A method for extracting 3D surface morphology features based on extended discrete mode decomposition

Abstract

The present invention relates to a method for extracting three-dimensional surface topography features based on extended discrete mode decomposition. Point cloud data of the surface topography of a part is obtained, a surface model is generated after preprocessing, and mechanical parameters are set. The surface model is projected onto different modal bases to generate multiple discrete modes, and the wavelengths of each discrete mode are calculated respectively. According to the national standard, a cut-off wavelength is set, and each wavelength range is divided into one of the surface topography features. The discrete modes in the same group are merged to obtain the surface topography features of the part. The method of the present invention realizes the extraction of three-dimensional engineering surface topography features and overcomes the problem of numerical distortion caused by the missing of end values, convolution distortion at the surface boundary and holes in the traditional surface filtering method.

Description

Technical Field

[0001] The invention belongs to the technical field of surface morphology analysis, and in particular relates to a three-dimensional surface morphology feature extraction method based on extended discrete modal decomposition. Background Art

[0002] The surface morphology of parts is an important indicator for evaluating the quality of part processing, and it has an important impact on the manufacturing cost and service performance of parts. At the same time, there are many factors in the part processing process, such as processing size error, system vibration, tool wear, and plastic deformation of parts and surface metals, which affect the generation of surface morphology. The surface morphology of parts is multi-scale, and the surface morphology characteristics of different scales are composed of surface components of different frequencies. According to the frequency, they can be divided into low-frequency component characteristics-shape error, medium-frequency component characteristics-waviness, and high-frequency component characteristics-roughness. These surface component characteristics are caused by different reasons and have different effects on the performance of parts such as wear resistance, corrosion resistance and sealing.

[0003] Engineering surface filtering is a key technology for extracting the three-dimensional surface morphology of parts. At present, Gaussian filters are regarded as standard filtering technologies, but Gaussian filters are prone to convolution distortion at surface boundaries and holes, resulting in boundary effect problems. Therefore, many filtering methods such as regional Gaussian filtering, spline filtering, wavelet filtering and morphological filtering have emerged to reduce the end effect.

[0004] In recent years, a filtering method based on structural dynamics problems has emerged. By solving the structural dynamics equations, the measured part surface is projected into a specific modal basis to realize the feature extraction of the part surface signal, and a discrete mode decomposition (DMD) filtering method is proposed. Compared with the traditional filtering method, it divides the three-dimensional surface morphology into a series of discrete components, has a finer decomposition scale, obtains more accurate surface components of each scale, effectively solves the filtering distortion problem at the surface boundary and holes, and overcomes the end effect. However, the physical meaning of each order of discrete modes and the surface morphology characteristics of the original discrete mode decomposition method are still unclear. Summary of the invention

[0005] Based on the above-mentioned shortcomings and deficiencies in the prior art, one of the objects of the present invention is to at least solve one or more of the above-mentioned problems in the prior art. In other words, one of the objects of the present invention is to provide a three-dimensional surface morphology feature extraction method based on extended discrete modal decomposition that meets one or more of the aforementioned needs.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0007] A method for extracting three-dimensional surface morphology features based on extended discrete modal decomposition, the method comprising the steps of:

[0008] S1. Obtaining point cloud data of part surface topography;

[0009] S2, preprocessing the point cloud data;

[0010] S3, generating a surface model using point cloud data;

[0011] S4, setting mechanical parameters for the surface model;

[0012] S5, projecting the surface model onto a plurality of different modal bases to generate a plurality of discrete modes of the surface model;

[0013] S6. Calculate the discrete mode wavelengths of several discrete modes respectively;

[0014] S7, setting a plurality of cutoff wavelengths according to the surface morphology characteristic parameters, wherein the cutoff wavelengths classify the discrete modal wavelengths in a given range into one of the surface morphology characteristics, and classifying the plurality of discrete modes into a plurality of groups of modal groups representing one of the surface morphology characteristics according to the cutoff wavelengths;

[0015] S8. The discrete modes in the same mode group are merged respectively to obtain several surface morphological features of the parts.

[0016] As a preferred solution, step S51 is further included after step S5, setting N-order limits and discarding discrete modes after the N-order.

[0017] As a preferred solution, the surface morphology characteristics specifically include: shape error, waviness and roughness.

[0018] As a further preferred solution, in step S7, the cutoff wavelength of the shape error feature is set according to international standards in combination with the surface processing evaluation parameters Ra and Rz. λ f , cut-off wavelength of waviness characteristics λ c and the cut-off wavelength of the roughness feature λ s ; wavelength greater than λ f The discrete modes of the shape error represent the characteristics, with wavelengths between λ f and λ c The discrete modes between represent the waviness characteristics, with wavelengths between λ c and λ s The discrete modes between represent the roughness characteristics.

[0019] As a preferred solution, the calculation of step S6 is specifically as follows:

[0020] Perform Riezs transformation on discrete modes, construct the single-action signal of discrete modes, and calculate the wavelength of discrete modes.

[0021] As a preferred solution, in step S3, the surface model is generated by:

[0022] The point cloud data is meshed into a triangular network, and the triangular network surface is generated into a face to obtain a surface model.

[0023] As a preferred solution, in step S3, the mechanical parameters set are: material, density, surface elastic modulus, and Poisson's ratio of the part.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The method of the present invention establishes the relationship between a series of decomposed discrete modes and their physical dimensions, realizes the extraction of three-dimensional engineering surface morphology features, and overcomes the problem of numerical distortion caused by convolution distortion at surface boundaries and holes due to the missing terminal values ​​in traditional surface filtering methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic diagram of point cloud data according to the first embodiment of the present invention;

[0027] Figure 2 is a schematic diagram of a surface model of Embodiment 1 of the present invention;

[0028] Figure 3 is a schematic diagram of discrete mode vibration shapes of the first embodiment of the present invention;

[0029] Figure 4 is a schematic diagram of discrete mode wavelengths according to the first embodiment of the present invention;

[0030] Figure 5 is a schematic diagram of filtering results of the first embodiment of the present invention;

[0031] Figure 6 is a schematic diagram of point cloud data of the second embodiment of the present invention;

[0032] Figure 7 is a schematic diagram of a surface model of a second embodiment of the present invention;

[0033] Figure 8 is a schematic diagram of discrete mode vibration shapes of the second embodiment of the present invention;

[0034] Fig. 9 is a schematic diagram of discrete mode wavelengths according to the second embodiment of the present invention;

[0035] Fig.10 It is a schematic diagram of filtering results of the second embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to more clearly illustrate the embodiments of the present invention, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings and other implementation methods can be obtained based on these accompanying drawings without creative work.

[0037] Embodiment 1:

[0038] A three-dimensional surface morphology feature extraction method based on extended discrete mode decomposition in this embodiment is used to extract the surface morphology features of the back cover of a mobile phone. The method is specifically as follows:

[0039] S1. First, use a laser interferometer holographic measuring instrument to measure the part to obtain the point cloud data of the surface morphology of the part. In this embodiment, the collected point cloud data is as follows: Figure 1 As shown in Section 1.1, a total of 903694 point cloud data were measured, and a certain area of ​​the point cloud data is enlarged as shown in Section 1.2. During the measurement, the sampling resolution in the XY direction is 0.1mm, and the accuracy in the Z direction is 1μm.

[0040] S2. Since there will be deviations or erroneous data when collecting data in step S1, the point cloud data is preprocessed in this step to remove duplicate points and outliers in the collected point cloud data, so as to obtain preprocessed point cloud data that can truly reflect the three-dimensional surface morphology of the workpiece. After removing duplicate points and outliers, a 30mm×30mm area is randomly selected to obtain the following Figure 1 The 130294 point cloud data shown in 1.3.

[0041] S3, generating a surface model using point cloud data;

[0042] Specifically, in this embodiment, the surface model is generated by the following method: using engineering modeling software, such as SolidWorks, using the meshing function of the software to connect the point cloud data to generate a triangular mesh, and then using the surface generation function to fit the generated triangular mesh into a surface, so as to obtain Figure 2 The surface model of the part is 30mm×30mm.

[0043] Then, step S4 is performed to set mechanical parameters for the surface model according to the mechanical parameters of the parts. In this embodiment, the material of the back cover of the mobile phone is 7075 aluminum alloy, so ABAQUS6.14 is used to set the surface elastic modulus to 200 GPa and the density to , Poisson's ratio is 0.25.

[0044] After the setting is completed, proceed to step S5: Projecting onto different modal bases generates discrete modes of various orders and obtains each modal basis and the corresponding modal coordinates The discrete modes generated above are The linear superposition of can form the original model surface ,in is the residual. By solving the linear equation of structural dynamics , surface model Can be projected onto different modal bases Implement modal decomposition, where M and K are the mass matrix and stiffness matrix of the surface, and the displacement vector , and then get .

[0045] According to the above formula, the modal natural frequency is solved and the modal matrix , modal basis is the modal matrix The column vector of . With the modal matrix Projection relationship Solving for the modal coordinates , modal coordinates is the modal coordinate The diagonal elements of the ith matrix.

[0046] Furthermore, in order to filter out the errors caused by high-frequency and low-amplitude modes, this embodiment further includes step S41 after step S4, setting N=1400 order limit, taking only the first 1400 order modes for analysis in the above-generated discrete modes, wherein the vibration shapes of some modes (mode n) are as follows: Figure 3 shown.

[0047] S6, respectively calculate the discrete modal wavelengths of several discrete modes; Specifically, the calculation method of the discrete modal wavelength in this embodiment can be specifically as follows: Riezs transform is performed on each discrete mode selected in the above step S51, so as to construct a single-order signal of each discrete mode , solve the single-shot signal The phase and frequency of each discrete mode are obtained For each discrete mode Perform Riesz transform , is the convolution kernel of Riesz transform.

[0048] Use the above method to construct the single-order discrete mode signal , solve for the monotonic signal Phase ,frequency and wavelength .

[0049] S7. Set a number of cutoff wavelengths according to the surface morphology characteristic parameters of the parts. Specifically, in this embodiment, the surface tolerance requirement of the mobile phone back shell is and , refer to the international standard ISO 4287, set three cut-off wavelengths λ s =0.008mm, λ c =2.5mm and λ f =7.5mm, the three cutoff wavelengths are set as the cutoff wavelengths of shape error, waviness and roughness respectively. The discrete modes with wavelengths greater than 0.008mm and less than 2.5mm represent the roughness characteristics, the discrete modes with wavelengths greater than 2.5mm and less than 7.5mm represent the waviness characteristics, and the discrete modes with wavelengths greater than 7.5mm represent the shape error characteristics. Compare the wavelengths of the discrete modes of each order obtained in step S5 , select mode is the shape error, mode is the waviness, mode For roughness.

[0050] S8. Merge and reconstruct the modes of shape error, waviness, and roughness to extract surface morphology features. , waviness characteristics , roughness characteristics ,like Figure 5 As shown in the figure, the three-dimensional surface morphology features of the parts are finally extracted. Figure 5 It can be seen that the present invention solves the problem of numerical distortion caused by convolution distortion at surface boundaries and holes in traditional surface filtering methods, makes up for the shortcomings of the original discrete mode decomposition (DMD) surface component representation, and realizes the accurate extraction of various three-dimensional engineering surface morphological features.

[0051] Embodiment 2:

[0052] The three-dimensional surface morphology feature extraction method based on extended discrete mode decomposition of this embodiment is different from that of the first embodiment in that this embodiment is used to extract the surface morphology features of the cylinder head of an automobile engine. The method is specifically as follows:

[0053] S1. First, use a laser interferometer holographic measuring instrument to measure the surface of the automobile engine cylinder head to obtain point cloud data of the surface morphology. In this embodiment, the collected point cloud data is as follows: Figure 6 As shown in Section 6.1, a total of 794,354 point cloud data were measured. The sampling resolution in the XY direction was 0.1 mm, and the accuracy in the Z direction was 1 μm.

[0054] S2. Since there will be deviations or erroneous data when collecting data in step S1, the point cloud data is preprocessed in this step to remove duplicate points and outliers in the collected point cloud data, so as to obtain preprocessed point cloud data that can truly reflect the three-dimensional surface morphology of the workpiece. After removing duplicate points and outliers, a 30mm×60mm area is randomly selected to obtain the following Figure 6 86638 point cloud data shown in 6.2.

[0055] S3, generating a surface model using point cloud data;

[0056] Specifically, in this embodiment, the surface model is generated by the following method: using engineering modeling software, such as SolidWorks, using the meshing function of the software to connect the point cloud data to generate a triangular mesh, and then using the surface generation function to fit the generated triangular mesh into a surface, so as to obtain Figure 7 The surface model of the part is 30mm×60mm.

[0057] Then, step S4 is performed to set mechanical parameters for the surface model according to the mechanical parameters of the part. In this embodiment, the specific parameters are: the material of the automobile engine cylinder head is FC-250, so ABAQUS6.14 is used to set the surface elastic modulus to 120 GPa and the density , Poisson's ratio is 0.3.

[0058] After the setting is completed, proceed to step S5: Projecting onto different modal bases generates discrete modes of various orders and obtains each modal basis and the corresponding modal coordinates .

[0059] Furthermore, in order to filter out the errors caused by high-frequency and low-amplitude modes, this embodiment further includes step S51 after step S5, setting N=3000 order limit, taking only the first 3000 order modes for analysis in the above-generated discrete modes, wherein the vibration shapes of some modes (mode n) are as follows: Figure 8 shown.

[0060] S6. Calculate the discrete mode wavelengths of several discrete modes respectively, perform Riesz transform on each discrete mode, and construct the single-mode signal of each discrete mode. , thus obtaining Fig. 9 The wavelengths of the discrete modes shown Schematic diagram of

[0061] S7. Set a number of cutoff wavelengths according to the surface morphology characteristic parameters of the parts. Specifically, in this embodiment, the surface tolerance requirement of the automobile engine cylinder head is and , refer to the international standard ISO4287, set three cut-off wavelengths λ s =0.008mm, λ c =2.5mm and λ f =7.5mm, the three cutoff wavelengths are set as the cutoff wavelengths of shape error, waviness and roughness respectively. Discrete modes with wavelengths below 0.008mm represent fine scratches, discrete modes with wavelengths greater than 0.008mm and less than 2.5mm represent roughness characteristics, discrete modes with wavelengths greater than 2.5mm and less than 7.5mm represent waviness characteristics, and discrete modes with wavelengths greater than 7.5mm represent shape error characteristics. Compare the wavelengths of each order of discrete modes obtained in step S5 , select mode is the shape error, mode is the waviness, mode For roughness.

[0062] S8. Merge and reconstruct the modes of shape error, waviness, and roughness to extract surface morphology features. , waviness characteristics , roughness characteristics ,like Fig.10 As shown, the extraction of the three-dimensional surface morphology features of the parts is finally achieved.

[0063] It should be noted that the above embodiments are only detailed descriptions of the preferred embodiments and principles of the present invention. For ordinary technicians in this field, there will be changes in the specific implementation methods based on the ideas provided by the present invention, and these changes should also be regarded as the scope of protection of the present invention.

Claims

1. A three-dimensional surface morphology feature extraction method based on extended discrete mode decomposition, It is characterized in that The method comprises the steps of: S1. Obtaining point cloud data of part surface topography; S2. Preprocessing the point cloud data; S3, generating a surface model using the point cloud data; S4, setting mechanical parameters for the surface model; S5, projecting the surface model onto a plurality of different modal bases to generate a plurality of discrete modes of the surface model; S6, respectively calculating the wavelengths of the plurality of discrete modes; S7, setting a plurality of cutoff wavelengths according to the surface morphology characteristic parameters, wherein the cutoff wavelengths divide wavelengths in a given range into one of the surface morphology characteristics, and classifying the plurality of discrete modes into a plurality of groups of modes representing one of the surface morphology characteristics according to the cutoff wavelengths; S8, merging the discrete modes in the same mode group respectively to obtain several surface morphological features of the parts; Step S5 includes: The surface model V m Projecting onto different modal bases generates discrete modes of various orders, and obtains each modal base Q i and the corresponding modal coordinates γ i ; The discrete modes P generated above i (x,y)=γ i Q i The linear superposition of can form the original model surface where ε is the residual; by solving the linear equation of structural dynamics Surface Model V m can be projected onto different modal bases Q i Implement modal decomposition, where M and K are the mass matrix and stiffness matrix of the surface, and the displacement vector q i (t) = Q i cos(w i t), and then get According to the above formula, the modal natural frequency w is solved i and modal matrix Q, modal basis Q i is the column vector of the modal matrix Q; through the original surface V m The projection relationship with the modal matrix Q ((Q T *Q) -1 *QT)V m =γSolve the modal coordinates γ i , modal coordinate γ i is the diagonal element of the i-th modal coordinate γ matrix.

2. A three-dimensional surface morphology feature extraction method based on extended discrete mode decomposition as claimed in claim 1, It is characterized in that The step S51 is further included after the step S5, setting an N-order limit and discarding discrete modes after the N-order.

3. A three-dimensional surface morphology feature extraction method based on extended discrete mode decomposition as claimed in claim 1, It is characterized in that The surface morphology characteristics specifically include: shape error, waviness and roughness.

4. A three-dimensional surface morphology feature extraction method based on extended discrete mode decomposition as claimed in claim 3, It is characterized in that In step S7, the cutoff wavelength λ of the shape error feature is set according to international standards in combination with the surface processing evaluation parameters Ra and Rz. f , cut-off wavelength λ of waviness characteristics c and the cutoff wavelength λ of the roughness feature s ; wavelength greater than λ f The discrete modes of the shape error represent the characteristics, with wavelengths between λ f With λ c The discrete modes between represent the waviness characteristics, with wavelengths between λ c With λ s The discrete modes between represent the roughness characteristics.

5. A three-dimensional surface morphology feature extraction method based on extended discrete mode decomposition as claimed in claim 1, It is characterized in that The calculation of step S6 is specifically as follows: Performing Riezs transformation on the discrete mode, constructing a monophonic signal of the discrete mode, and calculating and obtaining the wavelength of the discrete mode.

6. A three-dimensional surface morphology feature extraction method based on extended discrete mode decomposition as claimed in claim 1, It is characterized in that In step S3, the surface model is generated by: The point cloud data is meshed into a triangular network, and the triangular network surface is generated into a face to obtain a surface model.

7. A three-dimensional surface morphology feature extraction method based on extended discrete mode decomposition as claimed in claim 1, It is characterized in that In step S4, the mechanical parameters set are: density, surface elastic modulus, and Poisson's ratio.

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