Corrosion damage quantitative evaluation method based on optical microscope

Abstract

The invention discloses a corrosion damage quantitative evaluation method based on an optical microscope, and the method comprises the steps: carrying out a corrosion test after carrying out the pretreatment of a to-be-tested sample; obtaining the pitting depth and the corrosion rate of the corrosion area of the to-be-tested sample; an optical microscope is used for collecting the surface damage morphology of the sample to be tested after the corrosion test, a sequence splicing three-dimensional contour image is obtained, and height distribution matrix data is extracted; processing data of the height distribution matrix to obtain a height frequency diagram of the to-be-tested sample after corrosion, comparing the height frequency diagram with a height frequency diagram of an uncorroded standard sample, determining a corrosion damage threshold value, and counting point, line, surface and body characteristic parameters of a corrosion damage part; and determining a subitem damage weight, calculating a weighted damage value, and quantitatively evaluating the corrosion resistance of the to-be-tested sample. According to the invention, the optical microscope is adopted to accurately acquire the three-dimensional morphology parameters, and the three-dimensional morphology parameters are compared and analyzed, so that the method has the advantage of intuitively and accurately reflecting the corrosion resistance, and the accurate quantitative evaluation of the corrosion resistance of the material can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of corrosion performance evaluation, and in particular to a quantitative evaluation method for corrosion damage based on an optical microscope. Background Art

[0002] Traditional corrosion performance characterization methods have played an important role in research. The gravimetric method is a commonly used corrosion performance characterization method. It evaluates the degree of corrosion by measuring the weight change of the material after the material is exposed to the corrosive medium for a certain period of time. The surface observation method makes a judgment on the corrosion resistance by analyzing the thickness, color, density and degree of matrix damage of the corrosion products. The electrochemical method can determine the corrosion potential, corrosion current density, charge transfer reaction process of the material, reveal the corrosion tendency of the material under specific electrochemical conditions, and provide quantitative data for the electrochemical properties and corrosion resistance of the material. However, these methods are often limited by long testing time, limited sample number, limited information acquisition, and inability to perform non-destructive testing. In order to understand the corrosion behavior of materials more comprehensively and accurately, more and more researchers have begun to explore and apply optical-based characterization techniques.

[0003] Optical inspection methods are fast, easy to use, and non-destructive. For example, the white light interferometer 3D profilometer, a relatively mature analytical instrument, measures the relative height of the sample surface by measuring the optical path difference. This allows for rapid acquisition of 3D data such as pit depth, peak height, and roughness across a wide range of surfaces. This enables fixed-point, continuous acquisition of corroded material surfaces, thereby providing information on macro- and microscopic corrosion characteristics, including surface topography and roughness, corrosion product film thickness, number, depth, and distribution of corrosion pits, and corrosion rate. Accurate corrosion damage assessment methods based on optical microscopy facilitate the acquisition and analysis of large-scale data and offer new insights and methods for refined quantitative analysis and assessment of corroded surface characteristics. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for quantitative evaluation of corrosion damage based on an optical microscope. The optical microscope is used to accurately collect three-dimensional morphological parameters and perform comparative analysis. It has the advantages of intuitively and accurately reflecting corrosion resistance, and can realize accurate quantitative evaluation of the corrosion resistance of materials.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for quantitatively evaluating corrosion damage based on an optical microscope comprises the following steps:

[0007] S1, after pre-treating the sample to be tested, a corrosion test is carried out, and after the test, uniform or local corrosion damage is formed on the surface of the sample to be tested, and a corrosion area is obtained;

[0008] S2, obtaining the pitting depth and corrosion rate of the corrosion area of ​​the test sample;

[0009] S3, setting the test parameters of the optical microscope, collecting the damage morphology of the surface of the test sample after the corrosion test in an equidistant scanning manner, obtaining a sequence of spliced ​​three-dimensional contour images, and extracting the height distribution matrix data corresponding to the X and Y coordinate positions;

[0010] S4, processing the height distribution matrix data, obtaining a new zero reference plane during the data processing process, obtaining the absolute height loss at each position in the corrosion area, and obtaining a height frequency map of the test sample after corrosion;

[0011] S5, comparing the height frequency graph of the corroded sample to be tested with the height frequency graph of the uncorroded standard sample, determining the threshold of corrosion damage through difference analysis, and based on the threshold, calculating the characteristic parameters of points, lines, surfaces, and volumes at the corrosion damage as the damage quantification value;

[0012] S6. Select corrosion areas of the same area, comprehensively compare the differences in corrosion damage data of multiple test samples made of different materials and under different test conditions, and determine the sub-item damage weights; calculate the weighted damage value based on the sub-item damage weights to quantitatively evaluate the corrosion resistance of the test samples; wherein the corrosion damage data includes pitting depth, corrosion rate, and damage quantification value.

[0013] Furthermore, in S1, the sample to be tested is pretreated and then subjected to a corrosion test, specifically including:

[0014] The test specimens are machined to ensure that their surfaces are flat and smooth. After grinding and polishing, corrosion tests are carried out according to national standards or simulations of actual service conditions.

[0015] Wherein, the sample to be tested is a regular flat sample or an irregularly shaped sample;

[0016] The types of corrosion tests include seawater corrosion, atmospheric corrosion, erosion corrosion, galvanic corrosion, and intergranular corrosion.

[0017] Furthermore, the step S2, obtaining the pitting depth and corrosion rate of the corrosion area of ​​the sample to be tested, specifically includes:

[0018] Select a local corrosion area and use a micrometer to preliminarily measure the maximum corrosion depth of the pitting pits on the surface of the test sample. Test the maximum corrosion depth and average corrosion depth of the corrosion pit damage area, and use the weight loss method to calculate the corrosion rate of the surface of the test sample.

[0019] Furthermore, in S3, the optical microscope is a laser confocal microscope, a white light interferometer or an ultra-depth-of-field microscope.

[0020] Furthermore, in said S3, the test parameters include test time, magnification, data fluctuation range, collection interval, and collection area;

[0021] The optimization method of the test parameters is to comprehensively judge the optimal test parameters based on the statistical data capture rate, the relative standard deviation within the 95% confidence interval, and the data resolution.

[0022] Furthermore, in said S4, the height distribution matrix data is processed, including data pre-processing, flattening processing, and data post-processing;

[0023] Data pre-processing includes: using interpolation methods to perform data repair and edge repair, where interpolation methods include adjacent point interpolation and bilinear interpolation; using an iterative algorithm to fill missing data, setting the number of iterations, and selecting the number of iterations from 0 to 30; using Gaussian filtering to reduce noise and errors; and using plane tilt correction to remove the overall linear tilt of the sample.

[0024] The leveling process includes: setting a data mask, the mask shape includes regular and irregular areas, using the data mask to remove the surface height fluctuation area, performing linear leveling according to the inclination of the uncorroded area, and using the average height value of all points after leveling as the value of the new zero reference plane;

[0025] Data post-processing: Calculate the absolute height loss of each location in the corrosion area relative to the new zero reference level.

[0026] Furthermore, in said S5, the characteristic parameters of points, lines, surfaces and volumes at the corrosion damage are statistically calculated, specifically including:

[0027] Point feature parameters include the average value of all height data points, the number of valid data points, the percentage of valid data points, and the average roughness of some data points;

[0028] The line feature parameters contain at least one complete contour line;

[0029] Surface data parameters include surface data average roughness, root mean square roughness, the height value of the highest point in the area, and the depth value of the lowest point;

[0030] Volume data parameters include natural volume values ​​and standard volume values.

[0031] Furthermore, the step S6 selects corrosion areas of the same area, comprehensively compares the differences in corrosion damage data of multiple test samples made of different materials and under different test conditions, and determines the weights of the sub-items of damage, specifically including:

[0032] The analytic hierarchy process is used, with the material corrosion resistance as the target layer, the pitting depth, corrosion rate, and damage quantification value of the surface of the test sample as the criterion layer, and multiple test samples of different materials and different test conditions as the solution layer. The following formula is used:

[0033]

[0034] The weights of pitting depth, corrosion rate and damage quantification values ​​are calculated respectively;

[0035] Among them, ω i represents the final weight of the i-th factor, n represents the total number of factors involved in the comparison, a ij Indicates the importance of the i-th element relative to the j-th element; k represents the temporary column sum index, a kj Indicates the importance of the kth factor relative to the jth factor, It represents the sum of the importance scores of all factors compared with the jth factor.

[0036] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the present invention provides a quantitative evaluation method for corrosion damage based on an optical microscope, (1) an optical microscope is used to collect the damage morphology of the surface of the test sample after the corrosion test in an equidistant scanning manner, which can quickly and non-destructively obtain high-resolution three-dimensional morphological data in the millimeter to centimeter range, which is convenient for intuitively and accurately reflecting the corrosion resistance of different materials; (2) the damage threshold is determined based on the comparison of the height frequency diagram of the corroded and uncorroded samples, replacing subjective experience judgment, ensuring the scientificity and repeatability of feature extraction, and using the threshold to screen out all corrosion feature parameters that meet the definition, covering the key features of corrosion damage, avoiding the randomness of the results, and making the test results representative; (3) pitting depth, corrosion rate, and damage quantification value (point / line / surface / body feature parameters) are used as sub-item loss data to achieve comprehensive quantitative characterization of multi-dimensional damage such as uniform corrosion, pitting, and product film, so that the difference in corrosion resistance of different materials can be clearly seen. The final weighted comprehensive score is a single, quantifiable indicator, which achieves accurate and quantitative evaluation of corrosion resistance.

[0037] In summary, the corrosion damage quantitative evaluation method provided by the present invention has the advantages of intuitively and accurately reflecting corrosion resistance, and can realize the precise evaluation of corrosion resistance; it solves the problem of quantitative testing of corrosion damage, and can realize accurate and efficient evaluation of corrosion types such as pitting corrosion, crevice corrosion, erosion corrosion, and intergranular corrosion. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 This is a workflow diagram of the method for quantitatively evaluating corrosion damage based on an optical microscope of the present invention;

[0040] Figure 2 The three-dimensional profile and line profile of the corrosion process according to the embodiment of the present invention are shown in Figure 1, where (a) is the three-dimensional profile after treatment, (b) is the line profile along the X direction, and (c) is the line profile along the Y direction.

[0041] Figure 3 The macroscopic morphology of the corrosion test of three samples to be tested in the present invention, wherein (a) is sample 1#, (b) is sample 2#, and (c) is sample 3#;

[0042] Figure 4 This is the three-dimensional contour diagram of the damage 2#-1 of the present invention;

[0043] Figure 5 This is the height distribution frequency diagram of the damaged area 2#-1 of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] The purpose of this invention is to provide a quantitative evaluation method for corrosion damage based on an optical microscope. Through the three-in-one technical architecture of optical precision detection + data intelligent analysis + dynamic weight evaluation, a leap-forward upgrade of corrosion damage from "empirical description" to "digital quantification" is achieved. The evaluation results are more objective, the damage characterization is more comprehensive, the analysis efficiency is more efficient, and the engineering guidance is more accurate, providing an innovative solution for material corrosion evaluation.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] like Figure 1 As shown, the method for quantitatively evaluating corrosion damage based on an optical microscope provided by the present invention comprises the following steps:

[0048] S1, after pre-treating the sample to be tested, conduct a corrosion test, after which uniform or localized corrosion damage is formed on the surface of the sample to be tested, and obtain the corrosion area; specifically, the following steps are included:

[0049] The test specimens are machined to ensure that their surfaces are flat and smooth. After grinding and polishing, corrosion tests are carried out according to national standards or simulations of actual service conditions.

[0050] Wherein, the sample to be tested is a regular flat sample or an irregularly shaped sample;

[0051] The types of corrosion tests include seawater corrosion, atmospheric corrosion, erosion corrosion, galvanic corrosion, intergranular corrosion, etc.

[0052] S2, obtain the pitting depth and corrosion rate of the corrosion area of ​​the test sample; specifically, select a local corrosion area, use a micrometer to preliminarily measure the maximum corrosion depth of the pitting pit on the surface of the test sample, test the maximum corrosion depth and average corrosion depth of the corrosion pit damage area, and use the weight loss method to calculate the corrosion rate of the surface of the test sample.

[0053] The maximum corrosion depth and average corrosion depth of the relatively macroscopic local damage sites such as pitting pits and corrosion pits in S2, and the corrosion rate data calculated by the weight loss method serve as supplementary data for the evaluation method of the present invention.

[0054] S3, setting the test parameters of the optical microscope, collecting the damage morphology of the surface of the test sample after the corrosion test in an equidistant scanning manner, obtaining a sequence of spliced ​​three-dimensional contour images, and extracting the height distribution matrix data corresponding to the X and Y coordinate positions;

[0055] Among them, the test parameters include test time, magnification, data fluctuation range, collection spacing, collection area, etc.

[0056] The test parameter optimization method involves setting the optical microscope to the optimal test parameters based on the characteristics of the test sample. The optimal test parameters are then determined based on the statistical data capture rate, relative standard deviation within a 95% confidence interval, and data resolution. For example, the quantitative standards of statistical data capture rate ≥ 90%, relative standard deviation ≤ 3%, and data resolution ≤ 1μm are used as the criteria for determining optimal test parameters. Furthermore, the lens magnification must be high enough to clearly capture surface details, and the data acquisition time must be relatively short to avoid wasted resources and instrument crashes.

[0057] The 3D profile image data collected in S3 must be representative, and the number of local corrosion features collected in the field of view must be no less than a certain number. Whether the number of local features meets the requirement is determined by statistically analyzing the variance of the sample. For example, the quantitative results of each local field of view are statistically analyzed. As the number of statistical fields increases, the variance of the statistical data is calculated. Initially, the variance tends to decrease with the increase in the number of statistical fields. When the variance does not change with the increase in the number of statistical fields, it indicates that the statistical data of the local field of view is representative.

[0058] Optical microscopes used in S3 include, but are not limited to, laser confocal microscopy, white-light interferometry, or ultra-depth-of-field microscopy. For example, the data collected by white-light interferometry includes corrosion surface damage morphology and corrosion height data. The topography image is a sequentially spliced ​​surface distribution map; the height distribution matrix data includes X, Y, and Z three-dimensional data information.

[0059] S4, processing the height distribution matrix data, obtaining a new zero reference plane during the data processing, obtaining the absolute height loss at each position in the corrosion area, and obtaining a height frequency map of the test sample after corrosion; wherein the processing of the height distribution matrix data includes data pre-processing, flattening processing, and data post-processing;

[0060] For test samples with drastic height changes and low reflectivity, data pre-processing includes: using interpolation methods to repair data and edges, among which interpolation methods include adjacent point interpolation and bilinear interpolation; using an iterative algorithm to fill missing data, setting the number of iterations, and selecting 0 to 30 times; using Gaussian filtering to reduce noise and reduce errors;

[0061] Among them, Gaussian filtering uses long-wave filtering to eliminate micro-area roughness components. Its main purpose is to make large-scale surface structures easier to identify and measure;

[0062] Plane tilt correction is used to remove the overall linear tilt of the specimen; the linear tilt calculation formula for removing the surface measurement is:

[0063] ax+by+c=z

[0064] Where a and b are fitting coefficients, representing the slope of the sample along the x and y directions, respectively; c is a constant term, representing the base height offset of the fitting plane; x and y represent the two-dimensional coordinates of the measurement point in the horizontal plane, and z represents the original measurement value of the measurement point in the vertical direction (height direction).

[0065] The leveling process includes: setting a data mask, the mask shape includes regular and irregular areas, using the data mask to remove the surface height fluctuation area, performing linear leveling according to the inclination of the uncorroded area, and using the average height value of all points after leveling as the value of the new zero reference plane;

[0066] Data post-processing: Calculate the absolute height loss of each location in the corrosion area relative to the new zero reference level.

[0067] S5, comparing the height frequency graph of the corroded sample to be tested with the height frequency graph of the uncorroded standard sample, determining the threshold of corrosion damage through difference analysis, and based on the threshold, calculating the characteristic parameters of points, lines, surfaces, and volumes at the corrosion damage as the damage quantification value;

[0068] Among them, the characteristic parameters of points, lines, surfaces and volumes at the corrosion damage are statistically analyzed, including:

[0069] Point feature parameters include the average value H of all height data points Ave , the number of valid data points D, the percentage of valid data points P, the average roughness of some data points R a ;

[0070] in,

[0071] Where M and N are the number of data points along the X and Y directions of the matrix, and Z ij is the average height;

[0072] Average roughness R of data points a :

[0073]

[0074] The line feature parameters contain at least one complete contour line;

[0075] Surface data parameters include surface data average roughness, root mean square roughness, the height value of the highest point in the area, and the depth value of the lowest point;

[0076]

[0077] Where S a Indicates the average roughness of the surface data, S q It represents the root mean square roughness of surface data, A represents the evaluation area of ​​the measured surface, and Z(x,y) represents the height deviation value of a certain point (x,y) on the surface.

[0078] The volume data parameter contains the natural volume value V n , standard volume value V s :

[0079]

[0080] in,

[0081] S=ηA

[0082] Where A is the test area, η is the proportion of the damaged area, and S is the lateral area of ​​the damaged area.

[0083] The above statistical data include global characteristics of corrosion damage, such as average height, median, standard deviation, damage area, and volume.

[0084] Among them, the standard specimen can refer to the specimen after precision machining or the area close to the original position before corrosion;

[0085] The threshold determination process involves comparing the height frequency distribution of the sample to be tested with that of the standard sample. The threshold value may be 0, -100, -200, etc.

[0086] S6. Select corrosion areas of the same area, comprehensively compare the differences in corrosion damage data of multiple test samples made of different materials and under different test conditions, and determine the sub-item damage weights; calculate the weighted damage value based on the sub-item damage weights to quantitatively evaluate the corrosion resistance of the test samples; wherein the corrosion damage data includes pitting depth, corrosion rate, and damage quantification value.

[0087] Specifically, S6 selects corrosion areas of the same area, comprehensively compares the differences in corrosion damage data of multiple test samples made of different materials and under different test conditions, and determines the weights of the sub-items of damage, which specifically includes:

[0088] The analytic hierarchy process is used, with the material corrosion resistance as the target layer, the pitting depth, corrosion rate, and damage quantification value of the surface of the test sample as the criterion layer, and multiple test samples of different materials and different test conditions as the solution layer. The following formula is used:

[0089]

[0090] The weights of pitting depth, corrosion rate and damage quantification values ​​are calculated respectively;

[0091] Among them, ω i represents the final weight of the i-th factor, n represents the total number of factors involved in the comparison, a ij Indicates the importance of the i-th element relative to the j-th element, k represents the temporary column sum index, a kj Indicates the importance of the kth factor relative to the jth factor, It represents the sum of the importance scores of all factors compared with the jth factor.

[0092] In addition, when evaluating sub-item damage, hierarchical analysis is used to score and evaluate each sub-item damage, and weights can also be set based on expert experience.

[0093] This example uses 316L stainless steel with three different treatment processes as test samples, and the specific evaluation method is as follows:

[0094] Step 1: Machining the sample to be tested to ensure that the upper and lower surfaces of the sample are flat, grinding and polishing, and then conducting corrosion tests according to standards or simulating actual service conditions;

[0095] In this example, three 316L stainless steel samples with different treatment processes were selected as test samples. Three parallel samples were selected for each material. The sample cross-section was polished to 180 mesh and corroded in 0.05 mol / L hydrochloric acid solution at 50° C. for 24 h according to GB / T 10127-2002.

[0096] Step 2: Select a local area to measure the maximum pit depth of the test surface of the sample, the maximum corrosion depth and the average corrosion depth of the test area, and calculate the overall corrosion rate of the sample.

[0097] After the corrosion test, the overall corrosion rate of the sample was calculated by the weight loss method; ten deeper pits were selected on the sample surface to test the maximum depth of the corrosion pits on the test surface; the maximum corrosion pit depth in the crevice corrosion area and the average corrosion depth at ten locations were tested; the test results of the three materials are shown in Table 1. The macroscopic morphology of the corrosion test of the three samples to be tested is as follows: Figure 3 As shown in Table 1, there are differences in the macroscopic corrosion rates and the maximum pit depths of the test surfaces of the three materials, while the average corrosion depths are basically the same. Therefore, the maximum pit depth and average corrosion depth in the crevice corrosion area cannot be used to accurately judge the difference in corrosion resistance.

[0098] Table 1 Corrosion data test results of three tested materials

[0099]

[0100]

[0101] In Table 1, after each numbered sample, -1, -2, and -3 represent the data of three parallel samples of each material.

[0102] Step 3: Use a white light interferometer to set appropriate test parameters, collect the surface damage morphology of the corroded sample at equal intervals, and obtain sequential spliced ​​three-dimensional contour image data and height distribution matrix data corresponding to the X and Y positions;

[0103] In this example, two materials to be tested, 1# and 2#, were placed on the 3D profilometer test platform. The test parameters are shown in Table 2. Due to the obvious characteristics and considering the evaluation efficiency, the magnification was selected as 5×0.55, the data fluctuation range was ±500μm, the acquisition spacing was 2.484μm, and the acquisition area was 16mm×16mm. The complete damage morphology of the sample surface after corrosion was collected for 5 hours, and the sequence spliced ​​3D profile image data was obtained, with a data volume of 2.8×107 2#-1 damage three-dimensional contour diagram as shown Figure 4 shown.

[0104] Table 2 Corrosion data test parameters

[0105]

[0106] Step 4: Post-process the matrix data. Post-processing includes data filtering, setting a data mask, and leveling. During the data processing, a new zero reference plane is obtained to obtain the absolute height loss at each location in the corrosion area.

[0107] In this embodiment, the data array is post-processed. In order to improve the data integrity and reduce the proportion of invalid data, data repair is first performed. The interpolation method is selected as the repair method, and the number of iterations is set. Under this method, the more iterations, the more complete the data repair, but the data accuracy will decrease. In this case, the number of iterations is set to 30, and edge repair is performed at the same time to supplement the missing data at the edge. The data measured by white light interferometry inevitably have surface errors and edge distortion errors. In white light interferometry, surface error refers to the deviation between the macroscopic geometric shape of the measured surface and the ideal shape (such as a plane, a sphere, etc.). Gaussian regression filter processing can be used to reduce noise to reduce errors and improve data accuracy. The long wave (Long Wavelength Pass) filter in the Gaussian filter is used to remove small-scale roughness components. In order to prevent excessive correction of corrosion data at the filter, the filter order (Order) is zero order. The initial data measured by the instrument has a certain tilt and cannot be used directly. It needs to be leveled. The linear leveling method ax+by+c=z is selected for leveling. At the same time, in order to prevent the data of the corrosion area from affecting the leveling, it is necessary to screen it. First, use a mask to cover the damaged area. When masking, based on the annular corrosion characteristics of the test sample, use the circular tool to select and cover the damaged area. After setting the mask, the leveling will be linearly leveled according to the inclination of the uncorroded area to make the data more accurate. At this time, the new zero reference plane is the average height value of the uncorroded area, and the absolute height of each position in the corrosion damage area can be obtained to facilitate horizontal comparison between different samples. 2#-1 Three-dimensional contour image after macro corrosion morphology processing is shown in Figure 2 Middle (a).

[0108] Step 5: Statistically analyze the processed matrix data, compare the height frequency diagram of the corroded sample to be tested with that of the standard sample, perform difference analysis, and determine the threshold of corrosion damage; and calculate the height data of points, lines, surfaces, and volumes at the corrosion damage locations;

[0109] In this embodiment, the processed matrix data is statistically analyzed. In order to determine the corrosion damage threshold and facilitate the screening of damaged areas, a frequency distribution diagram is first developed to compare the difference between the height frequency diagram of the corroded sample to be tested and the standard sample. After determining the threshold based on the difference, the data mask is used to analyze the damaged area. Based on the annular corrosion characteristics of the test sample, the circular tool is still used to expose the damaged area. The area of ​​the damaged area is calculated using S=ηA and the formula is used. Calculate the average height of the damaged area, calculate the median height and the standard deviation of the height. Analyze the overall surface data, first remove the data mask, and operate on the overall matrix data. Calculate the average roughness R of the point data separately a , surface data average roughness S a In order to achieve accurate calculation of the damage volume, first select the appropriate height plane to fully expose the damage area data, and use V n =∫∫ A |z(x,y)|dxdy, V s =V n / S calculate the natural volume V n , standard volume V s ;

[0110] The height distribution frequency of the test sample is shown in Figure 5 Here, the threshold is set to 0 for data statistics, and areas with a height less than 0 are considered corrosion damage areas. The statistical data are shown in Tables 3 and 4. The three-dimensional data of the damage areas measured for the two materials differ. Because the measured data volume is in the tens of millions, the average corrosion depth obtained is more effective in identifying differences in corrosion resistance than in Step 2.

[0111] Table 3 Test results of corrosion damage area data of two materials

[0112] Sample Average height (μm) Median height (μm) Height standard deviation <![CDATA[Area (mm 2 )]]> 1# -90.14 -84.07 59.26 52.173 2# -129.44 -112.78 89.92 61.824

[0113] Table 4 Corrosion data test results of two materials

[0114]

[0115] Step 6: Select matrix areas of the same area, comprehensively compare the differences in corrosion damage of different materials under different test conditions, and accurately evaluate the corrosion resistance of the samples to be tested;

[0116] In this embodiment, two different treatment processes of 316L stainless steel were tested. After the test, the pitting corrosion depth, corrosion rate and damage quantification value of the sample surface were counted. In order to reasonably compare the corrosion resistance of the two materials, the hierarchical analysis method was used to take the material corrosion resistance as the target layer, the pitting corrosion depth, corrosion rate and damage quantification value of the sample surface as the criterion layer, and the different test materials as the solution layer. The formula The pitting depth on the specimen surface, the corrosion rate on the specimen surface, and the weights of the damage quantification values ​​were calculated. By comparing the relative importance of each element at the same level, a judgment matrix was constructed. The expert scores are shown in Table 5.

[0117] Table 5 Corrosion performance evaluation scoring table

[0118]

[0119] For the maximum corrosion pit depth (mm) of the test surface, experts scored samples 1# and 2# according to Table 1. By comparing the relative importance of each element in the same level, a judgment matrix was constructed. The scoring results are shown in Table 6.

[0120] Table 6 Scoring table for maximum corrosion pit depth on test surface

[0121]

[0122] For the corrosion rate (mm / a), experts scored samples 1# and 2# according to Table 1. By comparing the relative importance of each element in the same level, a judgment matrix was constructed. The scoring results are shown in Table 7.

[0123] Table 7 Corrosion rate scoring table

[0124] Corrosion rate (mm) 1# 2# 1# 1 2 2# 1 / 2 1

[0125] For the crevice corrosion damage (mm / a) data quantified by white light interferometry, experts scored samples 1# and 2# according to Tables 3 and 4. By comparing the relative importance of each element in the same level, a judgment matrix was constructed. The scoring results are shown in Table 8.

[0126] Table 8 Corrosion damage scoring table

[0127] Crevice corrosion damage (mm) 1# 2# 1# 1 3 2# 1 / 3 1

[0128] Using the analytic hierarchy process, the weights of each layer were combined to calculate the final weight of the solution layer for the overall goal. The results are shown in Table 9. After calculating the consistency ratio, the composite matrix requirements were obtained. The weights of pit depth, surface corrosion rate, and corrosion damage were 11%, 31%, and 58%, respectively. The overall score for test sample 1 was 0.73, while the overall score for test sample 2 was 0.27, indicating that test sample 1 had better overall crevice corrosion resistance than test sample 2.

[0129] Table 9 Weight coefficients of corrosion factors

[0130]

[0131] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for quantitative evaluation of corrosion damage based on an optical microscope, characterized in that: The following steps are involved: S1, after pre-treating the sample to be tested, a corrosion test is carried out, and after the test, uniform or local corrosion damage is formed on the surface of the sample to be tested, and a corrosion area is obtained; S2, obtaining the pitting depth and corrosion rate of the corrosion area of ​​the test sample; S3, setting the test parameters of the optical microscope, collecting the damage morphology of the surface of the test sample after the corrosion test in an equidistant scanning manner, obtaining a sequence of spliced ​​three-dimensional contour images, and extracting the height distribution matrix data corresponding to the X and Y coordinate positions; S4, processing the height distribution matrix data, obtaining a new zero reference plane during the data processing process, obtaining the absolute height loss at each position in the corrosion area, and obtaining a height frequency map of the test sample after corrosion; S5, comparing the height frequency graph of the corroded sample to be tested with the height frequency graph of the uncorroded standard sample, determining the threshold of corrosion damage through difference analysis, and based on the threshold, calculating the characteristic parameters of points, lines, surfaces, and volumes at the corrosion damage as the damage quantification value; S6, select the corrosion area of ​​the same area, comprehensively compare the differences in corrosion damage data of multiple test samples with different materials and different test conditions, and determine the sub-item damage weights; Combined with the item damage weights, a weighted damage value is calculated to quantitatively evaluate the corrosion resistance of the test sample; wherein, the corrosion damage data includes pitting depth, corrosion rate, and damage quantification value.

2. The method for quantitatively evaluating corrosion damage based on an optical microscope according to claim 1, wherein: In S1, the sample to be tested is pretreated and then subjected to a corrosion test, which specifically includes: The test specimens are machined to ensure that their surfaces are flat and smooth. After grinding and polishing, corrosion tests are carried out according to national standards or simulations of actual service conditions. Wherein, the sample to be tested is a regular flat sample or an irregularly shaped sample; The types of corrosion tests include seawater corrosion, atmospheric corrosion, erosion corrosion, galvanic corrosion, and intergranular corrosion.

3. The method for quantitatively evaluating corrosion damage based on an optical microscope according to claim 1, wherein: The step S2, obtaining the pitting depth and corrosion rate of the corrosion area of ​​the sample to be tested, specifically includes: Select a local corrosion area and use a micrometer to preliminarily measure the maximum corrosion depth of the pitting pits on the surface of the test sample. Test the maximum corrosion depth and average corrosion depth of the corrosion pit damage area, and use the weight loss method to calculate the corrosion rate of the surface of the test sample.

4. The method for quantitatively evaluating corrosion damage based on an optical microscope according to claim 1, wherein: In S3, the optical microscope is a laser confocal microscope, a white light interferometer or an ultra-depth-of-field microscope.

5. The method for quantitatively evaluating corrosion damage based on an optical microscope according to claim 1, wherein: In S3, the test parameters include test time, magnification, data fluctuation range, collection interval, and collection area; The optimization method of the test parameters is to comprehensively judge the optimal test parameters based on the statistical data capture rate, the relative standard deviation within the 95% confidence interval, and the data resolution.

6. The method for quantitatively evaluating corrosion damage based on an optical microscope according to claim 1, wherein: In said S4, the height distribution matrix data is processed, including data pre-processing, flattening processing, and data post-processing; Data pre-processing includes: using interpolation methods to perform data repair and edge repair, where interpolation methods include adjacent point interpolation and bilinear interpolation; using an iterative algorithm to fill missing data, setting the number of iterations, and selecting the number of iterations from 0 to 30; using Gaussian filtering to reduce noise and errors; and using plane tilt correction to remove the overall linear tilt of the sample. The leveling process includes: setting a data mask, the mask shape includes regular and irregular areas, using the data mask to remove the surface height fluctuation area, performing linear leveling according to the inclination of the uncorroded area, and using the average height value of all points after leveling as the value of the new zero reference plane; Data post-processing: Calculate the absolute height loss of each location in the corrosion area relative to the new zero reference level.

7. The method for quantitatively evaluating corrosion damage based on an optical microscope according to claim 1, wherein: In said S5, the characteristic parameters of points, lines, surfaces and volumes at the corrosion damage are counted, specifically including: Point feature parameters include the average value of all height data points, the number of valid data points, the percentage of valid data points, and the average roughness of some data points; The line feature parameters contain at least one complete contour line; Surface data parameters include surface data average roughness, root mean square roughness, the height value of the highest point in the area, and the depth value of the lowest point; Volume data parameters include natural volume values ​​and standard volume values.

8. The method for quantitatively evaluating corrosion damage based on an optical microscope according to claim 1, wherein: S6, selecting a corrosion area of ​​the same area, comprehensively comparing the differences in corrosion damage data of multiple test samples made of different materials and under different test conditions, and determining the weight of each damage item, specifically includes: The analytic hierarchy process is used, with the material corrosion resistance as the target layer, the pitting depth, corrosion rate, and damage quantification value of the surface of the test sample as the criterion layer, and multiple test samples of different materials and different test conditions as the solution layer. The following formula is used: The weights of pitting depth, corrosion rate and damage quantification values ​​are calculated respectively; Among them, ω i represents the final weight of the i-th factor, n represents the total number of factors involved in the comparison, a ij Indicates the importance of the i-th element relative to the j-th element; k represents the temporary column sum index, a kj Indicates the importance of the kth factor relative to the jth factor, It represents the sum of the importance scores of all factors compared with the jth factor.

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