate that the units of the leftmost endpoint "3" and the rightmost endpoint "5" are both h (hours), and both have the same meaning as 3h~5h. Similar descriptions of other parameters such as temperature and size are understood in the same manner.

[0087] The weights of the relevant components mentioned in the embodiments or examples of this application can indicate the content of each component and can also represent the proportional weight relationship between the components; therefore, any proportional expansion or contraction according to the content of the relevant components in the embodiments or examples of this application is within the scope described herein. Furthermore, the weights relating to the embodiments or examples of this application may be in mass units known in the chemical industry, such as μg, mg, g, and kg. Unless otherwise stated, a mass ratio is equal to the weight ratio in question; for example, if substance A has a mass of m1 and a weight of W1, and substance B has a mass of m2 and a weight of W2, then the mass ratio of both, m1 / m2, is numerically equal to the weight ratio W1 / W2.

[0088] In this application, unless otherwise specified, wt% represents a weight percentage based on weight and is numerically equivalent to the corresponding mass percentage based on mass.

[0089] In this application, "greater than or equal to" and "greater than or equal to" may both be represented as "≧", "less than or equal to" and "less than or equal to" may both be represented as "≦", "greater than" may be equivalently represented as ">", and "less than" may be equivalently represented as "<". In this application, unless otherwise specified, "greater than or equal to" and "≧" may be considered to provide two further solutions: "greater than" and "equal to". In this application, unless otherwise specified, "less than or equal to" and "≦" may be considered to provide two further solutions: "less than" and "equal to".

[0090] In this application, when there are exemplary descriptions such as "in some embodiments (or examples)" or "in one embodiment (or example)," these solutions can be appropriately combined with other solutions to form new technical solutions, but are not limited to this.

[0091] Currently, research on the corrosion behavior and mechanisms of fuel cell casing materials often focuses only on the electrochemical corrosion behavior of fuel cell casing materials or the corrosion rates in different corrosive media, making it difficult to provide effective guidelines for the design and development of fuel cell casing materials. The problem that research on the corrosion behavior of fuel cell casing materials is not sufficiently systematic and in-depth is becoming increasingly apparent. For example, the mechanism of white rust corrosion of fuel cell casing materials is not yet clear, the influence of metallic materials (e.g., alloy materials) on the corrosion behavior of fuel cell casing materials is not yet clear, and there is still no corresponding related research on the relationship between fuel cell casing materials and their mechanical performance and service life.

[0092] Through various embodiments and examples of this application, this application provides a method and application for constructing a corrosion resistance-mechanical performance analysis model for metallic materials. This application relates to power consumption devices, including battery systems.

[0093] Through various embodiments and examples of this application, this application provides a method for constructing a corrosion resistance-mechanical performance analysis model for metallic materials, a method and apparatus for analyzing the service life of metallic materials, a method and apparatus for analyzing the mechanical performance of metallic materials, computer equipment, computer-readable storage media, and power consumption devices. This corrosion resistance-mechanical performance analysis model for metallic materials can be effectively used to predict the mechanical performance and evaluate the service life of metallic materials. The metallic materials may include, but are not limited to, aluminum alloy materials, and may also include, but are not limited to, battery case materials (e.g., fuel cell case materials).

[0094] According to a first aspect of this application, a method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material is provided, which may be used to construct an analytical model between the degree of corrosion and mechanical performance under corrosion test conditions simulating the target actual usage environment of the metallic material, or it may be used to predict the mechanical performance of the metallic material. When the mechanical performance is related to the inactive behavior of the metallic material, this construction method can be used to obtain an analytical model between the degree of corrosion and the inactive-related mechanical performance under corrosion test conditions simulating the target actual usage environment of the metallic material, and may be used for predictive analysis of the mechanical performance and evaluation of the service life of the metallic material.

[0095] In some embodiments, this application provides a method for constructing a corrosion resistance-mechanical performance analysis model for metallic materials.

[0096] In some embodiments, the method for constructing this analytical model includes the steps of: taking a metallic material as the test subject and obtaining experimental data sets of corrosion performance tests at different equivalent corrosion points under corrosion test conditions, and experimental data sets of mechanical performance tests at different degrees of corrosion corresponding to different equivalent corrosion points; and establishing empirical relationships between corrosion parameters and mechanical parameters that change with the equivalent corrosion time, respectively. Furthermore, the corrosion test conditions may be used to simulate the target actual usage environment.

[0097] Based on the corrosion performance test experimental dataset, a first set of empirical relationships can be established that show how corrosion parameters change with equivalent corrosion time.

[0098] Based on the experimental data set of mechanical performance tests, a second set of empirical relationships can be established that show how mechanical parameters change with equivalent corrosion time.

[0099] In some embodiments, a method for constructing a corrosion resistance-mechanical performance analysis model for metallic materials is provided, which is, A step of taking a metal material as the test subject and obtaining experimental data sets of corrosion performance tests at different equivalent corrosion points under corrosion test conditions and experimental data sets of mechanical performance tests at different degrees of corrosion corresponding to different equivalent corrosion points, wherein the corrosion test conditions are used to simulate the target actual usage environment, The method includes the steps of establishing a first set of empirical relations that change with the equivalent corrosion time of corrosion parameters based on a corrosion performance test experimental dataset, and further establishing a second set of empirical relations that change with the equivalent corrosion time of mechanical parameters based on a mechanical performance test experimental dataset.

[0100] This analytical model can be effectively used to predict the mechanical properties and evaluate the service life of metallic materials, which may include aluminum alloy materials and may further include battery case materials.

[0101] In some embodiments, a method for constructing a corrosion resistance-mechanical performance analysis model for metallic materials is provided, which includes the following steps (see Figure 1).

[0102] S110: A metal material is used as the test subject, and test values ​​of corrosion parameters at different equivalent corrosion points under corrosion test conditions are obtained to obtain a corrosion performance test experimental dataset. Furthermore, test values ​​of mechanical parameters at different corrosion degrees corresponding to different equivalent corrosion points are obtained to obtain a mechanical performance test experimental dataset. Here, the corrosion test conditions are used to simulate the target actual usage environment of the metal material, and the corrosion parameters include at least one of the corrosion degree and corrosion rate. S120: Based on the corrosion performance test experimental dataset, establish a first set of empirical relations that change with the equivalent corrosion time of corrosion parameters, and further establish a second set of empirical relations that change with the equivalent corrosion time of mechanical parameters based on the mechanical performance test experimental dataset.

[0103] In some embodiments, a method for constructing a corrosion resistance-mechanical performance analysis model for metallic materials is provided, which includes the following steps (see Figure 2).

[0104] S110: A metal material is used as the test subject, and a corrosion performance test experimental dataset is obtained by acquiring test values ​​of corrosion parameters at different equivalent corrosion points under corrosion test conditions. Furthermore, a mechanical performance test experimental dataset is obtained by acquiring test values ​​of mechanical parameters at different corrosion degrees corresponding to different equivalent corrosion points. Here, the corrosion test conditions are used to simulate the target actual usage environment of the metal material, the corrosion parameters include at least one of corrosion degree and corrosion rate, and the mechanical parameters include at least one of tensile strength and elongation at break. S120: Based on the corrosion performance test experimental dataset, establish a first set of empirical relations that change with the equivalent corrosion time of corrosion parameters, and further establish a second set of empirical relations that change with the equivalent corrosion time of mechanical parameters based on the mechanical performance test experimental dataset.

[0105] In this application, unless otherwise specified, "metallic material" refers to a solid material having a certain macroscopic size, but is not limited to, for example, a plate or a block.

[0106] In this application, unless otherwise specified, “actual usage environment” refers to the environment in actual application scenarios of metallic materials.

[0107] In this application, unless otherwise specified, "degree of corrosion" refers to the degree of structural loss of a metal material compared to when it is not corroded, and the degree of corrosion can be characterized by "mass loss compared to when it is not corroded," but is not limited to this. The greater the mass loss, the higher the degree of corrosion.

[0108] In this application, unless otherwise specified, "equivalent corrosion time" refers to the fact that under possible corrosion test conditions that simulate the target actual usage environment, there is a certain correspondence between the corrosion test time experienced by the metal material and the actual usage time in the target actual usage environment, and that the corrosion test time can indirectly reflect the actual usage time conditions. Therefore, this corrosion test time is also referred to as "equivalent corrosion time."

[0109] In this application, unless otherwise specified, "corrosion rate" represents the amount of corrosion loss per unit time. Corrosion loss may, but is not limited to, be expressed in terms of mass, volume, or line length (e.g., corrosion depth). The corrosion rate can be obtained by dividing the total loss during a given period by the length of that period.

[0110] In this application, unless otherwise specified, "tensile strength" and "elongation at break" have the meanings known in the art. "Tensile strength" may also be written as tensile strength or ultimate tensile strength, and in a tensile test, it refers to the maximum tensile stress that a sample can withstand, and is numerically equal to the tensile force that the sample experiences when it breaks under tensile force. "Elongation at break" refers to the percentage of the deformation of the length of the sample at tensile break relative to its original length, where the original length is the length of the sample when no tensile force is applied. For a sample having a certain length L0, a tensile force is applied in the longitudinal direction, and the tensile force F experienced at the time of breakage of the sample is defined as follows: max The ratio of the cross-sectional area A is equal to the "tensile strength," "cross-section" refers to the cross-section perpendicular to the longitudinal direction of the sample, and the "elongation at break" δ = (L - L0) × 100%, where L is the length at which the sample breaks under tensile force.

[0111] By using metallic materials as test subjects, employing corrosion test conditions to simulate the target actual usage environment of metallic materials, and obtaining experimental data sets of corrosion performance at different equivalent corrosion points and experimental data sets of mechanical performance at different corrosion degrees corresponding to different equivalent corrosion points, and by linking the equivalent corrosion time under corrosion test conditions with the mechanical performance at different corrosion degrees, we can establish a first set of empirical relational expressions that change with the equivalent corrosion time for corrosion parameters including at least one of corrosion degree and corrosion rate, and a second set of empirical relational expressions that change with the equivalent corrosion time for at least one mechanical parameter. This allows us to construct an analytical model between the degree of corrosion resistance and mechanical performance of metallic materials, support the understanding of the corrosion mechanism of metallic materials, predict and analyze the mechanical performance of metallic materials under target actual usage conditions based on the corrosion behavior of metallic materials, and evaluate the service life of metallic structural members in the target actual usage environment.

[0112] Based on any suitable embodiment of this application, and in some embodiments, the mechanical parameter includes at least one of tensile strength and elongation at break.

[0113] If the mechanical parameters in this method may include at least one of tensile strength and fracture elongation, the resulting corrosion resistance-mechanical performance analysis model for metallic materials can be applied to metallic structural members that are prone to being rendered ineffective under tensile stress.

[0114] Based on any suitable embodiment of this application, in some embodiments, the mechanical parameters in the mechanical performance test experimental dataset include at least one of tensile strength and elongation at break, and more selectively include yield strength.

[0115] Based on any suitable embodiment of this application, and in some embodiments, the mechanical parameters in the mechanical performance test experimental dataset include at least tensile strength and elongation at break, and more selectively, yield strength.

[0116] Based on any suitable embodiment of this application, in some embodiments, the mechanical parameters include yield strength.

[0117] By selecting appropriate parameters and using them to fit the functional relationships, the fitting effect is advantageous in obtaining a more favorable second set of empirical relationships, and further advantageous in obtaining a more effective corrosion resistance degree-mechanical performance analysis model.

[0118] In this application, unless otherwise specified, tensile tests may be performed on an electronic tensile testing machine, for example, on a Z20 TEW electronic tensile testing machine. In this application, unless otherwise specified, the load rate when performing tensile tests may be 0.5 mm / min to 1.5 mm / min.

[0119] Tensile tests can be performed using samples of the sizes shown in Figure 23 without limitation. The sample in Figure 23 is a plate-shaped tensile specimen with a length of 60 millimeters (mm) and a thickness of 2 mm.

[0120] In addition, when performing a tensile test, the tensile sample to be measured may be wrapped in blue film, leaving only the test surface exposed. The total duration of the test may be set to 600 hours (h), and a group of samples may be taken every 24 hours during the test process to test their tensile performance. At least three parallel samples may be set up for each test.

[0121] Salt spray corrosion testing experiments may be conducted using commonly used salt spray corrosion testing equipment in this field. Without restriction, the LP / YWX-250 model salt spray corrosion testing equipment may also be used. During the test process, the sample may be left on a V-shaped bracket, allowing the salt spray to fall slowly and evenly onto the sample surface.

[0122] In this application, unless otherwise stated, the corrosion rate can be characterized by employing the average corrosion depth per unit time, and furthermore, The corrosion rate can be calculated using the formula: Corrosion rate = (K × W) / (A × T × D) mm / y (I). In equation (I), K = 8.64 × 10 4 And K is a time constant, W represents the mass difference before and after the test, and its unit is mg. A is the area of ​​the test surface, and its unit is cm. 2 And, T represents the test duration, and its unit is h. D = 2.7 g / cm³ 3 And D is the density of the test sample.

[0123] In formula (I), the unit of corrosion rate is millimeters per year and may be written as "mm / y".

[0124] In some embodiments based on any suitable embodiment of this application, the metallic material is an aluminum alloy material. The metallic material may further be a metallic structural member, or an aluminum alloy structural member.

[0125] Based on any suitable embodiment of this application, in some embodiments the metal material may be a battery case material, and may be any one of the battery case materials. In some embodiments the battery case material may include a fuel cell case material. In some embodiments the battery case material may include a lithium battery case material.

[0126] In some embodiments, the metal material includes at least some structural members of the battery case. In some embodiments, the battery case structural members may include at least some structural members of the fuel cell case. In some embodiments, the battery case structural members may include at least some structural members of the lithium battery case.

[0127] In this application, unless otherwise specified, "structural member" may be an independent object or a part of a structure within an independent object. "Metal structural member" is a structural member made of metal.

[0128] In the application, unless otherwise specified, “battery case material” refers to the main material constituting the battery case, which is the main material of at least a portion of the structure in the battery case, and the mass occupancy of the battery case material in this portion of the structure may exceed 80%, further exceed 90%, further approach 100%, or be 100%. In some embodiments, the battery case material may also refer to the constituent material of the battery case, i.e., at least a portion of the structural members of the battery case are made of this “battery case material”.

[0129] In the application, unless otherwise specified, “fuel cell case” refers to a battery case in which a fuel cell cell is housed, and “fuel cell case material” refers to the main material constituting the fuel cell case, which is the main material of at least a portion of the structure in the fuel cell case, and unless otherwise specified, the mass occupancy of this portion of the structure of the fuel cell case material may exceed 80%, may exceed 90%, may approach 100%, or be 100%. In some embodiments, the fuel cell case material may also refer to the constituent material of the fuel cell case, i.e., at least a portion of the structural members of the fuel cell case are made of this “fuel cell case material”.

[0130] In this application, unless otherwise specified, "fuel cell" has the meaning known in the art and refers to a chemical device that directly converts the chemical energy of a fuel into electrical energy. The actual operating environment in which the fuel cell case is located is prone to contact with various corrosive media, including moisture, rainfall, salt spray, and various organic and inorganic liquids used in vehicle or cleaning processes.

[0131] Pressure-cast aluminum alloys have advantages such as low density, high specific strength, excellent thermal stability, good machinability, and low cost, and may be used to manufacture a variety of parts, including, but not limited to, automobile cylinder blocks, generator cases, fuel cell cases, and various engine brackets.

[0132] The metallic material may be an aluminum alloy material or a metallic structural member, and may further be an aluminum alloy structural member. Pressure-cast aluminum alloys have advantages such as low density, high specific strength, excellent thermal stability, good machinability, and low cost, and may be used to manufacture various parts including, but not limited to, automobile cylinder blocks, generator cases, fuel cell cases, and various engine brackets. Here, since the aluminum alloy material is applicable to the battery technology field and can be used as the main material (including compositional material) of a battery case or an aluminum alloy structural member in a battery case, the metallic material may be a battery case material, and the battery case may include, but not limited to, a fuel cell case, and may further include, but not limited to, a lithium battery case, and accordingly, the metallic material may include at least some structural members of the battery case. When obtaining corrosion parameters and mechanical parameters based on the aluminum alloy material, the corrosion resistance degree-mechanical performance analysis model of the metallic material constructed can be applied to predicting the mechanical performance and evaluating the lifespan of the aluminum alloy material, and in this case, it is also advantageous for understanding the white rust corrosion mechanism of the aluminum alloy material. When aluminum alloy materials are used as the main material or component material of a battery case or structural member within a battery case, the corrosion resistance-mechanical performance analysis model for metallic materials can be applied to predicting the mechanical performance and evaluating the lifespan of the battery case or structural member within a battery case.

[0133] Using fuel cell cases as an example, this can further contribute to understanding the white rust corrosion mechanism of fuel cell case materials. When fuel cell case materials are used as test subjects, the corrosion behavior of fuel cell case materials can be analyzed, the corrosion rates of different fuel cell case materials can be determined, and a relationship between the degree of corrosion of fuel cell case materials and their mechanical performance can be established. This has value as a model for the safe operation of fuel cells and, by extension, power consumption devices including fuel cells.

[0134] Based on any suitable embodiment of this application, in some embodiments, the corrosion performance test experimental dataset under corrosion test conditions includes at least the corrosion performance test experimental dataset under salt spray corrosion test conditions. Non-limitingly, the salt spray corrosion test conditions may include one or more of the following: NaCl aqueous solution salt spray conditions, acetic acid salt spray conditions, copper salt accelerated acetic acid salt spray conditions, and alternating salt spray corrosion conditions. In some embodiments, the salt spray corrosion test conditions include NaCl aqueous solution salt spray conditions.

[0135] When the corrosion performance test experimental dataset includes at least the corrosion performance test experimental dataset under salt spray corrosion test conditions, the resulting corrosion resistance-mechanical performance analysis model for metallic materials can be applied to metallic materials and their structural components or products in real-world usage environments in fields such as road transport, computer, electronic communications, and electrical equipment, and can further be applied to structural components or products in related real-world usage environments such as electroplating, coating, packaging boxes, and transportation equipment. Here, the road traffic field may include, but is not limited to, road vehicle electronic and electrical equipment, rail transport locomotive vehicle equipment and devices, automobile parts, and other equipment or their metal structural components; the computer field may include, but is not limited to, computers, displays, hosts, computer devices, precision instruments such as medical equipment, and other equipment and products or their metal structural components; the electronic communications field may include, but is not limited to, mobile phones, radio frequency devices, electronic communication devices, printed circuit boards (PCBs), printed circuit board assemblies (PCBAs), and other equipment and products or their metal structural components; and electrical equipment may include, but is not limited to, home appliances, lighting fixtures, substations, and other various home appliances and electrical equipment, instruments and meters, medical equipment, and other equipment and products or their metal structural components.

[0136] Models that obtain corrosion performance test experimental data sets based on NaCl aqueous solution salt spray conditions are applicable to determining the mass and uniformity of protective coatings and to comparing differences in salt spray corrosion resistance of samples with similar structures, but are not limited to these applications. Models that obtain corrosion performance test experimental data sets based on acetic acid salt spray conditions are applicable to coastal cities in southern China and relatively harsh salt spray environments. Models that obtain corrosion performance test experimental data sets based on copper salt accelerated acetic acid salt spray conditions are applicable to harsh salt spray environments. Models that obtain corrosion performance test experimental data sets based on alternating salt spray corrosion conditions are applicable to high-temperature and high-humidity environments.

[0137] Based on any suitable embodiment of this application, in some embodiments, the NaCl aqueous solution salt spray conditions include the parameter of simulated salt spray conditions for a 3 wt% to 6 wt% NaCl aqueous solution at 34 to 36°C. In some embodiments, the NaCl aqueous solution salt spray conditions include the parameter of simulated salt spray conditions for a 5 wt% NaCl aqueous solution at 35°C. In some embodiments, the NaCl aqueous solution salt spray conditions include the parameter of simulated salt spray conditions for a 3.5 wt% NaCl aqueous solution at 35°C.

[0138] The aforementioned test conditions for NaCl aqueous solution salt spraying are advantageous for determining the corrosion resistance of battery cases, automobile parts, and other materials.

[0139] Without restriction, the start time for placing the sample under corrosion test conditions may be the time of the initial sampling.

[0140] In addition, when conducting corrosion test experiments, sampling methods for different equivalent corrosion time points may be used as long as the total test time (i.e., the length of a predetermined time period) is ≥ 30 hours, and the time interval between adjacent sampling points may be ≥ 1 day. In addition, sampling may be performed in the following manner: The total test time is 600 hours, with sampling performed once every 24 hours, and the initial sampling point may be the start time when the sample is placed under corrosion test conditions. Furthermore, sampling may be performed in the following manner: The total test time is 30 days, with sampling performed once every 10 days, and the initial sampling point may be the start time when the sample is placed under corrosion test conditions.

[0141] To make it clearer, when conducting corrosion test experiments, at any given time point, one or more test analyses may be selectively performed on the sample, including macromorphological observation, micromorphological observation, and corrosion rate analysis. For example, one or more of the aforementioned test analyses may be performed every 1 to 10 days. In some embodiments, macromorphological observation, micromorphological observation, and corrosion rate analysis are performed every 10 days.

[0142] Unrestrictedly, "macromorphological observation" may be performed using methods such as stereomicroscopes and shadowless lamp observations.

[0143] Unrestrictedly, "micro-morphological observation" includes local morphology of the sample at different magnifications (e.g., 200 to 10,000x), and may be performed using means such as a metallurgical microscope or scanning electron microscope (SEM). Unrestrictedly, a CX40M metallurgical microscope or a field emission scanning electron microscope FEI NOVA NanoSEM 230 may be employed.

[0144] For salt spray corrosion tests, corrosion parameters and corresponding mechanical parameters may be obtained by sampling at intervals of 12 to 36 hours (e.g., every 24 hours), thereby obtaining a corrosion performance test experimental dataset and a mechanical performance test experimental dataset. The test duration may be ≥ 24 days, ≥ 25 days, and even ≥ 30 days.

[0145] Without limitation, the sample for the salt spray corrosion test may be a mass measuring 12 mm × 12 mm × 6 mm. Without limitation, the non-test surface may be wrapped in blue film. Before performing the salt spray corrosion test, pretreatment including barrel polishing of the test surface, polishing, ethanol washing, and drying may be performed. Before performing the salt spray corrosion test, the sample is weighed and the initial weight W0 is recorded. Unless otherwise specified, the following test parameters may be adopted. The total duration of the test is 600 h, and during the test process, samples may be taken and tested every 24 h for analysis, macromorphological and micromorphological observations may be performed, and then the corrosion products on the sample surface may be washed away, and the sample is weighed again, with the weighing result of the i-th sample recorded as Wi, and the duration of the test at the i-th sample recorded as Ti, and the corrosion rate at the i-th sample can be obtained by substituting W=Wi-W0 and T=Ti into equation (I) above. Unless otherwise specified, at least three parallel samples should be set up for each test. The sample surface may be washed with a chromic acid detergent (20 g / L Cr2O3 + 50 mL / L H3PO4) without restriction to remove corrosion products.

[0146] Based on any suitable embodiment of this application, and further in some embodiments, a first set of empirical relations that change with the equivalent corrosion time of corrosion parameters based on a corrosion performance test experimental dataset is established. This method involves segmenting and fitting each corrosion parameter based on the corresponding corrosion performance test experiment dataset, fitting the corresponding corrosion parameter as the dependent variable and the equivalent corrosion time as the independent variable for each segment's fitting interval, and constructing an empirical relationship between the type of corrosion parameter and the equivalent corrosion time corresponding to the fitting interval of each segment, thereby obtaining a first set of empirical relationships.

[0147] The establishment of a first set of empirical relationships that change with the equivalent corrosion time of corrosion parameters can be obtained by segmenting and fitting each corrosion parameter. The fitting method for each segment may be a power function or a linear function, and in this case the agreement between the fitting curve and the experimental test dataset will be higher and the model will be more effective, but it is not limited to the function type described above.

[0148] Based on any suitable embodiment of this application, and further in some embodiments, in the first set of empirical relations, the fitting method for the power function is y1 = A·x B The fitting method for the linear function is y1 = a + b·x, where x is the equivalent corrosion time, y1 is the corrosion parameter, A is a positive number, B is a negative number, b is a positive number, and a is a negative number.

[0149] Based on any suitable embodiment of this application, and in some embodiments further, A is a real number selected from 0.01 to 1.00, B is a real number selected from -0.3 to -0.8, b is a real number selected from 0.001 to 0.05, and a is a real number selected from 0.02 to -0.8. Furthermore, non-restrictive examples of A include, for example, 0.145, 0.755, 0.756, 0.125, 0.126, 0.134, etc. Non-restrictive examples of B include, for example, -0.533, -0.531, -0.532, -0.681, -0.680, -0.688, etc. a may further be a real number selected from the ranges -0.001 to -0.8, -0.001 to -0.7, -0.001 to -0.5, etc., and non-restrictive examples of a are, for example, -0.124, -0.0132, -0.00171, -0.366, -0.668, etc. b may further be a real number selected from 0.001 to 0.02, or further a real number selected from 0.001 to 0.01, and non-restrictive examples of b are, for example, 0.00713, 0.00334, 0.0197, 0.0311, 0.00222, etc. Furthermore, in some embodiments, the corrosion rate may be fitted, and even further, the corrosion rate may be expressed in terms of corrosion depth per unit time.

[0150] By selecting an appropriate fitting function type, the fitting effect is advantageous in obtaining a more favorable first set of empirical relations, and furthermore, a more effective corrosion resistance degree-mechanical performance analysis model is obtained.

[0151] Depending on the differences in the corrosion parameters for fitting, factors such as A, B, a, and b in the above function may be selected from different ranges, and for any one of these factors, an interval consisting of any two exemplary point values ​​described in this application may be selected if it is appropriate.

[0152] Based on any suitable embodiment of this application, and in some embodiments further, the corrosion parameters in the corrosion performance test experimental dataset include at least the corrosion rate.

[0153] Based on any suitable embodiment of this application, in some embodiments, the corrosion performance test experimental dataset further includes at least one of a corrosion performance test experimental dataset under electrochemical corrosion test conditions and a corrosion performance test experimental dataset under immersion corrosion test conditions. Not limited to, the corrosion parameters in the corrosion performance test experimental dataset under electrochemical corrosion test conditions may include at least one electrochemical corrosion parameter, which is the self-corrosion potential and the self-corrosion current. Not limited to, the corrosion parameters in the corrosion performance test experimental dataset under immersion corrosion test conditions may include at least one of the degree of corrosion and the corrosion rate.

[0154] When the corrosion performance test experimental dataset includes corrosion performance test experimental datasets under electrochemical corrosion test conditions, this corrosion resistance-mechanical performance analysis model for metallic materials can be applied to predicting the performance and lifespan of metallic materials in electrochemical environments, such as predicting the performance and lifespan of battery cases, but is not limited to that. It may also, but is not limited to, predicting the performance and lifespan of fuel cell cases.

[0155] When the corrosion performance test experimental dataset includes data from corrosion performance test experiments under immersion corrosion test conditions, this corrosion resistance-mechanical performance analysis model for metallic materials can be applied to predict the performance and life assessment of metallic materials in environments where they come into contact with corrosive liquids.

[0156] When the corrosion performance test experimental dataset includes both a corrosion performance test experimental dataset under electrochemical corrosion test conditions and a corrosion performance test experimental dataset under immersion corrosion test conditions, this corrosion resistance-mechanical performance analysis model for metal materials can be applied to predict the performance and lifespan of battery cases containing electrolytes, and may also include, but is not limited to, performance prediction and lifespan of fuel cell cases.

[0157] In some embodiments, the corrosion performance test experimental dataset under corrosion test conditions includes a corrosion performance test experimental dataset under electrochemical corrosion test conditions, and further, the electrochemical corrosion test may include one or more of the following: open-circuit voltage test, AC impedance test, dynamic potential polarization test, etc. Accordingly, the corrosion parameters in the corrosion performance test experimental dataset may include one or more of the following: open-circuit voltage, impedance, etc.

[0158] The test equipment for electrochemical corrosion testing may include an electrochemical workstation and a three-electrode system, where a schematic diagram of the three-electrode system can be found in Figure 24. Non-limitingly, different excitation signals may be applied to the three-electrode system by the electrochemical workstation, and the electrochemical corrosion behavior of the sample to be measured is established by recording the corresponding response signals. In some non-limiting embodiments, in the three-electrode system, the sample to be measured is the working electrode, the silver-silver chloride (Ag-AgCl) electrode is the reference electrode, the platinum (Pt) electrode is the auxiliary electrode, the electrolyte solution used may be a 3.5 wt% NaCl aqueous solution, and the three-electrode system is placed in a Faraday shield box during testing.

[0159] Unrestrictedly, the sample for the electrochemical corrosion test may be a plate-like object with dimensions of 10 mm x 10 mm x 2 mm. The test surface area is 1 cm². 2 This may also be done. Before testing, the sample may undergo pretreatment including cold fitting with epoxy resin, barrel polishing with sandpaper, polishing, ethanol washing, and drying.

[0160] Unrestrictedly, electrochemical corrosion testing may include measuring the open-circuit voltage (OCP) and performing an AC impedance (EIS) test after the OCP has stabilized for a period of time (e.g., after 60 minutes of OCP stability), and further measuring the electrochemical impedance spectrum of the sample at different AC frequencies. A 10mV AC sine wave may be used as the excitation voltage during testing, and the test frequency can be controlled within 0.01Hz to 105Hz. Generally, an equivalent circuit can be employed to fit the experimental results of the EIS, and the parameters of each element in the equivalent circuit diagram can be analyzed.

[0161] Unrestrictedly, the electrochemical corrosion test step may further include a dynamic potential polarization test after the AC impedance test, and the following test parameters may be employed: scanning from a potential of OCP -300mV at a scanning rate of 0.167mV / s, sustained until the current exceeds 1mA. Tafel fitting is performed on the polarization characteristic data of the sample using ZSimpWin v3.40 software.

[0162] In some embodiments, the corrosion performance test experimental dataset under corrosion test conditions includes a corrosion performance test experimental dataset under immersion corrosion test conditions. Non-limitingly, the corrosion parameters in the corrosion performance test experimental dataset under immersion corrosion test conditions may include at least one of the degree of corrosion and the corrosion rate.

[0163] In addition, when conducting immersion corrosion tests, sampling may be performed in the following manner: The total test period is 30 days, with sampling taken once every 10 days, and the initial sampling point may be the start time when the sample is placed under corrosion test conditions. At each sampling point, one or more test analyses may be selectively performed on the sample, including macromorphological observation, micromorphological observation, and corrosion rate analysis, but are not limited to these.

[0164] In some embodiments, the immersion corrosion test includes steps where the total test time is 30 days, sampling and testing are performed every 10 days, and the macro morphology, micro morphology, and corrosion rate of the samples can be tested and analyzed.

[0165] Without limitation, the sample for the immersion corrosion test may be a block of 15 mm × 15 mm × 2 mm, or may be a block of 50 mm × 25 mm × 2 mm, or may be other shaped samples that require further testing.

[0166] Without limitation, the composition of the corrosion solution for sample immersion can be determined according to the corrosion environment where the sample to be measured is located. A 500 mL corrosion solution may be employed. In some embodiments, the corrosion solution is an aqueous solution of NaCl with 3 wt% - 6 wt%, such as an aqueous solution of NaCl with 3 wt%, 3.5 wt%, 4 wt%, 4.5 wt%, 5 wt%, 5.5 wt% or 6 wt%.

[0167] Without limitation, when performing the immersion corrosion test, the ratio of the corrosion liquid to the sample test area can be maintained to be greater than 0.2 mL / mm 2 in the test process.

[0168] Without limitation, before performing the immersion corrosion test, pretreatment including steps such as barrel polishing, polishing the test surface, rinsing with deionized water and ethanol in sequence, drying, and weighing may be performed. After weighing, the sample may be placed in a desiccator for storage, if necessary.

[0169] Indefinitely, performing an immersion corrosion test involves placing a sample in a container and adding 500 mL of corrosion solution. After sealing, the entire container is placed in a constant temperature water bath set to 25°C. A sample is taken every 10 days (i.e., test times are 10d, 20d, and 30d respectively) and the macro and micro morphologies of the sample surface after corrosion are observed. Then, the corrosion products on the sample surface are removed, the sample mass before and after corrosion is compared, and the corresponding immersion corrosion rate is calculated using formula (I). At least three parallel samples are set up for each test.

[0170] In this application, when referring to units of time, unless otherwise specified, 1d refers to one day and 1h refers to one hour.

[0171] Based on any suitable embodiment of this application, and further in some embodiments, a second set of empirical relations that change with the equivalent corrosion time of mechanical parameters based on a mechanical performance test experimental dataset is established. This method involves segmenting and fitting each mechanical parameter in the mechanical parameters based on the corresponding mechanical performance test experimental dataset, fitting each segment's fitting interval with the corresponding mechanical parameter as the dependent variable and the equivalent corrosion time as the independent variable, in the form of a power function or linear function, constructing an empirical relationship between the corresponding type of mechanical parameter and the equivalent corrosion time, and obtaining a second set of empirical relationships.

[0172] The establishment of a second set of empirical relationships that change with the equivalent corrosion time of the mechanical parameters can be obtained by segmenting and fitting each mechanical parameter. The fitting method for each segment may employ a power function or a linear function, and in this case, the agreement between the fitting curve and the experimental test dataset will be higher and the model will be more effective, but it is not limited to the function type described above.

[0173] Based on any suitable embodiment of this application, and further in some embodiments, in the second set of empirical relations, the fitting method for the power function is y² = M·x N The fitting method for the linear function is y² = m + n·x, where x is the equivalent corrosion time, y² is the mechanical parameter, M is a positive number, N is a negative number, n is a negative number, and m is a positive number.

[0174] Based on any suitable embodiment of this application, and in some embodiments further, M is a real number selected from 1 to 250, N is a real number selected from -0.01 to -1, n is a real number selected from -0.05 to -5, and may further be selected from -0.1 to -1.5, and m is a real number selected from 1 to 300. Unrestricted examples of M are, for example, 233.54, 152.16, 1.60, etc. N may further be a real number selected from -0.01 to -0.5, and may further be a real number selected from -0.01 to -0.2, and unrestricted examples of N are, for example, -0.11, -0.07, -0.16, etc. Unrestricted examples of m include, for example, 269.59, 203.21, 9.69, 248.48, 134.55, 3.95, 248.21, 131.83, 8.31, etc. Unrestricted examples of n include, for example, -1.44, -1.06, -0.15, -1.91, -0.40, -0.08, -1.61, -0.73, -0.19, etc. n may also be a real number selected from the ranges -0.05 to -4, -0.05 to -3, -0.05 to -2, -1 to -2, -1 to -2, -0.5 to -1.5, -0.05 to -0.5, etc.

[0175] By selecting an appropriate fitting function type, the fitting effect is advantageous in obtaining a more favorable second set of empirical relations, and further advantageous in obtaining a more effective corrosion resistance degree-mechanical performance analysis model.

[0176] Depending on the differences in the mechanical parameters for fitting, the factors M, N, m, n, etc. in the above function may be selected from different ranges, and for any one of these factors, an interval consisting of any two exemplary point values ​​described in this application may be selected, if such an interval is appropriate, but is not limited to this. For example, when fitting tensile strength (UTS), n may be selected from any appropriate range such as -0.05 to -5, -0.1 to -5, -0.2 to -5, -0.05 to -4, -0.1 to -4, -0.2 to -4, -0.05 to -3, -0.1 to -3, -0.2 to -3, -0.3 to -3, etc., and m may be selected from any appropriate range such as 60 to 300, 80 to 300, 60 to 250, 80 to 250, 100 to 300, 100 to 250, 120 to 240, etc. For example, when fitting the elongation at break (El), n may be selected from any appropriate range such as 0.05 to -0.5, -0.05 to -0.4, -0.05 to -0.3, -0.05 to -0.2, or -0.06 to -0.2, and m may be selected from any appropriate range such as 0.1 to 50, 0.1 to 30, 0.1 to 20, 0.1 to 10, 0.5 to 50, 0.5 to 30, 0.5 to 20, 0.5 to 10, 1 to 50, 1 to 30, 1 to 20, or 1 to 10. For example, when fitting the yield strength (YS), n may be selected from any appropriate range such as -0.05 to -5, -0.1 to -5, -0.2 to -5, -0.5 to -5, -0.05 to -4, -0.1 to -4, -0.2 to -4, -0.5 to -4, -0.05 to -3, -0.1 to -3, -0.2 to -3, -0.3 to -3, -0.5 to -3, -0.8 to -2.5, -0.8 to -2.4, -0.8 to -2.0, and m may be selected from any appropriate range such as 50 to 300, 80 to 300, 50 to 250, 80 to 250, 100 to 300, 100 to 250, 120 to 250.

[0177] Based on any suitable embodiment of this application, in some embodiments, a method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material further includes the step of establishing a third set of empirical relationships between equivalent corrosion time under corrosion test conditions and actual use time in a target actual use environment.

[0178] By establishing an empirical relationship (which may be written as a third set of empirical relationships) between the equivalent corrosion time under corrosion test conditions and the actual usage time in the target actual usage environment, the equivalent corrosion time can be converted into the actual usage time in the target actual usage environment, thereby enabling a more direct prediction of the service life of metal materials and their structural components or products.

[0179] Without restriction, a third set of empirical relationships can be established between equivalent corrosion time under corrosion test conditions selected by existing standards, norms, or common experience in the art, and actual usage time in the target actual usage environment. For example, if a pressure-cast aluminum alloy part is exposed to a salt spray corrosion environment for 1 day (d), it can be equivalent to it being corroded for more than one year (i.e., ≥1 year) in the true actual usage environment.

[0180] In some embodiments, a method for constructing a corrosion resistance-mechanical performance analysis model for metallic materials is provided, which includes the following steps (see Figure 3).

[0181] S110: A metal material is used as the test subject, and test values ​​of corrosion parameters at different equivalent corrosion points under corrosion test conditions are obtained to obtain a corrosion performance test experimental dataset. Furthermore, test values ​​of mechanical parameters at different corrosion degrees corresponding to different equivalent corrosion points are obtained to obtain a mechanical performance test experimental dataset. Here, the corrosion test conditions are used to simulate the target actual usage environment of the metal material, and the corrosion parameters include at least one of the corrosion degree and corrosion rate. S120: Based on the corrosion performance test experimental dataset, establish a first set of empirical relational equations that change with the equivalent corrosion time of corrosion parameters. Furthermore, based on the mechanical performance test experimental dataset, establish a second set of empirical relational equations that change with the equivalent corrosion time of mechanical parameters. Furthermore, establish a third set of empirical relational equations between the equivalent corrosion time under corrosion test conditions and the actual usage time in the target actual usage environment.

[0182] Based on any suitable embodiment of this application, in some embodiments, a method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material includes the step of establishing a fourth set of empirical relations between the corrosion rate and the equivalent corrosion time under corrosion test conditions. In this case, the first set of empirical relations mentioned above includes the fourth set of empirical relations between the corrosion rate and the equivalent corrosion time under corrosion test conditions. In this case, the corrosion parameter in the corrosion performance test experimental dataset includes the corrosion rate.

[0183] By establishing an empirical relationship (which may be written as a fourth set of empirical relationships) between the corrosion rate and equivalent corrosion time under corrosion test conditions, the relationship between the corrosion behavior and mechanical properties of metallic materials in target real-world usage environments can be dynamically analyzed.

[0184] The corrosion rate obtained from the test analysis can be fitted to the corresponding equivalent corrosion time data points and analyzed.

[0185] In some embodiments, a method for constructing a corrosion resistance-mechanical performance analysis model for metallic materials is provided, which includes the following steps (see Figure 4).

[0186] S110: A metal material is used as the test subject, and test values ​​of corrosion parameters at different equivalent corrosion points under corrosion test conditions are obtained to obtain a corrosion performance test experimental dataset. Furthermore, test values ​​of mechanical parameters at different corrosion degrees corresponding to different equivalent corrosion points are obtained to obtain a mechanical performance test experimental dataset. Here, the corrosion test conditions are used to simulate the target actual usage environment of the metal material, and the corrosion parameters include at least one of the corrosion degree and corrosion rate. S120: Based on the corrosion performance test experimental dataset, a first set of empirical relations is established that change with the equivalent corrosion time of corrosion parameters. Furthermore, based on the mechanical performance test experimental dataset, a second set of empirical relations is established that change with the equivalent corrosion time of mechanical parameters. Here, the first set of empirical relations includes a fourth set of empirical relations between the corrosion rate and the equivalent corrosion time under corrosion test conditions.

[0187] In some embodiments, a method for constructing a corrosion resistance-mechanical performance analysis model for metallic materials is provided, which includes the following steps (see Figure 5).

[0188] S110: A metal material is used as the test subject, and test values ​​of corrosion parameters at different equivalent corrosion points under corrosion test conditions are obtained to obtain a corrosion performance test experimental dataset. Furthermore, test values ​​of mechanical parameters at different corrosion degrees corresponding to different equivalent corrosion points are obtained to obtain a mechanical performance test experimental dataset. Here, the corrosion test conditions are used to simulate the target actual usage environment of the metal material, and the corrosion parameters include at least one of the corrosion degree and corrosion rate. S120: Based on the corrosion performance test experimental dataset, a first set of empirical relations is established that change with the equivalent corrosion time of corrosion parameters. Furthermore, based on the mechanical performance test experimental dataset, a second set of empirical relations is established that change with the equivalent corrosion time of mechanical parameters. Furthermore, a third set of empirical relations is established between the equivalent corrosion time under corrosion test conditions and the actual usage time in the target actual usage environment. Here, the first set of empirical relations includes a fourth set of empirical relations between the corrosion rate under corrosion test conditions and the equivalent corrosion time.

[0189] In the embodiments shown in Figures 3, 4, and 5, each is independent, and the mechanical parameters in the mechanical performance test experimental dataset should refer to the definitions described above. Furthermore, the mechanical parameters may include at least one of tensile strength and elongation at break. In some of these embodiments, the mechanical parameters include tensile strength and elongation at break. The mechanical parameters may also include yield strength. In some of these embodiments, the mechanical parameters include yield strength. In some embodiments, the mechanical parameters include tensile strength, elongation at break, and yield strength.

[0190] According to a second aspect of this application, a method for analyzing the service life of a metallic material is provided, which analyzes the service life of a metallic material using a corrosion resistance-mechanical performance analysis model of the metallic material.

[0191] The metallic material may be defined in the first aspect of this application.

[0192] In some embodiments, a method for analyzing the service life of a metallic material is provided, which includes the following steps (see Figure 6).

[0193] S210: Determine the mechanical parameters related to the ineffective behavior of the metal material according to the target actual usage environment of the metal material, determine possible corrosion test conditions that simulate the target actual usage environment, and obtain test values ​​for the degree of corrosion under the corrosion test conditions of the metal material. S220: The service life parameter under corrosion test conditions for a metallic material is obtained from the corrosion degree test value, the effective state threshold of the mechanical parameters, and the corrosion degree-mechanical performance analysis model of the metallic material, where the corrosion degree-mechanical performance analysis model of the metallic material includes at least a first set of empirical relations that change with the equivalent corrosion time of the corrosion parameters characterizing the corrosion degree, and a second set of empirical relations that change with the equivalent corrosion time of the mechanical parameters.

[0194] This method for analyzing the service life of a metallic material involves selecting mechanical parameters related to the ineffective behavior of the metallic material based on the target actual usage environment of the metallic material, selecting possible corrosion test conditions that simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metallic material under the selected corrosion test conditions. The service life parameter of the metallic material under the selected corrosion test conditions can then be obtained from the corrosion degree test values, the effective state thresholds of the selected mechanical parameters, and the corrosion resistance degree-mechanical performance analysis model of the metallic material. This corrosion resistance degree-mechanical performance analysis model of the metallic material includes two sets of empirical relations: a first set of empirical relations that change with the equivalent corrosion time of a corrosion parameter including at least one of the corrosion degree and corrosion rate, and a second set of empirical relations that change with the equivalent corrosion time of the aforementioned mechanical parameters, with the mechanical performance at different corrosion degrees being linked to the equivalent corrosion time. This analysis method can be used to effectively and accurately analyze the service life of a metallic material in its target actual usage environment.

[0195] Based on any suitable embodiment of this application, and in some embodiments further, the service life parameter includes at least equivalent corrosion time.

[0196] The aforementioned analysis method allows us to obtain at least the equivalent corrosion time parameter of this metallic material, and reflect the service life status of this metallic material.

[0197] Based on any suitable embodiment of this application, and further in some embodiments, the service life parameters under corrosion test conditions for a metallic material can be obtained by using corrosion degree test values, effective state thresholds for mechanical parameters, and a corrosion resistance degree-mechanical performance analysis model for metallic materials. This method includes obtaining the surplus actual usage time of a metal material by using a third set of empirical relations between the corrosion degree test value, the effective state threshold of the mechanical parameters, the corrosion resistance degree-mechanical performance analysis model of the metal material, and the equivalent corrosion time under corrosion test conditions and the actual usage time in the target actual usage environment.

[0198] By using test values ​​for the degree of corrosion of the metal material, effective state thresholds for mechanical parameters related to the ineffective behavior of the metal material, a corrosion resistance-mechanical performance analysis model for the metal material, and an empirical relationship between the equivalent corrosion time under selected corrosion test conditions and the actual usage time in the target actual usage environment, the surplus actual usage time of the metal material can be obtained, enabling predictive analysis of the surplus service life of the metal material.

[0199] Based on any suitable embodiment of this application, in some embodiments, the mechanical parameters include at least two. Non-limitingly, the degree of ineffective response of the metallic material may be ranked in descending order based on each mechanical parameter, and a corrosion resistance-mechanical performance analysis model of the metallic material is obtained based on the highest-ranking mechanical parameter.

[0200] By ranking the degree of ineffective response of metallic materials in descending order based on each mechanical parameter, and obtaining a corrosion resistance-mechanical performance analysis model for metallic materials based on the highest-ranking mechanical parameter, it is possible to more effectively reflect the relationship between the corrosion behavior of metallic materials and the ineffectiveness of mechanical performance in the target actual usage environment.

[0201] Based on any suitable embodiment of this application, and in some embodiments further, a corrosion resistance degree-mechanical performance analysis model for a metallic material is obtained by constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material as described in the first aspect of this application.

[0202] The corrosion resistance degree-mechanical performance analysis model for the aforementioned method of analyzing the service life of metallic materials can be constructed by employing the construction method in the first aspect of this application, and by constructing it, a relationship formula can be obtained between the degree of corrosion of the metallic material and the mechanical performance of a mechanical parameter including at least one of tensile strength and elongation at break.

[0203] According to a third aspect of this application, a service life analyzer for metallic materials is provided, which includes a corrosion performance data acquisition module 310 and a corrosion performance data processing module 320. See Figure 7.

[0204] The metallic material may be defined in the first aspect of this application.

[0205] In some embodiments, a service life analyzer for metallic materials is provided, which, A corrosion performance data acquisition module 310 determines mechanical parameters related to the ineffective behavior of a metal material according to the target actual usage environment of the metal material, determines possible corrosion test conditions that simulate the target actual usage environment, and obtains test values ​​for the degree of corrosion of the metal material under the corrosion test conditions. A corrosion performance data processing module 320 for obtaining service life parameters under corrosion test conditions for a metallic material, using test values ​​for the degree of corrosion, effective state thresholds for mechanical parameters, and a corrosion resistance degree-mechanical performance analysis model for the metallic material, wherein the corrosion resistance degree-mechanical performance analysis model for the metallic material may be defined in the first or second aspect of this application.

[0206] A metal material service life analyzer according to a third aspect of this application may be used to implement a method for analyzing the service life of a metal material provided in a second aspect of this application.

[0207] According to a fourth aspect of this application, a method for analyzing the mechanical properties of a metallic material is provided, which analyzes the mechanical properties of a metallic material using a corrosion resistance-mechanical properties analysis model.

[0208] The metallic material may be defined in the first aspect of this application.

[0209] In some embodiments, a method for analyzing the mechanical properties of metallic materials is provided, which is, The process involves determining mechanical parameters related to the ineffective behavior of a metal material according to the target actual usage environment of the metal material, determining possible corrosion test conditions that simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metal material under the corrosion test conditions. A step of obtaining preliminary predictions of mechanical parameters after the target actual usage time of a metal material under corrosion test conditions, using a test value of the degree of corrosion, the target actual usage time of the metal material, a third set of empirical relational expressions between the equivalent corrosion time under corrosion test conditions and the actual usage time in the target actual usage environment, and a corrosion resistance degree-mechanical performance analysis model of the metal material, wherein the corrosion resistance degree-mechanical performance analysis model of the metal material may be defined in the first or second aspect of this application. The process involves comparing a preliminary prediction of a mechanical parameter with an effective state threshold for the mechanical parameter to obtain a prediction of the mechanical parameter after the target actual usage time under corrosion test conditions for the metallic material, wherein if the preliminary prediction of the mechanical parameter is greater than or equal to the effective state threshold for the mechanical parameter, the preliminary prediction is output as the effective prediction of the mechanical parameter after the target actual usage time under corrosion test conditions for the metallic material; and if the preliminary prediction of the mechanical parameter is less than the effective state threshold for the mechanical parameter, an invalid prediction result is output before the target actual usage time for the metallic material is reached (see Figures 8 and 9).

[0210] The aforementioned method for analyzing the mechanical performance of a metallic material involves selecting mechanical parameters related to the ineffective behavior of the metallic material based on the target actual usage environment of the metallic material, selecting possible corrosion test conditions that simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metallic material under the selected corrosion test conditions. By using the test values ​​for the degree of corrosion, the target actual usage time of the metallic material, an empirical relationship between the equivalent corrosion time under the selected corrosion test conditions and the actual usage time in the target actual usage environment, and a corrosion resistance degree-mechanical performance analysis model of the metallic material, preliminary predictions of the mechanical parameters after the target actual usage time under the selected corrosion test conditions can be obtained. By comparing these preliminary predictions with the effective state thresholds of the mechanical parameters, prediction results for the mechanical parameters after the target actual usage time under the selected corrosion test conditions can be obtained. If the preliminary predicted value of the mechanical parameters is greater than or equal to the effective state threshold for the mechanical parameters, then the effective predicted value of the mechanical parameters after the target actual use time under the selected corrosion test conditions for the metal material will be equal to the preliminary predicted value. If the preliminary predicted value of the mechanical parameters is less than the effective state threshold for the mechanical parameters, it means that the metal material will become invalid before reaching the target actual use time.

[0211] According to a fifth aspect of this application, a mechanical performance analysis apparatus for metallic materials is provided (see Figure 10), which includes a test data acquisition module 510, a test data processing module 520, and a mechanical performance classification and identification module 530.

[0212] The metallic material may be defined in the first aspect of this application.

[0213] In some embodiments, a device for analyzing the mechanical properties of metallic materials is provided, which, A test data acquisition module 510 determines mechanical parameters related to the ineffective behavior of a metal material according to the target actual usage environment of the metal material, determines possible corrosion test conditions that simulate the target actual usage environment, and obtains test values ​​for the degree of corrosion of the metal material under the corrosion test conditions. A test data processing module 520 for obtaining preliminary prediction values ​​of mechanical parameters after use for a target actual usage time under corrosion test conditions, using a test value of the degree of corrosion, a target actual usage time of the metal material, a third set of empirical relational expressions between the equivalent corrosion time under corrosion test conditions and the actual usage time in the target actual usage environment, and a corrosion resistance degree-mechanical performance analysis model of the metal material, wherein the corrosion resistance degree-mechanical performance analysis model of the metal material may be defined in the first or second aspect of this application, A mechanical performance classification identification module 530 for obtaining a prediction result of the mechanical parameters after use for a target actual usage time under corrosion test conditions for a metallic material, by comparing a preliminary prediction value of the mechanical parameters with an effective state threshold of the mechanical parameters, wherein if the preliminary prediction value of the mechanical parameters is greater than or equal to the effective state threshold of the mechanical parameters, the mechanical performance classification identification module 530 determines that the effective prediction value of the mechanical parameters after use for a target actual usage time under corrosion test conditions for a metallic material is equal to the preliminary prediction value.

[0214] The apparatus for analyzing the mechanical properties of a metallic material according to the fifth aspect of this application may be used to implement the method for analyzing the mechanical properties of a metallic material provided in the fourth aspect of this application.

[0215] According to the sixth aspect of this application, a computer device is provided.

[0216] In some embodiments, a computer device is provided which includes a memory and a processor on which a computer program is stored, and the processor implements the steps of a method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material as described in the first aspect of this application, or a method for analyzing the service life of a metallic material as described in the second aspect of this application, or a method for analyzing the mechanical performance of a metallic material as described in the fourth aspect, when executing the computer program.

[0217] This computer device may be a terminal, and its internal structure diagram may be as shown in Figure 11. This computer device includes a processor, memory, communication interface, display, and input device connected via a system bus. Here, the processor of this computer device is used to provide computation and control capabilities. The memory of this computer device includes a non-volatile storage medium and internal memory. The operating system and computer programs are stored in the non-volatile storage medium. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal by wired or wireless means, and the wireless method can be implemented by Wi-Fi, mobile cellular network, NFC (Near Field Communication), or other technology. When this computer program is executed by the processor, it implements a method for determining the performance of the energy storage system battery. The display of this computer device may be a liquid crystal display or an electronic ink display, and the input device of this computer device may be a touch layer covered on the display, a button, trackball, or touch panel installed on the computer device housing, or an external keyboard, touch panel, or mouse.

[0218] As those skilled in the art will understand, the structure shown in Figure 11 is merely a block diagram of the structure of a part relating to the present invention and does not constitute a limitation on the computer equipment to which the present invention is applied. Specific computer equipment may include more or fewer components than those shown in the figure, or may have a combination of several components or different arrangements of components.

[0219] According to the seventh aspect of this application, a computer-readable storage medium is provided in which a computer program is stored, and when the computer program is executed by a processor, steps of the method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material as described in the first aspect of this application, or the method for analyzing the service life of a metallic material as described in the second aspect of this application, or the method for analyzing the mechanical performance of a metallic material as described in the fourth aspect are realized.

[0220] According to another aspect of this application, a computer program product is provided, which includes a computer program, which, when the computer program is executed by a processor, implements the steps of the construction method of the first aspect of this application or the analysis method of the second or fourth aspect of this application.

[0221] As those skilled in the art will understand, the implementation of all or part of the flows in the embodiments of the above methods can be completed by instructing the relevant hardware with a computer program, which may be stored in a non-volatile computer-readable storage medium and may include the flows of each of the embodiments of the above methods when executed. Herein, any use of memory, database or other media in each embodiment of the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM®), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. For illustrative purposes only, and not as a limitation, RAM may be of various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The database in each embodiment of this application may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a distributed database based on blockchain. The processor in each embodiment of this application may be, but is not limited to, a general-purpose processor, a central processor, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc.

[0222] According to the eighth aspect of this application, a power consumption device including a battery system is provided.

[0223] In some embodiments, the power consumption device, A battery system comprising a battery case and battery cells located inside the battery case, wherein the battery case comprises a metal material as defined in the first aspect of this application, The present application includes a service life analyzer for metallic materials as described in the third aspect of this application, a mechanical performance analyzer for metallic materials as described in the fifth aspect of this application, a computer device as described in the sixth aspect of this application, and at least one of a computer-readable storage medium as described in the seventh aspect of this application.

[0224] In this application, unless otherwise specified, "battery cell" refers to a basic unit capable of mutually converting chemical energy and electrical energy.

[0225] In some embodiments, the battery cell includes a fuel cell. In this case, the battery system may be a fuel cell system, and the battery case may be a fuel cell case.

[0226] In this application, unless otherwise specified, "fuel cell" has the meaning known in the art and refers to a chemical device that directly converts the chemical energy of a fuel into electrical energy, and "fuel cell" refers to a battery cell that directly converts the chemical energy of a fuel into electrical energy. In this application, unless otherwise specified, "fuel cell case" refers to a battery case in which fuel cell battery cells are housed.

[0227] In some embodiments, the power consumption device is a fuel power consumption device, which includes a fuel cell system, which includes a fuel cell case and fuel cell cells located inside the fuel cell case, wherein the fuel cell case includes a metallic material as defined in the first aspect of this application.

[0228] In some embodiments, the battery cell includes a lithium battery cell. In this case, the battery system may be a lithium battery system, and the battery case may be a lithium battery case.

[0229] In this application, unless otherwise specified, "lithium battery" has the meaning known in the art and refers to a battery in which the active ions include lithium ions, and "lithium battery cell" refers to a battery cell in which the active ions include lithium ions. A lithium battery may also be a lithium-ion secondary battery. In this application, unless otherwise specified, "lithium battery case" refers to a battery case in which lithium battery cells are housed.

[0230] In some embodiments, the power consumption device includes a lithium battery system, which includes a lithium battery case and lithium battery cells located inside the lithium battery case, wherein the lithium battery case includes a metallic material as defined in the first aspect of this application.

[0231] Power-consuming devices may include, but are not limited to, mobile devices (e.g., mobile phones, laptops, etc.), electric vehicles (e.g., pure electric vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, electric bicycles, electric scooters, electric golf carts, electric trucks, etc.), electric trains, ships and satellites, and energy storage systems.

[0232] It can be used as a power consumption device, and the battery can be selected according to the usage demand.

[0233] Figure 12 shows an example of a power-consuming device. This power-consuming device is a pure electric vehicle, a hybrid electric vehicle, or a plug-in hybrid electric vehicle.

[0234] Other examples of such devices may include mobile phones, tablet computers, and laptop computers. These devices generally require a thin design and can utilize rechargeable batteries as their power source.

[0235] At least one of the service life analysis device of the metal material, the mechanical property analysis device of the metal material, computer equipment, computer-readable storage media, and computer program products may be installed on the power consumption device. It is used to implement the above-described analysis model construction method, the service life analysis method of the metal material, or the mechanical property analysis method of the metal material, which is advantageous for achieving more suitable management and maintenance for the power consumption device, advantageous for achieving safety maintenance for the battery case in the power consumption device, and further advantageous for assisting in the design of the battery case with a longer actual service time.

[0236] Hereinafter, some embodiments of the present application will be described. The embodiments described below are illustrative and are for interpreting the present application, and should not be construed as limitations to the present application. In the embodiments, when the technology or conditions are not explicitly stated, it shall be carried out according to the description in the foregoing text, or according to the technology or conditions described in the literature in the art or the product specifications. Reagents or equipment for which the manufacturer is not described are all commercially available ordinary products, or can be manufactured in the ordinary manner using commercially available products.

[0237] <\(0000901\)>In the following embodiments, room temperature refers to 20°C to 30°C.

[0238] I. Test samples: Fuel cell cases made of four aluminum alloy materials, namely A356.2, A380, AlSi10MgMn, and AlSi9MnMoZr, were adopted. After being cut into a certain size, they were used as test samples for corrosion tests and tensile tests. The elemental compositions of the four aluminum alloy materials can be referred to Table 1.

[0239] Here, various aluminum alloys could be obtained by the following method, based on the elemental composition shown in Table 1. Pure aluminum ingots were added to the blast furnace, and then other elements were added in several batches according to the melting point differences of the other constituent elements, with subsequent raw materials being added after the previously added raw materials had melted. After melting, the mixture was allowed to stand and slag was removed, a refining agent was added, slag was removed again, and after slag removal, some of the constituent elements were added, and the mixture was injected into a mold to obtain an ingot of the target size. The ingot was allowed to stand and kept warm, and then water-cooled to room temperature to obtain an aluminum alloy material for a battery case of the target size, which was also referred to as a pressure-cast aluminum alloy sample.

[0240] [Table 1]

[0241] In Table 1, "-" indicates that the element was not detected and may be considered absent based on the normal composition, while "Bal." represents the base element.

[0242] 2. Construct a corrosion resistance-mechanical performance analysis model based on salt spray corrosion tests. 1. Salt spray corrosion test method A 3.5 wt% NaCl aqueous solution was used, and the test was conducted at 35°C.

[0243] The macromorphology, micromorphology, corrosion mass loss, and corrosion rate of case materials with a total test time of 600 hours were analyzed at 24-hour intervals.

[0244] Before the test, fuel cell cases of known alloy materials were cut into 15mm x 15mm x 2mm blocks, and the non-test surfaces were wrapped in blue film. The test surfaces were barrel polished, and after polishing and drying, they were weighed (W0). The total test duration was 600h, and a group of samples were taken every 24 hours during the test process to prepare for observation of the surface macromorphology and micromorphology. After observation, corrosion products on the sample surface were removed using a chromic acid cleaning agent (20g / L Cr2O3 + 50mL / L H3PO4), and the samples were weighed again to calculate the corrosion rate and observe the morphology. The test duration at the time of the i-th sampling was recorded as Ti, and the weighing at the time of the i-th sampling was recorded as Wi. At least three parallel samples were set up for each test.

[0245] 2. Testing Method (1) Macromorphological testing method Sample: The corrosion point for sampling is described above.

[0246] Equipment: Shadowless lamp observation.

[0247] (2) Micromorphological testing method Sample: The corrosion point for sampling is described above.

[0248] Equipment: NOVA NanoSEM 230 Low Vacuum Ultra-High Resolution Field Emission Electron Microscope.

[0249] Method: The probe type (det) was ETD. Using Figure 13 as an example, the acceleration voltage (HV) was 5kV and the working distance was 30mm.

[0250] (3) Method for analyzing corrosion mass loss and corrosion rate The mass loss W = W0 - Wi for the i-th sampling.

[0251] Substitute the corrosion rate Ri at the i-th sampling into equation (I) and calculate: The corrosion rate can be obtained as (K × W) / (A × Ti × D) mm / y (I), In equation (I), K = 8.64 × 104 where K is the time constant, W is the mass difference value before and after the test = W0 - Wi, and the unit is mg. A is the area of the test surface, and the unit is cm 2 where 1.5 cm × 1.5 cm = 2.25 cm 2 and Ti is the test time for the i-th sampling, and the unit is h. D = 2.7 g / cm <00000​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​This value was obtained using SPSSAU linear regression, and it was explained that the closer this value is to 1, the better the fitting effect.

[0256] 3. Analysis of Salt Spray Corrosion Test Results Figure 13 shows the macromorphological diagrams (first row) and micromorphological diagrams of four types of aluminum alloy materials at different equivalent corrosion points in salt spray tests: (a) A356.2, (b) A380, (c) AlSi10MgMn, and (d) AlSi9MnMoZr. As can be seen from the surface macromorphology of the four pressure-cast aluminum alloy samples at different corrosion times, among the four aluminum alloy materials, the A380 alloy was the most severely corroded, with a relatively large amount of white corrosion product deposited on the sample. The other three pressure-cast aluminum alloys corroded relatively slowly, with relatively few corrosion products on the surface, similar to the electrochemical test results. For the A356.2 and A380 alloy materials, when the corrosion time was relatively short (1d-6d), the alloy surface was rapidly covered with corrosion products, which was presumed to be due to the occurrence of overall corrosion. The area and thickness of surface corrosion products both increased continuously with increasing corrosion time, and the sample surface exhibited a characteristic transition from being initially corroded and dark in color to being covered with a large amount of white corrosion products. For AlSi10MgMn and AlSi9MnMoZr alloy materials, in the early stages of corrosion (1d-6d), the sample surface mainly showed localized individual pitting pits, and corrosion products often accumulated near the pitting pits, presumably because the surface was protected by a passivation film. In the middle stages of corrosion (7d-18d), the corrosion further expanded, the passivation film was destroyed, and gradually showed uniform corrosion. In the later stages of corrosion (19d-24d), more and more locations on the sample surface were covered with corrosion products, and this continued until the entire surface was covered.

[0257] This paper presents micromorphological diagrams of four aluminum alloy materials (A356.2, A380, AlSi10MgMn, and AlSi9MnMoZr) at different equivalent corrosion points in salt spray testing, including microstructural diagrams (not shown) of the four pressure-cast aluminum alloy samples in the early and late stages of salt spray corrosion testing. In the initial stage of corrosion (1d), the A356.2 and A380 alloys showed a comprehensive and uniform corrosion morphology, but the A380 alloy showed significantly more severe corrosion and a relatively larger amount of corrosion products. The AlSi10MgMn and AlSi9MnMoZr alloys mainly showed pitting corrosion, with the AlSi10MgMn alloy showing more pronounced pitting corrosion, which resembled the result of electrochemical corrosion. In the final stage of corrosion (25d), all four alloy surfaces underwent comprehensive and uniform corrosion, with corrosion products essentially covering and accumulating on the sample surface. The A380 alloy showed a relatively large and thick amount of surface corrosion products, followed by the AlSi10MgMn alloy, and the A356.2 alloy was relatively close to the AlSi9MnMoZr alloy.

[0258] The micromorphological diagrams of four types of aluminum alloy materials (A356.2, A380, AlSi10MgMn, and AlSi9MnMoZr) after salt spray corrosion tests, 10 days after removal of corrosion products, include the microstructural diagram of the pressure-cast aluminum alloy after removal of corrosion products (not shown). Different degrees of corrosion occurred in the α-Al substrate of each of the four alloy materials. In the A356.2 alloy, a large amount of dispersed Si particles were present. Although the Si particles could not directly participate in the corrosion reaction, they acted to accelerate substrate corrosion. The presence of Si particles made it difficult for a dense oxide film to form on the alloy, which led to more pronounced corrosion of the substrate α-Al around the Si particles. As corrosion occurred and spread, the Si particles eventually peeled off. In the A380 alloy, a large number of corrosion pits were distributed around the Al-Si eutectic structure. The presence of the Al2Cu phase near the Al-Si eutectic structure further exacerbated the tendency for intergranular corrosion of the Al-Si eutectic structure. Furthermore, solid solution of a small amount of Cu in the substrate increases the potential difference between intergranular and intragranular regions, and the potential of the Al2Cu phase is relatively positive, as is the potential of the substrate phase with a relatively high Cu content. In this case, the substrate with a relatively low Cu content acts as the cathode phase, forming a localized electrochemical miniature cell with the nearby Cu-rich substrate phase and Al2Cu phase. This causes the Cu-poor solid solution substrate phase to be continuously corroded, leading to the disappearance of the grain boundaries. 15 (FeMn)3Si2 phase and AlSi9MnMoZr alloy 12 Because the potential of the Mn3Si2 phase is similar to that of the substrate, corrosion at the grain boundaries and Al-Si eutectic phase boundaries of the AlSi10MgMn and AlSi9MnMoZr alloys was relatively severe after the passivation film was destroyed. Since the passivation film of the AlSi9MnMoZr alloy is more stable and less prone to destruction, this characteristic was more pronounced in the AlSi10MgMn alloy at the grain boundaries and Al-Si eutectic phase boundaries.

[0259] Figure 14 shows the experimental results of mass loss and corrosion rate at different equivalent corrosion points for four types of aluminum alloy materials in a salt spray test. The corrosion weight loss and corrosion rate of the A380 alloy were both significantly higher than those of the other three alloys, further confirming its relatively poor corrosion resistance. In stage I (0-13d), the corrosion rate of the A356.2 alloy was higher than that of the AlSi10MgMn and AlSi9MnMoZr alloys. In stage II (14d-22d), as the passivation film ruptured and localized corrosion behavior worsened, the corrosion rates of the AlSi10MgMn and AlSi9MnMoZr alloys gradually increased, while the corrosion rate of the A356.2 alloy remained essentially at approximately 0.05 mm / y. In stage III (23-25d), the oxide film formed by self-passivation of the AlSi10MgMn and AlSi9MnMoZr alloy was almost completely destroyed, and galvanic corrosion by the second phase intensified, resulting in a clear increase in the corrosion rate of the AlSi10MgMn and AlSi9MnMoZr alloy.

[0260] Figures 15-18 show the fitting results between the corrosion rate and equivalent corrosion time for salt spray corrosion tests of the four types of aluminum alloy materials shown in Figure 14. After segmenting and fitting the corrosion rates of the four types of pressure-cast aluminum alloys, the fitting curves basically matched the test results. An empirical relationship between the corrosion rate and equivalent corrosion time (the first set of empirical relationships is also the fourth set of empirical relationships) was established, and the fitting results for the four samples are shown in Table 2.

[0261] [Table 2]

[0262] In Table 2, Reduced Chi-Sqr corresponds to the sum of squared residuals RSS / dof inside Anova, and R 2 This is the coefficient of determination (COD), and "the adjusted R 2 This was obtained using SPSSAU linear regression.

[0263] Based on the empirical formula described above, it was possible to substitute the corresponding equivalent corrosion time x based on the corrosive medium and actual usage time during the actual use process of pressure-cast aluminum alloy parts, calculate the corresponding corrosion rate y, and provide a certain guideline for evaluating the service life of the product.

[0264] Taking Shanghai, China as an example, a pressure-cast aluminum alloy part exposed to a salt spray corrosion environment for 1d is equivalent to being corroded for more than a year in its true environment (this corresponds to establishing a third set of empirical relations between the equivalent corrosion time under selected corrosion test conditions and the actual usage time in the target actual usage environment). Using the first set of empirical relations from equations (3-1) to (3-9), it was possible to establish a conservative model for predicting the corrosion rate of four types of pressure-cast aluminum alloy parts up to 25 years.

[0265] 4. Tensile Test Method (1) The size of the sample is in millimeters (mm), as shown in Figure 23, where R2.5 represents a radius of 2.5 millimeters.

[0266] (2) Test equipment: Z20 TEW Electronic Universal Material Tester.

[0267] (3) Test analysis method: Tensile samples of four types of pressure-cast aluminum alloys were wrapped in blue film, leaving only the test surface exposed. The total test duration was 600 hours, during which a group of samples was taken every 24 hours to test their tensile performance. At least three parallel samples were set up for each test.

[0268] Mechanical performance tests were performed on tensile samples at different stages of corrosion to obtain stress-strain curves, which were then analyzed to obtain tensile strength (abbreviated as UTS, or referred to as tensile strength or ultimate tensile strength), elongation at break (abbreviated as El), and yield strength (abbreviated as YS).

[0269] (4) Establish a second set of empirical relationships that change with the equivalent corrosion time of the mechanical parameters. The total test time was divided into three time periods, and the following functions were used to fit each period, selecting the function with the best fitting effect.

[0270] Selective function 1: y² = M·x N , Selective function 2: y² = m + n·x, Here, x is the equivalent corrosion time, y² is the mechanical parameter, M is a positive number, N is a negative number, n is a negative number, and m is a positive number.

[0271] Damd Chi-Sqr, R 2 And R after adjustment 2 We were able to evaluate the fitting effect by adopting at least one of these options.

[0272] (5) The first set of empirical relations and the second set of empirical relations constitute an empirical model of the relationship between the degree of corrosion and mechanical performance.

[0273] (6) Test analysis results Figures 19 to 22 show experimental and fitting results of mechanical parameters and equivalent corrosion time at different equivalent corrosion points in salt spray corrosion tests for four types of aluminum alloy materials: A356.2, A380, AlSi10MgMn, and AlSi9MnMoZr. Here, the mechanical parameters relate to tensile strength (UTS), elongation at break (El), and yield strength (YS).

[0274] Test analysis results showed that the yield strength and tensile strength of A380 alloy tended to decrease rapidly with increasing corrosion time, particularly in the initial stages of corrosion (1-4d). The yield strength and tensile strength decreased to 77.5% and 76% of their pre-corrosion levels, respectively. Subsequently, the rate of performance decay slowed, and ultimately, the yield strength and tensile strength decreased to 65.4% and 52.5% of their pre-corrosion levels, respectively. The yield strength and tensile strength decay rates of A356.2, AlSi10MgMn, and AlSi9MnMoZr alloys were relatively stable. The yield strength and tensile strength of the A356.2 alloy decreased to 84.3% and 82.6% of their pre-corrosion levels, respectively. The yield strength and tensile strength of the AlSi10MgMn alloy decreased to 84.4% and 75.6% of their pre-corrosion levels, respectively. The yield strength and tensile strength of the AlSi9MnMoZr alloy decreased to 85.8% and 80.3% of their pre-corrosion levels, respectively. Furthermore, at a corrosion time of 23 days, the tensile strength of the AlSi9MnMoZr alloy decreased to only 87.5% of its pre-corrosion level, which was higher than the other three alloys (A356.2: 86.4%, A380: 62.9%, AlSi10MgMn: 81.4%). This indicates that the AlSi9MnMoZr alloy exhibited the best corrosion resistance at relatively short corrosion times, while the A380 alloy consistently showed the worst corrosion resistance.

[0275] Compared to yield strength and tensile strength, the elongation rates of the four pressure-cast aluminum alloys all tended to decrease rapidly with increasing corrosion time. The elongation rates of A356.2, A380, AlSi10MgMn, and AlSi9MnMoZr alloys decreased to 38.6%, 25.6%, 41.5%, and 25.8% respectively, indicating that the loss of alloy elongation due to corrosion was far greater than its impact on strength. This is because, after corrosion occurs, corrosion products begin to form on the alloy surface, and simultaneously, Cl - The corrosion continuously penetrates the alloy, creating brittle regions that are highly prone to stress concentration, thereby promoting crack initiation and expansion during the tensile process. As the corrosion time increases, the number of brittle regions due to corrosion within the alloy increases, greatly promoting crack initiation and expansion during the tensile process, and causing a significant decrease in the alloy's plastic deformation capacity.

[0276] The three mechanical parameters UTS, El, and YS of the four pressure-cast aluminum alloys are fitted, and refer to Figures 19-22 and Table 3. The empirical relationships between the yield strength, ultimate tensile strength, elongation, and corrosion equivalent time of the four alloys are shown in equations (4-1) to (4-12), respectively, and refer to Table 3. As can be seen, the fitting curves and test results show no obvious deviations, and R 2 And R after adjustment 2 All of these values ​​were relatively high, basically above 0.9, and in some cases reached 0.99. Therefore, an empirical relationship model between the degree of corrosion and mechanical performance was established, with YS, UTS, and El representing yield strength, ultimate tensile strength, and elongation, respectively.

[0277] [Table 3]

[0278] 5. Analysis of the relationship between the degree of corrosion and mechanical parameters As corrosion progresses, it causes a decrease in the mechanical properties of the material. Generally speaking, the factors that affect mechanical properties are complex. In the case of battery cases, the specific application environment of the battery case has a high degree of impact on corrosion resistance. By analyzing the prediction of the mechanical properties of the metal material using corrosion performance as a guide, it was possible to evaluate the service life.

[0279] As can be seen by comparing the corrosion rate-equivalent corrosion time curves in Figures 15-18 with the mechanical parameter-equivalent corrosion time curves in Figures 19-22, there are relatively large differences in the response behavior of the corrosion rate and mechanical parameters to the equivalent corrosion time. This makes it difficult to effectively reflect the corrosion ineffective behavior of the sample by simply using the corrosion rate-equivalent corrosion time curve. It was not possible to determine the extent to which it affected the actual service life by simply determining the corrosion rate. The above example established a correspondence between corrosion performance and actual service life by establishing a relationship between corrosion rate and mechanical performance.

[0280] Failures of other analytical models: Many factors influence the decay of mechanical performance. In addition to corrosive factors, several environmental factors, such as temperature, humidity, and electrolyte type, also affect mechanical performance. This makes it difficult to construct predictive models that include only mechanical performance parameters.

[0281] Through research and analysis of tensile fracture surfaces at different degrees of corrosion, we discovered a relatively strong correlation between the degree of corrosion and mechanical parameters. We then verified the feasibility of using the aforementioned established corrosion degree-mechanical parameter analysis model to predict mechanical performance or service life. Characterization method for tensile fracture surfaces: Scanning electron microscopy (SEM) technique, NOVA NanoSEM 230 low vacuum ultra-high resolution field emission electron microscope.

[0282] The fracture morphologies of four pressure-cast aluminum alloy materials, A356.2, A380, AlSi10MgMn, and AlSi9MnMoZr, before salt spray corrosion were observed using SEM technology. Large and small dimple morphologies and cleavage steps were observed on the fracture surface of the A356.2 alloy, indicating a pseudo-cleavage fracture mode. The fracture surface of the A380 alloy exhibited large-area cleavage planes, essentially lacking dimple morphologies, and clearly possessing brittle fracture characteristics. The fracture surface morphologies of the AlSi10MgMn and AlSi9MnMoZr alloys were roughly the same as the grain size, which is because, in polycrystalline materials, the macro-fracture surface is approximately perpendicular to the direction of maximum tensile stress, but in the microstructure, the cleavage fracture surfaces of each grain are not all perpendicular to the tensile stress. Furthermore, small terraced cleavage steps and river-like patterns were observed on the fracture surface, and tear ridges due to plastic deformation were also present, indicating the occurrence of a certain degree of plastic deformation.

[0283] The fracture core and fracture edge morphology approaching the test surface of four types of pressure-cast aluminum alloy materials at different corrosion times (5d, 15d, and 25d) could be observed using SEM technology.

[0284] Fracture surface morphology at different corrosion times during salt spray corrosion testing of A356.2 alloy material: As corrosion time increased, the morphology of the fracture center of the A356.2 alloy remained very similar, still exhibiting a dimpled pseudo-cleavage morphology, but the number of dimples decreased and became less pronounced, and the fracture surface at 25d had already become essentially a cleavage morphology. The fracture edges of the A356.2 alloy were not all the same, and a large number of gray corrosion products appeared on the fracture surface, along with a gradual increase in the defect area on the fracture surface, and many faint cavities could be observed. As corrosion time increased, the volume of the cavities gradually increased, and their number gradually increased. The strength at these defect locations decreased significantly, and stress accumulated easily, causing crack budding and expansion, which significantly reduced the elongation of the A356.2 alloy.

[0285] Fracture surface morphology at different corrosion times during salt spray corrosion testing of A380 alloy material: Unlike A356.2 alloy, the fracture surface morphology of A380 alloy material showed that the fracture center of A380 alloy was cleavage fracture, with the presence of certain corrosion products and relatively deep corrosion. With increasing corrosion time, the cleavage step difference on the fracture surface gradually increased. Due to the occurrence of corrosion, the differentiation between different facets gradually disappeared, and large cleavage step differences were formed after multiple dissociation surfaces of different heights that were parallel or nearly parallel to each other met. At the fracture surface edge, relatively large cleavage planes first appear. Subsequently, as corrosion expands, these cleavage planes split into individual small planes, and many elongated microcracks appear between these small planes, indicating that the corrosion has already expanded into the interior of the A380 alloy grains. As a result, a large number of cracks penetrate deep into the grains from the surface of the A380 alloy, significantly reducing its elongation rate. Furthermore, the appearance of a relatively large number of corrosion products at the center of the fracture surface proves that the corrosion has already penetrated relatively deeply, leading to an increasing number of defects such as cracks appearing within the alloy, which has a relatively significant impact on its strength.

[0286] Fracture surface morphology at different corrosion times during salt spray corrosion testing of AlSi10MgMn alloy material: A large number of cleavage facets were present at the center of the fracture surface, and several dimples were observed at relatively short corrosion times (5d), indicating a pseudocleavage fracture morphology. A small cavity appeared at the center of the fracture surface, which was particularly evident at relatively long corrosion times (15d and 25d). Similarly, gray corrosion products were observed at the fracture surface edge, and as corrosion progressed, the fracture surface edge split from a relatively large cleavage step into small planes separated by individual cracks. Compared to A380 alloy, both the number of corrosion products and small planes were smaller, indicating that it has better corrosion resistance.

[0287] Fracture surface morphology at different corrosion times during salt spray corrosion tests of AlSi9MnMoZr alloy material: Compared to AlSi10MgMn alloy, AlSi9MnMoZr exhibited numerous faint dimples and clear tear ridges at the center of the fracture surface, indicating greater plastic deformation. Dimples are a major characteristic of plastic fracture, and a greater number of dimples indicates fainter size and stronger plastic deformation capacity of the material; therefore, its elongation rate was higher than that of AlSi10MgMn alloy. Furthermore, even when the fracture surface edge was corroded for a relatively short time (5d), a relatively large number of dimples and cleavage steps could still be observed, indicating that the performance degradation of AlSi9MnMoZr alloy was not severe at relatively short corrosion times. As the corrosion time increased, the fracture surface edge morphology gradually transitioned to a cleavage morphology, and small amounts of corrosion products began to appear on the edge. When the corrosion time was relatively long (25d), many faint pits appeared on the edges, but because the number of microcracks was very small, the decrease in yield strength and tensile strength of the AlSi9MnMoZr alloy was not evident macroscopically, and the elongation rate decreased rapidly.

[0288] As can be seen from the analysis results above, for alloy samples that exhibited different mechanically ineffective behaviors and generated different fracture surface morphologies in a corrosive environment, there was a relatively strong correlation between mechanical performance and equivalent corrosion time. Therefore, the method for constructing the corrosion degree-mechanical parameter analysis model established above has a certain degree of versatility, and the analytical model obtained by constructing it was able to effectively analyze mechanical performance or service life.

[0289] 3. Electrochemical corrosion test 1. Test and analysis methods The electrochemical corrosion test included an open-circuit voltage test, an AC impedance test, and a dynamic potential polarization test.

[0290] Before testing, pressure-cast aluminum alloy was cut into 10mm x 10mm x 2mm samples, and cold-fitted with epoxy resin to the samples. After cold-fitting, the area was 1cm². 2 We ensured that only the test surface was exposed. The test surface was barrel polished sequentially with sandpaper, then the sample surface was polished clean and shiny with an abrasive, the surface was washed with ethanol and dried, and then prepared for use.

[0291] During the test, the open-circuit voltage (OCP) of the sample was measured first. After the OCP stabilized for 60 minutes, an AC impedance test (EIS) was performed on the sample, and the electrochemical impedance spectrum of the sample at different AC frequencies was measured. A 10mV AC sine wave was used as the excitation voltage during the test, and the test frequency was controlled within 0.01 to 105Hz.

[0292] After the tests were completed, the EIS results were fitted to the equivalent circuit and the parameters of each element in the equivalent circuit diagram were analyzed. Following the AC impedance experiments, a dynamic potential polarization test was performed on the sample. Scanning was performed from a potential of OCP -300mV at a scan rate of 0.167 mV / s until the current exceeded 1mA. After the tests were completed, Tafel fitting was performed on the polarization characteristics of the sample using ZSimpWin v3.40 software.

[0293] The schematic diagram of the three-electrode system used in the electrochemical corrosion testing equipment is shown in Figure 24. R, mA, and V represent a resistance meter, ammeter, and voltmeter, respectively. The combination of these three can be understood as an electrochemical workstation, and by adjusting the set voltage and current, the sample signal could be collected as measurement data for corrosion parameters.

[0294] 2. Test results The curves of the change in open-circuit voltage (OCP) over time for four types of aluminum alloy materials can be found in Figure 25. The OCP values ​​of the four pressure-cast aluminum alloy materials stabilized at 300 s, and the potential remained stable at 1800 s, exhibiting very small fluctuations. The change in OCP value was related to the formation of a surface layer, which was able to control subsequent electrochemical reactions. The OCP values ​​for A356.2, A380, AlSi10MgMn, and AlSi9MnMoZr alloys were -0.474V, -0.457V, -0.603V, and -1.009V, respectively, and the different microstructures of the four pressure-cast aluminum alloys altered the formation of their surface layers and surface reactions.

[0295] The AC impedance test results for the four pressure-cast aluminum alloys can be found in Figure 26 and Table 4. Here, (a) is the Bode plot (EIS Bode plot) of the AC impedance spectrum, (b) is the phase plot, (c) is the Nyquist plot, and (d) is the equivalent circuit diagram. As can be seen from the Bode curve (a) and the Phase curve (b) in Figure 26, regardless of low or high frequency, the AlSi10MgMn alloy showed the highest impedance and phase angle, followed by the A356.2 and A380 alloys, and finally the AlSi9MnMoZr alloy. In the Nyquist plot ((c) in Figure 26), all four alloy materials exhibited a suppressed capacitance ring, with the AlSi10MgMn alloy having the largest diameter and showing better capacitance performance and higher charge transfer resistance. The specific parameters of each element can be fitted to the EIS test results using the equivalent circuit diagram in (d) of Figure 26, and then refer to Table 4.

[0296] In the equivalent circuit diagram, R s R represents the solution resistance related to the corrosive solution used for testing. sl , R ct The values ​​represent the surface resistance and charge transfer resistance of each alloy used as the working electrode. The deviation between the capacitance in the EIS and the ideal capacitance behavior is typically represented by a phase element (CPE), and n was used as an index to evaluate the degree of closeness between the actual test and the theoretical calculation. Q1 and Q2 represent the capacitance of the surface and charge transfer layer during the establishment process of the corrosion cell, respectively, corresponding to n1 and n2. The results show that since the corrosion solution was a 3.5 wt% NaCl solution, the R of the four alloys... s They declared that they would come very close. sl and R ctRegardless, all exhibited the characteristics of AlSi10MgMn > A356.2 > A380 > AlSi9MnMoZr, with AlSi10MgMn showing the best corrosion resistance among the four during the process of establishing a corrosion cell. In the EIS test, since the alloy was not corroded, the EIS results analyzed the corrosion behavior that was to begin only from the perspective of electrochemical thermodynamics. For some thermodynamically unstable metals, such as Al, Mg, and Cr, self-passivation occurs under appropriate conditions, converting from an uncorrosion-resistant alloy to a corrosion-resistant alloy. Therefore, studying corrosion behavior from a thermodynamic perspective alone is one-sided, and it was necessary to further consider the thermodynamic stability of pressure-cast aluminum alloy materials in the corrosion medium. Experimental data showed that the AlSi9MnMoZr alloy underwent self-passivation in a 3.5 wt% NaCl solution and exhibited excellent corrosion resistance.

[0297] [Table 4]

[0298] Figure 27 shows the dynamic potential polarization curve for electrochemical corrosion tests, and Table 5 lists the relevant dynamic potential polarization parameters calculated based on the Tafel extrapolation method. Corrosion potentials (E) of A356.2, A380, AlSi10MgMn, and AlSi9MnMoZr alloys. corr The values ​​were -0.450V, -0.415V, -0.585V, and -0.981V, respectively, which were very close to the OCP values. Based on the electrochemical corrosion behavior, these four pressure-cast aluminum alloy materials could be divided into two types: one type was alloys without apparent passivation, including A356.2 alloy and A380 alloy; the other type had a significant passivation region, including AlSi10MgMn alloy and AlSi9MnMoZr alloy.

[0299] When evaluating the corrosion resistance of alloys that do not clearly passivate the former, the corrosion current density (I) of the alloy is the main factor. corr ) can be compared, I corr The value is relatively low, R pAlloys with relatively high values ​​had relatively good corrosion resistance. As can be seen from the Tafel fittings, A356.2 is E of A380 alloy. corr It is very close to, but the I of A356.2 alloy corr It is only about a quarter of that of the A380, but its polarization resistance R p This was more than 20 times that of A380. Therefore, the corrosion resistance of A356.2 alloy was far superior to that of A380 alloy.

[0300] The latter exhibits a significant passivation region, and when studying its corrosion resistance, the passivation region of the alloy should have been analyzed. The corrosion current density slowly increased with the positive shift of the passivation region potential, which meant that the expansion of corrosion behavior slowed considerably. The passivation region inhibits the development of corrosion behavior, and the potential rises to the breakdown potential (E). b It persisted until it reached ), the passivation film was completely destroyed, and corrosion began to worsen. The result was that the AlSi9MnMoZr alloy had a more pronounced passivation region, E b The voltage is -0.411V, which is higher than that of AlSi10MgMn alloy (-0.430V), and the I of AlSi10MgMn alloy corr Even if the ratio is lower than that of the AlSi9MnMoZr alloy, the latter has a more stable passivation region, which significantly slows the expansion of corrosion behavior and results in better corrosion resistance. c and B a These represent the cathode reaction slope and anode reaction slope of the Tafel fitting, respectively, and the B of the four alloys. c All of them are B a It was stated that the electrochemical reaction was higher than that, and that it was primarily controlled by the cathode process.

[0301] [Table 5]

[0302] Regarding the surface morphology of alloy materials after dynamic polarization, the A356.2 and A380 alloys showed mainly uniform corrosion after polarization, and the corrosion products on the A380 alloy surface were greater than those on the A356.2 alloy, indicating relatively poor corrosion resistance. The AlSi10MgMn and AlSi9MnMoZr alloys, after polarization, showed mainly pitting corrosion characteristics due to the presence of a surface passivation film. Compared to the AlSi9MnMoZr alloy, the pitting pits of the AlSi10MgMn alloy were larger and deeper, and the passivation film of the AlSi9MnMoZr alloy was less prone to fracture, which was consistent with the results of the dynamic potential polarization curves.

[0303] Therefore, it was possible to construct a corrosion resistance-mechanical performance analysis model using parameters that characterize the degree of corrosion in electrochemical corrosion tests.

[0304] IV. Immersion Corrosion Test 1. Test Analysis Method Stepwise immersion corrosion involved analyzing the macromorphology, micromorphology, and corrosion rate of case materials with a total test time of 30 days, at 10d intervals. In the application, 1d = 1 day.

[0305] The sample used for the immersion corrosion test could have been a 15mm x 15mm x 2mm mass.

[0306] Before the test, the test surface was barrel polished, then rinsed with deionized water and ethanol in that order, dried, weighed, and stored in a drying oven for future use. During the test, the sample was placed in a container and 500 mL of etching solution was added. The etching solution was a 3.5 wt% NaCl aqueous solution. After sealing, the entire container was placed in a constant temperature water bath and the temperature was set to 25°C. A sample was taken once every 10 days (i.e., the test times were 10d, 20d, and 30d respectively), and the macro and micromorphology of the sample surface after etching was observed. After that, the etching products on the sample surface were removed, the sample mass before and after etching was compared, and the corresponding immersion etching rate was calculated. Three parallel samples were set up for each test.

[0307] 2. Analysis of test results Immersion corrosion tests were performed for 10d, 20d, and 30d, respectively. The macromorphology of the sample surfaces at different immersion corrosion times can be seen in Figure 28. After immersion in a 3.5 wt% NaCl solution, the surfaces of all four pressure-cast aluminum alloys were covered with obvious corrosion products. Compared to the sample appearance before corrosion, the sample surfaces after corrosion were rougher, exhibiting a black corrosion layer accompanied by gray punctate corrosion products. The corrosion products of A380 (Figure 28 (b)) and AlSi10MgMn alloy (Figure 28 (c)) were more pronounced, indicating that their corrosion resistance was inferior to that of A356.2 (Figure 28 (a)) and AlSi9MnMoZr alloy (Figure 28 (d)). Regardless of the 10d, 20d, and 30d samples, the A380 alloy surface showed the most corrosion products, demonstrating the worst corrosion resistance of the A380 alloy. When immersed and corroded for 10d, A356.2 and AlSi10MgMn exhibited a similar macroscopic corrosion morphology to that of the AISi9MnMoZr alloy. When immersed and corroded for 20d and 30d, the A356.2 alloy showed better corrosion resistance. Both AlSi10MgMn and AISi9MnMoZr alloys showed a large amount of white corrosion products on the surface due to the breakdown of the passivation film and the worsening of pitting corrosion. The surface of the AlSi0MgMn alloy was covered over a relatively large area with spotted white corrosion products, indicating that the AlSi9MnMoZr alloy had relatively good corrosion resistance, which was consistent with the salt spray corrosion results.

[0308] The immersion corrosion rate was calculated from the weight loss results of the samples before and after immersion corrosion, and the analysis results can be found in Figure 29. When the immersion time changed from 10d to 20d and then to 30d, the corrosion rates of the four alloy materials increased to different degrees. This was because, with the continuous occurrence of corrosion, the number of defect locations on the alloy surface increased, making it easier for corrosive Cl- to adsorb onto the defect locations, thereby accelerating corrosion. After immersion in a 3.5 wt% NaCl aqueous solution for 10d, the corrosion rates of A356.2, A380, AlSi9MgMn, and AlSi10MgMn alloys were 0.058 mm / y, 0.149 mm / y, 0.064 mm / y, and 0.048 mm / y, respectively, indicating that the AlSi9MnMoZr alloy has optimal corrosion resistance in the initial stages of corrosion. After 30d of immersion, the corrosion rates of A356.2, A380, AlSi9MgMn, and AlSi10MgMn alloys were 0.094 mm / y, 0.232 mm / y, 0.141 mm / y, and 0.111 mm / y, respectively, indicating that the A356.2 alloy exhibited better corrosion resistance in longer immersion corrosion tests. The main cause of the reduced corrosion resistance of the AlSi9MnMoZr alloy was the failure of the passivation film and the worsening of pitting behavior.

[0309] Therefore, it was possible to construct a corrosion resistance-mechanical performance analysis model using parameters related to the degree of corrosion in immersion corrosion tests.

[0310] 5. Application of Model Construction Methods (i) Test samples and test methods An aluminum alloy sample (which may be labeled as N1) can be used, and the composition of the alloy material can be found in Table 6.

[0311] [Table 6]

[0312] In Table 6, elemental content refers to the mass percentage content in the aluminum alloy material, and the unit is wt%.

[0313] Using the element Mn as an example, the corresponding compounding material containing Mn is listed as "Mn compounding material."

[0314] The composition of each element is AlSi 20 (Si compound material), AlCu 50 (Cu compound material), AlTi5 (Ti compound material), Zn (Zn compound material), Mg (Mg compound material), AlMn 10 (Mn compounded material), AlSr 10 The materials were (Sr-containing material) and Al. Using 1 kilogram (kg) as an example, the Zn burnout rate was 12%, and the Mg burnout rate was 15%.

[0315] The element Fe was an unavoidable impurity element introduced during the manufacturing process and was not added voluntarily.

[0316] The aluminum alloy material N1 was obtained by the following method.

[0317] (1) First, a pure aluminum ingot was placed in a blast furnace and heated to 740°C. After maintaining the temperature and melting, a Mn-containing material was added. After the Mn was melted, a Cu-containing material was added. When the temperature dropped to 710°C, a Si-containing material, a Ti-containing material, a Zn-containing material, and a Sr-containing material were added and melted. After the above elements were melted, a Mg-containing material was added. After the Mg-containing material melted, it was allowed to stand and the slag was removed. A refining agent was added, and after removing the slag, a Ti-containing material and a Sr-containing material were added. Finally, an ingot of a predetermined size was obtained by injection molding. Here, the predetermined size was the size of the sample required for the test.

[0318] (2) The ingot was left to stand at 550°C for 2 hours and then cooled with water to room temperature (20°C to 30°C). The resulting aluminum alloy material was a pressure-cast aluminum alloy sample. This aluminum alloy material can be used as a main body material or component material for battery cases, and can be used as a main body material or component material for lithium battery cases, fuel cell cases, etc., but is not limited to these.

[0319] Elemental analysis was performed using an inductively coupled plasma emission spectrometer (ICP instrument, Avio 5000) to accurately measure the actual composition of the aluminum alloy material. The ICP test results indicated that the actual composition of the aluminum alloy material was very close to its nominal composition, suggesting a relatively good smelting effect.

[0320] (2) Test method: The test conditions and sampling timings for the salt spray corrosion test, tensile test, electrochemical corrosion test, and immersion test were the same as those in the previous second, third, and fourth sections.

[0321] (3) Test Analysis Results The empirical relationship model between corrosion rate and equivalent corrosion time (the first set of empirical relationships) is shown in equations (5-1 to 5-3), as can be seen in Table 7 and Figure 30. As can be seen, the function y = a·b x After segmenting and fitting the corrosion rate using the function y=a+b·x, the fitting results essentially matched the test results.

[0322] The results of fitting the relationship curve between mechanical performance and corrosion time can be found in the second set of empirical relationships in Figure 31 and Table 8, which show how mechanical parameters change with equivalent corrosion time. The fitting results were in good agreement with the test results.

[0323] [Table 7]

[0324] [Table 8]

[0325] The descriptions of each embodiment and example in the preamble tend to emphasize the distinctions between each embodiment and example, and the same or similar parts may refer to each other; for the sake of brevity, this specification will not describe them further.

[0326] The technical features of each embodiment and example described above may be combined in any way, and for the sake of brevity, not all possible combinations of the technical features in each embodiment and example described above will be explained. However, as long as there is no inconsistency in these combinations of technical features, they should all be considered to fall within the scope described herein.

[0327] It should be noted that this application is not limited to the embodiments and examples described above. The embodiments and examples are merely examples, and any embodiment that has substantially the same configuration as the technical idea and produces the same effects within the scope of the technical proposal of this application is included within the scope of the technical proposal of this application. The embodiments and examples described above merely represent multiple embodiments of this application, and although the description is relatively detailed, it should not be understood as a limitation on the scope of the patent. Furthermore, other methods constructed by applying various modifications to the embodiments or examples that a person skilled in the art could conceive, and by combining some of the components of the embodiments or examples, are also included within the scope of this application, without departing from the spirit of this application. [Explanation of Symbols]

[0328] 310 Corrosion performance data acquisition module 320 Corrosion Performance Data Processing Module 510 Test Data Acquisition Module 520 Test Data Processing Module 530 Mechanical Performance Classification and Identification Module 6 Power consumption equipment 131 Auxiliary electrode 132 Working electrode 133 Reference electrode

Claims

1. A method for constructing a model for analyzing the degree of corrosion resistance of metallic materials and their mechanical performance, A step of obtaining a corrosion performance test experimental dataset by taking a metal material as the test subject, obtaining test values ​​of corrosion parameters at different equivalent corrosion points under corrosion test conditions, and further obtaining a mechanical performance test experimental dataset by obtaining test values ​​of mechanical parameters at different corrosion degrees corresponding to the different equivalent corrosion points, wherein the corrosion test conditions are used to simulate the target actual usage environment of the metal material, and the corrosion parameters include at least one of corrosion degree and corrosion rate. The steps include establishing a first set of empirical relational expressions that change with the equivalent corrosion time of the corrosion parameters based on the aforementioned corrosion performance test experimental dataset, and further establishing a second set of empirical relational expressions that change with the equivalent corrosion time of the mechanical parameters based on the aforementioned mechanical performance test experimental dataset, A method for constructing a corrosion resistance-mechanical performance analysis model for metallic materials, including the degree of corrosion resistance.

2. A method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material according to claim 1, wherein the mechanical parameter includes at least one of tensile strength and fracture elongation.

3. The method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material according to claim 1, wherein the mechanical parameters include yield strength.

4. The aforementioned metal material is an aluminum alloy material, A method for constructing a corrosion resistance degree-mechanical performance analysis model for a metal material according to claim 1, wherein the metal material is a metal structural member, and is selectively an aluminum alloy structural member, satisfying at least one of these characteristics.

5. The aforementioned metal material is one of the battery case materials, and selectively, the battery case material includes a fuel cell case material, and selectively, the battery case material includes a lithium battery case material. A method for constructing a corrosion resistance degree-mechanical performance analysis model for a metal material according to claim 1, wherein the metal material includes at least a portion of the structural members of a battery case, and selectively the battery case structural members include at least a portion of the structural members of a fuel cell case, and selectively the battery case structural members include at least a portion of the structural members of a lithium battery case, wherein the metal material satisfies at least one of these characteristics.

6. The aforementioned corrosion performance test experiment dataset includes at least the corrosion performance test experiment dataset under salt spray corrosion test conditions, Selectively, the salt spray corrosion test conditions include one or more of the following: NaCl aqueous solution salt spray conditions, acetic acid salt spray conditions, copper salt accelerated acetic acid salt spray conditions, and alternating salt spray corrosion conditions. Selectively, the salt spray corrosion test conditions include NaCl aqueous solution salt spray conditions. A method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material according to claim 1, wherein the NaCl aqueous solution salt spray conditions selectively include the parameter of simulated salt spray conditions for a 3 wt% to 6 wt% NaCl aqueous solution at 34 to 36°C.

7. Based on the aforementioned corrosion performance test experimental dataset, establishing a first set of empirical relational equations that change with the equivalent corrosion time of the corrosion parameters is: A method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material according to claim 1, comprising: segmenting and fitting each corrosion parameter in the aforementioned corrosion parameters based on the corresponding corrosion performance test experiment dataset; fitting each segment's fitting interval with the corresponding corrosion parameter as the dependent variable and the equivalent corrosion time as the independent variable in the form of a power function or a linear function; constructing an empirical relationship between a type of corrosion parameter and the equivalent corrosion time corresponding to the fitting interval of each segment; and obtaining the first set of empirical relationship formulas.

8. In the first set of empirical relations mentioned above, the fitting method for the power function is y1 = A・x Ｂ The fitting method for the linear function is y1 = a + b・x, where x is the equivalent corrosion time, y1 is the corrosion parameter, A is a positive number, B is a negative number, b is a positive number, and a is a real number. A method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material according to claim 7, wherein a is selectively a negative number.

9. A is a real number selected from 0.01 to 1.00, B is a real number selected from -0.3 to -0.8, b is a real number selected from 0.001 to 0.05, and a is a real number selected from -0.02 to -0.

8. Selectively, a is a real number selected from -0.001 to -0.8, and further selectively, a is a real number selected from -0.001 to -0.

5. A method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material according to claim 8, wherein b is selectively a real number selected from 0.001 to 0.02, and further selectively b is a real number selected from 0.001 to 0.

01.

10. The aforementioned corrosion performance test experiment dataset includes at least one of the following: a corrosion performance test experiment dataset under electrochemical corrosion test conditions and a corrosion performance test experiment dataset under immersion corrosion test conditions. Selectively, the corrosion parameters in the experimental data set for corrosion performance tests under the electrochemical corrosion test conditions include at least one of the self-corrosion potential and the self-corrosion current. A method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material according to claim 1, wherein the corrosion parameters in the experimental data set of corrosion performance tests under the immersion corrosion test conditions selectively include at least one of the degree of corrosion and the corrosion rate.

11. Based on the aforementioned mechanical performance test experimental dataset, establishing a second set of empirical relational equations that change with the equivalent corrosion time of the mechanical parameters is: A method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material according to claim 1, comprising: segmenting and fitting each mechanical parameter in the mechanical parameters based on the corresponding mechanical performance test experimental dataset; fitting each segment's fitting interval with the corresponding mechanical parameter as the dependent variable and the equivalent corrosion time as the independent variable in the form of a power function or a linear function; constructing an empirical relationship between the corresponding type of mechanical parameter and the equivalent corrosion time; and obtaining the second set of empirical relationship equations.

12. In the second set of empirical relations mentioned above, the fitting method for the power function is y² = M·x Ｎ The method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material according to claim 11, wherein the fitting method for the linear function is y² = m + n * x, where x is the equivalent corrosion time, y² is a mechanical parameter, M is a positive number, N is a negative number, n is a negative number, and m is a positive number.

13. M is a real number selected from 1 to 250, N is a real number selected from -0.01 to -1, n is a real number selected from -0.05 to -5, and m is a real number selected from 1 to 300. Selectively, N is a real number chosen from -0.01 to -0.5, and further selectively, N is a real number chosen from -0.01 to -0.

2. A method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material according to claim 12, wherein n is selectively selected from -1 to -3, selectively selected from -0.5 to -1.5, and selectively selected from -0.05 to -0.

5.

14. A method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material according to claim 1, further comprising the step of establishing a third set of empirical relational expressions between the equivalent corrosion time under the aforementioned corrosion test conditions and the actual usage time in the target actual usage environment.

15. A method for constructing a corrosion resistance-mechanical performance analysis model for a metallic material according to claim 1, comprising the step of establishing a fourth set of empirical relations between the corrosion rate and the equivalent corrosion time under the aforementioned corrosion test conditions, wherein the first set of empirical relations includes the fourth set of empirical relations.

16. A method for analyzing the service life of a metallic material, wherein the metallic material is as defined in claim 1, The method for analyzing the service life of the aforementioned metal material is: The steps include determining mechanical parameters related to the ineffective behavior of the metal material according to the target actual usage environment of the metal material, determining corrosion test conditions that can simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metal material under the corrosion test conditions, A method for analyzing the service life of a metallic material, comprising the steps of obtaining a service life parameter of the metallic material under the corrosion test conditions, using the corrosion degree test value, the effective state threshold of the mechanical parameter, and the corrosion degree-mechanical performance analysis model of the metallic material, the steps of including a first set of empirical relational expressions that change with the equivalent corrosion time of the corrosion parameter characterizing the corrosion degree and a second set of empirical relational expressions that change with the equivalent corrosion time of the mechanical parameter.

17. The method for analyzing the service life of a metallic material according to claim 16, wherein the service life parameter includes at least equivalent corrosion time.

18. Obtaining the service life parameter of the metal material under the corrosion test conditions using the above-mentioned corrosion degree test value, the effective state threshold of the mechanical parameter, and the corrosion resistance degree-mechanical performance analysis model of the metal material is possible. A method for analyzing the service life of a metal material according to claim 16, comprising obtaining the surplus actual service time of the metal material by means of a test value of the degree of corrosion, an effective state threshold of the mechanical parameter, a corrosion resistance degree-mechanical performance analysis model of the metal material, and a third set of empirical relational expressions between the equivalent corrosion time under the corrosion test conditions and the actual service time in the target actual service environment.

19. The method for analyzing the service life of a metallic material according to claim 16, wherein the mechanical parameters include at least two, the degree of ineffective response of the metallic material is ranked in descending order based on each mechanical parameter, and a corrosion resistance degree-mechanical performance analysis model of the metallic material is obtained based on the highest-ranking mechanical parameter.

20. The corrosion resistance degree-mechanical performance analysis model of the metal material is constructed by the method for constructing the corrosion resistance degree-mechanical performance analysis model of the metal material described in claim 1, and is obtained by the method for constructing the corrosion resistance degree-mechanical performance analysis model of the metal material described in claim 1, and is the method for analyzing the service life of the metal material described in claim 16.

21. A device for analyzing the service life of a metallic material, wherein the metallic material is as defined in claim 1, The metal material service life analyzer is A corrosion performance data acquisition module for determining mechanical parameters related to the ineffective behavior of the metal material according to the target actual usage environment of the metal material, determining corrosion test conditions that can simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metal material under the corrosion test conditions, A metal material service life analyzer comprising a corrosion performance data processing module for obtaining a service life parameter of a metal material under the corrosion test conditions, wherein the corrosion performance data processing module is defined in claim 1, based on the corrosion degree test value, the effective state threshold of the mechanical parameter, and the corrosion degree-mechanical performance analysis model of the metal material, the corrosion degree-mechanical performance analysis model of the metal material being the corrosion performance data processing module defined in claim 1.

22. A method for analyzing the mechanical properties of a metallic material, wherein the metallic material is as defined in claim 1, The method for analyzing the mechanical properties of the aforementioned metallic material is: The steps include determining mechanical parameters related to the ineffective behavior of the metal material according to the target actual usage environment of the metal material, determining corrosion test conditions that can simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metal material under the corrosion test conditions, A step of obtaining preliminary predicted values ​​of the mechanical parameters after the target actual usage time of the metal material under the corrosion test conditions, using the test value of the degree of corrosion, the target actual usage time of the metal material, a third set of empirical relational expressions between the equivalent corrosion time under the corrosion test conditions and the actual usage time in the target actual usage environment, and a corrosion resistance degree-mechanical performance analysis model of the metal material, wherein the corrosion resistance degree-mechanical performance analysis model of the metal material is defined in claim 1, A method for analyzing the mechanical performance of a metallic material, comprising the steps of: comparing a preliminary predicted value of the mechanical parameter with an effective state threshold of the mechanical parameter to obtain a predicted result of the mechanical parameter after the metallic material has been used for a target actual usage time under the corrosion test conditions, wherein if the preliminary predicted value of the mechanical parameter is greater than or equal to the effective state threshold of the mechanical parameter, the preliminary predicted value is output as the effective predicted value of the mechanical parameter after the metallic material has been used for a target actual usage time under the corrosion test conditions; and if the preliminary predicted value of the mechanical parameter is less than the effective state threshold of the mechanical parameter, an invalid prediction result is output before the metallic material reaches the target actual usage time.

23. An analytical apparatus for the mechanical properties of a metallic material, wherein the metallic material is as defined in claim 1, The analytical apparatus for analyzing the mechanical properties of the aforementioned metallic material is: A test data acquisition module for determining mechanical parameters related to the ineffective behavior of the metal material according to the target actual usage environment of the metal material, determining corrosion test conditions that can simulate the target actual usage environment, and obtaining test values ​​for the degree of corrosion of the metal material under the corrosion test conditions, A test data processing module for obtaining preliminary predicted values ​​of the mechanical parameters after the target actual usage time of the metal material under the corrosion test conditions, using the test value of the degree of corrosion, the target actual usage time of the metal material, a third set of empirical relational expressions between the equivalent corrosion time under the corrosion test conditions and the actual usage time in the target actual usage environment, and a corrosion resistance degree-mechanical performance analysis model of the metal material, wherein the corrosion resistance degree-mechanical performance analysis model of the metal material is defined in the test data processing module of claim 1, A mechanical performance classification and identification module for obtaining a prediction result of the mechanical parameters after the target actual usage time of the metal material under the corrosion test conditions, which includes a mechanical performance classification and identification module that compares a preliminary prediction value of the mechanical parameters with an effective state threshold of the mechanical parameters, wherein if the preliminary prediction value of the mechanical parameters is greater than or equal to the effective state threshold of the mechanical parameters, the effective prediction value of the mechanical parameters after the target actual usage time of the metal material under the corrosion test conditions is equal to the preliminary prediction value.

24. A computer device comprising a memory for storing a computer program and a processor, wherein the processor, when executing the computer program, realizes the steps of the method for constructing a corrosion resistance degree-mechanical performance analysis model for a metallic material as described in claim 1.

25. A computer-readable storage medium in which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the method for constructing a corrosion resistance degree-mechanical performance analysis model of a metal material described in claim 1 are realized.

26. A power consumption device, A battery system comprising a battery case containing a metal material as defined in any one of claims 1 to 3, and a battery cell located inside the battery case, A power consumption device comprising a metal material service life analyzer according to claim 21, a metal material mechanical performance analyzer according to claim 23, and at least one of a computer device according to claim 24 and a computer-readable storage medium according to claim 25.

27. The power consumption device according to claim 26, wherein the battery cell includes a fuel cell.

28. The power consumption device according to claim 26, wherein the battery cell includes a lithium battery cell.

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