A digital simulation method and system for scraping surface topography

Abstract

The present invention discloses a method and system for digital simulation of scraped surface topography. The method comprises the following steps: 1: measuring the scraped surface to obtain three-dimensional topography data of the scraped surface; 2: calculating the overall surface parameters, probability material ratio curve, and autocorrelation function of the three-dimensional topography data of the scraped surface; 3: obtaining three sets of surface height characteristic parameters based on the probability material ratio curve, using these as input to generate a Gaussian surface and selectively superimposing them to obtain a surface with specified height characteristics; 4: calculating the power spectral density of the scraped surface topography based on the autocorrelation function of the three-dimensional topography data of the scraped surface; 5: using the surface with specified height characteristics and the power spectral density of the scraped surface topography as input to generate a surface with a specified autocorrelation function; 6: reordering the surface with specified height characteristics according to the sequence of the surfaces with the specified autocorrelation function to update the surface with specified height characteristics; and 7: repeating steps 5 and 6 until convergence to obtain a scraped surface with specified height characteristic parameters and spatial characteristic parameters. The present invention is suitable for digital simulation of scraped surfaces and multi-process surfaces.

Description

Technical Field

[0001] The invention relates to a digital simulation method and system for scraping and grinding surface topography, which are suitable for digital simulation of scraping and grinding surfaces and multi-process surfaces. Background Art

[0002] Every leap forward in manufacturing is inseparable from improvements in machine tool manufacturing precision. Machine tool machining accuracy is directly linked to guideway accuracy. Scraping is essential for producing machine tool guideways or workpieces that are too long to be finished on a grinding machine, or for workpieces that require extremely precise flatness.

[0003] Scraping is a process in which high points on the workpiece surface are revealed by grinding the surfaces of two mating workpieces, or between the workpiece surface and a standard surface, together. The elevated areas are then removed using a scraper in a micro-cutting manner, resulting in a workpiece with higher shape, position, and dimensional accuracy and lower surface roughness. Scraping is an important method for improving lubrication and smoothing surfaces. Scraping creates uniform contact points and shallow pits, which form oil-retaining gaps. Scraping has unique characteristics, resulting in complex and random surface morphologies. Scraping surfaces are multi-process surfaces and can be characterized and simulated using a layered approach. Layered surface characterization methods can accurately determine the height characteristic parameters of the component surfaces, but not the precise spatial characteristic parameters. This is because, after separating the component surfaces from the layered surface, the component surfaces contain a large number of missing points. The presence of these missing points makes it impossible to accurately calculate the spatial characteristic parameters of the component surfaces, making it impossible to accurately simulate the layered surface. Existing simulation methods struggle to simulate scraped surfaces that possess both specified height and spatial characteristics.

[0004] In view of this, the present invention is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for digital simulation of scraped surface topography, which can realize digital simulation of scraped surface with specified height characteristic parameters and spatial characteristic parameters.

[0006] In order to achieve the above object, the technical solution of the present invention is:

[0007] In a first aspect, a method for digitally simulating scraping surface topography is provided, comprising:

[0008] Step 1: Measure the scraped surface and obtain the three-dimensional topography data of the scraped surface;

[0009] Step 2: Calculate the overall surface parameters and the probability material ratio curve of the scraped surface 3D topography data based on the scraped surface 3D topography data; Calculate the autocorrelation function of the scraped surface 3D topography data based on the scraped surface 3D topography data and the overall surface parameters of the scraped surface 3D topography data;

[0010] Step 3: Based on the probability material ratio curve, three sets of surface height characteristic parameters are obtained, which are used as input to generate a Gaussian surface and selectively superimposed to obtain a surface with specified height characteristics;

[0011] Step 4: Calculate the power spectrum density of the scraped surface morphology through the autocorrelation function of the scraped surface three-dimensional morphology data;

[0012] Step 5: Generate a surface with a specified autocorrelation function by taking the surface with specified height features and the power spectrum density of the scraped surface as input;

[0013] Step 6: reordering the surface of the specified height feature according to the sequence of the surface of the specified autocorrelation function to update the surface of the specified height feature;

[0014] Step 7: Repeat steps 5 and 6 until convergence, and obtain the scraped surface with the specified height characteristic parameters and spatial characteristic parameters.

[0015] Furthermore, the step 2 is specifically as follows:

[0016] Step 2.1, calculate the overall surface parameters of the scraped surface three-dimensional topography data, the overall surface parameters include surface size (L x ,L y ), number of measurement points (M, N), root mean square S q , skewness S sk and kurtosis S ku Among them, L x 、L y Respectively represent the length of the scraped surface in the x direction and the y direction; M and N represent the number of measurement points in the x direction and the y direction of the scraped surface;

[0017] Step 2.2, calculating the probability material ratio curve of the scraped surface three-dimensional topography data;

[0018] Step 2.3: Calculate the autocorrelation function of the scraped surface three-dimensional topography data based on the scraped surface three-dimensional topography data and the overall surface parameters of the scraped surface three-dimensional topography data.

[0019] Furthermore, the autocorrelation function ACF(p,q) of the three-dimensional topography data of the scraped surface is calculated as follows:

[0020]

[0021] Among them, S q represents the root mean square; M and N represent the number of measurement points in the x and y directions of the scraped surface respectively; z i,j Indicates the three-dimensional shape data obtained when the number of measurement points in the xy direction is i and j respectively, z i+p,j+q Same thing.

[0022] Furthermore, the step 3 is specifically as follows:

[0023] Step 3.1: Obtain three sets of surface height characteristic parameters by fitting the probability material ratio curve. The surface height characteristic parameters include the root mean square σ k and mean μ k , k is 1, 2, 3;

[0024] Step 3.2: Based on the root mean square and autocorrelation length of the component surface height characteristic parameters, construct the component surface parameters (σ k ,λ x ,λ y ); According to the composition surface parameter (σ k ,λ x ,λ y ) generates three Gaussian surfaces; where λ x and λ y is the autocorrelation length in the x and y directions;

[0025] Step 3.3: Adjust the mean surface of each Gaussian surface to the corresponding mean μ k to determine the superposition position of each Gaussian surface; at each measurement point, retain the smaller of the three Gaussian surface heights to generate a surface with specified height characteristics.

[0026] Furthermore, the scraped surface morphology power spectral density PSD (ω p ,ω q ), the calculation formula is:

[0027]

[0028] Where FFT is Fourier transform; M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface, respectively; ACF(p,q) is the autocorrelation function of the three-dimensional topography data of the scraped surface.

[0029] Furthermore, the surface Z with a specified autocorrelation function is generated by taking the surface with a specified height feature and the power spectrum density of the scraped surface as input. s (x,y), the calculation formula is:

[0030] Z s (x,y)=MN·IFFT(PSD·exp(i1·angle(FFT(Z h )))

[0031] Among them, IFFT is the inverse Fourier transform; angle is the function for calculating the complex phase angle, i1 is a complex number; FFT (Z h ) represents the scraped surface Z of the specified height featureh Perform Fourier transform; PSD represents the power spectral density of the scraped surface topography; M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface, respectively.

[0032] Furthermore, the step 6 is specifically as follows:

[0033] Step 6.1: Convert the surface of the specified height feature and the surface of the specified autocorrelation function into the first matrix of 1×MN arranged from small to large. and the second matrix Wherein, M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface respectively;

[0034] Step 6.2: Record the second matrix The position of each element in the surface matrix of the specified autocorrelation function is stored in the third matrix Z c middle;

[0035] Step 6.3, the first matrix According to the third matrix Z c Sort the records in order to get the fourth matrix

[0036] Step 6.4, the fourth matrix The fifth matrix is ​​converted into M×N, and the fifth matrix is ​​the surface of the specified height feature after reordering and updating.

[0037] Furthermore, in step 7, the error between the PMRC of the surface with the specified height feature and the surface with the specified autocorrelation function after the update is used to perform convergence judgment, and the error formula is:

[0038]

[0039] Among them, err represents the error, Z h _PMRC y The PMRC ordinate, Z, of the surface representing the updated specified height feature s _PMRC y The PMRC ordinate represents the surface of the specified autocorrelation function, and mean represents the mean function. If the error requirement is met, it indicates convergence, and the surface of the specified height feature is updated to the simulation result. If not, repeat steps 5 and 6.

[0040] In a second aspect, a digital simulation system for scraping and grinding surface topography is provided, comprising:

[0041] The measurement module is used to perform step 1: measuring the scraped surface to obtain three-dimensional topography data of the scraped surface;

[0042] The first calculation module is configured to execute step 2: calculating the overall surface parameters and the probability material ratio curve of the scraped surface three-dimensional topography data based on the scraped surface three-dimensional topography data; and calculating the autocorrelation function of the scraped surface three-dimensional topography data based on the scraped surface three-dimensional topography data and the overall surface parameters of the scraped surface three-dimensional topography data;

[0043] The first acquisition module is used to execute step 3: according to the probability material ratio curve, obtain three sets of surface height characteristic parameters, use them as input to generate a Gaussian surface and selectively superimpose to obtain a surface with specified height characteristics;

[0044] The second calculation module is used to perform step 4: calculating the power spectrum density of the scraped surface morphology through the autocorrelation function of the three-dimensional morphology data of the scraped surface;

[0045] A generation module is used to execute step 5: generating a surface with a specified autocorrelation function by taking a surface with specified height features and a power spectrum density of a scraped surface as input;

[0046] An updating module, configured to execute step 6: reordering the surface of the specified height feature according to the sequence of the surface of the specified autocorrelation function to update the surface of the specified height feature;

[0047] The second acquisition module is used to execute step 7: repeat steps 5 and 6 until convergence, and obtain the scraped surface with specified height characteristic parameters and spatial characteristic parameters.

[0048] In a third aspect, a terminal is provided, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute any one of the above-described methods for digitally simulating the scraping surface morphology.

[0049] The beneficial effects of the present invention are:

[0050] Compared with the existing technology, the present invention is a digital simulation method for scraped surfaces that can specify height characteristic parameters and spatial characteristic parameters. It can be applied to the digital simulation of scraped surfaces and multi-process surfaces. This method overcomes the technical difficulty of the existing layered surface simulation method, which causes large errors in the simulated spatial characteristic parameters of the scraped surfaces due to the inability to accurately obtain the spatial characteristic parameters of the constituent surfaces due to missing points on the constituent surfaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Flowchart of the present invention;

[0052] Figure 2 is the measured three-dimensional topography of the scraped surface;

[0053] Figure 3 is the autocorrelation function of the three-dimensional topography of the scraped surface;

[0054] Figure 4 is the probability material ratio curve fitting result;

[0055] Figure 5 is a surface with specified height characteristics;

[0056] Figure 6 is the surface of the specified autocorrelation function;

[0057] Figure 7 To simulate scraping the surface;

[0058] Figure 8 3 is a comparison diagram of the autocorrelation functions of the simulated surface and the measured surface in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other in any way.

[0060] Example 1: Figure 1-8 According to a first aspect of an embodiment of the present invention, a method for digitally simulating scraping surface topography is provided, comprising:

[0061] Step 1: Measure the scraped surface and obtain the three-dimensional topography data of the scraped surface;

[0062] Step 2: Calculate the overall surface parameters and the probability material ratio curve of the scraped surface 3D topography data based on the scraped surface 3D topography data; Calculate the autocorrelation function of the scraped surface 3D topography data based on the scraped surface 3D topography data and the overall surface parameters of the scraped surface 3D topography data;

[0063] Step 3: Based on the probability material ratio curve, three sets of surface height characteristic parameters are obtained, which are used as input to generate a Gaussian surface and selectively superimposed to obtain the surface Z with the specified height characteristic. h ;

[0064] Step 4: Calculate the power spectral density (PSD) of the scraped surface topography using the autocorrelation function of the scraped surface three-dimensional topography data;

[0065] Step 5: Take Z h The surface Z with the specified autocorrelation function is generated as input by the power spectral density PSD of the scraped surface topography s (x,y);

[0066] Step 6: Specify the surface Z of the height feature h Surface Z of the specified autocorrelation function s The sequence is reordered to update the surface Z of the specified height feature h ;

[0067] Step 7: Repeat steps 5 and 6 until convergence, and obtain the scraped surface with the specified height characteristic parameters and spatial characteristic parameters.

[0068] Furthermore, the step 2 is specifically as follows:

[0069] Step 2.1, calculate the overall surface parameters of the scraped surface three-dimensional topography data, the overall surface parameters include surface size (L x ,L y ), number of measurement points (M, N), root mean square S q , skewness S sk and kurtosis S ku ;

[0070] S q 、S sk and S ku The calculation formula is:

[0071]

[0072] Among them, L x 、L y Respectively represent the length of the scraped surface in the x direction and the y direction; M and N represent the number of measurement points in the x direction and the y direction of the scraped surface; i,j It represents the three-dimensional topography data obtained when the number of measurement points in the xy direction is i and j respectively;

[0073] Step 2.2: Calculate the probability material ratio curve (PMRC) of the scraped surface three-dimensional topography data, specifically:

[0074] Statistical analysis is performed on the three-dimensional topography data of the scraped surface, and the cumulative distribution function of the three-dimensional topography data of the scraped surface is calculated;

[0075] Calculating the material ratio curve of the scraped surface three-dimensional topography data based on the cumulative distribution function of the scraped surface three-dimensional topography data;

[0076] Obtaining a probability material ratio curve of the scraped surface three-dimensional topography data according to the material ratio curve of the scraped surface three-dimensional topography data;

[0077] It should be noted that the present invention extracts three-dimensional morphological data of the scraped surface, and the data structure is in matrix form. Therefore, when the probability material ratio curve (PMRC) is calculated using the above method, the matrix data is converted into a single line for calculation.

[0078] Step 2.3: Calculate the autocorrelation function ACF(p,q) of the scraped surface three-dimensional topography data. The calculation formula is:

[0079]

[0080] Among them, S q represents the root mean square; M and N represent the number of measurement points in the x and y directions of the scraped surface respectively; z i,j Represents the three-dimensional topography data obtained when the number of measurement points in the x and y directions are i and j respectively.

[0081] Furthermore, the step 3 is specifically as follows:

[0082] Step 3.1, obtain three sets of surface height characteristic parameters by fitting the probability material ratio curve PMRC, the surface height characteristic parameters include root mean square σ k and mean μ k , k is 1, 2, 3;

[0083] Step 3.2: Based on the root mean square and autocorrelation length of the surface height characteristic parameters, construct the surface parameters (σ k ,λ x ,λ y ); According to the composition surface parameter (σ k ,λ x ,λ y ) generates three Gaussian surfaces; where λ x and λ y is the autocorrelation length in the x and y directions, and the surface size L is taken as x and L y r%, r is less than 5, that is, λ x and λ y Take the surface size L x and L y The experiment shows that when the autocorrelation length is set to be less than 5% of the surface size, the Gaussian surface obtained by simulation has higher accuracy.

[0084] Step 3.3: Adjust the mean surface of each Gaussian surface to the corresponding mean μ k to determine the superposition position of each Gaussian surface; at each measurement point, retain the smaller of the three Gaussian surface heights to generate a surface with specified height characteristics.

[0085] Furthermore, the scraped surface morphology power spectral density PSD (ω p ,ω q ), the calculation formula is:

[0086]

[0087] Where FFT is Fourier transform; M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface, respectively; ACF(p,q) is the autocorrelation function of the three-dimensional topography data of the scraped surface.

[0088] Furthermore, the Z h The surface Z with the specified autocorrelation function is generated as input by the power spectral density PSD of the scraped surface topography s (x,y), the calculation formula is:

[0089] Z s (x,y)=MN·IFFT(PSD·exp(i1·angle(FFT(Z h )))

[0090] Among them, IFFT is the inverse Fourier transform; angle is the function for calculating the complex phase angle, i1 is a complex number; FFT (Z h ) indicates Z h Perform Fourier transform; PSD represents the power spectral density of the scraped surface topography; M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface, respectively.

[0091] Furthermore, the step 6 is specifically as follows:

[0092] Step 6.1, specify the surface Z of the height feature h and the surface Z of the specified autocorrelation function s Convert to the first matrix of 1×MN arranged from small to large and the second matrix

[0093] Step 6.2: Record the second matrix Each element in the surface Z of the specified autocorrelation function s The position in the matrix and saved in the third matrix Z c middle;

[0094] Step 6.3, the first matrix According to the third matrix Z c Sort the records in order to get the fourth matrix

[0095] Step 6.4, the fourth matrix Converted to the fifth matrix of M×N, the fifth matrix is ​​the surface Z of the specified height feature after reordering and updating h .

[0096] Furthermore, in step 7, the surface Z of the updated specified height feature is used. h and the surface Z of the specified autocorrelation function sThe error between the PMRCs is used to determine convergence, and the error formula is:

[0097]

[0098] Among them, err represents the error, Z h _PMRC y Represents the updated surface Z of the specified height feature h The PMRC ordinate, Z s _PMRC y Surface Z representing the specified autocorrelation function s The PMRC ordinate, mean represents the mean function; if the error requirement is met, it means convergence, then the surface Z of the specified height feature is updated h is the simulation result. If it is not satisfied, repeat steps 5 and 6. err is set to 0.01. If the error is less than 0.01, it meets the requirement.

[0099] Taking the real scraped surface as an example, according to Figure 1 The principle flow chart shown here illustrates the digital simulation method of scraping surface morphology of the present invention.

[0100] 1) Use a non-contact three-dimensional profiler to measure the actual scraped surface and extract the three-dimensional profile data of the scraped surface, such as Figure 2 shown.

[0101] 2) Based on the scraped surface three-dimensional topography data, calculate the overall surface parameters and probability material ratio curve PMRC of the scraped surface three-dimensional topography data; based on the scraped surface three-dimensional topography data and the overall surface parameters of the scraped surface three-dimensional topography data, calculate the autocorrelation function AFC of the scraped surface three-dimensional topography data;

[0102] a) Overall surface parameters, surface size L x =L y =4700μm, number of measurement points M=N=3770, using the calculation formula:

[0103]

[0104]

[0105] Calculate the root mean square S q =3.4769μm, skewness S sk =–0.8741μm, kurtosis S ku =5.0839 μm as measured value;

[0106] b) Statistically analyze the scraped surface three-dimensional topography data and calculate the cumulative distribution function of the scraped surface three-dimensional topography data; calculate the material ratio curve based on the cumulative distribution function; obtain the probability material ratio curve of the scraped surface three-dimensional topography data based on the material ratio curve, and the probability material ratio curve is as follows: Figure 4 As shown;

[0107] c) Using the formula

[0108]

[0109] Calculate the autocorrelation function AFC, such as Figure 3 shown.

[0110] 3) According to the probability material ratio curve, three sets of surface height characteristic parameters are obtained, which are used as input to generate Gaussian surface and selectively superimposed to obtain the surface Z with specified height characteristics. h :

[0111] a) By fitting PMRC, we obtain three sets of surface height characteristic parameters: σ1 = 2.8237 μm, μ1 = 39.0210 μm, σ2 = 4.6022 μm, μ2 = 39.0136 μm, σ3 = 9.7653 μm and μ3 = 49.6517 μm. The fitting results are shown in the figure. Figure 4 As shown;

[0112] b) According to the composition surface parameters (σ1, λ x ,λ y ),(σ2,λ x ,λ y ) and (σ3,λ x ,λ y ) generates three Gaussian surfaces, λ x =λ y =235μm;

[0113] c) Adjust the mean surface of each Gaussian surface to the corresponding mean μ k To determine the superposition position of each Gaussian surface; at each measurement point, retain the smaller of the three Gaussian surface heights to generate a surface Z with a specified height feature h ,like Figure 5 shown.

[0114] 4) Using the formula The power spectral density (PSD) of the scraped surface morphology was calculated.

[0115] 5) Using formula Z s (x,y)=MN·IFFT(PSD·exp(i·angle(FFT(Z h )))Generates a surface Z with a specified autocorrelation function s,like Figure 6 shown.

[0116] 6) By the method described in step 6 of the technical solution in the invention content, the surface Z of the specified height feature is h Surface Z of the specified autocorrelation function s The sequence is reordered to update the surface Z of the specified height feature h .

[0117] 7) Using the formula Determine whether the simulation results meet the error requirements. Set err to 0.01. If the error is less than 0.01, it meets the requirements. If the error requirements are met, it means convergence, and the surface Z of the specified height feature is updated. h is the simulation result. If it does not meet the requirements, repeat steps 5) and 6) until the error requirements are met and output the simulation surface. Figure 7 The root mean square (RMS) of the overall surface parameters calculated for the simulated surface is shown in Figure 2. q =3.5021μm, skewness S sk =-0.8401μm and kurtosis S ku =5.0559μm, which is consistent with the overall surface parameters obtained by measurement, indicating that the simulated surface meets the specified height characteristic parameters. Figure 8 As shown, through Figure 8 It can be seen that the spatial characteristic parameters of the simulated surface are consistent with those of the measured surface.

[0118] According to a second aspect of an embodiment of the present invention, a digital simulation system for scraping surface morphology is provided, including: a measurement module for executing step 1: measuring the scraping surface to obtain three-dimensional morphological data of the scraping surface; a first calculation module for executing step 2: calculating the overall surface parameters and probability material ratio curve of the three-dimensional morphological data of the scraping surface based on the three-dimensional morphological data of the scraping surface; calculating the autocorrelation function of the three-dimensional morphological data of the scraping surface based on the three-dimensional morphological data of the scraping surface and the overall surface parameters of the three-dimensional morphological data of the scraping surface; a first acquisition module for executing step 3: obtaining three groups of surface height characteristic parameters based on the probability material ratio curve, and generating a Gaussian surface based on these as input. And selectively superimpose to obtain a surface with specified height characteristics; a second calculation module, used to execute step 4: calculate the power spectrum density of the scraped surface morphology through the autocorrelation function of the three-dimensional morphology data of the scraped surface; a generation module, used to execute step 5: generate a surface with a specified autocorrelation function using the surface with specified height characteristics and the power spectrum density of the scraped surface morphology as input; an update module, used to execute step 6: reorder the surface with specified height characteristics according to the sequence of the surface of the specified autocorrelation function to update the surface with specified height characteristics; a second acquisition module, used to execute step 7: repeat steps 5 and 6 until convergence to obtain a scraped surface with specified height characteristic parameters and spatial characteristic parameters. The term "module" as used above can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the above embodiment is preferably implemented in software, hardware, or a combination of software and hardware, is also possible and conceivable. For parts that are not described in detail in each module, please refer to the relevant description of other embodiments.

[0119] According to a third aspect of an embodiment of the present invention, a terminal is provided, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute any one of the above-described methods for digitally simulating scraping surface morphology.

[0120] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, can be centralized on a single computing device, or can be distributed across a network of multiple computing devices. They can be implemented using program code executable by the computing device, and thus, can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described herein can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0121] The specific embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

Claims

1. A digital simulation method for scraping surface topography, characterized in that: include: Step 1: Measure the scraped surface and obtain the three-dimensional topography data of the scraped surface; Step 2: Calculate the overall surface parameters and the probability material ratio curve of the scraped surface 3D topography data based on the scraped surface 3D topography data; Calculate the autocorrelation function of the scraped surface 3D topography data based on the scraped surface 3D topography data and the overall surface parameters of the scraped surface 3D topography data; Step 3: Based on the probability material ratio curve, three sets of surface height characteristic parameters are obtained, which are used as input to generate a Gaussian surface and selectively superimposed to obtain a surface with specified height characteristics; Step 4: Calculate the power spectrum density of the scraped surface morphology through the autocorrelation function of the scraped surface three-dimensional morphology data; Step 5: Generate a surface with a specified autocorrelation function by taking the surface with specified height features and the power spectrum density of the scraped surface as input; Step 6: reordering the surface of the specified height feature according to the sequence of the surface of the specified autocorrelation function to update the surface of the specified height feature; Step 7: Repeat steps 5 and 6 until convergence, and obtain the scraped surface with the specified height characteristic parameters and spatial characteristic parameters; The step 6 is specifically as follows: Step 6.1: Convert the surface of the specified height feature and the surface of the specified autocorrelation function into the first matrix of 1×MN arranged from small to large. and the second matrix Wherein, M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface, respectively; Step 6.2: Record the second matrix The position of each element in the surface matrix of the specified autocorrelation function is stored in the third matrix Z c middle; Step 6.3, the first matrix According to the third matrix Z c Sort the records in order to get the fourth matrix Step 6.4, the fourth matrix The fifth matrix is ​​converted into M×N, and the fifth matrix is ​​the surface of the specified height feature after reordering and updating.

2. The digital simulation method for scraping surface topography according to claim 1, characterized in that: The step 2 is specifically as follows: Step 2.1, calculate the overall surface parameters of the scraped surface three-dimensional topography data, the overall surface parameters include surface size (L x ,L y ), number of measurement points (M, N), root mean square S q , skewness S sk and kurtosis S ku Among them, L x 、L y Respectively represent the length of the scraped surface in the x-direction and y-direction; M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface; Step 2.2, calculating the probability material ratio curve of the scraped surface three-dimensional topography data; Step 2.3: Calculate the autocorrelation function of the scraped surface three-dimensional topography data based on the scraped surface three-dimensional topography data and the overall surface parameters of the scraped surface three-dimensional topography data.

3. The digital simulation method for scraping surface topography according to claim 2, characterized in that: The autocorrelation function ACF(p,q) of the scraped surface three-dimensional topography data is calculated as follows: Among them, S q represents the root mean square; M and N represent the number of measurement points in the x and y directions of the scraped surface respectively; z i,j Represents the three-dimensional topography data obtained when the number of measurement points in the x and y directions are i and j respectively.

4. The digital simulation method for scraping surface topography according to claim 1, characterized in that: The step 3 is specifically as follows: Step 3.1: Obtain three sets of surface height characteristic parameters by fitting the probability material ratio curve. The surface height characteristic parameters include the root mean square σ k and mean μ k , k is 1, 2, 3; Step 3.2: Based on the root mean square and autocorrelation length of the component surface height characteristic parameters, construct the component surface parameters (σ k ,λ x ,λ y ); According to the composition surface parameter (σ k ,λ x ,λ y ) generates three Gaussian surfaces; among them, λ x and λ y is the autocorrelation length in the x and y directions; Step 3.3: Adjust the mean surface of each Gaussian surface to the corresponding mean μ k to determine the superposition position of each Gaussian surface; at each measurement point, retain the smaller of the three Gaussian surface heights to generate a surface with specified height characteristics.

5. The digital simulation method for scraping surface topography according to claim 1, characterized in that: The scraped surface morphology power spectral density PSD (ω p ,ω q ), the calculation formula is: Where FFT is Fourier transform; M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface respectively; ACF(p,q) is the autocorrelation function of the three-dimensional topography data of the scraped surface.

6. The digital simulation method for scraping surface topography according to claim 1, characterized in that: The surface Z with the specified autocorrelation function is generated by taking the surface with the specified height feature and the power spectrum density of the scraped surface as input. s (x,y), the calculation formula is: Z s (x,y)=MN·IFFT(PSD·exp(i1·angle(FFT(Z h ))) Among them, IFFT is the inverse Fourier transform; angle is the function for calculating the complex phase angle, i1 is a complex number; FFT (Z h ) represents the scraped surface Z of the specified height feature h Perform Fourier transform; PSD represents the power spectral density of the scraped surface topography; M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface, respectively.

7. The digital simulation method for scraping surface topography according to claim 1, characterized in that: In step 7, the error between the probability material ratio curve PMRC of the surface with the specified height feature and the surface with the specified autocorrelation function after the update is used to make a convergence judgment. The error formula is: Among them, err represents the error, Z h _PMRC y The PMRC ordinate, Z, of the surface representing the updated specified height feature s _PMRC y The PMRC ordinate represents the surface of the specified autocorrelation function, and mean represents the mean function. If the error requirement is met, it indicates convergence, and the surface of the specified height feature is updated to the simulation result. If not, repeat steps 5 and 6.

8. A digital simulation system for scraping surface topography, characterized in that: include: The measurement module is used to perform step 1: measuring the scraped surface to obtain three-dimensional topography data of the scraped surface; The first calculation module is configured to execute step 2: calculating the overall surface parameters and the probability material ratio curve of the scraped surface three-dimensional topography data based on the scraped surface three-dimensional topography data; and calculating the autocorrelation function of the scraped surface three-dimensional topography data based on the scraped surface three-dimensional topography data and the overall surface parameters of the scraped surface three-dimensional topography data; The first acquisition module is used to execute step 3: according to the probability material ratio curve, obtain three sets of surface height characteristic parameters, use them as input to generate a Gaussian surface and selectively superimpose to obtain a surface with specified height characteristics; The second calculation module is used to perform step 4: calculating the power spectrum density of the scraped surface morphology through the autocorrelation function of the three-dimensional morphology data of the scraped surface; A generation module is used to execute step 5: generating a surface with a specified autocorrelation function by taking a surface with specified height features and a power spectrum density of a scraped surface as input; An updating module, configured to execute step 6: reordering the surface of the specified height feature according to the sequence of the surface of the specified autocorrelation function to update the surface of the specified height feature; The second acquisition module is used to execute step 7: repeating steps 5 and 6 until convergence, and obtaining a scraped surface with specified height characteristic parameters and spatial characteristic parameters; The step 6 is specifically as follows: Step 6.1: Convert the surface of the specified height feature and the surface of the specified autocorrelation function into the first matrix of 1×MN arranged from small to large. and the second matrix Wherein, M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface, respectively; Step 6.2: Record the second matrix The position of each element in the surface matrix of the specified autocorrelation function is stored in the third matrix Z c middle; Step 6.3, the first matrix According to the third matrix Z c Sort the records in order to get the fourth matrix Step 6.4, the fourth matrix The fifth matrix is ​​converted into M×N, and the fifth matrix is ​​the surface of the specified height feature after reordering and updating.

9. A terminal, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute the digital simulation method for scraping surface topography according to any one of claims 1 to 7.

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