Method for modeling surface topography of corroded steel based on frequency spectrum generation

Abstract

The application discloses a kind of based on spectrum generation's rusted steel surface morphology modeling method, comprising the following steps: using ruler rusted steel thickness, obtain rusted steel point thickness data set, calculate the representative value of the ruler thickness of rusted steel, the representative value of the artificial ruler thickness of rusted steel is further corrected according to the determined artificial ruler error correction proportion coefficient, obtain the thickness of corrected rusted steel plate, approximate evaluation uniform corrosion degree;Obtain the material category of rusted steel, search the pre-constructed each kind of steel non-uniform corrosion evaluation data set, calculate the simulation surface curve of corresponding rusted steel non-uniform corrosion;Combined with the thickness of corrected rusted steel plate and the simulation surface curve of non-uniform corrosion Artificially synthesized surface is generated, a model that can be used for the finite element analysis of the tensile properties of rusted steel is constructed.The present application only needs a ruler thickness parameter, can quickly generate three-dimensional surface that accords with actual steel corrosion, solves the problem of complex modeling of rusted steel surface.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rusted steel analysis, in particular to a rusted steel surface morphology modeling method based on spectrum generation. BACKGROUND

[0002] Steel structures or steel components are exposed to complex and variable natural environment, and are extremely susceptible to corrosion. The performance of steel materials subjected to natural environment corrosion is severely degraded, and in severe cases, the structure may even collapse, causing serious economic losses and casualties. In the case of rapid expansion of newly built steel structures, it is necessary to conduct in-depth research on the mechanical properties of corroded steel in order to better expand the application of steel in building structures. At this time, the finite element modeling analysis of corroded steel is particularly important.

[0003] Currently, the modeling methods for corroded steel are mainly manual measurement and on-site three-dimensional scanning. Steel corrosion exists in uniform corrosion and non-uniform corrosion components. For uniform corrosion, the three-dimensional scanning result is accurate, but the equipment requirement is high and the operation is difficult. Manual measurement is simple to operate, but its result usually overestimates the uniform corrosion component.

[0004] Although the above methods can achieve modeling of corroded steel to some extent, the large error of manual measurement, high cost and high operation difficulty of on-site three-dimensional scanning are still not conducive to rapid and accurate modeling of corroded steel. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art and provide a rusted steel surface morphology modeling method based on spectrum generation. The rusted steel surface morphology modeling method based on spectrum generation can quickly perform finite element modeling analysis while ensuring accuracy.

[0006] The purpose of the present application is achieved by the following technical solution: the rusted steel surface morphology modeling method based on spectrum generation comprises the following steps:

[0007] S1, measure the thickness of the rusted steel to obtain a rusted steel point thickness data set, calculate the representative value of the rusted steel measurement thickness, and further correct the representative value of the rusted steel manual measurement thickness according to a determined manual measurement error correction proportionality coefficient to obtain a corrected rusted steel plate thickness, and approximately evaluate the uniform corrosion degree;

[0008] S2, obtain the material category of the rusted steel, search for a pre-constructed non-uniform corrosion evaluation data set of various steels, and calculate the simulated surface curve of the non-uniform corrosion of the corresponding rusted steel;

[0009] S3, combine the corrected rusted steel plate thickness and the simulated surface curve of the non-uniform corrosion to generate an artificially synthesized surface, and construct a model that can be used for finite element analysis of the tensile properties of the rusted steel.

[0010] Preferably, before step S1, there is the following step:

[0011] The rust of steel material is divided into uniform corrosion and non-uniform corrosion.

[0012] Preferably, step S1 includes the following specific steps:

[0013] S11, determine the artificial gauge error correction proportionality coefficient;

[0014] S12, use a vernier caliper to measure the thickness of several points of the rusted steel material to be measured to obtain a rusted steel material point thickness data set;

[0015] S13, average the rusted steel material point thickness data set to obtain a representative value of the gauge thickness of the rusted steel material to be measured;

[0016] S14, using the artificial gauge error correction proportionality coefficient, further corrects the artificial gauge thickness representative value of the rusted steel material to be measured according to the gauge error to obtain a corrected rusted steel plate thickness, which is used to approximate the uniform corrosion degree, wherein the corrected rusted steel plate thickness is:

[0017] h1=a1y0

[0018] Where h1 is the corrected rusted steel plate thickness, a1 is the artificial gauge error correction proportionality coefficient, and y0 is the artificial gauge thickness representative value.

[0019] Preferably, step S11 includes the following specific steps:

[0020] S111, three-dimensional scanning of a large number of different rusted steel plates to obtain a point cloud thickness average;

[0021] S112, using a vernier caliper to measure the thickness of the steel plate at multiple locations;

[0022] S113, taking the average of the measured multiple steel plate thickness values to obtain an artificial gauge thickness representative value;

[0023] S114, the point cloud thickness average and the artificial gauge thickness representative value of each rusted steel plate are used as a group of data, and after obtaining a plurality of groups of data, a database is formed;

[0024] S115, compare each group of measurement values in the database, take the average of the ratio of each group to obtain an artificial gauge error correction proportionality coefficient with wide application.

[0025] Preferably, step S2 includes the following specific steps:

[0026] S21, construct a non-uniform corrosion evaluation data set for various types of steel materials;

[0027] S22, obtaining the material category of the rusted steel to be measured;

[0028] S23, searching the pre-constructed non-uniform corrosion evaluation data set of each type of steel to obtain the non-uniform corrosion steel frequency domain feature of the rusted steel to be measured, each frequency and the corresponding component;

[0029] S24, calculating the simulation surface curve of non-uniform corrosion by using the non-uniform corrosion steel frequency domain feature of the rusted steel to be measured, each frequency and the corresponding component:

[0030]

[0031] wherein, A i is the frequency corresponding to the frequency amplitude; ω i is the frequency, taking 1-50; b1 is the frequency-amplitude correlation coefficient, which is related to the material type of the rusted steel; b2 is the frequency-amplitude correlation index, which is related to the material type of the rusted steel; y2 is the non-uniform corrosion coordinate offset; x is the length coordinate of the middle of the test piece. is a random number representing the phase.

[0032] Preferably, step S21 includes the following specific steps:

[0033] S211, detecting and collecting data of a plurality of samples of each type of rusted steel;

[0034] S212, selecting 5 or more sample lines in the length direction on each rusted steel sample;

[0035] S213, extracting the non-uniform corrosion surface concave-convex situation on the sample line and recording it as a two-dimensional non-periodic signal;

[0036] S214, the non-periodic signal is decomposed into the sum of sine and cosine functions of different frequencies by Fourier transform.

[0037] Preferably, in step S314, the decomposition formula of the non-periodic signal is:

[0038]

[0039] In the formula, ω is the frequency; F(ω) is the frequency domain function (phase spectrum, amplitude spectrum) with frequency as the independent variable; x represents the length coordinate of the middle of the test piece; f(x) is a function with x as the independent variable.

[0040] Preferably, step S3 includes the following specific steps:

[0041] S31, taking the corrected rusted steel thickness as the rusted steel model reference thickness;

[0042] S32, the uneven corrosion simulation surface curve is taken as the rusted steel model surface profile curve;

[0043] S33, the rusted steel model surface profile curve fluctuates up and down with the rusted steel model reference thickness as the reference line, an artificial synthetic surface is generated, and a model capable of being used for finite element analysis of the tensile properties of the rusted steel is constructed.

[0044] Preferably, the artificial synthetic surface in step S33 is generated by using the following formula:

[0045] y = h1 + y2

[0046] wherein y is the total offset of each point of the artificial synthetic surface, h1 is the corrected thickness of the rusted steel plate, and y2 is the uneven corrosion coordinate offset.

[0047] The present application has the following advantages over the prior art:

[0048] The method of the present application determines the uniform corrosion thickness by using a ruler, the uneven corrosion thickness is obtained by using the inverse Fourier spectrum transformation means, the uniform corrosion thickness and the uneven corrosion thickness are superimposed, the artificial synthetic corrosion surface is generated, the corrosion profile characteristics of the rusted steel are accurately characterized, and the finite element model capable of being used for simulating the rusted steel and analyzing the structure is constructed. The method of the present application only needs one ruler thickness parameter, can quickly generate the three-dimensional surface that matches the actual steel corrosion, and solves the problem of complex modeling of the surface of the rusted steel. Therefore, the present application solves the problems of high equipment requirement, large operation difficulty, poor accuracy and high cost of the traditional technology. Meanwhile, the present application only needs one ruler thickness parameter, can realize the finite element model analysis, and improves the analysis efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 A flowchart of a rusted steel surface profile modeling method based on spectrum generation provided by an embodiment of the present application.

[0050] Figure 2 A regression curve of the frequency domain characteristics of the Q275 low-carbon steel obtained in an embodiment of the present application.

[0051] Figure 3 A regression curve of the frequency domain characteristics of the 600MPa grade high-strength steel obtained in an embodiment of the present application.

[0052] Figure 4 A schematic diagram of five typical artificial generated surfaces based on the uniform corrosion surface in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The present application will be further described below in combination with the drawings and embodiments.

[0054] The present embodiment takes the establishment of a database, the determination of a measurement error correction proportionality coefficient, the construction and application of a non-uniform corrosion evaluation data set of various types of steel materials as examples. As shown in Figure 1 The present embodiment is based on a rusted steel material surface morphology modeling method generated by a spectrum, and includes the following steps:

[0055] First, the rust of the steel material is divided into uniform corrosion and non-uniform corrosion. Then the following operations are performed:

[0056] S1, the thickness of the rusted steel material is measured by a measuring ruler, a rusted steel material point thickness data set is obtained, a rusted steel material measurement thickness representative value is calculated, and the rusted steel material manual measurement thickness representative value is further corrected according to the determined manual measurement error correction proportionality coefficient to obtain a corrected rusted steel plate thickness, and the uniform corrosion degree is approximately evaluated; step S1 includes the following specific steps:

[0057] S11, a manual measurement error correction proportionality coefficient is determined; step S11 includes the following specific steps:

[0058] S111, a point cloud thickness mean value is obtained by three-dimensional scanning of a large number of different rusted steel plates;

[0059] S112, the thickness values of the steel plates at multiple locations are measured using a vernier caliper;

[0060] S113, the measured thickness values of the steel plates at multiple locations are averaged to obtain a manual measurement thickness representative value;

[0061] S114, the point cloud thickness mean value and the manual measurement thickness representative value of each rusted steel plate are taken as a group of data, and a database is formed after a plurality of groups of data are obtained;

[0062] S115, the ratio of each group of measurement values in the database is averaged to obtain a manual measurement error correction proportionality coefficient with wide application.

[0063] Specifically, the manual measurement error correction proportionality coefficient determined by the present embodiment is shown in Table 1, and the database formed by measuring 18 rusted steel plate samples and the manual measurement error correction proportionality coefficient. The thickness mean value obtained by the point cloud is about 0.96 times the thickness measured by the ruler, because the ruler ignores the existence of rust pits and overestimates the remaining thickness of the rusted steel plate.

[0064] Table 1: Point cloud thickness mean value and measurement thickness

[0065]

[0066] By correcting the deviation of the vernier caliper measurement result by revising the gauge thickness, a more accurate uniform corrosion component is obtained, which is more accurate and convenient for the evaluation of the corrosion degree of the steel structure in the actual engineering. When the database volume is large enough, the artificial gauge error correction proportion coefficient obtained is more widely applicable, so that the correction effect is more accurate. As shown in Table 1, the artificial gauge error correction proportion coefficient of the embodiment is 0.96.

[0067] S12, using a vernier caliper to measure the thickness of several points of the rusted steel to be measured, to obtain a rusted steel point thickness data set;

[0068] S13, performing an average value processing on the rusted steel point thickness data set to obtain a gauge thickness representative value of the rusted steel to be measured;

[0069] S14, using an artificial gauge error correction proportion coefficient, further correcting the artificial gauge thickness representative value of the rusted steel to be measured according to the gauge error to obtain a corrected rusted steel plate thickness, which is used to approximate evaluate the uniform corrosion degree, wherein the corrected rusted steel plate thickness is:

[0070] h1=a1y0

[0071] Wherein, h1 is the corrected rusted steel plate thickness, a1 is the artificial gauge error correction proportion coefficient, and y0 is the artificial gauge thickness representative value.

[0072] S2, obtaining the material category of the rusted steel, searching for the pre-constructed non-uniform corrosion evaluation data set of each type of steel, and calculating the simulation surface curve of the non-uniform corrosion of the corresponding rusted steel; step S2 includes the following specific steps:

[0073] S21, constructing a non-uniform corrosion evaluation data set of each type of steel; step S21 includes the following specific steps:

[0074] S211, detecting and collecting data of a plurality of samples of each type of rusted steel;

[0075] S212, selecting 5 or more sample lines in the length direction on the upper and lower surfaces of each rusted steel sample;

[0076] S213, extracting the non-uniform corrosion surface concave-convex situation on the sample line and recording it as a two-dimensional non-periodic signal;

[0077] S214, the non-periodic signal is decomposed into a sum of sine and cosine functions of different frequencies by Fourier transform. In step S214, the decomposition formula of the non-periodic signal is:

[0078]

[0079] In the formula, ω is frequency; F(ω) is a frequency-domain function (phase spectrum, amplitude spectrum) with frequency as the independent variable; x represents the length coordinate of the middle part of the test piece; and f(x) is a function with x as the independent variable.

[0080] S22, obtaining the material category of the rusted steel to be measured;

[0081] S23, searching for pre-constructed non-uniform corrosion evaluation data sets of various steels to obtain the non-uniform corrosion steel frequency-domain features of the rusted steel to be measured, and the corresponding components of each frequency;

[0082] The non-uniform corrosion evaluation data sets of various steels are constructed, frequency-domain feature analysis is performed on 12 Q275 low-carbon steel rusted steel test pieces and 6 600 MPa grade high-strength steel rusted steel test pieces, the amplitude spectrum law is obtained, regression analysis is performed, and the frequency-domain features of the surface morphology after corrosion of Q275 low-carbon steel and 600 MPa grade high-strength steel are obtained, as shown in Figure 2 and Figure 3 .

[0083] The relationship between each frequency and the corresponding component obtained by regression is:

[0084]

[0085] wherein, A i is the component corresponding to the frequency; ω i is the frequency, which is 1-50.

[0086] With the expansion of the data set, more accurate and more comprehensive non-uniform corrosion steel frequency-domain features will be obtained after regression analysis, and the non-uniform corrosion coordinate offset is more accurate, thereby generating a more effective simulation surface curve of non-uniform corrosion. The non-uniform corrosion steel frequency-domain features are related to the type and nature of the steel, so the construction of the data set requires a large amount of data accumulation for each type of steel.

[0087] The pre-constructed non-uniform corrosion evaluation data sets of various steels are searched for the type of the rusted steel to be measured, and the frequency-domain features of the surface morphology after corrosion of the rusted steel to be measured are obtained.

[0088] S24, calculating the simulation surface curve of non-uniform corrosion by using the non-uniform corrosion steel frequency-domain features of the rusted steel to be measured, the corresponding components of each frequency:

[0089]

[0090] wherein, A i is the frequency amplitude corresponding to the frequency; ω iwherein, f is frequency, taking 1~50; b1 is frequency-amplitude correlation coefficient, which is related to the type of the corroded steel material; b2 is frequency-amplitude correlation index, which is related to the type of the corroded steel material; y2 is the coordinate offset of uneven corrosion; x is the length coordinate of the middle of the test piece. is a random number representing the phase.

[0091] S3, combining the modified thickness of the corroded steel plate and the simulated surface curve of uneven corrosion to generate an artificial synthetic surface and build a model that can be used for finite element analysis of the tensile properties of the corroded steel material. Step S3 includes the following specific steps:

[0092] S31, taking the modified thickness of the corroded steel material as the reference thickness of the corroded steel material model;

[0093] S32, taking the simulated surface curve of uneven corrosion as the surface morphology curve of the corroded steel material model;

[0094] S33, the surface morphology curve of the corroded steel material model fluctuates up and down with the reference line of the reference thickness of the corroded steel material model to generate an artificial synthetic surface and build a model that can be used for finite element analysis of the tensile properties of the corroded steel material.

[0095] The artificial synthetic surface generated in step S33 is generated using the following formula:

[0096] y = h1 + y2

[0097] wherein, y is the total offset of each point of the artificial synthetic surface, h1 is the modified thickness of the corroded steel plate, and y2 is the coordinate offset of uneven corrosion.

[0098] Because the phase spectrum uses random numbers in the process of generating the surface morphology, it may cause fluctuations in the calculation results of the model. Therefore, 30 different surface morphology models were generated for each of the 18 test pieces and the results were calculated. The results show that the average value of the fluctuation value of the peak force of the test piece is about 4%, and the average value of the fluctuation value of the peak strain is about 40%.

[0099] For example, the tensile test of the 18 corrosion test pieces was carried out, and the finite element results and experimental results were compared respectively. Table 2 further compares the modeling results of the artificial surface and the experimental results.

[0100] Table 2 Peak load and peak strain of steel plate

[0101]

[0102] The analysis results of the artificial surface modeling take into account the overestimation of the size of the uniform corrosion on the one hand, and use the artificial size error correction proportion coefficient, so the peak force is closer to the experimental results. On the other hand, the uneven corrosion component is considered, and the peak strain is also closer to the experimental results.

[0103] The above specific embodiments are the preferred embodiments of the present application, and cannot limit the present application, any changes or other equivalent replacement manners made without departing from the technical solutions of the present application are included in the protection scope of the present application.

Claims

1. A method for modeling surface topography of corroded steel based on spectral generation, characterized by, Comprise the following steps: S1, adopt the ruler rusted steel thickness, obtain the rusted steel point thickness data set, calculate the rusted steel ruler thickness representative value, further correct the artificial ruler thickness representative value of the rusted steel according to the determined artificial ruler error correction proportion coefficient, obtain the corrected rusted steel plate thickness, approximate evaluation uniform corrosion degree; S2, obtain the material category of the rusted steel, search the pre-constructed non-uniform corrosion evaluation data set of each type of steel, and calculate the simulation surface curve of the non-uniform corrosion of the corresponding rusted steel; Step S2 includes the following specific steps: S21, construct the non-uniform corrosion evaluation data set of each type of steel; Step S21 includes the following specific steps: S211, a plurality of sample detection and data collection are carried out on each type of rusted steel; S212, select 5 or more sample lines in the length direction on each upper and lower surface of each rusted steel sample; S213, extract the uneven corrosion surface concave-convex situation on the sample line and record it as a two-dimensional non-periodic signal; S214, the non-periodic signal is decomposed into the sum of sine and cosine functions of different frequencies by Fourier transform; S22, obtain the material category of the rusted steel to be tested; S23, search the pre-constructed non-uniform corrosion evaluation data set of each type of steel, and obtain the non-uniform corrosion steel frequency domain characteristics, each frequency and the corresponding component of the rusted steel to be tested; S24, the simulation surface curve of the non-uniform corrosion is calculated by using the non-uniform corrosion steel frequency domain characteristics, each frequency and the corresponding component of the rusted steel to be tested: wherein A i is the frequency corresponding to the amplitude; ω i is the frequency, i is the order, i is 1-50; b1 is the amplitude-frequency correlation coefficient, which is related to the type of the corroded steel material; b2 is the amplitude-frequency correlation index, which is related to the type of the corroded steel material; y2 is the uneven corrosion coordinate offset; x is the length coordinate of the middle of the test piece; is a random number representing the phase; S3, combine the corrected rusted steel plate thickness and the simulation surface curve of the non-uniform corrosion to generate an artificial synthetic surface, and construct a model that can be used for finite element analysis of the tensile properties of the rusted steel; Step S3 includes the following specific steps: S31, let the corrected rusted steel thickness be the rusted steel model reference thickness; S32, let the simulation surface curve of the non-uniform corrosion be the rusted steel model surface topography curve; S33, the rusted steel model surface topography curve fluctuates up and down with the rusted steel model reference thickness as the reference line to generate an artificial synthetic surface, and construct a model that can be used for finite element analysis of the tensile properties of the rusted steel; The artificial synthetic surface in step S33 is generated by using the following formula: y=h1+y2 Wherein, y is the total offset of each point of the artificial synthetic surface, h1 is the corrected rusted steel plate thickness, and y2 is the non-uniform corrosion coordinate offset.

2. The method of claim 1, wherein, Before step S1, there are the following steps: Divide the rust of the steel into uniform corrosion and non-uniform corrosion.

3. The method of claim 1, wherein the method is based on a spectrum generated rusted steel surface topography modeling method. Step S1 includes the following specific steps: S11, determine the artificial ruler error correction proportion coefficient; S12, measure the thickness of several points of the rusted steel to be tested using a vernier caliper to obtain a rusted steel point thickness data set; S13, average the rusted steel point thickness data set to obtain the ruler thickness representative value of the rusted steel to be tested; S14, further correct the artificial ruler thickness representative value of the rusted steel to be tested according to the ruler error by using the artificial ruler error correction proportion coefficient, obtain the corrected rusted steel plate thickness, and approximate evaluate the uniform corrosion degree, wherein the corrected rusted steel plate thickness is: h1=a1y0 Wherein, h1 is the modified thickness of the rusted steel plate, a1 is the manual measurement error correction coefficient, y0 is the manual measurement thickness representative value.

4. The method of claim 3, wherein the method is based on a spectrum generated by rusting steel material surface morphology modeling. Step S11 includes the following specific steps: S111, a large number of different rusted steel plates are three-dimensionally scanned to obtain the point cloud thickness mean value; S112, the thickness values of the steel plate at multiple positions are measured using a vernier caliper; S113, the multiple measured thickness values of the steel plate are averaged to obtain the manual measurement thickness representative value; S114, the point cloud thickness mean value and the manual measurement thickness representative value of each rusted steel plate are taken as a group of data, and after a plurality of groups of data are obtained, a database is formed; S115, each group of measurement values in the database is compared, and the ratio of each group is averaged to obtain the manual measurement error correction coefficient with wide application.

5. The method of claim 1, wherein the method is based on a spectrum- generated surface morphology modeling of rusted steel. In step S214, the decomposition formula of the non-periodic signal is: In the formula, ω is the frequency; F(ω) is the frequency domain function with frequency as the independent variable; x represents the middle length coordinate of the test piece; f(x) is a function with x as the independent variable.

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