Scraping surface topography digital simulation method and system

Abstract

The invention discloses a scraping surface morphology digital simulation method and system, and the method comprises the steps: 1, measuring a scraping surface, and obtaining the three-dimensional morphology data of the scraping surface; 2, calculating an overall surface parameter, a probability material ratio curve and an autocorrelation function of the three-dimensional shape data of the scraping surface; 3, obtaining three groups of surface height characteristic parameters according to the probability material ratio curve, generating a Gaussian surface by taking the parameters as input, and selectively superposing the Gaussian surface to obtain a surface with specified height characteristics; 4, calculating the power spectrum density of the scraping surface morphology through the autocorrelation function of the three-dimensional morphology data of the scraping surface; 5, generating a surface with a specified self-correlation function by taking the surface with the specified height feature and the power spectral density of the scraping surface morphology as input; step 6, reordering the surface with the specified height feature according to the sequence of the surface with the specified autocorrelation function so as to update the surface with the specified height feature; and 7, the step 5 and the step 6 are repeated until convergence is achieved, and the scraping surface with the specific height characteristic parameters and the specific space characteristic parameters is obtained. The method is suitable for digital simulation of scraping surfaces and multi-process surfaces.

Description

Technical Field

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

[0002] Every leap forward in the manufacturing industry is inseparable from the improvement of machine tool manufacturing accuracy, and the machining accuracy of machine tools is directly related to the accuracy of guideways. Scraping is essential for producing machine tool guideways or workpieces that are too long to be completed with a grinder, or for workpieces that require extremely precise flatness.

[0003] Scraping and grinding is to show the high points of the workpiece surface by grinding the surfaces of the workpieces that match each other or between the workpiece surface and the standard surface, and then use a scraper to scrape off the high parts in a micro-cutting manner, so that the workpiece can obtain higher shape, position, dimensional accuracy and smaller surface roughness. Scraping and grinding is an important means to improve the lubrication state and level the surface. Scraping and grinding the surface can form uniform contact points and some shallow pits, which can form oil storage gaps. The scraping and grinding process has unique characteristics, and the resulting machined surface morphology is complex and random. The scraped surface is a multi-process surface, which can be characterized and simulated by layering. The layered surface characterization method can accurately obtain the height characteristic parameters of the component surface, but cannot obtain accurate spatial characteristic parameters. The reason is that after the layered surface is separated from the component surface, there are a large number of missing points on the component surface. The existence of missing points makes it impossible to accurately calculate the spatial characteristic parameters of the component surface, so it is impossible to accurately simulate the layered surface. Existing simulation methods are difficult to simulate a scraped surface with both specified height characteristics 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 scraping surface morphology, which can realize digital simulation of scraping surface with specified height characteristic parameters and space 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 digital simulation of scraping surface morphology is provided, comprising:

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

[0009] Step 2: Calculate the overall surface parameters and probability material ratio curve of the scraped surface three-dimensional topography data based on the scraped surface three-dimensional topography data; 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;

[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 three-dimensional morphology data of the scraped surface;

[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 to obtain a scraped surface with specified height characteristic parameters and spatial characteristic parameters.

[0015] Furthermore, the step 2 is specifically:

[0016] Step 2.1, calculate the overall surface parameters of the scraped surface three-dimensional morphology 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 morphology data;

[0018] Step 2.3, based on the three-dimensional morphology data of the scraped surface and the overall surface parameters of the three-dimensional morphology data of the scraped surface, calculate the autocorrelation function of the three-dimensional morphology data of the scraped surface.

[0019] Furthermore, the autocorrelation function ACF(p,q) of the scraped surface three-dimensional morphology data 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-direction and y-direction of the scraped surface respectively; z i,j represents 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 reason.

[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; among them, 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 measuring 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 morphology data of the scraped surface.

[0029] Furthermore, the surface Z having a specified autocorrelation function is generated by taking the surface with specified height characteristics 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 morphology; 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 a 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 order of the records 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 for convergence judgment, and the error formula is:

[0038]

[0039] Among them, err represents the error, Z h _PMRC y The PMRC ordinate of the surface representing the updated specified height feature, Z 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 means convergence, and the surface of the specified height feature is updated as the simulation result. If not, repeat steps 5 and 6.

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

[0041] The measuring 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 used to execute step 2: calculate the overall surface parameters and the probability material ratio curve of the scraped surface three-dimensional topography data according to the scraped surface three-dimensional topography data; calculate the autocorrelation function of the scraped surface three-dimensional topography data according to 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 a specified height characteristic;

[0044] The second calculation module is used to execute 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 generating module is used to execute step 5: generating a surface with a specified autocorrelation function by taking a surface with a specified height feature and a power spectrum density of a scraped surface as input;

[0046] An updating module, used 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 scraping surface with specified height characteristic parameters and space 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 prior art, 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 that the spatial characteristic parameters of the simulated scraped surfaces have large errors due to the missing points on the constituent surfaces and cannot be accurately obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the present invention;

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

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

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

[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 the scraping surface;

[0058] Figure 8 It 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] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with 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 the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily without conflict.

[0060] Example 1: Figure 1-8 As shown, according to a first aspect of an embodiment of the present invention, a method for digital simulation of scraping surface morphology is provided, comprising:

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

[0062] Step 2: Calculate the overall surface parameters and probability material ratio curve of the scraped surface three-dimensional topography data based on the scraped surface three-dimensional topography data; 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;

[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 specified height characteristics. h ;

[0064] Step 4: Calculate the power spectral density PSD of the scraped surface morphology through the autocorrelation function of the three-dimensional morphology data of the scraped surface;

[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 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 to obtain a scraped surface with specified height characteristic parameters and spatial characteristic parameters.

[0068] Furthermore, the step 2 is specifically:

[0069] Step 2.1, calculate the overall surface parameters of the scraped surface three-dimensional morphology 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; z 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 morphology 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] Calculate the material ratio curve of the three-dimensional morphology data of the scraped surface according to the cumulative distribution function of the three-dimensional morphology data of the scraped surface;

[0076] According to the material ratio curve of the three-dimensional topography data of the scraped surface, a probability material ratio curve of the three-dimensional topography data of the scraped surface is obtained;

[0077] It should be noted that what is extracted in the present invention is the three-dimensional morphology 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 morphology 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-direction and y-direction of the scraped surface respectively; z i,j It represents the three-dimensional shape data obtained when the number of measurement points in the xy direction is 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 the 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 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, respectively taking the surface size L 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 measuring 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 morphology 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 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 morphology; 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 order of the records 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 make convergence judgment, 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 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 illustrates the digital simulation method of scraping surface morphology of the present invention.

[0100] 1) Use a non-contact 3D profiler to measure the actual scraped surface and extract the 3D profile data of the scraped surface, such as Figure 2 shown.

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

[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 three-dimensional morphology data of the scraped surface, calculate the cumulative distribution function of the three-dimensional morphology data of the scraped surface; calculate the material ratio curve based on the cumulative distribution function; obtain the probability material ratio curve of the three-dimensional morphology data of the scraped surface 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 a Gaussian surface and selectively superimposed to obtain the surface Z with specified height characteristics. h :

[0111] a) By fitting PMRC, three sets of surface height characteristic parameters are obtained: 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 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 At each measuring point, the smaller of the three Gaussian surface heights is retained 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 the 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, then update the surface Z of the specified height feature. 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 of the overall surface parameters S calculated for the simulated surface 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 measuring module for executing step 1: measuring the scraping surface to obtain three-dimensional morphological data of the scraping surface; a first calculating 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 obtaining 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 with 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 with 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, and 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, the implementation of hardware, or a combination of software and hardware, is also possible and conceived. 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, there is provided a terminal 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.

[0120] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0121] The specific implementation modes of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A digital simulation method for scraping surface morphology, characterized in that: include: Step 1: Measure the scraped surface to obtain three-dimensional topography data of the scraped surface; Step 2: Calculate the overall surface parameters and probability material ratio curve of the scraped surface three-dimensional topography data based on the scraped surface three-dimensional topography data; 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; 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 three-dimensional morphology data of the scraped surface; 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 to obtain a scraped surface with specified height characteristic parameters and spatial characteristic parameters.

2. The method for digital simulation of scraping surface morphology according to claim 1, characterized in that: The step 2 is specifically: Step 2.1, calculate the overall surface parameters of the scraped surface three-dimensional morphology 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; Step 2.2, calculating the probability material ratio curve of the scraped surface three-dimensional morphology data; Step 2.3, based on the three-dimensional morphology data of the scraped surface and the overall surface parameters of the three-dimensional morphology data of the scraped surface, calculate the autocorrelation function of the three-dimensional morphology data of the scraped surface.

3. The method for digital simulation of scraping surface morphology 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-direction and y-direction of the scraped surface respectively; z i,j It represents the three-dimensional shape data obtained when the number of measurement points in the xy direction is i and j respectively.

4. The method for digital simulation of scraping surface morphology according to claim 1, characterized in that: The step 3 is specifically: 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 measuring point, retain the smaller of the three Gaussian surface heights to generate a surface with specified height characteristics.

5. The method for digital simulation of scraping surface morphology 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 morphology data of the scraped surface.

6. The method for digital simulation of scraping surface morphology 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 for a specified height feature h Perform Fourier transform; PSD represents the power spectral density of the scraped surface morphology; M and N represent the number of measurement points in the x-direction and y-direction of the scraped surface, respectively.

7. The method for digital simulation of scraping surface morphology according to claim 1, characterized in that: 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 a 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 order of the records 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.

8. The method for digital simulation of scraping surface morphology according to claim 1, characterized in that: 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 for convergence judgment, and the error formula is: Among them, err represents the error, Z h _PMRC y The PMRC ordinate of the surface representing the updated specified height feature, Z 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 means convergence, and the surface of the specified height feature is updated as the simulation result. If not, repeat steps 5 and 6.

9. A digital simulation system for scraping surface morphology, characterized in that: include: The measuring 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 used to execute step 2: calculate the overall surface parameters and the probability material ratio curve of the scraped surface three-dimensional topography data according to the scraped surface three-dimensional topography data; calculate the autocorrelation function of the scraped surface three-dimensional topography data according to 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 a specified height characteristic; The second calculation module is used to execute 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 generating module is used to execute step 5: generating a surface with a specified autocorrelation function by taking a surface with a specified height feature and a power spectrum density of a scraped surface as input; An updating module, used 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: repeat steps 5 and 6 until convergence, and obtain the scraping surface with specified height characteristic parameters and space characteristic parameters.

10. 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 method for digital simulation of scraping surface morphology as claimed in any one of claims 1 to 8.

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