Method and system for establishing oilstone working surface morphology quantitative description model

Abstract

The invention relates to the technical field of oilstone surface topography, and provides a method and system for establishing an oilstone working surface topography quantitative description model. The method comprises the following steps: determining a reasonable sampling interval for shooting the surface appearance of the oilstone according to the specification of the oilstone; selecting an optical measuring instrument meeting the requirement, shooting the surface appearance of the oilstone, and outputting original measurement data; re-sampling the original data by adopting an expansion sampling interval method to obtain new morphology data; identifying abrasive particles on the surface of the oilstone by adopting an eight-point method, and counting and analyzing the bulge height distribution, the position distribution and the number of abrasive particles in unit area of the abrasive particles on the surface of the oilstone; the abrasive particle size distribution is quantitatively described by adopting normal distribution, the abrasive particle bulge height distribution is quantitatively described by adopting normal distribution or logarithmic normal distribution, and the abrasive particle position distribution is quantitatively described by adopting uniform distribution; an oilstone working surface appearance quantitative description model is established by adopting abrasive particle size distribution, abrasive particle number per unit area, abrasive particle bulge height distribution and abrasive particle position distribution.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil stone surface topography research, and particularly relates to a method and system for establishing a quantitative description model of an oil stone working surface topography. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] Superfinishing is a kind of finishing method, which can greatly reduce the surface roughness of a workpiece in a short time, remove grinding defects and modified layer, and improve the service life of the workpiece, and is generally used as the final machining process for parts such as bearing rings. As a superfinishing tool, the surface topography of the oil stone has a crucial influence on the quality and precision of the machined surface. At present, in order to obtain a high-quality superfinishing surface, a large amount of time is spent on experiments to determine reasonable superfinishing process parameters, and the experimental workload is huge. By establishing a superfinishing surface topography prediction model, theoretically analyzing the influence of superfinishing parameters on the superfinishing surface topography, and further determining reasonable superfinishing process parameters, the experimental workload can be greatly reduced, and the production efficiency can be improved. The modeling of the working surface topography of the oil stone is a prerequisite for establishing the superfinishing surface topography prediction model.

[0004] At present, in the modeling of the surface topography of the oil stone, only optical measuring instruments are used to shoot the surface topography of the oil stone, and the surface topography characteristics of the oil stone are qualitatively analyzed. Due to the lack of an oil stone surface grain recognition method, the statistical and analytical data of the input parameters required for the modeling of the surface topography of the oil stone, including the grain size distribution, the grain protrusion height distribution, the grain position distribution, and the number of grains per unit area, are insufficient, and the modeling of the surface topography of the oil stone cannot be realized. SUMMARY

[0005] In order to solve the technical problems in the background art, the present application provides a method and system for establishing a quantitative description model of an oil stone working surface topography. The present application first uses an optical measuring instrument to shoot the surface topography of the oil stone, then performs a discrete post-processing on the shot image of the surface topography of the oil stone, and further establishes an oil stone surface grain recognition method. On this basis, the input parameters required for the modeling of the surface topography of the oil stone are statistically analyzed, and finally the modeling of the surface topography of the oil stone is realized.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: The first aspect of the present application provides a method for establishing a quantitative description model of an oil stone working surface topography.

[0007] According to the particle size of the oil stone grain M , a reasonable sampling interval required for shooting the surface topography of the oil stone is calculated ; Adopting optical measuring instrument meeting shooting requirements, shooting oilstone surface topography, calculating oilstone surface topography original measurement data Z 0( x , y ), x and y represent position coordinates of pixel points, Z 0 represents topography height at pixel point; Adopting extended sampling interval method, resampling original measurement data Z 0( x , y ) along X axis and Y axis direction, screening out pixel points with interval Z 0( x , y ) in , obtaining first oilstone surface topography data Z 1( x , y ); Based on first oilstone surface topography data Z 1( x , y ), adopting eight-point method to identify oilstone surface abrasive grains, and screening out pixel points at positions of abrasive grains, forming second oilstone surface topography data Z 2( x , y ); Based on second oilstone surface topography data Z 2( x , y ), statistically analyzing abrasive grain number per unit area of oilstone surface, position distribution law and abrasive grain protrusion height distribution law; adopting normal distribution, quantitatively describing size distribution law of oilstone abrasive grains; adopting normal distribution or logarithmic normal distribution, quantitatively describing protrusion height distribution law of oilstone surface abrasive grains; adopting uniform distribution, quantitatively describing position distribution law of oilstone surface abrasive grains; Adopting abrasive grain size distribution, abrasive grain number distribution per unit area, abrasive grain protrusion height distribution and abrasive grain position distribution, establishing oilstone working surface topography quantitative description model.

[0008] Further, the reasonable sampling interval is calculated by following formula:

[0009] wherein, d mean is average size of abrasive grains, which can be obtained by referring to relevant standards or estimated by following formula: d mean= 15.2 / M .

[0010] Further, the optical measuring instrument satisfying the shooting requirements is adopted, including: requiring reasonable sampling interval is an integer multiple of the initial sampling interval of the optical measuring instrument, that is , i= 1, 2, 3, ….

[0011] Further, the initial sampling interval is calculated by the following formula:

[0012] Wherein, L x , L y respectively represent the length and width of the shooting window, A × B indicates the resolution of the optical measuring instrument.

[0013] Further, the eight-point method is used to identify the abrasive grains on the oil stone surface, which includes: taking a certain pixel point as the center, comparing it with the eight nearest points around it, if the topography height of the point is higher than that of the eight points around it, it is defined that there is an abrasive grain at the point.

[0014] Further, based on the second oil stone surface topography data Z 2( x , y ), the number of abrasive grains per unit area, the position distribution law and the abrasive grain protrusion height distribution law of the oil stone surface are statistically analyzed; the method includes: Counting the number of all pixel points in the second oil stone surface topography data Z 2( x , y ), that is, the total number of abrasive grains on the oil stone surface in the shooting window, recorded as N h , the measured value of the number of abrasive grains per unit area N s is calculated by the following formula: N s = N h / ( L x × L y ) Any pixel point in the second oil stone surface topography data Z 2( x , y )​i The height of the shape is recorded as h i ,Will h i The maximum and minimum values ​​of h max and h min , then the range of the height of the abrasive protrusion is [ h min , h max ]; divide the range into several sub-ranges evenly, and count the number of pixels in each sub-range, and further obtain the height of the wear particle protrusion in the range [ h min , h max ] within the cumulative probability distribution; Statistical analysis of the second oilstone surface morphology data Z 2( x , y ) of all pixels within X axis, Y Axis coordinates, X The distribution range of the axis coordinates is recorded as [0, x max ], Y The distribution range of the axis coordinates is recorded as [0, y max ]. The rectangular area [0, x max ]×[0, y max ] is evenly divided into several subdomains, and the number of pixels in each subdomain is counted to further obtain the distribution position of the wear particles in the rectangular area [0, x max ]×[0, y max ] is the cumulative probability distribution within .

[0015] Furthermore, the method of using normal distribution to quantitatively describe the size distribution of oilstone abrasive particles includes: Normal distribution probability density function of wear particle size f ( d ), can be calculated using the following formula:

[0016] in, σ d is the standard deviation of the abrasive particle size and is calculated using the following formula: σ d =( d max- d min ) / 6 wherein, d min and d max are the minimum and maximum abrasive grain sizes respectively, which can be determined by referring to relevant national standards.

[0017] Further, the normal distribution or the lognormal distribution is adopted to quantitatively describe the protruding height distribution law of the abrasive grains on the oilstone surface, and the method comprises the following steps. the average value of the protruding height of the abrasive grains h mean is calculated by using the following formula:

[0018] the standard deviation of the protruding height of the abrasive grains σ h is calculated by using the following formula:

[0019] the truncated normal distribution probability density function of the protruding height of the abrasive grains f bn ( h ) is calculated by using the following formula:

[0020] wherein, f n ( h ) is the ideal normal distribution probability density function, which is calculated by using the following formula:

[0021] the average value of the logarithm of the protruding height of the abrasive grains h mean_ln is calculated by using the following formula:

[0022] the standard deviation of the logarithm of the protruding height of the abrasive grains σ h_ln is calculated by using the following formula:

[0023] the truncated lognormal distribution probability density function of the protruding height of the abrasive grains f bln ( h ) is calculated by using the following formula:

[0024] wherein, f ln (h ) is the ideal lognormal distribution probability density function, which is calculated using the following formula: .

[0025] Furthermore, the method of using uniform distribution to quantitatively describe the position distribution of abrasive particles on the surface of the oilstone includes: The uniform distribution probability density function of the abrasive particle distribution position on the oilstone surface is calculated using the following formula: .

[0026] Furthermore, the method of establishing a quantitative description model of the working surface morphology of the oilstone by using the abrasive particle size distribution, the number of abrasive particles per unit area, the abrasive particle protrusion height distribution and the abrasive particle position distribution includes: In the rectangular coordinate system, the working surface of the oilstone is established, and its distribution range is the rectangular area [0, x max ]×[0, y max ]; Randomly generate the coordinates of the wear particle position in the rectangular area in accordance with the uniform distribution law ( x i , y i ), generating a total of N t points, and N t = N s ·x max ·y max ; Apply abrasive grain protrusion height that conforms to normal distribution or log-normal distribution to all points in the rectangular area. h i ; At each point with a protrusion height in the rectangular area, a spherical abrasive particle with a size that conforms to the normal distribution is established, and finally a quantitative description model of the oilstone working surface morphology is established.

[0027] A second aspect of the present invention provides a system for establishing a quantitative description model of the working surface morphology of an oilstone.

[0028] A system for establishing a quantitative description model of the surface morphology of an oilstone working surface, comprising: The data acquisition module is configured to: M , calculate the reasonable sampling interval required to photograph the surface morphology of the oilstone ; Use optical measuring instruments that meet the shooting requirements to shoot the surface morphology of the oilstone and calculate the original measurement data of the surface morphology of the oilstoneZ 0( x , y ), x and y Indicates the position coordinates of the pixel point, Z 0 represents the height of the topography at the pixel point; The first sampling module is configured to: use the extended sampling interval method to sample the original measurement data Z 0( x , y ) to resample along X Axis and Y The axis direction will Z 0( x , y ) Middle interval The pixel points are filtered out to obtain the first oilstone surface morphology data Z 1( x , y ); The second sampling module is configured to: based on the first oilstone surface morphology data Z 1( x , y ), the eight-point method is used to identify the abrasive particles on the surface of the oilstone, and the pixel points where the abrasive particles are located are screened out to form the second oilstone surface morphology data Z 2( x , y ); The statistical analysis module is configured to: based on the second oilstone surface morphology data Z 2( x , y ) to statistically analyze the number of abrasive particles per unit area on the oilstone surface, the distribution of their positions, and the distribution of the height of the abrasive particles protruding from the oilstone; using normal distribution to quantitatively describe the size distribution of the abrasive particles on the oilstone; using normal distribution or lognormal distribution to quantitatively describe the height distribution of the abrasive particles protruding from the oilstone surface; using uniform distribution to quantitatively describe the position distribution of the abrasive particles on the oilstone surface; The model building module is configured to establish a quantitative description model of the oilstone working surface morphology by using abrasive particle size distribution, abrasive particle number per unit area, abrasive particle protrusion height distribution and abrasive particle position distribution.

[0029] A third aspect of the present invention provides a computer device, comprising: a processor adapted to execute a computer program; Computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the method for establishing a quantitative description model of the surface morphology of an oilstone working surface as described in the first aspect above are implemented.

[0030] Compared with the prior art, the present application has the beneficial effects that: The present application provides a method for establishing a quantitative description model of the working surface topography of oilstone, and based on the method, a surface topography prediction model of the superfinishing of oilstone can be further established, the internal correlation between the superfinishing process parameters and the surface topography of the superfinishing of oilstone can be analyzed, and the experimental workload required for formulating a superfinishing process scheme can be greatly reduced, so that the production efficiency can be improved and the production cost can be reduced.

[0031] The present application can further establish a surface topography prediction model of the superfinishing of oilstone by establishing a quantitative description model of the working surface topography of oilstone, and the influence mechanism of the surface topography of oilstone on the surface topography of the superfinishing of oilstone can be revealed, so that theoretical guidance can be provided for the formula design of oilstone. BRIEF DESCRIPTION OF DRAWINGS

[0032] The drawings accompanying the specification of the present application form part of the present application and serve to further understand the present application, and the schematic embodiments of the present application and the description thereof serve to explain the present application and do not constitute an improper limitation on the present application.

[0033] Figure 1 is a flow chart of the establishment of a quantitative description model of the working surface topography of oilstone according to the embodiment of the present application; Figure 2 is a schematic diagram of the relationship between the initial sampling interval and the shooting window and the resolution; Figure 3 is a principle diagram of the extended sampling interval method according to the embodiment of the present application; Figure 4 is a principle diagram of the eight-point method for identifying the abrasive grains of oilstone according to the embodiment of the present application; Figure 5 is an oilstone surface topography image shot by a super-depth microscope according to the embodiment of the present application; Figure 6 is a comparison diagram of the cumulative probability distribution of the protrusion height of abrasive grains according to the embodiment of the present application; Figure 7 is a distribution position diagram of the abrasive grains of oilstone according to the embodiment of the present application; Figure 8 is a comparison diagram of the cumulative probability distribution of the distribution position of abrasive grains according to the embodiment of the present application; Figure 9 is a probability density distribution diagram of normal distribution according to the embodiment of the present application; Figure 10 is an established oilstone working surface topography diagram according to the embodiment of the present application; Figure 11 is a measured oilstone working surface topography diagram according to the embodiment of the present application; Figure 121 is a schematic structural diagram of a system for establishing a quantitative description model of the working surface topography of an oilstone according to an embodiment of the present invention; Figure 13 It is a structural diagram of a computer device shown in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0036] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0037] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to the various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code can include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of the boxes in the flowchart and / or block diagram, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0038] The present invention is described in detail below through several embodiments: Example 1 like Figure 1 As shown, this embodiment provides a method for establishing a quantitative description model of the surface morphology of an oilstone working surface, the method comprising the following steps: Step (1), according to the particle size of the oilstone abrasive M , calculate the reasonable sampling interval required to photograph the surface morphology of the oilstone , calculated using the following formula:

[0039] in, d mean is the average size of the abrasive particles, which can be obtained by referring to relevant standards or estimated using the following formula: d mean =15.2 / M Step (2): Select an optical measuring instrument that meets the shooting requirements and requires a reasonable sampling interval is the initial sampling interval of the optical measuring instrument An integer multiple of , i= 1, 2, 3, …. Initial sampling interval Calculated using the following formula:

[0040] Where, L x 、 L y Respectively represent the length and width of the shooting window, A × B Indicates the resolution of the optical measuring instrument, the initial sampling interval The relationship between the shooting window and resolution is as follows Figure 2 shown.

[0041] Step (3): Use an optical measuring instrument that meets the shooting requirements to shoot the surface morphology of the oilstone, and use post-processing software to output the original measurement data of the surface morphology of the oilstone. Z 0( x , y ), x and y Indicates the position coordinates of the pixel point, Z 0 represents the height of the topography at the pixel point.

[0042] Step (4) uses the extended sampling interval method to Z 0( x , y ) to resample and obtain the morphological data Z 1( x , y ), that is, along x and y The direction will Z 0( x , y ) Middle interval The pixel points are screened out to form the first oilstone surface morphology data Z1 x , y ). The principle of the extended sampling interval method is shown in Figure 3 .

[0043] Step (5), based on the first oil stone surface topography data Z 1 x , y , the eight-point method is used to identify the oil stone surface abrasive particles, and the pixel points corresponding to the abrasive particles are screened out to form the second oil stone surface topography data Z 2 x , y .

[0044] The principle of the eight-point method for identifying the oil stone surface abrasive particles is shown in Figure 4 , taking a pixel point Z ( i +1, j +1) as the center, it is compared with the eight nearest neighboring points around it, if the topography height of the point is higher than that of the eight points around it, i.e.

[0045] , it is defined that there is an abrasive particle at the point. Based on this principle, the pixel points that meet the eight-point method are screened out by cyclic calculation, and the second oil stone surface topography data Z 2 x , y is formed.

[0046] Step (6), based on the second oil stone surface topography data Z 2 x , y , the number of abrasive particles per unit area, the position distribution law and the protrusion height distribution law of the oil stone surface are statistically analyzed.

[0047] The number of all pixel points in the second oil stone surface topography data Z 2 x , y is counted, that is, the total number of abrasive particles on the oil stone surface in the shooting window, denoted as N h , then the measured value of the number of abrasive particles per unit area is N s , calculated by the following formula: N s = N h / ( L x × L y ) The second oil stone surface topography data Z 2x , y ) of any pixel point in the image i is recorded as h i , i.e. h i = Z 2( x i , y i ), the maximum and minimum values of h i are recorded as h max and h min , respectively. The value range of the protruding height of the abrasive particles is [ h min , h max ]. The value range is uniformly divided into several sub-ranges, and the number of pixel points in each sub-range is counted to further obtain the cumulative probability distribution of the protruding height of the abrasive particles in the value range [ h min , h max ].

[0048] Statistical analysis of the surface topography data of the second oil stone Z 2( x , y ) of all pixel points in the image X axis, Y axis coordinate, X axis coordinate is recorded as [0, x max ], Y axis coordinate is recorded as [0, y max ]. The rectangular region [0, x max ]×[0, y max ] is uniformly divided into several sub-regions, and the number of pixel points in each sub-region is counted to further obtain the cumulative probability distribution of the abrasive particle distribution position in the rectangular region [0, x max ]×[0, y max ].

[0049] Step (7), the size distribution of the oil stone abrasive particles is quantitatively described by using normal distribution. The normal distribution probability density function of the abrasive particle size f ( d ) is calculated by using the following formula:

[0050] in, σ d is the standard deviation of the abrasive particle size and is calculated using the following formula: σ d =( d max - d min ) / 6 in, d min and d max They are the minimum and maximum abrasive sizes, respectively, which can be determined by checking relevant national standards.

[0051] The normal distribution or lognormal distribution is used to quantitatively describe the distribution of the height of the abrasive grains on the oilstone surface. The average height of the abrasive grains is h mean , calculated using the following formula:

[0052] Standard deviation of abrasive grain protrusion height σ h , calculated using the following formula:

[0053] Probability density function of truncated normal distribution of wear particle protrusion height f bn ( h ), calculated using the following formula:

[0054] in, f n ( h ) is the probability density function of the ideal normal distribution, which is calculated using the following formula:

[0055] Average value of the logarithm of the height of the abrasive grains h mean_ln , calculated using the following formula:

[0056] Standard deviation of the logarithm of the wear particle height σ h_ln , calculated using the following formula:

[0057] Probability density function of truncated lognormal distribution of wear particle protrusion height f bln (h ), calculated by the following formula:

[0058] wherein, f ln ( h ) is the ideal lognormal distribution probability density function, calculated by the following formula:

[0059] The uniform distribution is adopted to quantitatively describe the position distribution law of the abrasive grains on the oilstone surface. The uniform distribution probability density function of the abrasive grain distribution position on the oilstone surface is calculated by the following formula:

[0060] Step (8), the abrasive grain size distribution, the number of abrasive grains per unit area, the abrasive grain protrusion height distribution and the abrasive grain position distribution are adopted to establish the quantitative description model of the oilstone working surface topography.

[0061] Firstly, in the rectangular coordinate system, the oilstone working surface is established, and the distribution range thereof is a rectangular region [0, x max ]x[0, y max ].

[0062] Then, the abrasive grain position coordinates ( x i , y i ) conforming to the uniform distribution law are randomly generated in the rectangular region, and a total of N t points are generated, and N t = N s ·x max ·y max .

[0063] Thirdly, the abrasive grain protrusion height h i conforming to the normal distribution law or the lognormal distribution law is applied to all the points in the rectangular region.

[0064] Finally, the spherical abrasive grains with the size conforming to the normal distribution are established at each point in the rectangular region having the protrusion height, and the quantitative description model of the oilstone working surface topography is finally established.

[0065] Further, in step (8), since the binder does not participate in the actual superfinishing process, only the abrasive grains are established when the oilstone surface topography is established, and the binder is ignored.

[0066] Example 2 This embodiment is one of the implementations of the first embodiment of the present invention and should not be understood as limiting the present invention.

[0067] (1) Determination of reasonable sampling interval: Taking the vitrified bond CBN oilstone with a concentration of 75% and a particle size of M3 / 6 as an example, the average abrasive particle size can be obtained by referring to "JB / T 7990-2012 Superhard abrasives: artificial diamond and cubic boron nitride powder". d mean 4.5μm, a reasonable sampling interval ∆ Should be between 1.125μm ~ 1.5μm.

[0068] (2) Selection of optical measuring instruments: The window size of the ultra-depth of field microscope is 0.488mm×0.689mm, the resolution is 1445×2040, and the initial sampling interval is 0.34μm, (1.36μm) at a reasonable sampling interval Therefore, the ultra-depth-of-field microscope meets the needs of photographing the surface morphology of oilstone.

[0069] (3) Photographing the surface morphology of the oilstone: Use an ultra-depth-of-field microscope to photograph the surface morphology of the oilstone, such as Figure 5 As shown. Use Gwyddion processing software to output the original measurement data of the oilstone surface morphology Z 0( x , y ), x and y Indicates the position coordinates of the pixel point, Z 0 represents the height of the topography at the pixel point.

[0070] (4) Resampling of morphological data: Using the extended sampling interval method, the original measurement data Z 0( x , y ) to resample along x and y The direction will Z 0( x , y ) Middle interval (1.36μm) pixels are screened out to form topography data Z 1( x , y ).

[0071] (5) Identification of wear particles on the oilstone surface: based on the surface morphology data of the oilstone Z 1( x , y), the eight-point method is used to identify the abrasive particles on the surface of the oilstone, and the pixel points corresponding to the abrasive particles are selected to form the morphological data Z 2( x , y ).

[0072] (6) Statistical analysis of input parameters: based on oilstone surface morphology data Z 2( x , y ), statistical analysis of the number of abrasive particles per unit area on the oilstone surface N s Equal to 38180 pieces / mm 2 .

[0073] The cumulative probability distribution of wear particle protrusion height is compared with the cumulative probability distribution of normal distribution and lognormal distribution, as shown in Figure 6 As shown, it can be seen that the height of the abrasive protrusion is more consistent with the log-normal distribution.

[0074] The actual distribution position of abrasive particles on the surface of oilstone is as follows: Figure 7 As shown in the figure, the analysis of its cumulative probability distribution shows that the distribution of wear particle positions conforms to the uniform distribution law. The comparison between the cumulative probability distribution of wear particle distribution positions and the cumulative probability distribution of uniform distribution is shown in the figure. Figure 8 As shown, it can be seen that there is good consistency between the two.

[0075] The abrasive particle size distribution range is 3μm~6μm. The normal distribution is used to quantitatively describe the size distribution law of oilstone abrasive particles. The normal distribution probability density function is as follows: Figure 9 shown.

[0076] (7) Modeling of oilstone surface morphology: Using the abrasive particle size distribution that conforms to the normal distribution, the abrasive particle protrusion height distribution that conforms to the logarithmic normal distribution, the abrasive particle position distribution that conforms to the uniform distribution, and the number of abrasive particles per unit area obtained in step (6), a quantitative description model of the oilstone working surface morphology is established, such as Figure 10 shown. Figure 11 is the measured surface morphology of the oilstone. Figure 10 and Figure 11 The binder was ignored and only the abrasive particles were retained. It can be seen that the calculated results of the oilstone surface morphology are in good agreement with the measured results.

[0077] At this point, a quantitative description model for the surface morphology of oilstone has been established.

[0078] Combination of the above Figure 1 The method for establishing a quantitative description model of the working surface morphology of an oilstone provided by an embodiment of the present invention is introduced in detail. Next, the system for establishing a quantitative description model of the working surface morphology of an oilstone provided by an embodiment of the present invention will be introduced with reference to the accompanying drawings.

[0079] Example 3 Figure 12 This is a schematic diagram of the structure of a system for establishing a quantitative description model of the working surface morphology of an oilstone according to an embodiment of the present invention, referring to Figure 12 , the system of the present invention comprises: The data acquisition module is configured to: M , calculate the reasonable sampling interval required to photograph the surface morphology of the oilstone ; Use optical measuring instruments that meet the shooting requirements to shoot the surface morphology of the oilstone and calculate the original measurement data of the surface morphology of the oilstone Z 0( x , y ), x and y Indicates the position coordinates of the pixel point, Z 0 represents the height of the topography at the pixel point; The first sampling module is configured to: use the extended sampling interval method to sample the original measurement data Z 0( x , y ) to resample along X Axis and Y The axis direction will Z 0( x , y ) Middle interval The pixel points are filtered out to obtain the first oilstone surface morphology data Z 1( x , y ); The second sampling module is configured to: based on the first oilstone surface morphology data Z 1( x , y ), the eight-point method is used to identify the abrasive particles on the surface of the oilstone, and the pixel points where the abrasive particles are located are screened out to form the second oilstone surface morphology data Z 2( x , y ); The statistical analysis module is configured to: based on the second oilstone surface morphology data Z 2( x , y ) to statistically analyze the number of abrasive particles per unit area on the oilstone surface, the distribution of their positions, and the distribution of the height of the abrasive particles protruding from the oilstone; using normal distribution to quantitatively describe the size distribution of the abrasive particles on the oilstone; using normal distribution or lognormal distribution to quantitatively describe the height distribution of the abrasive particles protruding from the oilstone surface; using uniform distribution to quantitatively describe the position distribution of the abrasive particles on the oilstone surface; The model building module is configured to establish a quantitative description model of the oilstone working surface morphology by using abrasive particle size distribution, abrasive particle number per unit area, abrasive particle protrusion height distribution and abrasive particle position distribution.

[0080] The system for establishing a quantitative description model of the working surface of an oilstone according to an embodiment of the present invention may correspond to the method described in the embodiment of the present invention, and the above and other operations and / or functions of each module of the system for establishing a quantitative description model of the working surface of an oilstone are respectively to achieve Figure 1 For the sake of brevity, the corresponding processes of each method in are not repeated here.

[0081] Example 4 See also Figure 13 The computer device shown in the figure has a structure diagram, which includes a processor, a communication interface, and a computer-readable storage medium. The processor, communication interface, and computer-readable storage medium can be connected via a bus or other means. The communication interface is used to receive and send data. The computer-readable storage medium can be stored in the memory of the computer device, and the computer-readable storage medium is used to store a computer program, which includes program instructions. The processor is used to execute the program instructions stored in the computer-readable storage medium. The processor (or CPU (Central Processing Unit)) is the computing core and control core of the computer device. It is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding steps in the embodiment of the method for establishing a quantitative description model of the working surface morphology of an oilstone.

[0082] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0083] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxesFigure 1 A device that provides the functions specified in a block or multiple blocks.

[0084] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes ​ A step that specifies a function in one or more boxes.

[0086] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0087] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for establishing a quantitative description model of the working surface morphology of an oilstone, characterized in that: include: According to the particle size of the oilstone M , calculate the reasonable sampling interval required to photograph the surface morphology of the oilstone ; Use optical measuring instruments that meet the shooting requirements to shoot the surface morphology of the oilstone and calculate the original measurement data of the surface morphology of the oilstone Z 0( x , y ), x and y Indicates the position coordinates of the pixel point, Z 0 represents the height of the topography at the pixel point; The extended sampling interval method is used to Z 0( x , y ) to resample along X Axis and Y The axis direction will Z 0( x , y ) Middle interval The pixel points are filtered out to obtain the first oilstone surface morphology data Z 1( x , y ); Based on the surface morphology data of the first oilstone Z 1( x , y ), the eight-point method is used to identify the abrasive particles on the surface of the oilstone, and the pixel points where the abrasive particles are located are screened out to form the second oilstone surface morphology data Z 2( x , y ); Based on the surface morphology data of the second oilstone Z 2( x , y ) to statistically analyze the number of abrasive particles per unit area on the oilstone surface, the distribution of their positions, and the distribution of the height of the abrasive particles protruding from the oilstone; using normal distribution to quantitatively describe the size distribution of the abrasive particles on the oilstone; using normal distribution or lognormal distribution to quantitatively describe the height distribution of the abrasive particles protruding from the oilstone surface; using uniform distribution to quantitatively describe the position distribution of the abrasive particles on the oilstone surface; A quantitative description model of the oilstone working surface morphology was established using the abrasive particle size distribution, the number of abrasive particles per unit area, the abrasive particle protrusion height distribution and the abrasive particle position distribution.

2. The method for establishing a quantitative description model of the working surface morphology of an oilstone according to claim 1, characterized in that: Use optical measuring instruments that meet the shooting requirements, including: requiring a reasonable sampling interval is the initial sampling interval of the optical measuring instrument An integer multiple of , i= 1, 2, 3, …; The reasonable sampling interval , calculated using the following formula: in, d mean is the average abrasive particle size, or it can be estimated using the following formula: d mean = 15.2 / M ； The initial sampling interval , calculated using the following formula: in, L x 、 L y Respectively represent the length and width of the shooting window, A × B Indicates the resolution of an optical measuring instrument.

3. The method for establishing a quantitative description model of the working surface morphology of an oilstone according to claim 1, characterized in that: The eight-point method is used to identify abrasive particles on the surface of the oilstone. The method includes: taking a certain pixel point as the center, comparing it with the eight nearest points around the point, and if the height of the point is higher than the eight surrounding points, it is defined that there is an abrasive particle at the point.

4. The method for establishing a quantitative description model of the working surface morphology of an oilstone according to claim 1, characterized in that: Based on the surface morphology data of the second oilstone Z 2( x , y ), statistically analyzing the number of abrasive grains per unit area on the oilstone surface, the distribution pattern of their positions, and the distribution pattern of the height of the abrasive grain protrusions; the method includes: Statistics of the second oilstone surface morphology data Z 2( x , y ), that is, the total number of abrasive particles on the oilstone surface in the shooting window, is recorded as N h , then the measured value of the number of abrasive particles per unit area is N s , calculated using the following formula: N s = N h / ( L x × L y ) The surface morphology data of the second oilstone Z 2( x , y ) any pixel within i The height of the shape is recorded as h i ,Will h i The maximum and minimum values ​​of h max and h min , then the range of the height of the abrasive protrusion is [ h min , h max ]; divide the range into several sub-ranges evenly, and count the number of pixels in each sub-range, and further obtain the height of the wear particle protrusion in the range [ h min , h max ] within the cumulative probability distribution; Statistical analysis of the second oilstone surface morphology data Z 2( x , y ) of all pixels within X axis, Y Axis coordinates, X The distribution range of the axis coordinates is recorded as [0, x max ], Y The distribution range of the axis coordinates is recorded as [0, y max ]. The rectangular area [0, x max ]×[0, y max ] is evenly divided into several subdomains, and the number of pixels in each subdomain is counted to further obtain the distribution position of the wear particles in the rectangular area [0, x max ]×[0, y max ] is the cumulative probability distribution within .

5. The method for establishing a quantitative description model of the working surface morphology of an oilstone according to claim 1, characterized in that: The method of using normal distribution to quantitatively describe the size distribution of oilstone abrasive particles includes: Normal distribution probability density function of wear particle size f ( d ), can be calculated using the following formula: in, σ d is the standard deviation of the abrasive particle size and is calculated using the following formula: σ d =( d max - d min ) / 6 in, d min and d max are the minimum and maximum abrasive grain sizes, respectively.

6. The method for establishing a quantitative description model of the working surface morphology of an oilstone according to claim 1, characterized in that: The method of using normal distribution or lognormal distribution to quantitatively describe the distribution law of the protrusion height of the abrasive particles on the surface of the oilstone includes: Average height of abrasive grain protrusion h mean , calculated using the following formula: Standard deviation of abrasive protrusion height σ h , calculated using the following formula: Probability density function of truncated normal distribution of wear particle protrusion height f bn ( h ), calculated using the following formula: in, f n ( h ) is the probability density function of the ideal normal distribution, which is calculated using the following formula: Average value of the logarithm of the height of the abrasive grains h mean_ln , calculated using the following formula: Standard deviation of the logarithm of the wear particle height σ h_ln , calculated using the following formula: Probability density function of truncated lognormal distribution of wear particle protrusion height f bln ( h ), calculated using the following formula: in, f ln ( h ) is the ideal lognormal distribution probability density function, which is calculated using the following formula: 。 7. The method for establishing a quantitative description model of the working surface morphology of an oilstone according to claim 1, characterized in that: The method of using uniform distribution to quantitatively describe the position distribution of abrasive particles on the surface of the oilstone includes: The uniform distribution probability density function of the abrasive particle distribution position on the oilstone surface is calculated using the following formula: 。 8. The method for establishing a quantitative description model of the working surface morphology of an oilstone according to claim 1, characterized in that: The method of establishing a quantitative description model of the working surface morphology of the oilstone by using the abrasive particle size distribution, the abrasive particle number distribution per unit area, the abrasive particle protrusion height distribution and the abrasive particle position distribution includes: In the rectangular coordinate system, the working surface of the oilstone is established, and its distribution range is the rectangular area [0, x max ]×[0, y max ]; Randomly generate the coordinates of the wear particle position in the rectangular area in accordance with the uniform distribution law ( x i , y i ), generating a total of N t points, and N t = N s ·x max ·y max ; Apply abrasive grain protrusion height that conforms to normal distribution or log-normal distribution to all points in the rectangular area. h i ; At each point with a protrusion height in the rectangular area, a spherical abrasive particle with a size that conforms to the normal distribution is established, and finally a quantitative description model of the oilstone working surface morphology is established.

9. A system for establishing a quantitative description model of the surface morphology of an oilstone working surface, characterized in that: include: The data acquisition module is configured to: M , calculate the reasonable sampling interval required to photograph the surface morphology of the oilstone ; Use optical measuring instruments that meet the shooting requirements to shoot the surface morphology of the oilstone and calculate the original measurement data of the surface morphology of the oilstone Z 0( x , y ), x and y Indicates the position coordinates of the pixel point, Z 0 represents the height of the topography at the pixel point; The first sampling module is configured to: use the extended sampling interval method to sample the original measurement data Z 0( x , y ) to resample along X Axis and Y The axis direction will Z 0( x , y ) Middle interval The pixel points are filtered out to obtain the first oilstone surface morphology data Z 1( x , y ); The second sampling module is configured to: based on the first oilstone surface morphology data Z 1( x , y ), the eight-point method is used to identify the abrasive particles on the surface of the oilstone, and the pixel points where the abrasive particles are located are screened out to form the second oilstone surface morphology data Z 2( x , y ); The statistical analysis module is configured to: based on the second oilstone surface morphology data Z 2( x , y ) to statistically analyze the number of abrasive particles per unit area on the oilstone surface, the distribution of their positions, and the distribution of the height of the abrasive particles protruding from the oilstone; using normal distribution to quantitatively describe the size distribution of the abrasive particles on the oilstone; using normal distribution or lognormal distribution to quantitatively describe the height distribution of the abrasive particles protruding from the oilstone surface; using uniform distribution to quantitatively describe the position distribution of the abrasive particles on the oilstone surface; The model building module is configured to establish a quantitative description model of the oilstone working surface morphology by using abrasive particle size distribution, abrasive particle number per unit area, abrasive particle protrusion height distribution and abrasive particle position distribution.

10. A computer device, characterized in that: a processor adapted to execute a computer program; A computer-readable storage medium having a computer program stored therein, wherein the computer program, when executed by the processor, implements the steps of the method for establishing a quantitative description model of the working surface morphology of an oilstone as described in any one of claims 1 to 8.

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