CN113570703B - Three-dimensional digital model porosity calculation method based on image recognition technology - Google Patents
Three-dimensional digital model porosity calculation method based on image recognition technology Download PDFInfo
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Abstract
The invention discloses a three-dimensional digital model porosity calculation method based on an image recognition technology, which comprises the following steps: s1, slicing the three-dimensional digital model; s2, binarizing the slice image; s3, calculating pore information of the slice by an image recognition method; s4, calculating the porosity of the three-dimensional digital entity; the invention has the advantages that: the three-dimensional digital model slice is processed into a photo form, the three-dimensional entity porosity is approximately deduced through the information of the two-dimensional photo, the model complexity is insensitive, the porosity is directly obtained through the image, and under the condition that the digital model precision is enough and the image resolution is large enough, the theoretical calculation porosity result can be infinitely approximate to the true value, the processing operation is simple and convenient, the applicability is strong, and the calculation result meets the precision requirement.
Description
Technical Field
The invention relates to the technical field of aperture ratio calculation, in particular to a three-dimensional digital model aperture ratio calculation method based on an image recognition technology.
Background
In post-processing analysis of numerical simulation, quality assessment of a workpiece is often involved, and the existence of pores can cause a plurality of adverse effects in a printed part, so that calculation of porosity is an important part in the quality assessment process;
the existing numerical simulation post-processing software does not integrate a porosity calculation module according to the requirements of certain problems, and the porosity is often calculated by manually extracting result data; and if the operation calculation object is directly a three-dimensional digital object, the extracted data is often huge in information quantity and can be difficult to process due to high complexity of the model.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a three-dimensional digital model aperture ratio calculation method based on an image recognition technology. And the operation object is subjected to dimension reduction treatment and then is subjected to analysis and calculation, the porosity is directly obtained through the image, the sensitivity to the model is low, the calculation is rapid and accurate, and the application range is wide.
In order to achieve the above object, the present invention adopts the following technical scheme:
a three-dimensional digital model porosity calculation method based on an image recognition technology comprises the following steps:
s1, slicing the three-dimensional digital model; the related information of the three-dimensional digital model is extracted from a result file output by a solving calculation program, and the related information specifically comprises but is not limited to the space coordinates of the nodes;
the specific process of slicing:
s11, setting a proper hidden function to perform excision calculation on the digital model, selecting excision parts of the three-dimensional digital model, wherein the excision parts have the characteristics of the digital model, namely, the integral pore situation can be mapped through the parts.
S12, after setting the hidden function, traversing all unit nodes in the input digital model, and removing nodes of the unit cells outside the defined hidden function space. When a cell is traversed by a clipping plane defined by the hidden function, the cell will be clipped by generating a new node on the clipping plane. The remaining nodes can form a graph outline through the node coordinates thereof, and an image with specific resolution is output.
S2, binarizing the slice image;
the image is divided into a plurality of images with gray values of 0 and a hole and background part with gray values of 255 by the above processing, wherein the gray value of the pixel is higher than the threshold value and the gray value of the pixel is lower than the threshold value is set as 255 according to the set threshold value. In the binarized image, 0 represents the entity, and 1 represents the pore and the background. The threshold value may be selected from the gray values of the entities.
S3, calculating pore information of the slice by an image recognition method;
s31, calculating total pixel sum A of slices;
a is the number of black and white pixels.
S32, calculating pixel sum B of the slice hole and the boundary;
b is the number of white pixel points in the binary image.
S33, removing boundaries of the binary image to obtain a new binary image;
removing the boundary refers to all pixels connected with the image boundary, wherein the image boundary does not refer to the boundary between an object and a background in the image, but refers to the boundary of an actual image display. The values of the pixel points of the pixel region connected to the boundary pixel point are all set to 0, i.e., black pixel points.
S34, calculating pixels and C of the new binary image pore;
the pixel sum of the aperture is the white pixel point in the new binary image.
S35, calculating the number of pixels occupied by the material and the holes;
the number of material and hole pixels is A- (B-C).
S4, calculating the porosity of the three-dimensional digital entity;
in the calculation process, the existence of pores is considered to be the case by artificially defining how many pores consist of pixels, and a proper threshold value is selected through screening.
Three-dimensional solid porosity calculation formula:
further, the hidden function in S11 may optionally include: rectangular faces and triangular faces.
Further, in S12, the resolution is selected by the result of the experimental back-thrust or by the convergence result under different resolutions to select the resolution meeting the accuracy requirement of the result.
Compared with the prior art, the invention has the advantages that:
the three-dimensional digital model slice is processed into a photo form, the three-dimensional entity porosity is approximately deduced through the information of the two-dimensional photo, the model complexity is insensitive, the porosity is directly obtained through the image, and under the condition that the digital model precision is enough and the image resolution is large enough, the theoretical calculation porosity result can be infinitely approximate to the true value, the processing operation is simple and convenient, the applicability is strong, and the calculation result meets the precision requirement.
Drawings
FIG. 1 is a flow chart of a method for computing porosity of a three-dimensional digital model according to an embodiment of the present invention;
FIG. 2 is a slice effect diagram of an embodiment of the present invention;
FIG. 3 is a diagram of resolution selection variation according to an embodiment of the present invention;
FIG. 4 is a diagram showing the effect of binarization processing according to an embodiment of the present invention;
FIG. 5 is a diagram showing the effect of removing the boundary according to the embodiment of the present invention;
FIG. 6 is a numerical model of an embodiment of the present invention after additive manufacturing;
FIG. 7 is a binary image of a model slice derived in accordance with an embodiment of the present invention;
FIG. 8 is a graph of calculated porosity results and errors for an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the accompanying drawings and by way of examples in order to make the objects, technical solutions and advantages of the invention more apparent.
As shown in fig. 1, a three-dimensional digital model porosity calculation method based on image recognition technology comprises the following steps:
s1, slicing the three-dimensional digital model;
the related information of the three-dimensional digital model specifically includes, but is not limited to, spatial coordinates of the nodes, and for a cloud image such as stress strain and the like which is wanted to be further output on the slice, the information such as stress strain and the like on the nodes can be further output. And extracting from the output result file of the solving calculation program. The building of related information such as space coordinates is the same as the building of a coordinate system of a model when solving problems. The specific process of slicing:
s11, setting a proper hidden function to perform excision calculation on the digital model, and selecting excision parts of the three-dimensional digital model, wherein the excision parts have the characteristics of the digital model (namely, the situation of integral pores can be mapped through parts). The hidden function may be selected in various forms, such as rectangular, triangular, etc.
S12, after setting the hidden function, traversing all unit nodes in the input digital model, and removing nodes of the unit cells outside the defined hidden function space. When a cell is traversed by a clipping plane defined by the hidden function, the cell will be clipped by generating a new node on the clipping plane. The remaining nodes can form a graph outline through the node coordinates thereof, and an image with specific resolution is output.
Resolution selection can be performed by experimental back-stepping results or by convergence results at different resolutions to select a resolution that meets the accuracy requirements of the results. As shown in fig. 2, the trend of porosity with the change of resolution is shown, and the slice image may be selected to have a resolution satisfying the accuracy, as shown in fig. 3.
S2, binarizing the slice image;
the image is divided into a plurality of images with gray values of 0 and a hole and background part with gray values of 255 by the above processing, wherein the gray value of the pixel is higher than the threshold value and the gray value of the pixel is lower than the threshold value is set as 255 according to the set threshold value. In the binarized image, 0 represents the entity, and 1 represents the pore and the background. The threshold value may be selected from the gray values of the entities, and the effect after the binarization process is shown in fig. 4.
S3, calculating pore information of the slice by an image recognition method;
s31, calculating total pixel sum A of slices;
a is the number of black and white pixels.
S32, calculating pixel sum B of the slice hole and the boundary;
b is the number of white pixel points in the binary image.
S33, removing boundaries of the binary image to obtain a new binary image;
removing the boundary refers to all pixels connected with the image boundary, wherein the image boundary does not refer to the boundary between an object and a background in the image, but refers to the boundary of an actual image display. The values of the pixel points of the pixel region connected to the boundary pixel point are all set to 0 (i.e., black pixel point), and the boundary removal effect is as shown in fig. 5.
S34, calculating the pixel sum C of the new binary image pore
The pixel sum of the aperture is the white pixel point in the new binary image.
S35, calculating the number (whole area) of pixels occupied by the material and the holes
The number of the material and the pore pixels is A- (B-C)
S4, calculating the porosity of the three-dimensional digital entity;
in the calculation process, it can be manually specified how many pores composed of pixels are considered to exist as pores, and a proper threshold value is selected through screening.
Three-dimensional solid porosity calculation formula:
example results show:
fig. 6 is a graph of results obtained by numerical simulation of a metal additive manufacturing process, the porosity of a cladding layer under different laser speeds is calculated, fig. 7 is a binary graph obtained by slicing a numerical model and deriving the result, the result of the porosity obtained by calculation is shown in fig. 8, X and Y represent slices along different directions, XYAVE is an average value of the porosities calculated by slicing along the X and Y directions, and a porosity threshold can be considered to be set in the calculation process, namely, the porosity greater than the number of pixels is considered to be included in statistics, and the obtained error is shown in fig. 8. The advantage of this example is that the image resolution is improved, as the device allows, and the result of the calculation will approximate the true value in the simulation environment.
Those of ordinary skill in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the practice of the invention and that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (3)
1. The three-dimensional digital model porosity calculation method based on the image recognition technology is characterized by comprising the following steps of:
s1, slicing the three-dimensional digital model; the related information of the three-dimensional digital model is extracted from a result file output by a solving calculation program, and the related information specifically comprises but is not limited to the space coordinates of the nodes;
the specific process of slicing:
s11, setting a proper hidden function to perform excision calculation on the digital model, selecting excision parts of the three-dimensional digital model, wherein the excision parts have the characteristics of the digital model, namely, the integral pore situation can be mapped through the parts;
s12, after setting the hidden function, traversing all unit nodes in the input digital model, and removing nodes of unit cells outside the defined hidden function space; when the cell crosses the shearing surface defined by the hidden function, the cell is sheared, and a new node is generated on the shearing surface; the left nodes can form a graph outline through the node coordinates thereof, and an image with specific resolution is output;
s2, binarizing the slice image;
the image is according to the threshold value that presumes, set up the pixel point that the gray value is higher than threshold value as 255, the pixel point that the gray value is lower than threshold value is set up as 0, through the above-mentioned processing, cut apart several pictures into the entity with gray value of 0, the aperture, background part with gray value of 255; in the binarized image, 0 represents an entity, and 1 represents a pore and a background; the threshold value is selected according to the gray value of the entity;
s3, calculating pore information of the slice by an image recognition method;
s31, calculating total pixel sum A of slices;
a is the number of black and white pixel points;
s32, calculating pixel sum B of the slice hole and the boundary;
b is the number of white pixel points in the binary image;
s33, removing boundaries of the binary image to obtain a new binary image;
removing boundaries refers to all pixels connected with the image boundaries, wherein the image boundaries do not refer to the boundaries of objects and backgrounds in the image, but refer to the boundaries of an actual image display; setting the values of the pixel points of the pixel area connected with the boundary pixel points to be 0, namely black pixel points;
s34, calculating pixels and C of the new binary image pore;
the pixel sum of the pore is the white pixel point in the new binary image;
s35, calculating the number of pixels occupied by the material and the holes;
the number of the material and the hole pixels is A- (B-C);
s4, calculating the porosity of the three-dimensional digital entity;
in the calculation process, manually defining how many pores consisting of pixels are considered to be pores at the position, and selecting a proper threshold value through screening;
three-dimensional solid porosity calculation formula:
2. the three-dimensional digital model porosity calculation method according to claim 1, characterized in that: the hidden function in S11 may optionally include: rectangular faces and triangular faces.
3. The three-dimensional digital model porosity calculation method according to claim 1, characterized in that: and S12, selecting the resolution ratio which meets the accuracy requirement of the result by using the result of experimental back-pushing or by using the convergence result under different resolution ratios.
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| CN104237103A (en) * | 2014-09-23 | 2014-12-24 | 中国石油天然气股份有限公司 | Quantitative characterization method and device for pore connectivity |
| KR20160069379A (en) * | 2014-12-08 | 2016-06-16 | 공주대학교 산학협력단 | Apparatus and method for calculating permeability and porosity of rock using image of slice of rock |
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| RU2576501C2 (en) * | 2011-02-28 | 2016-03-10 | Шлюмбергер Текнолоджи Б.В. | Methods of building 3-dimensional digital models of porous medium using combination of high and low resolution data and multi-point statistics |
| US11460462B2 (en) * | 2018-10-31 | 2022-10-04 | Exxonmobil Upstream Research Company | Microanalysis of fine grained rock for reservoir quality analysis |
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| CN104237103A (en) * | 2014-09-23 | 2014-12-24 | 中国石油天然气股份有限公司 | Quantitative characterization method and device for pore connectivity |
| KR20160069379A (en) * | 2014-12-08 | 2016-06-16 | 공주대학교 산학협력단 | Apparatus and method for calculating permeability and porosity of rock using image of slice of rock |
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