A high-precision test method for the corrosion resistance of stainless steel

Abstract

The present invention discloses a high-precision corrosion resistance testing method for stainless steel, which includes obtaining multi-dimensional microscopic characteristic data of the sample surface and determining the microscopic sensitive areas; applying a multi-field coupled dynamic excitation to the sensitive areas to determine the critical conditions for triggering corrosion; triggering initial corrosion according to the critical conditions and adaptively tracking the propagation process to obtain spatio-temporal data; monitoring the changes in micro-region environmental parameters based on the spatio-temporal data and analyzing the corresponding relationship between the surface topography changes and the environmental parameter changes to determine the key conditions for forming a feedback amplification effect; performing an accelerated test with multi-level oscillation strengthening according to the key conditions and conducting stage decomposition processing to obtain the corrosion resistance prediction results. The technical solution of the present invention can accurately reveal the dynamic feedback mechanism between the surface microscopic topography and the corrosion behavior, solve the evaluation deviation problem caused by the traditional method ignoring the microscopic topography evolution, and achieve the accurate prediction of the long-term corrosion resistance of high-precision stainless steel in complex service environments.

Description

Technical Field

[0001] The present invention relates to the technical field of corrosion performance testing of metal materials, and particularly to a high-precision corrosion resistance testing method for stainless steel. Background Art

[0002] With the continuous increase in the demand for precision components in modern industry, high-precision stainless steel, as a material with excellent corrosion resistance, mechanical strength, and machining accuracy, is widely used in fields such as aerospace, medical devices, precision instruments, and microelectronics. Since such materials are often exposed to complex corrosion environments in actual applications, corrosion resistance testing has become a crucial step in ensuring product performance and lifespan. Traditional corrosion resistance testing mainly includes two methods: one is the standard salt spray test, that is, placing the sample in a closed environment with 5% sodium chloride solution spray, continuously exposing it for hundreds of hours at 35 ± 2 °C, and evaluating the corrosion resistance by observing the surface corrosion state, weight loss, or corrosion depth under a microscope; the other is the electrochemical polarization curve test, which immerses the sample in a specific electrolyte solution through a three-electrode system (working electrode, reference electrode, and auxiliary electrode), applies a continuously changing potential, records the corresponding current density, plots the polarization curve, and extracts parameters such as corrosion potential and passivation current density from it to quantitatively evaluate the corrosion resistance of the material.

[0003] However, during the actual etching process of high-precision stainless steel, it has been found that as the etching depth increases, specific microtopographies will form on the material surface. These topographies include network-like grooves formed by preferential corrosion of grain boundaries (depth about 0.1 - 5 μm, width about 0.05 - 2 μm), tiny steps caused by differential corrosion between austenite phase and ferrite phase (height difference about 0.2 - 3 μm), and local pitting pits (diameter about 1 - 50 μm, depth-to-width ratio about 0.5 - 3) and other complex structures. These microtopographies will significantly change the local electrochemical environment: on the one hand, the chemical composition of the micro-region solution (such as pH value, metal ion concentration) formed in the grooves and pits is different from the surface solution; on the other hand, these microstructures change the potential distribution and solution flow state, and some areas may form stagnant zones or areas with accelerated flow rates. The combined effect of these changes results in subsequent etching behavior being significantly different from the initial stage, forming a complex dynamic feedback system. This makes it impossible for traditional testing methods to accurately predict the long-term corrosion resistance of materials in actual application environments, because standard tests are often based on steady-state assumptions or simple dynamic models, ignoring this complex surface topography-corrosion behavior feedback mechanism, resulting in a significant deviation between the evaluation results and the actual service performance, bringing serious challenges to product design and lifespan assessment. Summary of the Invention

[0004] The main objective of the present invention is to solve the technical problem of the deviation in corrosion resistance evaluation caused by the dynamic feedback mechanism between the surface microtopography and corrosion behavior in existing high-precision stainless steel corrosion resistance testing methods.

[0005] The first aspect of the present invention provides a high-precision stainless steel corrosion resistance testing method, and the high-precision stainless steel corrosion resistance testing method includes:

[0006] Obtain multi-dimensional microscopic characteristic data of the stainless steel sample surface, analyze and process the multi-dimensional microscopic characteristic data, determine the microscopic sensitive areas on the stainless steel sample surface, and obtain a microscopic sensitivity distribution map;

[0007] Based on the microscopic sensitivity distribution map, apply a multi-field coupled dynamic excitation to the microscopic sensitive areas of the stainless steel sample, obtain the surface response data of the microscopic sensitive areas under the multi-field coupled dynamic excitation, and determine the critical conditions for triggering corrosion;

[0008] According to the critical conditions, trigger the initial corrosion of the microscopic sensitive areas, adaptively track the corrosion propagation process, and implement corrosion path intervention to obtain the spatio-temporal data of corrosion evolution;

[0009] Based on the spatio-temporal data, monitor the dynamic changes of micro-region environmental parameters during the corrosion process, analyze the corresponding relationship between the surface topography changes and the dynamic changes of micro-region environmental parameters, and determine the key conditions for forming the corrosion feedback amplification effect;

[0010] According to the key conditions, the microscopic sensitivity distribution map, the critical conditions, and the spatio-temporal data, perform an accelerated test with multi-level shock reinforcement, perform stage decomposition processing on the accelerated test data, calculate the time conversion coefficient, and obtain the corrosion resistance performance prediction result of the stainless steel sample.

[0011] Preferably, the obtaining of the multi-dimensional microscopic characteristic data of the stainless steel sample surface, the analysis and processing of the multi-dimensional microscopic characteristic data, the determination of the microscopic sensitive areas on the stainless steel sample surface, and the obtaining of the microscopic sensitivity distribution map include:

[0012] Simultaneously obtain the microscopic structure data, mechanical property data, chemical composition data, and residual stress data of the same position of the stainless steel sample, where the microscopic structure data is used to identify the grain boundary network and phase boundary distribution, the mechanical property data is used to determine the hardness gradient region, the chemical composition data is used to locate the element segregation points, and the residual stress data is used to mark the stress concentration areas;

[0013] Divide the stainless steel sample surface into micro-grid units, perform correlation analysis on the microscopic structure data, mechanical property data, chemical composition data, and residual stress data within each micro-grid unit, and calculate the electrochemical activity index of each micro-grid unit;

[0014] According to the electrochemically active index, high-activity micro-regions are selected on the surface of the stainless-steel sample for weak electrochemical perturbation tests. The current density change rate and potential recovery time of the high-activity micro-regions under perturbation are recorded, the electrochemical response differences between different micro-regions are compared, and the region with the largest response difference is determined as the microscopic sensitive region;

[0015] Fine scanning is performed on the microscopic sensitive region to collect three-dimensional depth information. Combining the sensitivity data of the surface and subsurface, a microscopic sensitivity distribution map represented in the form of a probability heat map is constructed.

[0016] Preferably, according to the electrochemically active index, high-activity micro-regions are selected on the surface of the stainless-steel sample for weak electrochemical perturbation tests. The current density change rate and potential recovery time of the high-activity micro-regions under perturbation are recorded, the electrochemical response differences between different micro-regions are compared, and the region with the largest response difference is determined as the microscopic sensitive region, including:

[0017] Potential pulse perturbations with an amplitude less than 10 mV are applied to the micro-grid cells ranked in the top 30% of the electrochemically active index through a microelectrode array, and the perturbation duration is controlled within the range of 1 - 5 ms;

[0018] The transient current density curves of each microelectrode during the application of the perturbation and within 100 ms after the perturbation is withdrawn are synchronously collected. The ratio of the peak current density to the steady-state value is calculated as the current density change rate, and the time required for the potential to recover from the perturbation peak to 90% of the steady-state value is measured as the potential recovery time;

[0019] According to the current density change rate and potential recovery time, the electrochemical response sensitivity coefficient of each micro-region is calculated, where the electrochemical response sensitivity coefficient is equal to the weighted product of the current density change rate and the potential recovery time;

[0020] The electrochemical response sensitivity coefficient is normalized. The micro-regions with a normalized sensitivity coefficient greater than 0.8 are marked as high-sensitivity candidate regions, and topological connectivity analysis is performed on the high-sensitivity candidate regions to identify interconnected clusters of sensitive micro-regions as the microscopic sensitive region.

[0021] Preferably, based on the microscopic sensitivity distribution map, multi-field coupled dynamic excitation is applied to the microscopic sensitive region of the stainless-steel sample, the surface response data of the microscopic sensitive region under multi-field coupled dynamic excitation are obtained, and the critical conditions for triggering corrosion are determined, including:

[0022] According to the sensitivity differences in different regions of the microscopic sensitivity distribution map, the microscopic sensitive region of the stainless-steel sample is divided into a grain boundary sensitive region, a stress concentration region, and a phase boundary sensitive region;

[0023] Apply an interactive combined excitation with a chloride ion concentration ranging from 0.01 M to 0.5 M and a potential ranging from -300 mV to +200 mV to the grain boundary sensitive region, and record the current density-time curve and the passivation film breakdown potential under different combinations;

[0024] Apply an interactive combined excitation with a stress level of 20% to 70% of the yield strength and a pH value ranging from 3 to 10 to the stress concentration region, and measure the change rate of the polarization resistance and the critical pitting current density under different combinations;

[0025] Apply an interactive combined excitation with a temperature ranging from 25 °C to 65 °C and a dissolved oxygen concentration ranging from 2 ppm to 8 ppm to the phase boundary sensitive region, and obtain the change rate of the surface morphology and the corrosion potential drift value under different combinations;

[0026] Comprehensively analyze the response data of various sensitive regions under different excitation combinations, calculate the corrosion sensitivity index, identify the parameter combination threshold that causes a sudden increase in the corrosion sensitivity index, and determine the parameter combination threshold as the critical condition for triggering corrosion.

[0027] Preferably, the comprehensively analyzing the response data of various sensitive regions under different excitation combinations, calculating the corrosion sensitivity index, identifying the parameter combination threshold that causes a sudden increase in the corrosion sensitivity index, and determining the parameter combination threshold as the critical condition for triggering corrosion includes:

[0028] Perform numerical integration on the current density-time curve of the grain boundary sensitive region, calculate the charge transfer amount per unit time, take the reciprocal of the difference between the passivation film breakdown potential and the reference potential as the passivation film fragility coefficient, and define the product of the charge transfer amount per unit time and the passivation film fragility coefficient as the corrosion sensitivity index of the grain boundary sensitive region;

[0029] Calculate the ratio of the change rate of the polarization resistance in the stress concentration region to the initial polarization resistance as the polarization resistance coefficient, calculate the ratio of the critical pitting current density to the material reference corrosion current density as the pitting tendency coefficient, and divide the pitting tendency coefficient by the polarization resistance coefficient to obtain the corrosion sensitivity index of the stress concentration region;

[0030] Convert the change rate of the surface morphology in the phase boundary sensitive region into the surface roughness increment per unit time, calculate the ratio of the corrosion potential drift value to the standard deviation as the electrochemical stability coefficient, and define the product of the surface roughness increment per unit time and the electrochemical stability coefficient as the corrosion sensitivity index of the phase boundary sensitive region;

[0031] Normalize the corrosion sensitivity index of the grain boundary sensitive area, the corrosion sensitivity index of the stress concentration area, and the corrosion sensitivity index of the phase boundary sensitive area respectively, plot the response surface of the multi-field coupling parameters and the normalized corrosion sensitivity index, identify the inflection points on the surface where the gradient change rate exceeds 200%, and determine the parameter combination corresponding to the inflection point as the critical condition for triggering corrosion.

[0032] Preferably, according to the critical condition, trigger the initial corrosion of the micro-sensitive area, adaptively track the corrosion propagation process, and implement corrosion path intervention to obtain the spatio-temporal data of corrosion evolution, including:

[0033] According to the critical condition, apply a triggering stimulus to the micro-sensitive area, specifically including: applying a combination of critical chloride ion concentration and critical potential to the grain boundary sensitive area, applying a combination of critical stress level and critical pH value to the stress concentration area, and applying a combination of critical temperature and critical dissolved oxygen concentration to the phase boundary sensitive area until the formation of an initial corrosion point is observed;

[0034] Obtain corrosion propagation data through multi-scale monitoring methods, including macroscopic electrochemical parameter monitoring, micro-area electrochemical activity scanning, and real-time surface topography imaging. When the formation of an initial corrosion point is detected, dynamically track the corrosion propagation front and adjust the monitoring parameters according to the corrosion front movement speed;

[0035] During the corrosion propagation process, implement intervention measures at the positions where the corrosion rate changes, including changing the local electrochemical environment, adjusting the stress distribution, or modifying the surface state, and record the changes in the corrosion propagation rate before and after the intervention;

[0036] Collect data on the entire corrosion process, including data on the change of corrosion point density over time, data on the spatial distribution of corrosion depth, and data on the corrosion front propagation rate;

[0037] Integrate the data on the change of corrosion point density over time, data on the spatial distribution of corrosion depth, and data on the corrosion front propagation rate, identify the conversion points and spatial expansion characteristics of each corrosion stage, and form spatio-temporal data of corrosion evolution including time dimension and space dimension.

[0038] Preferably, based on the spatio-temporal data, monitor the dynamic changes of micro-area environmental parameters during the corrosion process, analyze the corresponding relationship between the surface topography changes and the dynamic changes of micro-area environmental parameters, and determine the key conditions for forming the corrosion feedback amplification effect, including:

[0039] Based on the corrosion active area determined by the spatio-temporal data, deploy a micro-area environmental monitoring lattice to monitor the dynamic change data of local pH value, metal ion concentration, dissolved oxygen concentration, potential distribution, and liquid flow state during the corrosion process, and obtain the dynamic change data of micro-area environmental parameters;

[0040] Extract the data of the change of corrosion point density over time, the spatial distribution data of corrosion depth, and the data of the corrosion front propagation rate from the spatio-temporal data, calculate the change rate of each data in the time dimension, and obtain the surface topography change rate;

[0041] Perform a time-series comparative analysis on the surface topography change rate and the dynamic change data of the micro-area environmental parameters, determine the sequence of topography change and micro-area environmental parameter change, and identify the coupling points where the change of environmental parameters causes the topography to change rapidly or the topography change causes the environmental parameters to change rapidly;

[0042] Calculate the environmental parameter influence coefficient and the topography reaction coefficient for the coupling points. When the product of the environmental parameter influence coefficient and the topography reaction coefficient is greater than 1, confirm the formation of a positive feedback loop;

[0043] Determine the key conditions for the formation of the corrosion feedback amplification effect according to the trigger parameter combination, threshold condition, and enhancement mechanism of the positive feedback loop.

[0044] Preferably, according to the key conditions, the microscopic sensitivity distribution map, the critical conditions, and the spatio-temporal data, perform an accelerated test with multi-level oscillation strengthening, perform a stage decomposition process on the accelerated test data, calculate the time conversion coefficient, and obtain the corrosion resistance prediction result of the stainless steel sample, including:

[0045] Compile a service environment characteristic spectrum according to the periodic changes, random fluctuations, and emergency characteristics of the actual service environment, and convert the service environment characteristic spectrum into a parameter sequence that can be reproduced in the laboratory;

[0046] Based on the parameter sequence, the key conditions, the microscopic sensitivity distribution map, and the critical conditions, design an accelerated test scheme with multi-level oscillation strengthening, specifically including: microscopic oscillation strengthening with high-frequency environmental parameter fluctuations, mesoscopic oscillation strengthening with periodic switching of corrosion conditions, and macroscopic oscillation strengthening with short-term extreme conditions applied at key time points;

[0047] Execute the accelerated test scheme with multi-level oscillation strengthening, obtain the corrosion depth data, corrosion current density data, and surface topography change data under the accelerated test conditions, and form the accelerated test data;

[0048] According to the corrosion evolution characteristics in the spatio-temporal data, decompose the corrosion process into four stages: latency period, nucleation period, expansion period, and stable period, and analyze the accelerated test data for each stage;

[0049] Compare the accelerated test data with the standard test data, establish the time correspondence relationship for each stage, calculate the time conversion coefficient for each stage, and obtain the corrosion resistance prediction result of the stainless steel sample through integral calculation.

[0050] Preferably, according to the corrosion evolution characteristics in the spatio-temporal data, the corrosion process is decomposed into four stages: the latency period, the nucleation period, the propagation period, and the stable period. Analyze the accelerated test data for each stage, including:

[0051] Perform curve fitting on the data of the change of corrosion point density with time, the spatial distribution data of corrosion depth, and the data of the propagation rate of the corrosion front in the spatio-temporal data, calculate the first derivative and the second derivative of the curve, and determine the conversion time points from the latency period to the nucleation period, from the nucleation period to the propagation period, and from the propagation period to the stable period according to the change of the derivative sign and the inflection point position;

[0052] According to the determined time ranges of the four stages, extract the accelerated test data in segments, analyze the passivation film stability for the accelerated test data in the latency period, analyze the growth law of corrosion point density for the accelerated test data in the nucleation period, analyze the corrosion depth propagation characteristics for the accelerated test data in the propagation period, and analyze the constant corrosion rate parameter for the accelerated test data in the stable period to complete the analysis of the accelerated test data for each stage.

[0053] The high-precision stainless steel corrosion resistance test method disclosed by the present invention first obtains multi-dimensional microscopic characteristic data on the surface of a stainless steel sample and conducts comprehensive analysis and processing. The data sources include microscopic structure data, mechanical property data, chemical composition data, and residual stress data, which are synchronously obtained through a microarray sampling technique, providing a basis for subsequent determination of sensitive areas. This process can not only identify the surface microscopic structure but also reveal factors such as hardness, chemical composition, and stress distribution that have important effects on corrosion behavior. Based on these data, the microscopic sensitive areas on the sample surface can be accurately determined, and a microscopic sensitivity distribution map can be generated.

[0054] Next, based on the microscopic sensitivity distribution map, the method applies multi-field coupled dynamic excitation to different sensitive areas. Through these excitations, the microscopic sensitive areas of the sample will simulate their possible corrosion responses in a real environment. Different excitation combinations (such as the interaction of chloride ion concentration, potential, stress level, etc.) make it possible to clarify the corrosion triggering conditions for each sensitive area. This process can reveal the critical conditions for corrosion, that is, under specific environmental stimuli, which microscopic areas will corrode first.

[0055] After determining the critical conditions for corrosion, the next step is to trigger the initial corrosion of the sample based on these conditions and adaptively track the corrosion propagation process. By real-time monitoring various data during the corrosion process (such as corrosion pit density, corrosion depth, corrosion rate, etc.), the test parameters can be dynamically adjusted to intervene during the corrosion path expansion. This adaptive tracking and intervention mechanism not only helps to grasp the corrosion progress in real time but also can slow down or accelerate the corrosion expansion by adjusting the local electrochemical environment, stress distribution, or surface state, etc., further helping to optimize the test process.

[0056] In addition, during this process, the dynamic changes of the micro-area environmental parameters during the corrosion process should also be monitored, and the corresponding relationship between the surface topography changes and the micro-area environmental parameter changes should be analyzed. Through this analysis, it is possible to identify which factors form a positive feedback loop of corrosion, thereby amplifying the corrosion effect. Based on this analysis, the key conditions for forming the corrosion feedback amplification effect can be determined, which provides a basis for further evaluating the corrosion resistance of materials in complex environments.

[0057] Finally, through the analysis of spatio-temporal data, an accelerated test with multi-level oscillatory strengthening is performed. This accelerated test combines the characteristics of periodic fluctuations, random changes, and extreme conditions in the actual service environment. By simulating these environmental changes in the laboratory, more realistic corrosion resistance prediction results can be obtained. Through stage-by-stage decomposition processing and calculating the time conversion coefficient for each stage, the performance of the material at different corrosion stages can be accurately predicted, and finally a reliable corrosion resistance evaluation can be obtained.

[0058] The present invention fundamentally solves the core problem that traditional test methods cannot accurately simulate corrosion behavior through these specific steps. First, through the acquisition and analysis of multi-dimensional microscopic characteristic data, a comprehensive understanding of the microscopic topography of the sample surface is ensured, which lays a foundation for accurately locating the corrosion-sensitive areas. Then, through dynamic excitation and determination of critical conditions, the dynamic changes in the real corrosion environment can be simulated, solving the problem that the corrosion behavior in complex environments cannot be reflected in traditional methods. Furthermore, through real-time monitoring and adaptive tracking, the corrosion expansion is precisely controlled, ensuring the dynamic adaptability and accuracy of the test results. Finally, through the accelerated test combined with spatio-temporal data analysis, more realistic corrosion resistance prediction results can be obtained in a shorter time. Therefore, the entire solution can effectively eliminate the deviation of traditional test methods and provide a more accurate corrosion resistance evaluation. Brief Description of the Drawings

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0060] Figure 1 It is a schematic diagram of an embodiment of the high-precision stainless steel corrosion resistance test method in the embodiments of the present invention.

[0061] The realization of the object of the present invention, functional features and advantages will be further described in conjunction with the embodiments with reference to the drawings. Specific embodiments

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0063] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, the directional indications are only used to explain the relative position relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0064] In addition, the descriptions involving "first", "second", etc. in the present invention are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" throughout the text includes three solutions. Taking A and / or B as an example, it includes the technical solution of A, the technical solution of B, and the technical solution that both A and B are satisfied at the same time. In addition, the technical solutions between the embodiments can be combined with each other, which must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0065] An embodiment of the present application provides a high-precision stainless steel corrosion resistance test method. Figure 1 It is a flowchart of a high-precision stainless steel corrosion resistance test method provided by an embodiment of the present application. In this embodiment, the method includes:

[0066] Please refer toFigure 1 , obtain multi-dimensional microscopic characteristic data of the surface of a stainless steel sample, analyze and process the multi-dimensional microscopic characteristic data, determine the microscopic sensitive area on the surface of the stainless steel sample, and obtain a microscopic sensitivity distribution map;

[0067] In an embodiment of the present invention, the obtaining of the multi-dimensional microscopic characteristic data of the surface of the stainless steel sample, the analysis and processing of the multi-dimensional microscopic characteristic data, the determination of the microscopic sensitive area on the surface of the stainless steel sample, and the obtaining of the microscopic sensitivity distribution map include:

[0068] Simultaneously obtain microscopic structure data, mechanical property data, chemical composition data, and residual stress data at the same position of the stainless steel sample, where the microscopic structure data is used to identify the grain boundary network and phase boundary distribution, the mechanical property data is used to determine the hardness gradient area, the chemical composition data is used to locate the element segregation point, and the residual stress data is used to mark the stress concentration area;

[0069] Divide the surface of the stainless steel sample into micro-grid units, perform correlation analysis on the microscopic structure data, mechanical property data, chemical composition data, and residual stress data within each micro-grid unit, and calculate the electrochemical activity index of each micro-grid unit;

[0070] According to the electrochemical activity index, select high-activity micro-regions on the surface of the stainless steel sample for weak electrochemical perturbation tests, record the current density change rate and potential recovery time of the high-activity micro-regions under perturbation, compare the electrochemical response differences between different micro-regions, and determine the region with the largest response difference as the microscopic sensitive area;

[0071] Perform a fine scan on the microscopic sensitive area, collect three-dimensional depth information, and combine the sensitivity data of the surface and subsurface to construct a microscopic sensitivity distribution map in the form of a probability heat map.

[0072] The following specifically describes the steps involved in the above embodiment:

[0073] First, it is necessary to synchronously acquire multi-dimensional data at multiple positions on the surface of the stainless steel sample. These data include microstructure data, mechanical property data, chemical composition data, and residual stress data. The microstructure data is obtained through a scanning electron microscope (SEM) or a transmission electron microscope (TEM) to identify the grain boundary network and phase boundary distribution. A grain boundary is the interface between adjacent grains in a crystal and is usually the place where the material preferentially reacts during the corrosion process; the phase boundary distribution reflects the distribution of different phases in the material, which may lead to different corrosion rates. The mechanical property data is obtained through a microhardness tester and is mainly used to identify the hardness gradient region. The existence of a hardness gradient usually means that the mechanical properties of the material will change in a local area, thus affecting its corrosion resistance. The chemical composition data is obtained through energy dispersive spectroscopy (EDS) or X-ray fluorescence (XRF) to locate the element segregation points. Element segregation points are usually hot spots for corrosion because the elemental composition at these positions may make this area more susceptible to corrosion. The residual stress data is obtained through X-ray diffraction (XRD) or strain gauge testing to mark the stress concentration areas, as stress concentration areas are often sensitive points where corrosion occurs. Through the synchronous acquisition of this multi-dimensional data, the internal structure, properties, and chemical characteristics of the sample can be comprehensively reflected at the microscopic level, providing detailed data support for the subsequent identification of sensitive areas.

[0074] It is necessary to divide the surface of the stainless steel sample into several micro-grid units. The size of each micro-grid unit is usually between dozens of micrometers and hundreds of micrometers, and the specific size selection needs to consider the microscopic characteristics of the material and the testing requirements. For each micro-grid unit, first, the microstructure data, mechanical property data, chemical composition data, and residual stress data involved are integrated through correlation analysis. Then, based on the comprehensive consideration of these data, the electrochemical activity index of each unit is calculated. The electrochemical activity index is a quantitative indicator used to measure the corrosion activity of this area relative to other areas. This indicator takes into account various factors, such as the hardness, chemical composition, residual stress, etc. of this area, and can comprehensively reflect the electrochemical corrosion activity of this area. In this way, the corrosion tendency of each micro-grid unit on the surface of the stainless steel sample can be quantified, thus helping to further identify potential corrosion areas.

[0075] Based on the electrochemically active index calculated in the second step, it is first necessary to select those regions with higher electrochemically active indices for further weak electrochemical perturbation tests. These regions are where the surface corrosion reaction is most active and may become the starting points of corrosion. A potential pulse with a small amplitude (usually less than 10 mV) is applied through a microelectrode array, and the duration of the perturbation is generally controlled between 1 - 5 milliseconds. The rate of change of current density and the potential recovery time are important parameters reflecting the electrochemical response of this region. The rate of change of current density reflects the activity of this region under electrochemical perturbation, and the potential recovery time can reflect the electrochemical stability of this region. By comparing and analyzing the differences in electrochemical responses between different microregions, the regions with the most significant response differences can be identified. These regions usually have higher corrosion sensitivity and are finally determined as microscopically sensitive regions. These sensitive regions show a more obvious corrosion tendency during the subsequent corrosion propagation process. Therefore, the accurate identification of them is crucial for the corrosion resistance test.

[0076] Fine scanning is usually completed by a high-resolution scanning electron microscope (SEM) or an atomic force microscope (AFM), which can obtain the three-dimensional depth information of this region. This information can show the surface morphology and subsurface structure of the microscopically sensitive region, thus providing important data support for the subsequent corrosion evolution. By comprehensively analyzing the sensitivity data of the surface and subsurface, a microscopically sensitivity distribution map in the form of a probability heat map can be constructed. The color depth of the heat map represents the corrosion sensitivity of different regions. Through this visualization method, the corrosion susceptibility and distribution of different regions can be intuitively understood. This map helps researchers deeply understand the corrosion behavior of the sample under different environmental conditions and facilitates targeted intervention during the experiment.

[0077] Through these steps, the present invention can comprehensively identify and analyze the corrosion resistance of stainless steel samples at the microscopic level. By using means such as electrochemically active index and fine scanning, it provides a more accurate corrosion assessment than traditional methods. This comprehensive analysis method can effectively overcome the problems of ignoring surface morphology changes and corrosion behavior feedback mechanisms in traditional methods, thus providing a more reliable basis for corrosion assessment in practical applications.

[0078] In an embodiment of the present invention, according to the electrochemically active index, high-activity microregions are selected on the surface of the stainless steel sample for weak electrochemical perturbation tests, the rate of change of current density and the potential recovery time of the high-activity microregions under perturbation are recorded, and the differences in electrochemical responses between different microregions are compared to determine the region with the largest response difference as the microscopically sensitive region, including:

[0079] A potential pulse perturbation with an amplitude less than 10 mV is applied to the micro-grid cells ranked in the top 30% of the electrochemical activity index through a microelectrode array, and the perturbation duration is controlled within the range of 1 - 5 ms;

[0080] The transient current density curves of each microelectrode during the perturbation application and within 100 ms after the perturbation cancellation are synchronously collected. The ratio of the peak current density to the steady-state value is calculated as the current density change rate, and the time required for the potential to recover from the perturbation peak to 90% of the steady-state value is measured as the potential recovery time;

[0081] According to the current density change rate and the potential recovery time, the electrochemical response sensitivity coefficient of each micro-region is calculated, where the electrochemical response sensitivity coefficient is equal to the weighted product of the current density change rate and the potential recovery time;

[0082] The electrochemical response sensitivity coefficient is normalized. The micro-regions with a normalized sensitivity coefficient greater than 0.8 are marked as high-sensitivity candidate regions, and topological connectivity analysis is performed on the high-sensitivity candidate regions to identify interconnected sensitive micro-region clusters as microscopic sensitive regions.

[0083] The following is a specific description of the steps involved in the above embodiments:

[0084] When a potential pulse perturbation with an amplitude less than 10 mV is applied to the micro-grid cells ranked in the top 30% of the electrochemical activity index through a microelectrode array and the perturbation duration is controlled within the range of 1 - 5 ms, it is possible to first confirm through the previous test results that these micro-grid cells have a relatively high electrochemical activity on the sample surface. Then, a pulse potential with an amplitude less than 10 mV is applied one by one using the microelectrode array. This amplitude can trigger significant and measurable transient electrochemical changes without causing surface structure damage under the existing test conditions. The perturbation duration is controlled within 1 - 5 ms to ensure that the initial rapid response of the sensitive region can be captured within a short time. For example, in some stainless steel samples, the micro-region current intensity will rise rapidly and then decay at a pulse duration of about 1 ms. If the pulse time is too long, it may lead to aggravated local corrosion, which is not conducive to subsequent multiple measurements and comparisons. Through such a pulse perturbation process, a relatively real transient electrochemical response can be obtained, which helps to accurately evaluate the sensitivity of the micro-region to corrosion induction and exclude data distortion caused by excessive perturbation in subsequent comparisons.

[0085] Synchronously collect the transient current density curves of each microelectrode during the application of the perturbation and within 100 ms after the perturbation is withdrawn, and calculate the ratio of the peak current density to the steady-state value as the current density change rate. At the same time, when measuring the time required for the potential to recover from the peak of the perturbation to 90% of the steady-state value as the potential recovery time, a multi-channel data acquisition system is needed to quickly record the transient response of each microelectrode. The ratio of the peak current density to the steady-state value can show the reaction intensity of the micro-region to the applied perturbation. If the peak current of a certain micro-region under the potential pulse is much higher than the steady-state value, it indicates that the activity of this micro-region is higher when subjected to corrosion stimulation; the potential recovery time can reflect the speed of the micro-region to return to stability after the perturbation is withdrawn. The shorter the recovery time, the relatively more stable the electrochemical environment of this region, and the longer it is, the more likely it is to show a continuous reaction. For example, when testing a stainless steel containing austenite and ferrite duplex phases, if some regions show a sharp increase in the multiple of the peak current under microsecond-level perturbations and the recovery time is prolonged, these regions tend to show a higher tendency of local corrosion in subsequent corrosion. By simultaneously collecting the current density changes during and within 100 ms after the perturbation, the interference of transient and short-period oscillations on the overall result can be excluded, so as to obtain more representative response data.

[0086] According to the current density change rate and the potential recovery time, calculate the electrochemical response sensitivity coefficient of each micro-region. When the electrochemical response sensitivity coefficient is equal to the weighted product of the current density change rate and the potential recovery time, the current density change rate and the potential recovery time corresponding to each micro-grid unit need to be imported in numerical form in the data analysis software, and by setting one or more weighting factors, the final electrochemical response sensitivity coefficient can be obtained. The current density change rate reflects the stimulation response intensity, and the potential recovery time reflects the system's return-to-stability rate. Through the weighted product, these two indicators can be combined. The larger the value, the higher the corrosion reaction intensity and the more obvious the persistence after being perturbed. For example, if the current density change rate of a certain region is 2.0 (relative to the reference) and the potential recovery time is 10 ms, under the one-to-one weighting setting, the electrochemical response sensitivity coefficient of this region is 2.0×10 = 20, which shows higher sensitivity compared with the result of another region with a change rate of 1.5 and a recovery time of 5 ms (1.5×5 = 7.5). Through this calculation process, a quantitative judgment standard for the corrosion sensitivity of the micro-region can be established, which is convenient for further identifying high-sensitivity regions.

[0087] Normalize the electrochemical response sensitivity coefficient, mark the microregions with a normalized sensitivity coefficient greater than 0.8 as high-sensitivity candidate regions, and perform topological connectivity analysis on the high-sensitivity candidate regions to identify interconnected sensitive microregion clusters. When identifying the microscopic sensitive regions, it is necessary to first compress the electrochemical response sensitivity coefficients of different microregions into the range of 0-1 through mathematical processing for subsequent comparison and visualization analysis. A normalized index greater than 0.8 means that this region has a relatively high activity in terms of corrosion induction. After marking it as a high-sensitivity candidate region, common graphic processing or spatial analysis algorithms can be used to perform topological connectivity analysis on these regions to determine whether there is a continuous or adjacent distribution geometrically. If some candidate regions form a contiguous distribution or have a close adjacency relationship, they are regarded as sensitive microregion clusters, and such clusters have more consistent responses to the external corrosion environment. For example, in some stainless steel parts with local stress concentration, if multiple high-sensitivity candidate regions are distributed at the edge of the stress concentration area and are connected to each other, it can be inferred that these regions are more likely to form through-corrosion channels under macroscopic corrosion conditions, thus playing a dominant role in the overall corrosion resistance performance. Through this identification process, the microscopic sensitive regions that are most likely to trigger corrosion can be effectively screened out, providing guidance for subsequent targeted corrosion monitoring and prevention measures.

[0088] Please continue to refer to Figure 1 , based on the microscopic sensitivity distribution map, apply a multi-field coupled dynamic excitation to the microscopic sensitive regions of the stainless steel sample, obtain the surface response data of the microscopic sensitive regions under the multi-field coupled dynamic excitation, and determine the critical conditions for triggering corrosion;

[0089] In one embodiment of the present invention, the applying a multi-field coupled dynamic excitation to the microscopic sensitive regions of the stainless steel sample based on the microscopic sensitivity distribution map, obtaining the surface response data of the microscopic sensitive regions under the multi-field coupled dynamic excitation, and determining the critical conditions for triggering corrosion includes:

[0090] According to the sensitivity differences in different regions of the microscopic sensitivity distribution map, divide the microscopic sensitive regions of the stainless steel sample into grain boundary sensitive regions, stress concentration regions, and phase boundary sensitive regions;

[0091] Apply an interactive combined excitation of chloride ion concentration from 0.01M to 0.5M and potential from -300mV to +200mV to the grain boundary sensitive regions, and record the current density-time curve and the passivation film breakdown potential under different combinations;

[0092] Apply an interactive combined excitation of stress level with a yield strength percentage from 20% to 70% and pH value from 3 to 10 to the stress concentration regions, and measure the change rate of polarization resistance and the critical pitting current density under different combinations;

[0093] Apply an interactive combined excitation with a temperature ranging from 25°C to 65°C and a dissolved oxygen concentration ranging from 2 ppm to 8 ppm to the phase boundary sensitive region, and obtain the surface morphology change rate and the corrosion potential drift value under different combinations.

[0094] Comprehensively analyze the response data of various sensitive regions under different excitation combinations, calculate the corrosion sensitivity index, identify the parameter combination threshold that causes a sudden increase in the corrosion sensitivity index, and determine the parameter combination threshold as the critical condition for triggering corrosion.

[0095] The following is a specific description of the steps involved in the above embodiments:

[0096] According to the sensitivity differences in different regions of the microscopic sensitivity distribution map, when dividing the microscopic sensitive regions of the stainless steel sample into grain boundary sensitive regions, stress concentration regions, and phase boundary sensitive regions, it is necessary to first make a segmented judgment on the microscopic sensitivity distribution map. By observing the numerical values of the electrochemical activity indicators and the structural characteristics of the surface and subsurface, distinguish the particularities of different regions in terms of grain boundary distribution, stress concentration degree, and phase boundary structure. The grain boundary sensitive region is mainly characterized by a significant increase in electrochemical activity at the grain boundaries; the stress concentration region corresponds to the location where residual stress or applied stress significantly accumulates; the phase boundary sensitive region is concentrated at the junction of the austenite phase and the ferrite phase or other phases. For example, if a high grain boundary network density and obvious current fluctuations are observed in a certain region, it can be determined as a grain boundary sensitive region. Such a division helps to apply targeted excitation conditions to different types of sensitive regions in the subsequent steps, avoiding treating all regions as the same type and resulting in a decrease in the accuracy of the test results. This targeted distinction is determined based on the comprehensive results of pre - stage multi - dimensional data and microscopic characterization. In stainless steel workpieces, different sensitive types often have significantly different response modes to external environmental stimuli, so a refined division is required.

[0097] When applying an interactive combination excitation of chloride ion concentration ranging from 0.01 M to 0.5 M and potential ranging from -300 mV to +200 mV to the grain boundary sensitive area and recording the current density-time curve and the passivation film breakdown potential under different combinations, a common electrochemical test system can be used. It can be completed by replacing chloride ion solutions with different concentrations and gradually adjusting the working electrode potential. The chloride ion concentration is selected between 0.01 M and 0.5 M to cover a relatively wide range of corrosion environment concentrations, which can not only reflect the slow corrosion at low salinity but also the accelerated corrosion at grain boundaries at high salinity. The potential range is set between -300 mV and +200 mV to cover the typical potential range from the cathodic region to near the anodic region, so that the influence of different polarization levels on grain boundary corrosion can be observed. For each combination of chloride ion concentration and potential, record the current density-time curve to reflect the dynamic changes of the corrosion process, and measure the passivation film breakdown potential to judge when stable corrosion channels are formed under this combination. Such an operation can further identify the risk points of corrosion failure at grain boundaries in environments such as salt spray and brine, and confirm the influence intensity of medium concentration and potential on the integrity of the passivation film during the observation process.

[0098] When applying an interactive combination excitation of stress level of yield strength percentage ranging from 20% to 70% and pH value ranging from 3 to 10 to the stress concentration area and measuring the change rate of polarization resistance and the critical pitting current density under different combinations, a loading device is needed to locally apply specific stress to the sample, usually presented in the form of the percentage of the material's yield strength. The stress level is set in the range of 20% to 70% considering the common load ranges in engineering practical applications, observing small deformations at low stress and evaluating the strengthening effect of significant stress concentration on local corrosion at high stress. The pH value range from 3 to 10 is used to simulate the corrosion environment under different acid-base degrees. By configuring solutions with corresponding pH values and applying them together with the external stress to the stress concentration area, then using an electrochemical workstation to measure the change rate of polarization resistance and the critical pitting current density. The change rate of polarization resistance can reflect whether the resistance of the material to external electrochemical polarization decreases under specific stress and acid-base conditions, while the critical pitting current density can indicate the transition critical point from the stable state to the occurrence of local pitting. In this way, it can be directly observed when obvious corrosion intensification occurs in the stress concentration area under different combinations of stress and pH value. The purpose of this is to judge the coupling effect between the strength load and the corrosion environment and determine under what conditions the area vulnerable to stress and acid-base shows significant deterioration of corrosion resistance.

[0099] When applying an interactive combined excitation of temperature ranging from 25°C to 65°C and dissolved oxygen concentration ranging from 2 ppm to 8 ppm to the phase boundary sensitive area to obtain the surface morphology change rate and corrosion potential drift value under different combinations, the sample needs to be placed in an electrochemical device with controllable temperature and dissolved oxygen. The temperature range from 25°C to 65°C is based on the medium and high temperature intervals common in many industrial and environmental conditions, which can simulate the corrosion scenarios at normal temperature and higher operating temperatures; the dissolved oxygen concentration from 2 ppm to 8 ppm can represent the transition from a lower oxygen content to a more oxygen-rich environment. By gradually adjusting the temperature and dissolved oxygen concentration and conducting a soaking or polarization experiment on the phase boundary sensitive area for a period of time, the change rate of the surface morphology can be recorded using an optical microscope or a scanning electron microscope, and the corrosion potential drift value can be monitored using an electrochemical measurement system. The change rate of the surface morphology usually reflects the increase or decrease rate in surface roughness or the number of local corrosion pits, and the corrosion potential drift value represents the deviation tendency of the material stability at this temperature and oxygen concentration. For example, when a higher temperature and high dissolved oxygen are superimposed on some duplex stainless steels, obvious corrosion grooves will appear at the phase boundary, resulting in the corrosion potential drifting in the negative direction. By observing these two indicators, the influence intensity of the coupling effect of temperature and dissolved oxygen on the corrosion rate and reaction characteristics at the phase boundary can be determined.

[0100] When comprehensively analyzing the response data of various sensitive areas under different excitation combinations, calculating the corrosion sensitivity index, identifying the parameter combination threshold that causes a sudden increase in the corrosion sensitivity index, and determining this parameter combination threshold as the critical condition for triggering corrosion, it is necessary to use data processing software to organize and fit the current density-time curve, passivation film breakdown potential, polarization resistance change rate, critical pitting current density, surface morphology change rate, and corrosion potential drift value obtained previously. The corrosion sensitivity index is generally obtained by quantitatively weighting these parameters. A significant increase in the index value often means that corrosion instability or acceleration is likely to occur under this specific parameter combination (such as chloride ion concentration - potential, stress level - pH value, temperature - oxygen concentration). If a sudden increase in the corrosion sensitivity index is observed under a certain condition combination, the environmental parameters or stress parameters at this point can be regarded as the critical conditions for triggering corrosion. For example, if the index value in the grain boundary sensitive area is significantly higher than other conditions when the chloride ion concentration is 0.3 M and the applied potential is +100 mV, it can be judged that this condition is more likely to induce grain boundary corrosion failure. In this way, the corrosion critical points of different sensitive areas under specific excitation combinations can be clarified, so as to guide the control and optimization of temperature, stress, chemical media, etc. in actual engineering to reduce or avoid the occurrence of local corrosion.

[0101] In one embodiment of the present invention, the comprehensive analysis of the response data of various sensitive regions under different excitation combinations, calculating the corrosion sensitivity index, identifying the parameter combination threshold that causes a sudden increase in the corrosion sensitivity index, and determining the parameter combination threshold as the critical condition for triggering corrosion includes:

[0102] Perform numerical integration on the current density-time curve of the grain boundary sensitive region to calculate the charge transfer amount per unit time. Take the reciprocal of the difference between the passivation film breakdown potential and the reference potential as the passivation film fragility coefficient. Define the product of the charge transfer amount per unit time and the passivation film fragility coefficient as the corrosion sensitivity index of the grain boundary sensitive region;

[0103] Calculate the ratio of the polarization resistance change rate of the stress concentration region to the initial polarization resistance as the polarization resistance coefficient. Calculate the ratio of the critical pitting current density to the material reference corrosion current density as the pitting tendency coefficient. Divide the pitting tendency coefficient by the polarization resistance coefficient to obtain the corrosion sensitivity index of the stress concentration region;

[0104] Convert the surface topography change rate of the phase boundary sensitive region into the surface roughness increment per unit time. Calculate the ratio of the corrosion potential drift value to the standard deviation as the electrochemical stability coefficient. Define the product of the surface roughness increment per unit time and the electrochemical stability coefficient as the corrosion sensitivity index of the phase boundary sensitive region;

[0105] Normalize the corrosion sensitivity index of the grain boundary sensitive region, the corrosion sensitivity index of the stress concentration region, and the corrosion sensitivity index of the phase boundary sensitive region respectively. Plot the response surface of the multi-field coupling parameters and the normalized corrosion sensitivity index. Identify the inflection points on the surface where the gradient change rate exceeds 200%. Determine the parameter combination corresponding to the inflection points as the critical condition for triggering corrosion.

[0106] The following specifically describes the steps involved in the above embodiment:

[0107] When numerically integrating the current density-time curve of the grain boundary sensitive region to calculate the charge transfer amount per unit time, and defining the reciprocal of the difference between the passivation film breakdown potential and the reference potential as the passivation film vulnerability coefficient, the current density-time curve of the grain boundary sensitive region under different environments or electrochemical conditions can be obtained first on an electrochemical tester. Taking time as the abscissa and current density as the ordinate, the area of the curve within a fixed time window is calculated through numerical integration software to obtain the charge transfer amount per unit time. For example, in an environment with a chloride ion concentration of 0.3 M, the potential change of the grain boundary sensitive region with time can be measured first and the potential at which the passivation film breakdown occurs can be recorded. If this breakdown potential is 150 mV lower than the known reference potential, then the reciprocal of the difference between the reference potential and the breakdown potential (150 mV) can be recorded as the passivation film vulnerability coefficient. After multiplying the charge transfer amount per unit time by the passivation film vulnerability coefficient, the corrosion sensitivity index of the grain boundary sensitive region can be obtained. The reason for this is that the charge transfer amount reflects the intensity of the electrochemical reaction actually occurring at the grain boundary, while the passivation film vulnerability coefficient can reflect the degree to which the passivation film is easily damaged. The combined result of the two can more quantitatively reflect the comprehensive sensitivity of the grain boundary to corrosion conditions.

[0108] When calculating the ratio of the polarization resistance change rate of the stress concentration region to the initial polarization resistance as the polarization resistance coefficient, and calculating the ratio of the critical pitting current density to the material reference corrosion current density as the pitting tendency coefficient, the corrosion sensitivity index of the stress concentration region can be obtained by dividing the two. In implementation, the polarization curve of the stress concentration region under a certain stress level and corrosion medium can be monitored first through an electrochemical workstation, and the polarization resistance values before and after polarization are compared to obtain the polarization resistance change rate. Then, the polarization resistance coefficient is obtained by dividing this change rate by the initial polarization resistance. If pitting is observed in this region under a specific combination of load and pH value, the corresponding critical pitting current density can be recorded, and then the ratio is calculated with the material reference corrosion current density (which can be measured in a mild environment) to obtain the pitting tendency coefficient. Dividing the pitting tendency coefficient by the polarization resistance coefficient can reflect the influence degree of the applied stress or internal stress aggregation on the pitting formation speed and the expansion of the range. For example, in an environment with a yield strength percentage of 50% and a pH value of 4, if this ratio is significantly higher than other combined conditions, it indicates that the stress concentration region is more likely to experience pitting failure in this environment.

[0109] The surface topography change rate in the phase boundary sensitive region is converted into the surface roughness increment per unit time, and the ratio of the corrosion potential drift value to the standard deviation is calculated as the electrochemical stability coefficient. Then, the product of these two is defined as the corrosion sensitivity index of the phase boundary sensitive region. This process requires first soaking or polarizing the phase boundary sensitive region for a period of time in an environment with a certain temperature and dissolved oxygen concentration. The local topography at the phase boundary is photographed by an optical microscope or a scanning electron microscope, and the surface roughness increment is quantified. Subsequently, the fluctuation amplitude of the corrosion potential during the experiment is measured under the same conditions, and the ratio of it to the standard deviation is calculated to reflect the electrochemical stability of this region. When the surface roughness increment is large and the electrochemical stability coefficient is low, it often indicates that local corrosion is more likely to occur at the phase boundary. This comprehensive evaluation method can visually quantify the amplitude of the corrosion morphology evolution at the phase boundary under external conditions such as temperature and dissolved oxygen. For example, in an environment with a temperature of 45°C and a dissolved oxygen concentration of 5 ppm, if the surface roughness increment value reaches 0.3 μm / hour and the electrochemical stability coefficient is only 0.6, it can be judged that the corrosion sensitivity index of the phase boundary increases significantly.

[0110] After normalizing the corrosion sensitivity index of the grain boundary sensitive region, the stress concentration region corrosion sensitivity index, and the phase boundary sensitive region corrosion sensitivity index respectively, a response surface of the multi-field coupling parameter and the normalized corrosion sensitivity index is plotted. The inflection points with a gradient change rate exceeding 200% on the surface are identified, and the parameter combination corresponding to this inflection point is determined as the critical condition for triggering corrosion. First, the numerical values of these three corrosion sensitivity indices need to be processed to the range of 0 to 1 through data analysis software to ensure their comparability. Subsequently, according to the conditions of different experimental combinations (such as different chloride ion concentration - potential, different stress levels - pH value, different temperature - dissolved oxygen concentration), each sensitivity index is corresponded to the parameter combination, and after establishing a multi-dimensional coordinate, surface fitting is carried out. If under a certain parameter condition, the gradient change rate of the surface (i.e., the slope of the index changing with the parameter) exceeds 200%, this point can be regarded as the boundary where the corrosion degree rises sharply. At this time, the combination of chloride ion concentration, potential, stress level, pH value, temperature or dissolved oxygen behind this condition is marked, and it can be judged that this combination has a significant promoting effect on local corrosion failure, thus determining it as the critical condition for triggering corrosion. In some high-temperature and high-oxygen environments, the phase boundary sensitivity index may increase sharply, or the grain boundary sensitivity index may have a significant jump under high chloride ion concentration and specific potential conditions. Such inflection points are often directly closely related to the material failure process and can provide accurate references for subsequent safety assessment and protection schemes.

[0111] Please continue to refer to Figure 1 According to the critical condition, trigger the initial corrosion of the microscopic sensitive region, adaptively track the corrosion propagation process, and implement corrosion path intervention to obtain the spatio-temporal data of corrosion evolution;

[0112] In one embodiment of the present invention, according to the critical conditions, triggering the initial corrosion of the microscopic sensitive area, adaptively tracking the corrosion propagation process, and implementing corrosion path intervention to obtain the spatio-temporal data of corrosion evolution, including:

[0113] According to the critical conditions, applying a triggering stimulus to the microscopic sensitive area, specifically including: applying a combination of critical chloride ion concentration and critical potential to the grain boundary sensitive area, applying a combination of critical stress level and critical pH value to the stress concentration area, and applying a combination of critical temperature and critical dissolved oxygen concentration to the phase boundary sensitive area until the formation of an initial corrosion point is observed;

[0114] Obtaining corrosion propagation data through multi-scale monitoring methods, including macroscopic electrochemical parameter monitoring, micro-area electrochemical activity scanning, and real-time surface topography imaging. After detecting the formation of an initial corrosion point, dynamically tracking the corrosion propagation front and adjusting the monitoring parameters according to the movement speed of the corrosion front;

[0115] During the corrosion propagation process, implementing intervention measures at the positions where the corrosion rate changes, including changing the local electrochemical environment, adjusting the stress distribution, or modifying the surface state, and recording the changes in the corrosion propagation rate before and after the intervention;

[0116] Collecting data on the entire corrosion process, including data on the change in corrosion point density over time, data on the spatial distribution of corrosion depth, and data on the corrosion front propagation rate;

[0117] Integrating the data on the change in corrosion point density over time, data on the spatial distribution of corrosion depth, and data on the corrosion front propagation rate, identifying the transition points and spatial expansion characteristics of each stage of corrosion, and forming spatio-temporal data of corrosion evolution including time dimension and space dimension.

[0118] The following specifically describes the steps involved in the above embodiment:

[0119] According to the critical conditions, when applying a triggering stimulus in the micro-sensitive region, corresponding combinations of critical chloride ion concentration and critical potential, critical stress level and critical pH value, and critical temperature and critical dissolved oxygen concentration can be selected for the grain boundary sensitive region, stress concentration region, and phase boundary sensitive region respectively, and continuous observation is carried out until the initial corrosion point appears. In specific implementation, a pre-prepared solution with a critical chloride ion concentration can be added to the grain boundary sensitive region on an electrochemical workstation and the corresponding potential is applied. A stress loading device is used to apply a set stress in the stress concentration region and control the pH value of the solution. For the phase boundary sensitive region, the thermostat and dissolved oxygen controller are adjusted to the predetermined temperature and dissolved oxygen concentration. Such an operation can capture the initial corrosion point in a relatively short time, reflecting the real corrosion triggering process of different regions under the most sensitive conditions. The selection of these specific critical parameter ranges is based on previous tests and multi-dimensional data analysis. For example, for the combination of chloride ion concentration and potential, if it exceeds a specific threshold, obvious local damage of the passivation film will be induced; for the combination of stress level and pH value, if it exceeds a certain range, local stress corrosion on the material surface will be intensified; for the combination of temperature and dissolved oxygen concentration, if both exceed specific critical values, the corrosion reaction at the phase boundary will be accelerated.

[0120] When obtaining corrosion propagation data through multi-scale monitoring methods, it is necessary to combine various means such as macroscopic electrochemical parameter monitoring, micro-region electrochemical activity scanning, and real-time surface topography imaging, and adjust the observation focus in a timely manner according to the monitoring results. In specific implementation, an electrochemical workstation can be used to continuously record macroscopic parameters such as the overall current and potential of the material, and local activity changes are detected using microelectrodes or microprobe arrays in the micro-region electrochemical activity scanning system. Then, the surface topography is periodically observed with an optical microscope or a scanning electron microscope. If it is detected that the initial corrosion point has been formed, further attention needs to be paid to the moving speed of the corrosion front at the microscale. If it is found that the corrosion front moves faster, the scanning frequency can be appropriately increased or the observation area can be encrypted to timely capture the dynamic changes during the corrosion process. Such a set of monitoring methods can find a balance between large-scale data acquisition and local detail capture, and ensure that the key nodes during the propagation process are accurately recorded.

[0121] During the corrosion propagation process, when implementing intervention measures at the positions where the corrosion rate changes, the local electrochemical environment can be changed, the stress distribution can be adjusted, or the surface state can be modified, and the differences in the corrosion propagation rate before and after the intervention can be observed. When implementing the intervention, the local solution environment can be adjusted by adding certain ionic inhibitors or adjusting the pH value on the electrochemical monitoring platform, the local stress can be redistributed or eliminated by mechanical methods, or a coating material can be applied to the local area to modify the surface. If the corrosion front propagation rate is significantly reduced after the intervention in a certain area, it indicates that this intervention measure has a positive effect on preventing local corrosion. The setting of the intervention measures and the targeted local parameter adjustment need to be reasonably selected in combination with the sample material and the actual service conditions to effectively control the corrosion propagation.

[0122] When collecting data throughout the corrosion process, it is necessary to summarize the change of the corrosion point density over time, the spatial distribution of the corrosion depth, and the corrosion front propagation rate. At this time, the number of corrosion points and their distribution information obtained from multiple observations can be corresponded to time and sorted through visualization tools or data management software to form multiple sets of data including the corrosion point density curve, the local depth detection results, and the curve of the propagation rate changing with time. Such a complete data system can show the corrosion progress of the material at different times and positions, providing support for further analyzing the corrosion transmission path and the key driving factors.

[0123] When integrating the data of the change of the corrosion point density over time, the spatial distribution data of the corrosion depth, and the corrosion front propagation rate data, and identifying the transition points and spatial expansion characteristics of each stage of corrosion, it is necessary to map the above multi-dimensional information in the same coordinate system or unified data structure, comprehensively consider the time factor and the spatial factor, and finally form spatio-temporal data. When implementing, a time index can be set up in the data processing software first, map the corrosion point distribution and corrosion depth values at different times to the corresponding positions, and find the turning moments from the start of corrosion to large-scale expansion, and then to stabilization or slowdown in combination with the change trend of the corrosion front propagation rate, and mark the important expansion characteristics in combination with the surface topography observation results. If there is a sudden increase in the corrosion point density or a sharp rise in the propagation rate in a certain stage, it can be determined that this stage is the rapid growth period. If the detected propagation rate slows down or the density tends to be stable, this stage can be regarded as the relatively stable period. By integrating this information, the corrosion evolution process of the material can be clearly presented in two dimensions of time and space, providing a quantitative reference basis for in-depth study of the corrosion mechanism under different environmental couplings and long-life prediction.

[0124] Please continue to refer to Figure 1 Based on the spatio-temporal data, monitor the dynamic changes of the micro-area environmental parameters during the corrosion process, analyze the corresponding relationship between the surface topography changes and the dynamic changes of the micro-area environmental parameters, and determine the key conditions for forming the corrosion feedback amplification effect;

[0125] In one embodiment of the present invention, based on the spatio-temporal data, monitoring the dynamic changes of the micro-region environmental parameters during the corrosion process, analyzing the corresponding relationship between the surface topography changes and the dynamic changes of the micro-region environmental parameters, and determining the key conditions for forming the corrosion feedback amplification effect, including:

[0126] Deploy a micro-region environmental monitoring lattice on the corrosion active region determined based on the spatio-temporal data, monitor the dynamic change data of the local pH value, metal ion concentration, dissolved oxygen concentration, potential distribution and liquid flow state during the corrosion process, and obtain the dynamic change data of the micro-region environmental parameters;

[0127] Extract the data of the corrosion point density changing with time, the spatial distribution data of the corrosion depth, and the corrosion front propagation rate data from the spatio-temporal data, calculate the change rate of each data in the time dimension, and obtain the surface topography change rate;

[0128] Perform a time-series comparative analysis on the surface topography change rate and the dynamic change data of the micro-region environmental parameters, determine the sequence of the topography change and the micro-region environmental parameter change, and identify the coupling points where the environmental parameter change causes the topography to change rapidly or the topography change causes the environmental parameter to change rapidly;

[0129] Calculate the environmental parameter influence coefficient and the topography reaction coefficient for the coupling points. When the product of the environmental parameter influence coefficient and the topography reaction coefficient is greater than 1, confirm the formation of a positive feedback loop;

[0130] According to the trigger parameter combination, threshold condition and enhancement mechanism of the positive feedback loop, determine the key conditions for forming the corrosion feedback amplification effect.

[0131] The following is a specific description of the steps involved in the above embodiment:

[0132] When deploying a micro-region environmental monitoring lattice on the corrosion active region determined based on spatio-temporal data, it is necessary to combine the previously obtained spatio-temporal information of corrosion evolution, select the range where the corrosion activities are relatively concentrated, and deploy probes or sensors that can monitor the pH value, metal ion concentration, dissolved oxygen concentration, potential distribution and liquid flow state at these positions. During the implementation process, common instruments such as micro pH electrodes, ion selective electrodes, dissolved oxygen detectors and micro flow meters can be used to continuously record the local electrochemical and flow parameters. If a certain place shows frequent movement of the corrosion front in the previous step, deploy sensors with a higher density at this position to obtain more detailed data. By recording the outputs of these sensors at different times and integrating them into dynamic change curves, the relationship between the topography evolution and the environmental parameter change can be compared in the subsequent steps.

[0133] When extracting data on the change of corrosion point density over time, the spatial distribution data of corrosion depth, and the corrosion front propagation rate data from spatio-temporal data, and calculating the change rate of each data in the time dimension, the number of corrosion points in each observation period can be read in data management software and converted into density values per unit area or unit volume. At the same time, the measurement results of the surface or cross-sectional morphology are digitized to obtain the corrosion depth distribution at different times. By dividing the difference between adjacent periods of these distribution data by the corresponding time interval, the speed of corrosion depth expansion or point density change can be obtained. The corrosion front propagation rate can be obtained in image recognition software by comparing consecutive surface imaging results, identifying the distance the corrosion boundary moves between two captures and dividing by the time interval. After normalizing or converting these change values to specified units, the surface morphology change rate can be obtained, which is used to reflect the increase or decrease rate of the external appearance or internal depth of material corrosion.

[0134] When performing a time-series comparative analysis of the surface morphology change rate and the dynamic change data of micro-area environmental parameters, in the data analysis platform, the morphology change rate curve obtained in the previous step needs to be synchronized with the change curves of pH value, metal ion concentration, dissolved oxygen concentration, potential distribution, and liquid flow state. After aligning the time axes, it can be identified whether the environmental parameters change significantly before the surface morphology in the same period, or whether the surface morphology changes first and then the environmental parameters fluctuate. If it is found that the pH value drops rapidly at a certain moment and the surface morphology change rate increases significantly in a short time afterwards, it can be judged that this moment is the coupling point of "environmental parameter change leading to accelerated morphology change"; conversely, if the metal ion concentration only starts to rise after the morphology change rate increases sharply, it can be judged as the situation where morphology change leads to accelerated change of environmental parameters. Through this comparative analysis, it helps to clarify the causal order between environmental parameters and corrosion propagation.

[0135] When calculating the environmental parameter influence coefficient and the morphology reaction coefficient for the coupling point, the calculation methods of the influence coefficient and the reaction coefficient can be set in the statistical model. The environmental parameter influence coefficient can be obtained by comparing the correlation between the difference in pH value or ion concentration before and after the coupling point and the morphology change rate. For example, the proportion of the significant increase in the morphology change rate after the pH value drops to a certain threshold can be regarded as the magnitude of the environmental induction effect; the morphology reaction coefficient can be measured according to the amplitude of the electrochemical environment change caused after the formation of corrosion pits or grooves. For example, the correlation between the degree of morphology depression and the enhancement of the local concentration cell effect. If the result of multiplying the two at a specific coupling point exceeds 1, it indicates that this coupling has a self-enhancing trend, that is, a positive feedback cycle is formed, which often causes the corrosion rate to accelerate and spread locally.

[0136] When determining the key conditions for forming the corrosion feedback amplification effect based on the trigger parameter combinations, threshold conditions, and enhancement mechanisms of the positive feedback loop, it is necessary to summarize multiple coupling points, compare the numerical values of the environmental parameter influence coefficient and the morphology reaction coefficient among them, and confirm the corresponding external environment or material conditions when exceeding the positive feedback critical value. If it is found that the chloride ion concentration and potential are likely to trigger coupling points within a certain range, or the combination of high stress and low pH significantly increases both the environmental parameter influence coefficient and the morphology reaction coefficient, it can be determined that the corrosion feedback amplification effect is more likely to occur in such scenarios. When selecting trigger parameters, the service environment of the sample and the results of laboratory accelerated tests are usually combined, and key parameters and thresholds are refined to guide subsequent corrosion prevention or monitoring. This can clarify the critical factors that cause great destructive power during the corrosion propagation process and provide a quantitative basis for material life assessment and process adjustment.

[0137] Please continue to refer to Figure 1 , perform an accelerated test with multi-level oscillatory strengthening according to the key conditions, the microscopic sensitivity distribution map, the critical conditions, and the spatio-temporal data, perform stage decomposition processing on the accelerated test data, calculate the time conversion coefficient, and obtain the corrosion resistance prediction result of the stainless steel sample.

[0138] In one embodiment of the present invention, the performing an accelerated test with multi-level oscillatory strengthening according to the key conditions, the microscopic sensitivity distribution map, the critical conditions, and the spatio-temporal data, performing stage decomposition processing on the accelerated test data, calculating the time conversion coefficient, and obtaining the corrosion resistance prediction result of the stainless steel sample includes:

[0139] According to the periodic changes, random fluctuations, and emergency characteristics of the actual service environment, compile a service environment characteristic spectrum, and convert the service environment characteristic spectrum into a parameter sequence that can be reproduced in the laboratory;

[0140] Based on the parameter sequence, the key conditions, the microscopic sensitivity distribution map, and the critical conditions, design an accelerated test scheme with multi-level oscillatory strengthening, specifically including: microscopic oscillatory strengthening with high-frequency environmental parameter fluctuations, mesoscopic oscillatory strengthening with periodic switching of corrosion conditions, and macroscopic oscillatory strengthening with short-term extreme conditions applied at key time points;

[0141] Execute the accelerated test scheme with multi-level oscillatory strengthening, obtain the corrosion depth data, corrosion current density data, and surface morphology change data under the accelerated test conditions, and form the accelerated test data;

[0142] According to the corrosion evolution characteristics in the spatio-temporal data, decompose the corrosion process into four stages: latency period, nucleation period, expansion period, and stable period, and analyze the accelerated test data for each stage;

[0143] Compare the accelerated test data with the standard test data, establish the time correspondence relationship for each stage, calculate the time conversion coefficient for each stage, and obtain the predicted corrosion resistance result of the stainless steel sample through integral calculation.

[0144] The following specifically describes the steps involved in the above embodiments:

[0145] When compiling the service environment characteristic spectrum according to the periodic changes, random fluctuations, and emergency characteristics of the actual service environment, it is necessary to summarize factors such as the load cycle information, temperature fluctuation range, chemical medium concentration change curve, and sudden shock (such as a sharp drop in pH value or an instantaneous increase in chloride ion concentration) collected at the engineering site or in simulation experiments. In the data analysis software, the environmental parameter records in different time periods can be classified first. The part that shows regular repetition is regarded as the periodic part, the deviation with a small amplitude but no fixed rule is regarded as random fluctuation, and the extremely few but drastic changes that will seriously change the corrosion conditions are regarded as emergencies. In order to reproduce these characteristics under laboratory conditions, they need to be converted into a series of programmable parameter sequences. For example, the temperature fluctuates according to the amplitude and frequency in certain time periods, the chemical medium randomly floats up and down in ion concentration in another period of time, and a one-time high-intensity stimulus is applied at a set moment. This is to try to restore the multiple stresses and environmental interferences suffered by the material in the actual use conditions and observe the typical evolution of the corrosion behavior in a short time.

[0146] When designing an accelerated test scheme with multi-level oscillation strengthening based on the parameter sequence, key conditions, microscopic sensitivity distribution map, and critical conditions, it is necessary to introduce multi-level fluctuation factors into the original accelerated corrosion idea, mainly including microscopic oscillation strengthening with high-frequency environmental parameter fluctuations, mesoscopic oscillation strengthening with periodic switching of corrosion conditions, and macroscopic oscillation strengthening with short-term extreme conditions applied at key time points. Microscopic oscillation strengthening is mainly based on rapidly changing temperature or micro-stress perturbation, which can be achieved by adjusting the frequency control module of the constant temperature device or the stress loading device; mesoscopic oscillation strengthening switches the concentration or potential of the corrosion medium within a cycle of several hours or days to simulate the regular operation and maintenance cycle in actual service; macroscopic oscillation strengthening applies a large-amplitude stimulus at key nodes, such as suddenly increasing the chloride ion concentration or decreasing the pH value when the temperature is already high. This multi-level design can highlight the response differences of various sensitive regions under different time scales and intensity conditions, which is beneficial to more comprehensively evaluating the corrosion resistance of materials.

[0147] When implementing an accelerated test plan with multi-level oscillation strengthening and obtaining corrosion depth data, corrosion current density data, and surface morphology change data under accelerated test conditions, the dynamic curve of current density over time can be recorded on an electrochemical workstation. At the same time, surface morphology images can be collected using a microscopic observation instrument (such as a scanning electron microscope) at different cycles, and the depth of local corrosion pits or grain boundary grooves can be quantified using a depth measurement device (such as a 3D profiler). Summarizing these data to form an accelerated test data set, it can be observed that at different oscillation intensities and different time nodes, the corrosion morphology and rate differences presented by the material. The selected parameter range and oscillation frequency should correspond to the previously compiled service environment characteristic spectrum to match the laboratory conditions with the actual use conditions, thereby improving the prediction accuracy of corrosion behavior in the real environment.

[0148] According to the corrosion evolution characteristics in the spatio-temporal data, the corrosion process is decomposed into four stages: the incubation period, the nucleation period, the propagation period, and the stable period. When analyzing the accelerated test data for each stage, different stages can be distinguished on the data analysis platform based on the change rate of corrosion point density, the increase rate of morphology depth, and the inflection point of the current density increase level. For example, if there are very few surface corrosion points and the growth is slow before a certain moment, this period can be regarded as the incubation period; when there is a sudden rapid increase in the morphology or current signal, it can be determined that the nucleation period has entered; if large-scale corrosion begins to connect or the pits accelerate to expand afterwards, it can be regarded as the propagation period; finally, if the corrosion front speed tends to be flat and the local corrosion depth no longer continues to increase, it indicates that the system has entered the stable period. The reason for distinguishing the stages in this way is to extract several representative periods from the complex corrosion process, so as to more specifically compare the accelerated test data in different stages and find the reasons for the sudden change in corrosion behavior under specific environments and loads.

[0149] When comparing the accelerated test data with the standard test data, establishing the time correspondence relationship for each stage and calculating the time conversion coefficient for each stage, and then obtaining the corrosion resistance prediction result of the stainless steel sample by integration, it is necessary to compare the corrosion stage duration and corrosion rate experienced by the material in the standard test (such as traditional salt spray test or potentiostatic polarization test) and the multi-level oscillatory strengthening test in a unified coordinate system. Assuming that there is a certain multiple relationship between the latency period in the accelerated test and the latency period in the standard test, by calculating the time ratio of the latency period, nucleation period, growth period, and stable period respectively, and correcting according to the corrosion magnitude of the material under accelerated test and standard conditions, the time conversion coefficient for each stage can be obtained. If it is found that the growth period is shortened several times in the accelerated test compared to the standard test, then this multiple is defined as the conversion coefficient of the growth period, and it is accumulated (or multiplied) with the coefficients of other stages to obtain the overall corrosion resistance prediction value. This method is applicable to quickly evaluating the corrosion risk faced by materials during long-term service under controllable laboratory conditions, and ensures the referenceability and engineering application value of the results through stage-by-stage comparison.

[0150] In an embodiment of the present invention, according to the corrosion evolution characteristics in the spatio-temporal data, the corrosion process is decomposed into four stages: a latency period, a nucleation period, a growth period, and a stable period. Analyzing the accelerated test data for each stage includes:

[0151] Performing curve fitting on the data of the change in corrosion point density over time, the spatial distribution data of corrosion depth, and the corrosion front propagation rate data in the spatio-temporal data, calculating the first derivative and the second derivative of the curve, and determining the conversion time points from the latency period to the nucleation period, from the nucleation period to the growth period, and from the growth period to the stable period according to the change in the derivative sign and the inflection point position;

[0152] According to the determined time ranges of the four stages, extracting the accelerated test data in segments, analyzing the passivation film stability for the accelerated test data of the latency period, analyzing the growth law of corrosion point density for the accelerated test data of the nucleation period, analyzing the corrosion depth propagation characteristics for the accelerated test data of the growth period, and analyzing the constant corrosion rate parameter for the accelerated test data of the stable period, thus completing the analysis of the accelerated test data for each stage.

[0153] The following specifically describes the steps involved in the above embodiment:

[0154] When performing curve fitting on the data of the corrosion point density varying with time, the spatial distribution data of the corrosion depth, and the data of the corrosion front propagation rate in the spatio-temporal data, calculating the first derivative and the second derivative of the curve, and determining the transition time points of the corrosion from the latent period to the nucleation period, from the nucleation period to the propagation period, and from the propagation period to the stable period according to the derivative sign change and the inflection point position, it is necessary to first organize the above three types of data into the same time series and select an appropriate function or polynomial for fitting. Specifically, for the data of the corrosion point density varying with time, if the corrosion points gradually increase in the initial stage, it may show an exponential growth trend, and in this case, an exponential function can be selected for fitting; if the growth is relatively gentle, a linear function or a quadratic polynomial can be selected for fitting to ensure a better fitting effect of the data. For the spatial distribution data of the corrosion depth, usually this data will tend to be stable after a certain time, and a power-law function or a logarithmic function can be considered to fit its growth process, because these functions can better describe the variation law of the corrosion depth with time. As for the data of the corrosion front propagation rate, if the corrosion propagation accelerates rapidly in the initial stage and gradually slows down in the later stage, a logarithmic function or a power function can be used for fitting, and these functions can better describe the acceleration and deceleration processes.

[0155] During the fitting process, common fitting methods such as the least squares method can be used to conduct multiple experiments on the data, and the goodness of fit (such as the R² value) can be used to judge the quality of the fitting result. For example, in the curve fitting of the corrosion front propagation rate, if the data shows an obvious trend of rapid initial expansion and then slowdown, a power function form can be selected for fitting, and then the second derivative of the fitting result can be analyzed to judge the inflection point of the corrosion. This process helps to capture the dynamic changes in each stage of the corrosion process, thereby accurately locating the turning time point of the corrosion and further improving the understanding of the corrosion behavior.

[0156] According to the time range of the four stages, the accelerated test data is extracted in segments, and the accelerated test data of the latent period is used to analyze the stability of the passive film, the accelerated test data of the nucleation period is used to analyze the growth law of the corrosion point density, the accelerated test data of the extension period is used to analyze the corrosion depth expansion characteristics, and the accelerated test data of the stable period is used to analyze the constant corrosion rate parameters. After the above time points are divided, the accelerated test data in each stage need to be collected separately. The accelerated test data of the latent period can be used to evaluate the ability of the passive film to maintain in different micro-sensitive areas before there is no obvious pitting or groove; the data of the nucleation period pays more attention to the increase and decrease relationship of the corrosion point density with time or with the change of the external environment; the data of the extension period includes the corrosion depth progress and the corrosion front advancement rate of each position; the data of the stable period focuses on monitoring the parameters after the overall corrosion rate tends to be constant. For example, in the nucleation period, the number of corrosion points appearing under different chloride ion concentrations can be regressed and the correlation coefficient of the corrosion point density growth formula can be evaluated, so as to further understand the influence of the medium concentration on the generation of new corrosion points. After splitting the stages in this way, the corrosion mechanism of each stage can be more easily analyzed separately, and the weaknesses or strengths of the material in different time periods can be discussed in a targeted manner. It is also convenient to take differentiated protection and monitoring measures for different stages in practical applications.

[0157] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A high-precision corrosion resistance testing method for stainless steel, characterized in that, Including: Obtain multi-dimensional microscopic characteristic data of the surface of a stainless steel sample, analyze and process the multi-dimensional microscopic characteristic data, determine the microscopic sensitive areas on the surface of the stainless steel sample, and obtain a microscopic sensitivity distribution map, including: Simultaneously obtain microscopic structure data, mechanical property data, chemical composition data, and residual stress data of the same position on the stainless steel sample, where the microscopic structure data is used to identify grain boundary networks and phase boundary distributions, the mechanical property data is used to determine hardness gradient regions, the chemical composition data is used to locate element segregation points, and the residual stress data is used to mark stress concentration areas; Divide the surface of the stainless steel sample into micro-grid units, perform correlation analysis on the microscopic structure data, mechanical property data, chemical composition data, and residual stress data within each micro-grid unit, and calculate the electrochemical activity index of each micro-grid unit; According to the electrochemical activity index, select high-activity micro-areas on the surface of the stainless steel sample for weak electrochemical perturbation tests, record the current density change rate and potential recovery time of the high-activity micro-areas under perturbation, compare the electrochemical response differences between different micro-areas, and determine the area with the largest response difference as the microscopic sensitive area; Perform a fine scan on the microscopic sensitive area, collect three-dimensional depth information, and combine the sensitivity data of the surface and subsurface to construct a microscopic sensitivity distribution map in the form of a probability heat map; Based on the microscopic sensitivity distribution map, apply a multi-field coupled dynamic excitation to the microscopic sensitive area of the stainless steel sample, obtain the surface response data of the microscopic sensitive area under the multi-field coupled dynamic excitation, and determine the critical conditions for triggering corrosion, including: According to the sensitivity differences in different regions in the microscopic sensitivity distribution map, divide the microscopic sensitive area of the stainless steel sample into grain boundary sensitive areas, stress concentration areas, and phase boundary sensitive areas; Apply a multi-field coupled dynamic excitation to the microscopic sensitive area of the stainless steel sample; Comprehensively analyze the response data of various sensitive areas under different excitation combinations, calculate the corrosion sensitivity index, identify the parameter combination threshold that causes a sudden increase in the corrosion sensitivity index, and determine the parameter combination threshold as the critical condition for triggering corrosion; According to the critical conditions, trigger the initial corrosion of the microscopic sensitive area, adaptively track the corrosion propagation process, and implement corrosion path intervention to obtain the spatio-temporal data of corrosion evolution; Based on the spatio-temporal data, monitor the dynamic changes of micro-area environmental parameters during the corrosion process, analyze the corresponding relationship between the surface morphology changes and the dynamic changes of micro-area environmental parameters, and determine the key conditions for forming a corrosion feedback amplification effect; According to the key conditions, the microscopic sensitivity distribution map, the critical conditions, and the spatio-temporal data, perform an accelerated test with multi-level oscillation strengthening, perform stage decomposition processing on the accelerated test data, calculate the time conversion coefficient, and obtain the corrosion resistance performance prediction result of the stainless steel sample.

2. The high-precision stainless steel corrosion resistance testing method according to claim 1, characterized in that According to the electrochemical activity index, high-activity microregions are selected on the surface of the stainless-steel sample for weak electrochemical perturbation tests. The current density change rate and potential recovery time of the high-activity microregions under perturbation are recorded, and the differences in electrochemical responses between different microregions are compared to determine the region with the largest response difference as the microscopic sensitive region, including: Applying a potential pulse perturbation with an amplitude less than 10 mV to the microgrid cells ranked in the top 30% of the electrochemical activity index through a microelectrode array, and controlling the perturbation duration within the range of 1 - 5 ms; Synchronously collecting the transient current density curves of each microelectrode during the perturbation application and within 100 ms after the perturbation is withdrawn, calculating the ratio of the peak current density to the steady-state value as the current density change rate, and measuring the time required for the potential to recover from the perturbation peak to 90% of the steady-state value as the potential recovery time; Calculating the electrochemical response sensitivity coefficient of each microregion according to the current density change rate and potential recovery time, where the electrochemical response sensitivity coefficient is equal to the weighted product of the current density change rate and the potential recovery time; Performing normalization processing on the electrochemical response sensitivity coefficient, marking the microregions with a normalized sensitivity coefficient greater than 0.8 as high-sensitivity candidate regions, and performing topological connectivity analysis on the high-sensitivity candidate regions to identify interconnected sensitive microregion clusters as the microscopic sensitive region.

3. The high-precision stainless-steel corrosion resistance test method according to claim 1, characterized in that Applying an interactive combined excitation of chloride ion concentration from 0.01 M to 0.5 M and potential from -300 mV to +200 mV to the grain boundary sensitive region, and recording the current density-time curve and passivation film breakdown potential under different combinations; Applying an interactive combined excitation of stress level with a yield strength percentage from 20% to 70% and pH value from 3 to 10 to the stress concentration region, and measuring the polarization resistance change rate and critical pitting current density under different combinations; Applying an interactive combined excitation of temperature from 25°C to 65°C and dissolved oxygen concentration from 2 ppm to 8 ppm to the phase boundary sensitive region, and obtaining the surface morphology change rate and corrosion potential drift value under different combinations.

4. The high-precision stainless steel corrosion resistance test method according to claim 3, characterized in that The comprehensive analysis of the response data of various sensitive regions under different excitation combinations, calculating the corrosion sensitivity index, identifying the parameter combination threshold that causes a sudden increase in the corrosion sensitivity index, and determining the parameter combination threshold as the critical condition for triggering corrosion, including: Performing numerical integration on the current density-time curve of the grain boundary sensitive region to calculate the charge transfer amount per unit time, taking the reciprocal of the difference between the passivation film breakdown potential and the reference potential as the passivation film fragility coefficient, and defining the product of the charge transfer amount per unit time and the passivation film fragility coefficient as the corrosion sensitivity index of the grain boundary sensitive region; Calculating the ratio of the polarization resistance change rate of the stress concentration region to the initial polarization resistance as the polarization resistance coefficient, calculating the ratio of the critical pitting current density to the material reference corrosion current density as the pitting tendency coefficient, and dividing the pitting tendency coefficient by the polarization resistance coefficient to obtain the corrosion sensitivity index of the stress concentration region; Convert the surface topography change rate in the phase boundary sensitive area into the surface roughness increment per unit time, calculate the ratio of the corrosion potential drift value to the standard deviation as the electrochemical stability coefficient, and define the product of the surface roughness increment per unit time and the electrochemical stability coefficient as the corrosion sensitivity index of the phase boundary sensitive area; Normalize the corrosion sensitivity index of the grain boundary sensitive area, the corrosion sensitivity index of the stress concentration area, and the corrosion sensitivity index of the phase boundary sensitive area respectively, plot the response surface of the multi-field coupling parameters and the normalized corrosion sensitivity index, identify the inflection points on the surface where the gradient change rate exceeds 200%, and determine the parameter combination corresponding to the inflection point as the critical condition for triggering corrosion.

5. The high-precision stainless steel corrosion resistance test method according to claim 1, wherein According to the critical condition, trigger the initial corrosion of the micro-sensitive area, adaptively track the corrosion propagation process, and implement corrosion path intervention to obtain the spatio-temporal data of corrosion evolution, including: Apply trigger stimuli to the micro-sensitive area according to the critical condition, specifically including: applying a combination of critical chloride ion concentration and critical potential to the grain boundary sensitive area, applying a combination of critical stress level and critical pH value to the stress concentration area, and applying a combination of critical temperature and critical dissolved oxygen concentration to the phase boundary sensitive area until the formation of an initial corrosion point is observed; Obtain corrosion propagation data through multi-scale monitoring methods, including macroscopic electrochemical parameter monitoring, micro-area electrochemical activity scanning, and real-time surface topography imaging. When the formation of an initial corrosion point is detected, dynamically track the corrosion propagation front and adjust the monitoring parameters according to the corrosion front movement speed; During the corrosion propagation process, implement intervention measures at the positions where the corrosion rate changes, including changing the local electrochemical environment, adjusting the stress distribution, or modifying the surface state, and record the change in the corrosion propagation rate before and after the intervention; Collect data on the entire corrosion process, including data on the change in corrosion point density over time, data on the spatial distribution of corrosion depth, and data on the corrosion front propagation rate; Integrate the data on the change in corrosion point density over time, data on the spatial distribution of corrosion depth, and data on the corrosion front propagation rate, identify the transition points and spatial propagation characteristics of each stage of corrosion, and form spatio-temporal data of corrosion evolution including time dimension and space dimension.

6. The high-precision stainless steel corrosion resistance testing method according to claim 1, characterized in that Based on the spatio-temporal data, monitor the dynamic changes of micro-area environmental parameters during the corrosion process, analyze the corresponding relationship between the surface topography changes and the dynamic changes of micro-area environmental parameters, and determine the key conditions for forming the corrosion feedback amplification effect, including: Deploy a micro-area environmental monitoring lattice in the corrosion active area determined based on the spatio-temporal data to monitor the dynamic change data of local pH value, metal ion concentration, dissolved oxygen concentration, potential distribution, and liquid flow state during the corrosion process, and obtain the dynamic change data of micro-area environmental parameters; Extract the data on the change in corrosion point density over time, data on the spatial distribution of corrosion depth, and data on the corrosion front propagation rate from the spatio-temporal data, calculate the change rate of each data in the time dimension, and obtain the surface topography change rate; Perform a sequential comparison analysis on the surface topography change rate and the dynamic change data of the micro-region environmental parameters to determine the sequence of topography change and micro-region environmental parameter change, and identify the coupling points where environmental parameter change causes accelerated topography change or topography change causes accelerated environmental parameter change; Calculate the environmental parameter influence coefficient and the topography reaction coefficient for the coupling points. When the product of the environmental parameter influence coefficient and the topography reaction coefficient is greater than 1, confirm the formation of a positive feedback loop; Determine the key conditions for the formation of the corrosion feedback amplification effect based on the trigger parameter combination, threshold conditions, and enhancement mechanism of the positive feedback loop.

7. The high-precision stainless steel corrosion resistance testing method according to claim 1, wherein Execute an accelerated test with multi-level oscillation strengthening according to the key conditions, the microscopic sensitivity distribution map, the critical conditions, and the spatio-temporal data. Perform a stage decomposition process on the accelerated test data, calculate the time conversion coefficient, and obtain the corrosion resistance prediction results of the stainless steel sample, including: Compile a service environment characteristic spectrum based on the periodic changes, random fluctuations, and emergency characteristics of the actual service environment, and convert the service environment characteristic spectrum into a parameter sequence that can be reproduced in the laboratory; Design an accelerated test scheme with multi-level oscillation strengthening based on the parameter sequence, the key conditions, the microscopic sensitivity distribution map, and the critical conditions, specifically including: microscopic oscillation strengthening with high-frequency environmental parameter fluctuations, mesoscopic oscillation strengthening with periodic switching of corrosion conditions, and macroscopic oscillation strengthening with short-term extreme conditions applied at key time points; Execute the accelerated test scheme with multi-level oscillation strengthening to obtain the corrosion depth data, corrosion current density data, and surface topography change data under the accelerated test conditions, and form the accelerated test data; According to the corrosion evolution characteristics in the spatio-temporal data, decompose the corrosion process into four stages: latency period, nucleation period, expansion period, and stable period, and analyze the accelerated test data for each stage; Compare the accelerated test data with the standard test data, establish the time correspondence relationship for each stage, calculate the time conversion coefficient for each stage, and obtain the corrosion resistance prediction results of the stainless steel sample through integral calculation.

8. The high-precision stainless steel corrosion resistance testing method according to claim 7, characterized in that According to the corrosion evolution characteristics in the spatio-temporal data, decompose the corrosion process into four stages: latency period, nucleation period, expansion period, and stable period, and analyze the accelerated test data for each stage, including: Perform curve fitting on the corrosion point density change data over time, the corrosion depth spatial distribution data, and the corrosion front propagation rate data in the spatio-temporal data, calculate the first derivative and second derivative of the curve, and determine the conversion time points from the latency period to the nucleation period, from the nucleation period to the expansion period, and from the expansion period to the stable period based on the derivative sign change and inflection point position; Extract the accelerated test data in segments according to the determined time ranges of the four stages, analyze the passivation film stability for the accelerated test data in the latency period, analyze the growth law of corrosion point density for the accelerated test data in the nucleation period, analyze the corrosion depth expansion characteristics for the accelerated test data in the expansion period, and analyze the constant corrosion rate parameter for the accelerated test data in the stable period to complete the analysis of the accelerated test data for each stage.

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