Continuous casting billet surface roughness modeling method and device and storage medium

Abstract

The invention discloses a continuous casting billet surface roughness modeling method and device and a storage medium. The method comprises the following steps that S1, a surface roughness height distribution function of a continuous casting billet is determined; s2, the surface roughness height distribution function is imported into an MATLAB software program; s3, building a point cloud model framework, and exporting a point cloud file; s4, constructing a COMSOL physical field model, and importing a point cloud file; and S5, optimizing physical field model parameters, and generating a model. The roughness model can be generated according to the surface roughness data of the continuous casting billet, so that the influence on ultrasonic detection in different roughness states is analyzed, and the ultrasonic detection precision of the continuous casting billets with different roughness surfaces is improved.

Description

Technical Field

[0001] The present application relates to the technical field of ultrasonic nondestructive testing, and in particular to a method, device and storage medium for modeling the surface roughness of a continuous casting billet. Background Art

[0002] As an intermediate product in the production of high-quality special steel, the internal quality of the steel billet has an important impact on the quality of the final product. Since the internal defects of the steel billet are hereditary, expansive, and acquired, it is of great significance to detect the flaws in high-quality special steel billets.

[0003] At present, the internal quality inspection of high-quality special steel billets mainly relies on ultrasonic non-destructive testing technology. However, the surface roughness of the billet machining has a great influence on ultrasonic non-destructive testing. In addition, there is currently a lack of a continuous casting billet surface roughness modeling method. Summary of the invention

[0004] The present application provides a continuous casting billet surface roughness modeling method, equipment and storage medium to solve the above-mentioned problems.

[0005] In one aspect, the present application provides a method for modeling the surface roughness of a continuous casting billet, the method comprising the following steps:

[0006] Step S1: determining the surface roughness height distribution function of the continuous casting slab;

[0007] Step S2: importing the surface roughness height distribution function into a MATLAB software program;

[0008] Step S3: Build a point cloud model framework and export point cloud files;

[0009] Step S4: construct a COMSOL physical field model and import the point cloud file;

[0010] Step S5: Optimize physical field model parameters and generate a model.

[0011] In one implementation of the present application, modeling includes simulation of a Gaussian distribution rough surface and simulation of a non-Gaussian distribution rough surface; wherein the simulation of a Gaussian distribution rough surface includes:

[0012] Matlab is used to simulate a noise distribution function φ(a, b) that conforms to the characteristics of normal distribution, and F(ω a ,ω b ), the specific method is: input φ(a, b) that conforms to the normal distribution into the two-dimensional filter to generate a random process function x(a, b);

[0013] in, h(ε a , ε b ) is the impulse response function of the filter; M = N = 128, m = n = 64;

[0014] Converted to:

[0015]

[0016] Where X(ω a ,ω b ), H(ω a ,ω b ), F(ω a ,ω b ) are x(a, b), h(ε a , ε b ), the Fourier transform of f(a, b);

[0017] The power spectral density Q of the output function is generated by Fourier transformation of the defined autocorrelation function J x (ω a ,ω b ), and determine the power spectrum density R of the input function; where J(ε a , ε b )=P[x(a,b),x(a+ε a , b+ε b )];

[0018] By formula The filter transfer function H(ω a ,ω b );

[0019] By the expression:

[0020]

[0021] Generate the Fourier transform X(ω) of the output function after the input function passes through the two-dimensional filter a ,ω b );

[0022] For X(ω a ,ω b )The surface roughness height distribution function x(a, b) is generated through inverse Fourier transform, and then the surface roughness model of the ingot is developed.

[0023] In one implementation of the present application, the simulation of a non-Gaussian distribution rough surface specifically includes:

[0024] Matlab is used to simulate the kurtosis and skewness of the discrete random process of the surface roughness distribution of the ingot under the condition of meeting the expectation, where the kurtosis expression is:

[0025]

[0026] is the fourth-order matrix of the random function, σ is the standard deviation. When K<3, it shows high kurtosis, and when K>3, it shows low kurtosis.

[0027] Skewed expression: η 3 is the third-order matrix of the random function, σ is the standard deviation; when S<0, it is negatively skewed, and when S>0, it is positively skewed;

[0028] The kurtosis and skewness of the actual output function are obtained by the following expressions;

[0029]

[0030] Among them, K sj , S sj is the kurtosis and skewness value of the actual output function, K φ , S cφ is the kurtosis and skewness value of the input function. ξ i =h(c,d), c=0,1,…,n-1; d=0,1,…,m-1; i=cm+d;

[0031] Output a random sequence φ(a, b) to make it conform to Gaussian distribution, and obtain the function φ(a, b) of kurtosis and skewness through Johnson transformation;

[0032] Using the theoretical method of simulating Gaussian surface, the filter transfer function H(ω a ,ω b );

[0033] A low-pass filter is used to generate the roughness height distribution function x(a, b), and then the surface roughness model of the ingot is developed.

[0034] In one implementation of the present application, the development of a point cloud file includes:

[0035] Establish roughness height distribution function;

[0036] Define the length values ​​in the X and Y directions;

[0037] Define the coordinate axis range and interpolation and coloring methods;

[0038] Generate MATLAB point cloud TXT file and model.

[0039] In one implementation of the present application, after defining the length values ​​in the X and Y directions, the method further includes:

[0040] Define the power spectral density value;

[0041] Generate a rough surface with a specified Gaussian distributed white noise autocorrelation function;

[0042] Define Fourier transform function, autocorrelation function, power spectrum density function, and transfer function respectively;

[0043] Establish the Fourier transform of surface height and surface height distribution function;

[0044] Defines the absolute value of a number and the magnitude of a complex number.

[0045] In one implementation of the present application, COMSOL model development specifically includes:

[0046] In the COMSOL software, select the Model Wizard, select 3D, Solid Mechanics, Add, and Finish.

[0047] Select Geometry Options, select More Voxels, Polygons, Data Source, From File, select the Matlab point cloud TXT file, and Build Selected Objects;

[0048] According to the construction results, further optimize the relative tolerance and maximum number of knots in the advanced settings;

[0049] Click the Rectangle option in the Geometry drop-down list, define the width, depth, and height in the Size and Shape list, define the X, Y, and Z values ​​in the Position list, and construct the selected object;

[0050] Optimize the parameters in the size and shape lists and position lists based on the construction results and border parameters;

[0051] Select the Geometry drop-down list, select Boolean Operation and Segmentation, Segmentation Domain, select the upper half of the crack in the above picture, select Surface as the segmentation method, click the crack segmentation line, and build the selected object;

[0052] Click the selection field, select the faces involved in the upper half of the object, right-click and select Delete, then select Geometry, Polygon, and Delete in sequence to get the final figure.

[0053] In one implementation of the present application, after selecting a Matlab point cloud TXT file and constructing the selected object, the method further includes:

[0054] Select the Geometry option, click Measure, and record the maximum and minimum values ​​of the COMSOL frame parameters X, Y, and Z in turn;

[0055] Select Global Definition, select Function, Interpolation, and Data Source File in turn; select Point Cloud File Import, and click Draw;

[0056] Select Geometry and use it to select more voxels, parameterized surfaces in the drop-down list, determine the minimum and maximum values ​​of the first and second parameters in the Parameter list, define the XYZ parameters in the Expression list and Position list to construct the selected object.

[0057] The present application also provides a continuous casting billet surface roughness modeling device, the device comprising:

[0058] at least one processor; and,

[0059] a memory communicatively connected to the at least one processor; wherein,

[0060] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can complete the aforementioned continuous casting billet surface roughness modeling method.

[0061] The present application also provides a non-volatile computer storage medium for modeling the surface roughness of a continuous casting billet, which stores computer executable instructions. The computer executable instructions are executed by a processor to implement the aforementioned method for modeling the surface roughness of a continuous casting billet.

[0062] The present application provides a continuous casting billet surface roughness modeling method, device and storage medium, which have the following beneficial effects:

[0063] (1) Roughness simulation is performed through COMSOL Multiphysics, which has a powerful multi-physics field coupling function and can comprehensively consider the influence of multiple physical fields such as acoustic field, temperature field, flow field, etc. on surface roughness, thereby obtaining more comprehensive and accurate simulation results. Traditional methods can often only consider a certain factor alone, with a simple model and low accuracy;

[0064] (2) The method of generating point cloud data through MATLAB can truly reflect the surface condition of the actual object, making the simulated roughness surface closer to the actual situation. Very complex roughness features can be simulated. Point cloud data can also be used for subsequent geometric analysis, acoustic analysis, etc., which helps to more deeply understand the impact of roughness on ultrasonic testing performance, which is difficult to achieve with traditional modeling methods;

[0065] (3) It can intuitively display the distribution of the surface roughness of the continuous casting billet, which is helpful for analyzing the problem. It can also quickly master the modeling method, while the traditional method may require deep professional knowledge;

[0066] (4) Using parametric modeling, various parameters can be easily adjusted. Model geometry, material properties, boundary conditions, etc. can be customized according to actual needs, making the model closer to the actual situation and quickly performing multiple simulations, thereby optimizing process parameters and reducing experimental costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0068] Figure 1 A flow chart of a method for modeling the surface roughness of a continuous casting billet provided in an embodiment of the present application;

[0069] Figure 2 The Matlab point cloud generation model provided in the embodiment of the present application;

[0070] Figure 3 The selected model is constructed by COMSOL geometry provided in the embodiment of the present application;

[0071] Figure 4 The model after importing the COMSOL point cloud file provided in the embodiment of the present application;

[0072] Figure 5 The COMSOL parameterized surface model provided in the embodiment of the present application;

[0073] Figure 6 The COMSOL cuboid model provided in the embodiment of the present application is constructed;

[0074] Figure 7 The COMSOL crack segmentation line model provided in the embodiment of the present application is constructed;

[0075] Figure 8 The surface roughness model of the continuous casting billet developed by COMSOL and Matlab point cloud provided in the embodiment of the present application;

[0076] Fig. 9 A schematic diagram of a continuous casting billet surface roughness modeling device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0078] The embodiment of the present application provides a method, device and storage medium for modeling the surface roughness of a continuous casting billet. The technical solution proposed in the embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0079] Figure 1 A flow chart of a method for modeling the surface roughness of a continuous casting billet provided in an embodiment of the present application. Figure 1 As shown, the method mainly includes the following steps:

[0080] Step S1: determining the surface roughness height distribution function of the continuous casting slab;

[0081] Step S2: importing the surface roughness height distribution function into a MATLAB software program;

[0082] Step S3: Build a point cloud model framework and export point cloud files;

[0083] Step S4: construct a COMSOL physical field model and import the point cloud file;

[0084] Step S5: Optimize physical field model parameters and generate a model.

[0085] This application demonstrates the surface roughness modeling method of continuous casting billet developed based on COMSOL Multiphysics and Matlab point cloud. Figure 1 As shown. It mainly includes Matlab point cloud file development and COMSOL model development. It includes the following steps:

[0086] Content 1: Matlab point cloud file development, mainly including the following steps:

[0087] Step 1: Establish the roughness height distribution function.

[0088] Step 2: Define the length values ​​in the X and Y directions;

[0089] Step 3: Define the power spectral density value;

[0090] Step 4: Generate a rough surface with a specified Gaussian distribution white noise autocorrelation function;

[0091] Step 5: Define Fourier transform function, autocorrelation function, power spectrum density function, and transfer function respectively;

[0092] Step 6: Establish the Fourier change of surface height and surface height distribution function;

[0093] Step 7: Define the absolute value of a number and the amplitude of a complex number;

[0094] Step 8: Define the coordinate axis range and interpolation and coloring methods;

[0095] Step 9: Generate Matlab point cloud TXT file and model. Figure 2 shown.

[0096] Content 2: COMSOL model development based on point cloud files mainly includes the following steps:

[0097] Step 1: Select "Model Wizard" in COMSOL software, and then select "3D", "Solid Mechanics", "Add", and "Finish".

[0098] Step 2: Select the "Geometry" option, select "More Voxels", "Polygons", "Data Source", "From File", select the Matlab point cloud TXT file, and build the selected object. Figure 3 shown.

[0099] Step 3: Select the "Geometry" option, click "Measure", and record the maximum and minimum values ​​of the COMSOL frame parameters X, Y, and Z in turn.

[0100] Step 4: Select "Global Definition", select "Function", "Interpolation", and "Data Source" files in turn. Select the point cloud file to import and click "Draw". Generate Figure 4 .

[0101] Step 5: Select "Geometry", select "More Voxels" and "Parametric Surface" in the drop-down list, determine the minimum and maximum values ​​of the first and second parameters in the parameter list, define the XYZ parameters in the expression list and position list (based on the bounding box parameters) to construct the selected object. Figure 5 shown.

[0102] Step 6: Based on the construction results, further optimize the relative tolerance and maximum number of knots in the advanced settings.

[0103] Step 7: Click the "Cuboid" option in the "Geometry" drop-down list, define the width, depth, and height in the Size and Shape lists, and define the X, Y, and Z values ​​in the Position list to construct the selected object. Figure 6 shown.

[0104] Step 8: Based on the construction results and border parameters, optimize the parameters in the size and shape list and the position list.

[0105] Step 9: Select the "Geometry" drop-down list, select "Boolean Operations and Segmentation", "Segmentation Domain", select the upper half of the crack in the above picture, select the surface as the segmentation method, click the crack segmentation line, and construct the selected object. Figure 7 shown.

[0106] Step 10: Click "Selection Domain", select the face involved in the upper half of the object, right-click and select Delete. Select "Geometry", "Polygon", and "Delete" in sequence to get the final figure. Figure 8 shown.

[0107] The above is a method for modeling the surface roughness of a continuous casting billet provided in an embodiment of the present application. Based on the same inventive concept, an embodiment of the present application also provides a device for modeling the surface roughness of a continuous casting billet. Fig. 9 A schematic diagram of a continuous casting billet surface roughness modeling device provided in an embodiment of the present application, such as Fig. 9 As shown, the device mainly includes: at least one processor 901; and a memory 902 communicatively connected to the at least one processor; wherein the memory 902 stores instructions executable by the at least one processor 901, and the instructions are executed by the at least one processor 901 so that the at least one processor 901 can complete the aforementioned continuous casting billet surface roughness modeling method.

[0108] In addition, an embodiment of the present application further provides a non-volatile computer storage medium for modeling the surface roughness of a continuous casting billet, which stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the aforementioned method for modeling the surface roughness of a continuous casting billet.

[0109] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0112] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0113] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0114] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0115] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for modeling the surface roughness of a continuous casting billet, characterized in that: The method comprises the following steps: Step S1: determining the surface roughness height distribution function of the continuous casting slab; Step S2: importing the surface roughness height distribution function into a MATLAB software program; Step S3: Build a point cloud model framework and export point cloud files; Step S4: construct a COMSOL physical field model and import the point cloud file; Step S5: Optimize physical field model parameters and generate a model.

2. The method for modeling the surface roughness of a continuous casting billet according to claim 1, characterized in that: The modeling includes the simulation of Gaussian distribution rough surface and the simulation of non-Gaussian distribution rough surface; wherein, the simulation of Gaussian distribution rough surface includes: Matlab is used to simulate a noise distribution function φ(a, b) that conforms to the characteristics of normal distribution, and F(ω a ,ω b ), the specific method is: input φ(a,b) that conforms to the normal distribution into the two-dimensional filter to generate a random process function x(a,b); in, ε a =1,…,N;ε b =1,…,N; h(ε a ,ε b ) is the impulse response function of the filter; M = N = 128, m = n = 64; Converted to: Where X(ω a ,ε b ), H(ω a ,ε b ), F(ω a ,ε b ) are x(a,b), h(ε a ,ε b ), the Fourier transform of f(a,b); The power spectral density Q of the output function is generated by Fourier transformation of the defined autocorrelation function J x (ω a ,ω b ), and determine the power spectrum density R of the input function; where J(ε a ,ε b )=P[x(a,b),x(a+ε a ,b+ε b )]; By formula The filter transfer function H(ω a ,ω b ); By the expression: Generate the Fourier transform X(ω) of the output function after the input function passes through the two-dimensional filter a ,ω b ); For X(ω a ,ω b )The surface roughness height distribution function x(a,b) is generated through inverse Fourier transform, and then the surface roughness model of the ingot is developed.

3. A continuous casting billet surface roughness modeling method according to claim 2, characterized in that: Simulation of non-Gaussian rough surfaces, including: Matlab is used to simulate the kurtosis and skewness of the discrete random process of the surface roughness distribution of the ingot under the condition of meeting the expectation, where the kurtosis expression is: is the fourth-order matrix of the random function, σ is the standard deviation. When K<3, it shows high kurtosis, and when K>3, it shows low kurtosis. Skewed expression: η3 is the third-order matrix of the random function, σ is the standard deviation; when S<0, it is negatively skewed, and when S>0, it is positively skewed; The kurtosis and skewness of the actual output function are obtained by the following expressions; Among them, K sj , S sj is the kurtosis and skewness value of the actual output function, K φ , S cφ is the kurtosis and skewness value of the input function. ξ i =h(c,d),c=0,1,…,n-1; d=0,1,…,m-1; i=cm+d; Output a random sequence φ(a,b) to make it conform to the Gaussian distribution, and obtain the kurtosis value and skewness function φ(a,b) through Johnson transformation; Using the theoretical method of simulating Gaussian surface, the filter transfer function H(ω a ,ω b ); A low-pass filter is used to generate the roughness height distribution function x(a, b), and then the surface roughness model of the ingot is developed.

4. The method for modeling the surface roughness of a continuous casting billet according to claim 1, characterized in that: Development of point cloud files, including: Establish roughness height distribution function; Define the length values ​​in the X and Y directions; Define the coordinate axis range and interpolation and coloring methods; Generate MATLAB point cloud TXT file and model.

5. A continuous casting billet surface roughness modeling method according to claim 4, characterized in that: After defining the length values ​​in the X and Y directions, the method further includes: Define the power spectral density value; Generate a rough surface with a specified Gaussian distributed white noise autocorrelation function; Define Fourier transform function, autocorrelation function, power spectrum density function, and transfer function respectively; Establish the Fourier transform of surface height and surface height distribution function; Defines the absolute value of a number and the magnitude of a complex number.

6. The method for modeling the surface roughness of a continuous casting billet according to claim 1, characterized in that: COMSOL model development, including: In the COMSOL software, select the Model Wizard, select 3D, Solid Mechanics, Add, and Finish. Select Geometry Options, select More Voxels, Polygons, Data Source, From File, select the Matlab point cloud TXT file, and Build Selected Objects; According to the construction results, further optimize the relative tolerance and maximum number of knots in the advanced settings; Click the Rectangle option in the Geometry drop-down list, define the width, depth, and height in the Size and Shape list, define the X, Y, and Z values ​​in the Position list, and construct the selected object; Optimize the parameters in the size and shape lists and position lists based on the construction results and border parameters; Select the Geometry drop-down list, select Boolean Operation and Segmentation, Segmentation Domain, select the upper half of the crack in the above picture, select Surface as the segmentation method, click the crack segmentation line, and build the selected object; Click the selection field, select the faces involved in the upper half of the object, right-click and select Delete, then select Geometry, Polygon, and Delete in sequence to get the final figure.

7. A continuous casting billet surface roughness modeling method according to claim 6, characterized in that: After selecting the Matlab point cloud TXT file and constructing the selected object, the method further includes: Select the Geometry option, click Measure, and record the maximum and minimum values ​​of the COMSOL frame parameters X, Y, and Z in turn; Select Global Definition, select Function, Interpolation, and Data Source File in turn; select Point Cloud File Import, and click Draw; Select Geometry and use it to select more voxels, parameterized surfaces in the drop-down list, determine the minimum and maximum values ​​of the first and second parameters in the Parameter list, define the XYZ parameters in the Expression list and Position list to construct the selected object.

8. A continuous casting billet surface roughness modeling device, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can complete the continuous casting billet surface roughness modeling method described in any one of claims 1-7.

9. A non-volatile computer storage medium for modeling the surface roughness of a continuous casting billet, storing computer executable instructions, characterized in that: The computer executable instructions are executed by a processor to implement a continuous casting billet surface roughness modeling method as described in any one of claims 1 to 7.

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