A strip steel surface morphology uniformity index processing method and system

Abstract

The application discloses a strip steel surface morphology distribution uniformity index processing method and system. In view of the problem that there is no index for evaluating the strip steel surface morphology distribution uniformity at present, the embodiment obtains a plurality of height data of the strip steel surface in a measurement range, performs interpolation and Fourier transform processing on the plurality of height data to obtain an amplitude spectrum, and then obtains the frequency concentration degree of the data based on the amplitude spectrum, so as to use the frequency concentration degree as an index for characterizing the strip steel surface morphology distribution uniformity, thereby solving the problem of the lack of the index for evaluating the strip steel surface morphology distribution uniformity at present.

Description

Technical Field

[0001] This application relates to the field of steel rolling technology, and in particular to a method and system for processing the index of uniformity of surface morphology distribution of strip steel. Background Technology

[0002] The surface morphology of strip steel refers to the degree of unevenness of the strip steel surface at a microscopic level. It is generally divided into three levels according to wavelength: shape and size (>10mm), waviness (1mm~10mm), and roughness (<1mm).

[0003] Surface morphology is crucial to the use of steel strips. For example, roughness affects the stamping performance of automotive steel sheets, while waviness affects their coating performance. Poor uniformity in surface morphology distribution will reduce both the stamping and coating performance of the automotive steel sheets.

[0004] However, there are currently no indicators for evaluating the uniformity of the surface morphology distribution of strip steel. Summary of the Invention

[0005] This invention provides a method and system for processing the index of uniformity of surface morphology distribution of strip steel, so as to solve or partially solve the problem of the lack of indexes for evaluating the uniformity of surface morphology distribution of strip steel.

[0006] To solve the above-mentioned technical problems, the present invention provides a method for processing the index of uniformity of surface morphology distribution of strip steel, the method comprising:

[0007] Obtain several height data points of the strip surface within the measurement range;

[0008] Linear interpolation is performed based on the aforementioned height data to obtain several interpolated data; wherein, the several interpolated data includes the aforementioned height data;

[0009] A Fourier transform is performed on the interpolated data to obtain the amplitude spectrum corresponding to the interpolated data.

[0010] The frequency concentration of the interpolated data is calculated based on the amplitude spectrum, and the frequency concentration is used to characterize the uniformity of the surface morphology distribution of the strip.

[0011] Preferably, after acquiring several height data of the strip surface within the measurement range, the method further includes: performing low-pass filtering on the several height data to obtain corresponding several filtered data;

[0012] The step of performing linear interpolation based on the several height data to obtain several interpolated data includes: performing linear interpolation based on the several filtered data to obtain the several interpolated data.

[0013] Preferably, the linear interpolation based on the height data to obtain several interpolated data includes:

[0014] Linear interpolation is performed between adjacent height data of the plurality of height data to obtain the plurality of interpolated data.

[0015] Preferably, the step of calculating the frequency concentration of the plurality of interpolated data based on the amplitude spectrum includes:

[0016] Using formula Determine the frequency concentration; where J is the frequency concentration, A center The center frequency is the maximum value within the amplitude spectrum [k1, k2], where k is the number of frequencies and N is the number of interpolation data.

[0017] This invention also discloses a system for processing the index of uniformity of surface morphology distribution of strip steel, comprising:

[0018] The acquisition module is used to acquire several height data of the strip surface within the measurement range;

[0019] An interpolation module is used to perform linear interpolation based on the plurality of height data to obtain a plurality of interpolated data; wherein, the plurality of interpolated data includes the plurality of height data;

[0020] The transformation module is used to perform Fourier transform based on the interpolated data to obtain the amplitude spectrum corresponding to the interpolated data.

[0021] The calculation module is used to calculate the frequency concentration of the several interpolated data based on the amplitude spectrum, and the frequency concentration is used to characterize the uniformity of the surface morphology distribution of the strip steel.

[0022] Preferably, the system further includes: a filtering module, used to perform low-pass filtering on the plurality of height data to obtain corresponding plurality of filtered data;

[0023] The interpolation module is specifically used to: perform linear interpolation based on the plurality of filtered data to obtain the plurality of interpolated data.

[0024] Preferably, the interpolation module is specifically used to perform linear interpolation between adjacent height data of the plurality of height data to obtain the plurality of interpolated data.

[0025] Preferably, the calculation module is specifically used to utilize formulas Determine the frequency concentration; where J is the frequency concentration, A center The center frequency is the maximum value within the amplitude spectrum [k1, k2], where k is the number of frequencies and N is the number of interpolation data.

[0026] The present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0027] The present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0028] Through one or more technical solutions of the present invention, the present invention has the following beneficial effects or advantages:

[0029] This invention discloses a method and system for processing the index of the uniformity of strip surface morphology distribution. Addressing the current lack of an index for evaluating the uniformity of strip surface morphology distribution, this embodiment acquires several height data points on the strip surface within a measurement range, performs interpolation and Fourier transform on these height data points to obtain an amplitude spectrum, and then obtains the frequency concentration of the data based on the amplitude spectrum, using this as an index to characterize the uniformity of strip surface morphology distribution, thereby solving the current problem of missing indexes for evaluating the uniformity of strip surface morphology distribution.

[0030] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0031] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0032] In the attached diagram:

[0033] Figure 1 A flowchart illustrating the implementation of a method for processing the index of uniformity of strip surface morphology distribution according to an embodiment of the present invention is shown.

[0034] Figures 2A-2B A schematic diagram showing the height data of two strips (a, b) according to an embodiment of the present invention is provided.

[0035] Figures 3A-3B A schematic diagram of two strips (a, b) after filtering is shown according to an embodiment of the present invention;

[0036] Figures 4A-4B A schematic diagram of the amplitude spectra of two strips (a, b) according to an embodiment of the present invention is shown;

[0037] Figure 5 A diagram of an index processing system for the uniformity of strip surface morphology distribution according to an embodiment of the present invention is shown. Detailed Implementation

[0038] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0039] This invention provides a method and system for processing the index of the uniformity of strip surface morphology distribution. Addressing the current lack of an index for evaluating the uniformity of strip surface morphology distribution, this embodiment acquires several height data points on the strip surface within a measurement range, performs interpolation and Fourier transform on these height data points to obtain an amplitude spectrum, and then obtains the frequency concentration of the data based on the amplitude spectrum. This frequency concentration is used as an index characterizing the uniformity of strip surface morphology distribution, thereby solving the problem of the lack of current indexes for evaluating the uniformity of strip surface morphology distribution.

[0040] To further illustrate and explain the present invention, please refer to the following: Figure 1 This is a flowchart illustrating the implementation of a method for processing the uniformity of strip surface morphology distribution according to an embodiment of the present invention. The method in this embodiment is mainly applicable to characterizing the uniformity of strip surface morphology at a microscale of 1 mm, but it is not a limitation. The method includes the following steps:

[0041] Step 101: Obtain several height data of the strip surface within the measurement range.

[0042] In this embodiment, a roughness meter or a white light interferometer is used to obtain several height data. The roughness meter or the white light interferometer has its own measurement range. For example, the measurement range of the white light interferometer is 1.3 mm. If the strip surface is illuminated with a white light interferometer, several height data within 1.3 mm will be obtained. The specific number of height data depends on the instrument used and the surface morphology of the strip.

[0043] In some optional implementations, after acquiring several height data points, the height data is further smoothed and denoised. Specifically, the several height data points are low-pass filtered to obtain corresponding filtered data. For example, an 8th-order Butterworth low-pass filter can be used to filter the several height data points. Suppose the cutoff frequency with an amplitude of -3dB is set to ω. c The filter is: in, s is the dummy variable after the Laplace transform. After filtering with a low-pass filter, several filtered data points will be obtained.

[0044] Step 102: Perform linear interpolation based on several height data to obtain several interpolated data.

[0045] The interpolated data includes several height data points and interpolation point data points. It's worth noting that if low-pass filtering is applied to the height data, then during linear interpolation, linear interpolation will be performed based on the filtered data, resulting in several interpolated data points. In this case, the interpolated data points include both filtered data points and interpolation point data points.

[0046] Since the linear interpolation methods for altitude data and filtered data are the same, the following embodiments will use linear interpolation of altitude data as an example for description, while the linear interpolation of filtered data can be processed in the same way, and will not be described again in the embodiments.

[0047] When performing linear interpolation on altitude data, linear interpolation is performed between adjacent altitude data points to obtain several interpolated data points. The main purpose of linear interpolation is to improve the accuracy of subsequent frequency concentration calculations. Therefore, considering the calculation accuracy, linear interpolation can be performed on the altitude data once or multiple times, with the number of linear interpolations related to the calculation accuracy. If multiple linear interpolations are performed on the altitude data, the first linear interpolation is performed between adjacent altitude data points to obtain the first interpolated data. Then, a second linear interpolation is performed between adjacent interpolated data points of the first interpolated data, and so on, to obtain the interpolated data corresponding to multiple linear interpolations.

[0048] Furthermore, since the interpolation point data is obtained by interpolating between adjacent height data, the coordinate data of the interpolation point data can be calculated based on the coordinate data of the preceding and following height data. Specifically, this can be done according to the coordinate relationship formula. Calculate the coordinate data of the interpolation points. Where x is the x-coordinate of the interpolation point, and L is the y-coordinate of the interpolation point. i L is the x-coordinate of the height data preceding the interpolation point. i x is the ordinate of the height data preceding the interpolation point. i+1 L is the x-coordinate of the height data after the interpolation point. i+1 This is the ordinate of the height data after the interpolation point.

[0049] Step 103: Perform Fourier transform based on several interpolated data to obtain the amplitude spectrum corresponding to several interpolated data.

[0050] In the process of performing a Fourier transform on several interpolated data points, the data are first converted into a complex form F(k), where k is the number of frequencies corresponding to the interpolated data points, and then the amplitude spectrum is obtained accordingly. Specifically, the formula is first used... Converting several interpolated data points into complex form F(k), since... Therefore, F(k) can be transformed into: F(k) = X(k) + i*Y(k); where X(k) is the real part, Y(k) is the imaginary part, and c j The data are interpolated, j = 0, 1…N-1, where i is the imaginary unit and N is the number of interpolated data points. Next, the amplitude spectrum A(k) corresponding to several interpolated data points is calculated.

[0051] Step 104: Calculate the frequency concentration of several interpolated data based on the amplitude spectrum. The frequency concentration is used to characterize the uniformity of the surface morphology distribution of the strip steel.

[0052] In this embodiment, since the amplitude spectrum is used to characterize the amplitude variation of several interpolated data points with frequency, the frequency concentration of the several interpolated data points can be determined based on the amplitude spectrum. Specifically, this is achieved using the formula... Determine the frequency concentration. Where J is the frequency concentration, and A... center The center frequency is the maximum value within the amplitude spectrum [k1, k2], where k is the number of frequencies and N is the number of interpolation data.

[0053] Furthermore, an interval [k1, k2] can be determined in the amplitude spectrum A(k), and the maximum value in [k1, k2] can be taken as the center frequency, that is: A center =max(A(k)∈[k1,k2]), and then use the above formula to determine the frequency concentration in the amplitude spectrum A(k) located in [k1,k2]. The larger the frequency concentration, the better the uniformity of the strip surface morphology distribution.

[0054] To further illustrate and explain the present invention, specific examples are used below.

[0055] In this example, to verify the effectiveness of frequency concentration in characterizing the uniformity of strip surface morphology distribution, two strips with different surface morphologies were selected for analysis.

[0056] First, the height data of the two strips (a and b) were obtained using a white light interferometer, such as... Figures 2A-2B As shown, the horizontal axis represents the position of the strip surface topography data, and the vertical axis represents the height of the strip surface topography data.

[0057] Secondly, an 8th-order Butterworth low-pass filter was used to filter the altitude data, with the cutoff frequency set in the range of [0.0814, 0.135] when the amplitude was -3dB. Figures 3A-3B This is a schematic diagram after filtering. The horizontal axis represents the position of the strip surface topography data, and the vertical axis represents the height of the strip surface topography data. From... Figure 2A , Figure 2B , Figure 3A , Figure 3B It can be seen that the peak-to-trough intervals in (a) data are uneven, with varying widths. The peak-to-trough intervals in (b) data are more uniform compared to those in (a).

[0058] Next, the filtered data of the strips (a, b) are subjected to fourth-order linear interpolation, and then Fourier transform is performed to obtain their respective amplitude spectra, such as... Figures 4A-4B As shown. The vertical axis represents the amplitude of the corresponding wavelength, and the horizontal axis represents the number of cycles of the corresponding wavelength within 1 mm. From 4A- Figure 4B It can be seen that there is a clear concentrated frequency in the amplitude spectrum of data (b), indicating that the spectral distribution is more concentrated. In contrast, the amplitude spectrum of data (a) is more dispersed.

[0059] Finally, the frequency concentration of each strip (a) and (b) is calculated based on their respective amplitude spectra. The value range of k1 is [5, 10], and the value range of k2 is [30, 50]. Using the aforementioned formula, the frequency concentrations of strips (a) and (b) are determined to be 2.257 and 3.137, respectively. A higher frequency concentration indicates better uniformity of the strip's surface morphology distribution. Therefore, it can be seen here that strip (b) has better uniformity of surface morphology distribution.

[0060] Based on the same inventive concept as in the foregoing embodiments, this invention also provides an index processing system for the uniformity of strip surface morphology distribution, see below. Figure 5 ,include:

[0061] The acquisition module 501 is used to acquire several height data of the strip surface within the measurement range;

[0062] Interpolation module 502 is used to perform linear interpolation based on the plurality of height data to obtain a plurality of interpolated data; wherein, the plurality of interpolated data includes the plurality of height data;

[0063] The transformation module 503 is used to perform a Fourier transform based on the plurality of interpolated data to obtain the amplitude spectrum corresponding to the plurality of interpolated data.

[0064] The calculation module 504 is used to calculate the frequency concentration of the plurality of interpolated data based on the amplitude spectrum, wherein the frequency concentration is used to characterize the uniformity of the surface morphology distribution of the strip steel.

[0065] In some optional implementations, the system further includes: a filtering module, used to perform low-pass filtering on the plurality of height data to obtain corresponding plurality of filtered data;

[0066] The interpolation module 502 is specifically used to: perform linear interpolation based on the plurality of filtered data to obtain the plurality of interpolated data.

[0067] In some optional implementations, the interpolation module 502 is specifically used to perform linear interpolation between adjacent height data of the plurality of height data to obtain the plurality of interpolated data.

[0068] In some alternative implementations, the calculation module 504 is specifically used to utilize formulas Determine the frequency concentration; where J is the frequency concentration, A center The center frequency is the maximum value within the amplitude spectrum [k1, k2], where k is the number of frequencies and N is the number of interpolation data.

[0069] Based on the same inventive concept as in the foregoing embodiments, this embodiment of the invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0070] Based on the same inventive concept as in the foregoing embodiments, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.

[0071] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0072] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0073] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0074] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0075] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0076] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components of the gateway, proxy server, or system according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing some or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0077] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. A method for processing the index of uniformity of surface morphology distribution of strip steel, characterized in that, The method includes: Obtain several height data points of the strip surface within the measurement range; Linear interpolation is performed based on the aforementioned height data to obtain several interpolated data; wherein, the several interpolated data includes the aforementioned height data; A Fourier transform is performed on the interpolated data to obtain the amplitude spectrum corresponding to the interpolated data. Calculating the frequency concentration of the interpolated data based on the amplitude spectrum includes: Using formula Determine the frequency concentration; where J is the frequency concentration. The center frequency is the intrinsic frequency of the amplitude spectrum. The maximum value in the range, where k is the number of frequencies. N The frequency concentration is the number of interpolated data points, used to characterize the uniformity of the surface morphology distribution of the strip steel.

2. The method as described in claim 1, characterized in that, After acquiring several height data of the strip surface within the measurement range, the method further includes: performing low-pass filtering on the several height data to obtain corresponding several filtered data; The step of performing linear interpolation based on the several height data to obtain several interpolated data includes: performing linear interpolation based on the several filtered data to obtain the several interpolated data.

3. The method as described in claim 1, characterized in that, The linear interpolation based on the aforementioned height data yields several interpolated data points, including: Linear interpolation is performed between adjacent height data of the plurality of height data to obtain the plurality of interpolated data.

4. A system for processing the index of uniformity of surface morphology distribution of strip steel, characterized in that, include: The acquisition module is used to acquire several height data of the strip surface within the measurement range; An interpolation module is used to perform linear interpolation based on the plurality of height data to obtain a plurality of interpolated data; wherein, the plurality of interpolated data includes the plurality of height data; The transformation module is used to perform Fourier transform based on the interpolated data to obtain the amplitude spectrum corresponding to the interpolated data. The calculation module is used to calculate the frequency concentration of the interpolated data based on the amplitude spectrum, specifically using the formula... Determine the frequency concentration; where J is the frequency concentration. The center frequency is the intrinsic frequency of the amplitude spectrum. The maximum value in the range, where k is the number of frequencies. N The frequency concentration is the number of interpolated data points used to characterize the uniformity of the surface morphology distribution of the strip steel.

5. The index processing system as described in claim 4, characterized in that, The system further includes a filtering module, used to perform low-pass filtering on the plurality of height data to obtain corresponding plurality of filtered data; The interpolation module is specifically used to: perform linear interpolation based on the plurality of filtered data to obtain the plurality of interpolated data.

6. The index processing system as described in claim 4, characterized in that, The interpolation module is specifically used to perform linear interpolation between adjacent height data of the plurality of height data to obtain the plurality of interpolated data.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-3.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1-3.

Images