CN113344823A - Three-dimensional roughness characterization method for morphology of ablation area of silver linear contact - Google Patents
Three-dimensional roughness characterization method for morphology of ablation area of silver linear contact Download PDFInfo
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Abstract
The application provides a three-dimensional roughness characterization method for the morphology of a silver wire type contact ablation area, which comprises the steps of obtaining a contact surface morphology image and an original point cloud data set corresponding to the surface morphology image; extracting the outline of the ablation area of the contact on the surface topography image of the contact; mapping the outline of the contact ablation area to an original point cloud data set to obtain an ablation area point cloud data set corresponding to the contact ablation area; fitting the ablation area point cloud data set to obtain a reference surface of the surface appearance of the contact; calculating the height deviation between the ablation area point cloud data set and the corresponding point on the reference surface to obtain a corrected point cloud data set; and calculating the characterization parameters of the three-dimensional roughness of the contact ablation area morphology corresponding to the corrected point cloud data set. The method can avoid containing a large amount of irrelevant data, has accurate result and small error, and the obtained characterization parameters can accurately characterize the three-dimensional roughness of the ablation area morphology of the silver linear contact, and have very important significance for researching the surface functional characteristics of the contact and the reliability of the relay.
Description
Technical Field
The application relates to the technical field of electrical element contact roughness characterization, in particular to a three-dimensional roughness characterization method for an ablation area morphology of a silver linear contact.
Background
The surface topography of the relay contact directly influences the contact state and the relay service life, the surface three-dimensional topography information has the characteristics of complexity and comprehensiveness, and the surface three-dimensional topography information has anisotropic and multi-scale characteristics, namely, the roughness changes along with the changes of the scale and the direction. At present, roughness calculation is suitable for a rectangular area, and the method has limitation only by taking a rectangular roughness parameter as a morphology parameter for measuring an accurate contact ablation area, is easy to contain a large amount of irrelevant data, and generates a large error on the analysis of the roughness morphology of the ablation area. Therefore, the application provides a three-dimensional roughness characterization method for the appearance of the ablation area of the silver wire type contact.
Disclosure of Invention
The application aims to solve the problems and provides a three-dimensional roughness characterization method for the appearance of the ablation area of a silver linear contact.
The application provides a three-dimensional roughness characterization method for a silver linear contact ablation area, which comprises the following steps:
acquiring a contact surface topography image and an original point cloud data set corresponding to the surface topography image;
extracting the outline of the ablation area of the contact on the surface topography image of the contact;
mapping the outline of the contact ablation area to an original point cloud data set to obtain an ablation area point cloud data set corresponding to the contact ablation area;
fitting the ablation area point cloud data set to obtain a reference surface of the surface appearance of the contact;
calculating the height deviation between the ablation area point cloud data set and the corresponding point on the reference surface to obtain a corrected point cloud data set;
and calculating the characterization parameters of the three-dimensional roughness of the contact ablation area morphology corresponding to the corrected point cloud data set.
According to the technical scheme provided by some embodiments of the present application, the extracting the outline of the ablation area of the contact on the surface topography image of the contact specifically includes:
carrying out noise reduction treatment on the contact surface topography image to obtain a noise reduction image;
masking the noise reduction image of the surface topography of the contact to obtain a third image;
and processing the third image by using a sobel edge detection operator method, and extracting the outline of the ablation area of the contact.
According to the technical scheme provided by some embodiments of the present application, the denoising processing of the contact surface topography image to obtain a denoised image specifically includes:
performing illumination homogenization treatment on the contact surface topography image by adopting a two-dimensional gamma function to obtain a first image;
carrying out gray level processing on the first image to obtain a second image;
and carrying out gray level histogram equalization processing on the second image to obtain a noise reduction image.
According to the technical scheme provided by some embodiments of the application, the method for obtaining the reference plane of the contact surface topography by fitting the ablation area point cloud data set comprises the following steps: and performing polynomial surface fitting on the point cloud data set of the fitting ablation area by adopting a least square method to obtain a reference surface of the surface appearance of the contact.
According to the technical scheme provided by some embodiments of the application, the method for performing polynomial surface fitting on the point cloud data set of the fitting ablation area by adopting a least square method comprises the following steps:
the equation of the surface appearance reference surface of the contact to be fitted is set as follows:
f(x,y)＝a0+a1x+a2y+a3x2+a4xy+a5y2
the fitting error is then:
wherein x isi,jThe abscissa value of the point in the ith row and the jth column in the point cloud data set of the ablation area; y isi,jThe ordinate value of the point in the ith row and the jth column in the point cloud data set of the ablation area; z is a radical ofi,jHeight values of points in the ith row and the jth column in the point cloud data set of the ablation area;
a0、a1、a2、a3、a4and a5Respectively solving the deviation of the fitting errors:
the matrix form of the obtained parameters is:
according to the technical solution provided by some embodiments of the present application, the characterization parameters include ten-point height of the surface, arithmetic mean deviation of the surface, root mean square deviation of the surface, maximum peak height of the surface, maximum valley depth of the surface, skewness of distribution of the height of the surface, and kurtosis of distribution of the height of the surface.
Compared with the prior art, the beneficial effect of this application: the three-dimensional roughness characterization method for the silver wire type contact ablation area morphology comprises the steps of firstly, extracting a contact ablation area outline on a contact surface morphology image, and mapping the contact ablation area outline to an original point cloud data set to obtain an ablation area point cloud data set corresponding to a contact ablation area; fitting the ablation area point cloud data set to obtain a reference surface of the surface appearance of the contact; then calculating the height deviation between the ablation area point cloud data set and the corresponding point on the reference surface to obtain a corrected point cloud data set; finally, calculating the characterization parameters of the three-dimensional roughness of the ablation area morphology of the contact corresponding to the corrected point cloud data set, thereby realizing the analysis of the three-dimensional roughness of the ablation area morphology of the silver wire type relay contact; the method processes the contact surface topography image and the original point cloud data set to obtain a corrected point cloud data set, and calculates the characterization parameters of the three-dimensional roughness of the contact ablation area topography according to the corrected point cloud data set, compared with the existing rectangular area roughness calculation technology, the method can avoid containing a large amount of irrelevant data, the roughness of the region of interest is calculated more accurately, the calculation error is smaller, the obtained characterization parameters can more accurately characterize the three-dimensional roughness of the ablation region of the silver line type contact, namely the surface morphology of the contact, the method also provides reference basis for the calculation of three-dimensional roughness of the area morphology with irregular profiles, the characterization parameters are in direct connection with scratches, ablation, fusion welding, pit protrusions on the microscopic surface of the contact, the distribution condition of metal particles, the surface flatness of the contact and the like, the method has great significance for researching the surface functional characteristics of the contact, the reliability of the relay and the like.
Drawings
FIG. 1 is a flowchart of a method for characterizing three-dimensional roughness of an ablation area topography of a silver wire contact according to an embodiment of the present application;
FIG. 2 is a contact surface topography image of a silver wire type contact provided in accordance with an embodiment of the present application;
fig. 3 is a first point cloud image provided in an embodiment of the present application;
FIG. 4 is a first image provided by an embodiment of the present application;
FIG. 5 is a second image provided by an embodiment of the present application;
FIG. 6 is a noise-reduced image provided by an embodiment of the present application;
FIG. 7 is a third image provided by an embodiment of the present application;
FIG. 8 is a profile image provided by an embodiment of the present application;
FIG. 9 is a second point cloud image provided by an embodiment of the present application;
FIG. 10 is a third point cloud image provided by an embodiment of the present application;
fig. 11 is a fourth point cloud image provided in the embodiment of the present application.
Detailed Description
The following detailed description of the present application is given for the purpose of enabling those skilled in the art to better understand the technical solutions of the present application, and the description in this section is only exemplary and explanatory, and should not be taken as limiting the scope of the present application in any way.
The embodiment provides a method for characterizing three-dimensional roughness of an ablation area of a silver linear contact, wherein a flow chart of the method is shown in fig. 1, and the method comprises the following steps:
and S1, acquiring the contact surface topography image and the original point cloud data set corresponding to the surface topography image.
Acquiring a contact surface topography image of the silver line type contact and an original point cloud data set corresponding to the image by using a non-contact three-dimensional topography instrument; FIG. 2 is a contact surface topography image of a silver wire contact, and FIG. 3 is a point cloud image of the contact surface topography image of the silver wire contact, denoted as a first point cloud image; the original point cloud data set corresponds to the first point cloud image shown in fig. 3. The original point cloud data set comprises position information and corresponding height information of all pixel points on the contact surface topography image, wherein the position information of a certain pixel point refers to an abscissa value and an ordinate value of the pixel point, and the height information refers to a height value. The abscissa value, the ordinate value and the height value of all pixel points on the contact surface topography image can respectively form an mxn matrix, namely three original matrices, m is the row number of the original matrices, n is the column number of the original matrices, point cloud data refers to a set of vectors in a three-dimensional coordinate system, and any point cloud data in the original point cloud data set can be represented as (x 0)i,j，y0i,j，z0i,j) Wherein i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n.
S2, extracting the outline of the contact ablation area on the contact surface topography image, which specifically comprises the following steps:
and S21, carrying out noise reduction processing on the contact surface topography image to obtain a noise reduction image.
The contact surface topography image obtained in step S1 may have problems of uneven brightness, noise introduced during shooting, and the like, and if not processed, the result is easily affected by non-negligible influences, so that the contact surface topography image needs to be preprocessed, that is, noise reduction processing is performed.
The method for reducing the noise of the contact surface topography image to obtain the noise reduction image specifically comprises the following steps:
s211, carrying out illumination homogenization treatment on the contact surface topography image by adopting a two-dimensional gamma function to obtain a first image, wherein the first image is shown in figure 4.
When the collected image has the phenomenon of uneven illumination, the problem of directly influencing the result is brought, for example, important detail information of the image cannot be shown or even can be covered, the visual effect and the application value of the image are greatly influenced, and therefore illumination homogenization treatment needs to be carried out on the contact surface appearance image.
Firstly, extracting the illumination component of the contact surface topography image by adopting a Gauss function, wherein the expression of the Gauss function is as follows:
where λ is a normalized constant and c represents a scale factor.
And expressing the gray value of the contact surface topography image by using A (x, y), and performing convolution by using a multi-scale Gauss function and A (x, y) to obtain an illumination component C (x, y) in A (x, y):
C(x,y)＝A(x,y)B(x,y)；
secondly, adjusting parameters of the 2D-Gamma function according to the distribution characteristics of the illumination components, and realizing illumination homogenization treatment on the contact three-dimensional topography map to obtain a first image, wherein the brightness value of the first image is as follows:
where γ is an index whose magnitude is determined by the characteristics of the illumination component and the luminance average value of the illumination component, and is expressed as follows:
where k is the mean luminance value of the illumination components.
S212, perform gray scale processing on the first image to obtain a second image, where the second image is shown in fig. 5.
If the gray values of the foreground and the background of the image are too close, that is, the contrast of the image is too small, the determination of the image boundary value is difficult during the edge detection. In order to enhance the image contrast, the edge contour of the ablation area is more prominent and easy to detect, and the first image is subjected to gray processing to obtain a second image.
S213, performing gray histogram equalization on the second image to obtain a noise-reduced image, where the noise-reduced image is shown in fig. 6.
The gray distribution of the image can be visually represented by a gray histogram, the process of carrying out nonlinear stretching on the gray of the image is called gray histogram equalization, and as a result, the gray histogram is uniformly distributed, so that the effect of gray equalization of all levels is achieved.
And S22, performing masking treatment on the noise reduction image of the surface topography of the contact to obtain a third image, wherein the third image is shown in FIG. 7.
The image mask processing method is to extract the interested part in the image by using the selected image or object and carry out the shielding processing on the rest part. The image for masking is a specific image which has a masking function and is a binary image composed of 0 and 1, and in the image masking process, a region having a value of 1 is reserved and a region having a value of 0 is masked. In this embodiment, on the basis of the noise reduction image, a third image for extracting the outline of the ablation region of the contact is generated, that is, the mask is multiplied by the gray value of the corresponding position of the noise reduction image, so as to obtain an image of the region of interest, such as the third image shown in fig. 7.
S23, processing the third image by adopting a sobel edge detection operator method, and extracting the outline of the ablation area of the contact to obtain an outline image, as shown in FIG. 8. The contact ablation area profile edges are represented by white interrupted edge points in fig. 8.
And S3, mapping the outline of the contact ablation area to the original point cloud data set to obtain an ablation area point cloud data set corresponding to the contact ablation area.
The specific implementation method comprises the steps of extracting position information and corresponding height information of all pixel points on and in the outline of the contact ablation area from the original point cloud data set, and selectingDiscarding all pixel points outside the outline of the ablation area of the contact to obtain a point cloud data set of the ablation area; any point cloud data in the ablation region point cloud dataset may be represented as (x)i,j，yi,j，zi,j) Wherein m ismin≤i≤mmax，nmin≤j≤nmaxWherein m isminAnd mmaxRespectively representing the minimum value and the maximum value of the row number of each point cloud data in the ablation area point cloud data set in the corresponding original matrix; n isminAnd nmaxRespectively is the minimum value and the maximum value of the column number of each point cloud data in the ablation area point cloud data set in the corresponding original matrix.
The point cloud image corresponding to the ablation area point cloud dataset is recorded as a second point cloud image, as shown in fig. 9.
And S4, fitting the ablation area point cloud data set to obtain a reference surface of the contact surface topography.
The method for obtaining the reference surface of the contact surface topography by fitting the ablation area point cloud data set comprises the following steps: performing polynomial surface fitting on the point cloud data set of the fitting ablation area by adopting a least square method to obtain a reference surface of the surface appearance of the contact, and specifically comprising the following steps of:
the equation of the surface appearance reference surface of the contact to be fitted is set as follows:
f(x,y)＝a0+a1x+a2y+a3x2+a4xy+a5y2
the fitting error is then:
wherein x isi,jThe abscissa value of the point in the ith row and the jth column in the point cloud data set of the ablation area; y isi,jThe ordinate value of the point in the ith row and the jth column in the point cloud data set of the ablation area; z is a radical ofi,jHeight values of points in the ith row and the jth column in the point cloud data set of the ablation area;
a0、a1、a2、a3、a4and a5Respectively solving the deviation of the fitting errors:
the matrix form of the obtained parameters is:
the equation of the reference surface of the surface topography of the contact is obtained.
And (3) fitting the ablation area point cloud data set to obtain a point cloud image corresponding to the reference surface, and recording the point cloud image as a third point cloud image as shown in fig. 10.
In this example, the values of the parameters obtained are shown in table 1:
TABLE 1
| a0 | 878.29130352 |
| a1 | 4.7486787859×10-7 |
| a2 | 0.11477040006 |
| a3 | 5.90538×10-5 |
| a4 | 2.884004×10-6 |
| a5 | -0.0002129434 |
S5, calculating the height deviation between the ablation area point cloud data set and the corresponding point on the reference surface to obtain a corrected point cloud data set; the point cloud image corresponding to the corrected point cloud data set is recorded as a fourth point cloud image, as shown in fig. 11.
Eta (x) for height value of each point in point cloud data set after correctioni,j,yi,j) Is represented by eta (x)i,j,yi,j) The expression of (a) is: eta (x)i,j,yi,j)＝zi,j-f(xi,j,yi,j)。
And S6, calculating characterization parameters of the three-dimensional roughness of the contact ablation area morphology corresponding to the corrected point cloud data set. The characterization parameters include surface ten point height, surface arithmetic mean deviation, surface root mean square deviation, surface maximum peak height, surface maximum valley depth, skewness of surface height distribution, and kurtosis of surface height distribution.
(1) Ten points height of surface
The ten-point height of the surface indicates the integral deviation degree of the curved surface, the larger the value, the larger the integral deviation degree of the curved surface, and the value of an absolutely smooth plane is 0; to eta (x)i,j,yi,j) The values of (A) are sorted, and the top 5 five values are taken and are used as etahiFive numerical values representing, and ranking 5 names by etaliRepresenting, then obtaining the ten-point height of the surface:
(2) mean deviation of surface arithmetic
The surface arithmetic mean deviation refers to the arithmetic mean or geometric mean of the distances between points in the contour surface and the central plane, the surface arithmetic mean deviation reflects the arithmetic mean distribution of the height information of the surface roughness, and is a main measurement value with a concentration trend, the magnitude of the value can be expressed as the overall distance of the contour surface from the central plane, the larger the value is, the larger the deviation degree is, the expression is:
wherein M is the sum of the information points of the profile height of the ablation area of the contact, namely eta (x)i,j,yi,j) The number of the corresponding point cloud data in the corrected point cloud data set.
(3) Root mean square deviation of surface
The surface root mean square deviation represents the root mean square value from the actual surface to the reference surface, the value can be expressed as the integral surface root mean square deviation condition of the contour surface from the central plane, the larger the value is, the larger the deviation degree is, and the expression is:
wherein M is the sum of the information points of the profile height of the ablation area of the contact, namely eta (x)i,j,yi,j) The number of the corresponding point cloud data in the corrected point cloud data set.
(4) Maximum peak height of surface
The maximum peak height of the surface is eta (x)i,j,yi,j) The absolute value of the maximum, the greater this value, indicates that the extreme distribution of roughness of the surface is more severe, expressed as:
Sp＝|maxη(xi,j,yi,j)|
(5) maximum valley depth of surface
The maximum valley depth of the surface is eta (x)i,j,yi,j) The absolute value of the minimum, the larger this value, indicates that the extreme distribution of roughness of the surface is more severe, expressed as:
Sv＝|minη(xi,j,yi,j)|
(6) skewness of surface height distribution
The expression for the skewness of the surface height distribution is:
when the surface height is symmetrically distributed, the skewness is equal to 0; when the surface height distribution has a large "spike" above the evaluation datum, SskIs greater than 0; when the surface height distribution has a large "spike" on the side lower than the evaluation reference plane, Ssk＜0。
(7) Kurtosis of surface height distribution
The expression for the kurtosis of the surface height distribution is:
the kurtosis of the surface height distribution can be used to identify the stability of the surface. When the profile offset follows Gaussian distribution, the kurtosis value is close to 3, and when the kurtosis value is larger than 3, the shape of the amplitude distribution curve is steeper by taking the shape of the Gaussian distribution curve as a reference, and the curve is called as a sharp-peak state curve; when the kurtosis value is less than 3, the amplitude distribution curve is wider and flatter, and is called as a low-kurtosis curve.
In this embodiment, the values of the characterization parameters of the three-dimensional roughness of the contact ablation area topography corresponding to the corrected point cloud data set are shown in table 2:
TABLE 2
| Parameter name | Numerical value (Unit μm) |
| Sz | 20.6432 |
| Sa | 4.3261 |
| Sq | 6.2392 |
| Sp | 17.4912 |
| Sv | 31.5067 |
| Ssk | -1.9065 |
| Sku | 4.4452 |
Aiming at the contact analysis research of the silver wire type relay, firstly, extracting the outline of a contact ablation area on a contact surface topography image, and mapping the outline of the contact ablation area to an original point cloud data set to obtain an ablation area point cloud data set corresponding to the contact ablation area; fitting the ablation area point cloud data set to obtain a reference surface of the surface appearance of the contact; then calculating the height deviation between the ablation area point cloud data set and the corresponding point on the reference surface to obtain a corrected point cloud data set; finally, calculating the characterization parameters of the three-dimensional roughness of the ablation area morphology of the contact corresponding to the corrected point cloud data set, thereby realizing the analysis of the three-dimensional roughness of the ablation area morphology of the silver wire type relay contact; the analysis result is as follows: ten points of surface height Sz20.6432 μm, surface arithmetic mean deviation Sa4.3261 μm, surface root mean square deviation Sq6.2392 μm, the maximum peak height S of the surfacep17.4912 μm, the maximum valley depth S of the surfacev31.5067 μm, the above parametersThe deviation degree of the contact surface is expressed and compared with good contact surface roughness parameters, so that the deviation degree of the surface appearance, the degradation characteristics and the ablation condition can be obtained; value S of the degree of skewness of the surface height distributionskIs-1.9065 μm, SskLess than 0, indicating a large valley in the plane of the contact, which may be ablation loss; kurtosis S of surface height distributionku4.4452 μm, SkuGreater than 3 indicates that the contact surface has a steeper peak shape.
The method comprises the steps of processing a contact surface appearance image and an original point cloud data set to obtain a corrected point cloud data set, and calculating characterization parameters of three-dimensional roughness of the appearance of the contact ablation area according to the corrected point cloud data set.
The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. The foregoing is only a preferred embodiment of the present application, and it should be noted that there are no specific structures which are objectively limitless due to the limited character expressions, and it will be apparent to those skilled in the art that a plurality of modifications, decorations or changes can be made without departing from the principle of the present invention, and the technical features mentioned above can be combined in a suitable manner; such modifications, variations, combinations, or adaptations of the invention in other instances, which may or may not be practiced, are intended to be within the scope of the present application.
Claims (6)
1. A three-dimensional roughness characterization method for the morphology of an ablation area of a silver linear contact is characterized by comprising the following steps:
acquiring a contact surface topography image and an original point cloud data set corresponding to the surface topography image;
extracting the outline of the ablation area of the contact on the surface topography image of the contact;
mapping the outline of the contact ablation area to an original point cloud data set to obtain an ablation area point cloud data set corresponding to the contact ablation area;
fitting the ablation area point cloud data set to obtain a reference surface of the surface appearance of the contact;
calculating the height deviation between the ablation area point cloud data set and the corresponding point on the reference surface to obtain a corrected point cloud data set;
and calculating the characterization parameters of the three-dimensional roughness of the contact ablation area morphology corresponding to the corrected point cloud data set.
2. The method for characterizing the three-dimensional roughness of the ablation area morphology of the silver wire contact according to claim 1, wherein the extracting the contact ablation area profile on the contact surface morphology image specifically comprises:
carrying out noise reduction treatment on the contact surface topography image to obtain a noise reduction image;
masking the noise reduction image of the surface topography of the contact to obtain a third image;
and processing the third image by using a sobel edge detection operator method, and extracting the outline of the ablation area of the contact.
3. The method for characterizing the three-dimensional roughness of the ablation area morphology of the silver wire contact according to claim 2, wherein the noise reduction processing is performed on the surface morphology image of the contact to obtain a noise-reduced image, and specifically comprises:
performing illumination homogenization treatment on the contact surface topography image by adopting a two-dimensional gamma function to obtain a first image;
carrying out gray level processing on the first image to obtain a second image;
and carrying out gray level histogram equalization processing on the second image to obtain a noise reduction image.
4. The method for characterizing the three-dimensional roughness of the ablation area topography of the silver-wire contact according to claim 1, wherein the step of fitting the point cloud data set of the ablation area to obtain the reference plane of the contact surface topography comprises the following steps: and performing polynomial surface fitting on the point cloud data set of the fitting ablation area by adopting a least square method to obtain a reference surface of the surface appearance of the contact.
5. The method for characterizing the three-dimensional roughness of the ablation area morphology of the silver wire contact as claimed in claim 4, wherein the method for performing polynomial surface fitting on the point cloud data set of the fitted ablation area by using the least square method is as follows:
the equation of the surface appearance reference surface of the contact to be fitted is set as follows:
f(x,y)＝a0+a1x+a2y+a3x2+a4xy+a5y2
the fitting error is then:
wherein x isi,jThe abscissa value of the point in the ith row and the jth column in the point cloud data set of the ablation area; y isi,jThe ordinate value of the point in the ith row and the jth column in the point cloud data set of the ablation area; z is a radical ofi,jHeight values of points in the ith row and the jth column in the point cloud data set of the ablation area;
a0、a1、a2、a3、a4and a5Respectively solving the deviation of the fitting errors:
the matrix form of the obtained parameters is:
6. the method of claim 1, wherein the characterization parameters include ten point height of the surface, arithmetic mean deviation of the surface, root mean square deviation of the surface, maximum peak height of the surface, maximum valley depth of the surface, skewness of the distribution of the height of the surface, and kurtosis of the distribution of the height of the surface.
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| CN202110730278.4A Active CN113344823B (en) | 2021-06-29 | 2021-06-29 | Three-dimensional roughness characterization method for ablation area morphology of silver wire type contact |
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| WO2024045440A1 (en) * | 2022-08-31 | 2024-03-07 | 西安热工研究院有限公司 | Method for determining ablation degree of blade of gas turbine |
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