CN115662553B - Method for constructing material microstructure information database - Google Patents
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Abstract
The invention relates to a method for constructing a material microstructure information database, which relates to the technical field of metal material microstructure analysis and comprises the following steps: s01, acquiring material microstructure characteristic data by adopting an EBSD technology to obtain a pixel visual field information table and a crystal grain information table; s02, carrying out data classification by adopting an image-based region growing algorithm, and establishing a mapping relation between the pixel visual field information table and the crystal grain information table; and S03, importing the pixel visual field information table, the crystal grain information table and the mapping relation thereof into a database, and constructing a material microstructure information database. The invention enriches the construction of a database by carrying out mathematical operation processing on the fitting ellipse, roundness and convexity standardized parameters of the crystal grains and provides accurate data preparation for the research of subsequent materials.
Description
Technical Field
The invention relates to the technical field of microstructure analysis of metal materials, in particular to a method for constructing a material microstructure information database.
Background
EBSD (Electron back scattering Diffraction) is a current material microstructure characterization technique, which uses a scanning Electron microscope as an experimental instrument to study the metallographic characteristics of a crystal microstructure. For example, martensite is an important crystal phase in the microstructure of a steel material, and due to the high strength characteristic and the high toughness characteristic of the martensite, the martensite is continuously and deeply researched, and the EBSD technology can be used for obtaining the microstructure characteristic data of the martensite, so that the subsequent deep research on the crystal structure, the morphological characteristic and the structural property of the martensite is facilitated.
However, the data form obtained by the EBSD is single, the relationship is simple, and the EBSD is mostly structured data in the form of a table, and two data tables, namely a pixel view information table and a crystal grain information table, can be obtained after the EBSD scans the microstructure of the material. The pixel visual field information table reflects point-by-point microstructure characteristic data during EBSD scanning, the grain information table reflects grain characteristic data counted after scanning is completed, and obviously, the data in the two tables are in an association relationship, but the EBSD does not give out the association between the data in the two tables, which causes great difficulty in subsequent deep analysis of the microstructure of the material.
Disclosure of Invention
The invention aims to provide a method for constructing a material microstructure information database, which is used for realizing the correspondence of a single crystal grain in a crystal grain information table to a corresponding EBSD scanning point group in a pixel field information table, thereby establishing a mapping relation between the two tables, constructing a database of the relation between the two data tables under the MySQL environment and providing convenience for the in-depth analysis of a material microstructure.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention provides a method for constructing a material microstructure information database on one hand, which is characterized by comprising the following steps:
s01, acquiring material microstructure characteristic data by adopting an EBSD technology to obtain a pixel visual field information table and a crystal grain information table;
s02, adopting an image-based region growing algorithm to classify data, and establishing the pixel visual field information table and the crystal grains
Mapping relation of the information table;
s03, importing the pixel visual field information table, the crystal grain information table and the mapping relation thereof into a database, and constructing a material micro-structure
And viewing and organizing the information database.
Further, the step S02 includes the following steps:
s021, reconstructing a gray image of the original crystal grain by adopting a BC value;
s022, reading the gravity center point of the gray level image to generate a seed point;
s023, initializing a mark matrix seed, initializing an empty seed column seed for storing seed points;
s024, taking out a first element in the seed as an initial seed point for the next search;
s025, searching four adjacent domain points of the initial seed point, and judging the gray values of the four adjacent domain points and the initial seed point
Whether the difference exceeds a set threshold value or not and whether the four adjacent domain points are marked or not is judged, if the gray value difference does not exceed the threshold value and the four adjacent domain points are not marked, the four adjacent domain points are added to the position of the first element in the seed;
s026, if the seedlist length is not empty, then returning to S024, if the seedlist length is empty, then stopping
Stopping growth.
Further, the step S02 further includes at least one of the following steps:
deleting duplicate pictures by deleting pictures having the same MD5 value;
removing the crystal grains with smaller sizes by deleting the pictures with the sizes smaller than the set size threshold;
and removing black isolated points or small holes in the processing area by adopting a mode of firstly performing expansion operation and then performing corrosion operation.
Further, step S021 is to reconstruct an RGB image of the original crystal grain by using an euler angle.
Further, the white areas grown from each seed point are numbered, and the marks of all white pixel points in the graph are marked
And marking as 1, marking a different label in the growth area of each other seed point, and establishing a mapping relation between the pixel visual field information table and the crystal grain information table.
Further, the step S03 includes the steps of:
s031, carry on the standardized parameter to the fitting ellipse, circularity and convexity of the crystalline grain and process;
s032, creating a database and a corresponding table in MySQL, and importing the material microstructure characteristic data into the table
Forming a pixel view data table;
s033, building a new crystal grain data table in the database, responsible for storing upper layer crystal grain information, mainly including
The grain id, the roundness of the grains, fitting ellipse information of the grains, the perimeter of the grains, the area of the grains, and the convexity information of the grains.
Further, when the pixel visual field data table is established, index of a pixel visual field point is set as a main key, and one is added
And the name of the off-column key is GrainID and establishes a mapping relation with another crystal grain data table.
Further, the data source of the graninid is information corresponding to the tag.
Another aspect of the present invention provides a storage medium having a computer program stored thereon, characterized in that: the computer program, when executed by a processor, implements the steps of the method of constructing a database of material microstructure information.
Another aspect of the present invention provides a computer apparatus, characterized in that: comprises a storage medium, a processor and a computer program stored in the storage medium and executable by the processor, wherein the computer program realizes the steps of the method for constructing the material microstructure information database when being executed by the processor.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
(1) The invention provides a data classification optimization algorithm based on an image region growing algorithm, which realizes that a single crystal grain in a crystal grain information table corresponds to a corresponding EBSD scanning point group in a pixel visual field information table, thereby establishing a mapping relation between the two tables;
(2) According to the classification result of the combined region growth and the data obtained by the EBSD processing, a database of the relationship between the two data tables under the MySQL environment is constructed;
(3) And mathematical operation processing is carried out on the fitting ellipse, roundness and convexity standardized parameters of the crystal grains, so that the construction of a rich database is facilitated, and accurate data preparation is provided for the research of subsequent materials.
Drawings
FIG. 1 is a gray scale diagram of an original grain reconstructed from BC values according to an embodiment of the present invention;
FIG. 2 is a center of gravity point distribution diagram according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the region growing result according to the embodiment of the present invention;
FIG. 4 is a graph illustrating the result of post-processing region growth according to an embodiment of the present invention;
FIG. 5 is an RGB image of original grains reconstructed from Euler angles according to an embodiment of the present invention;
FIG. 6 shows the region growing result of the RGB image according to the embodiment of the present invention;
FIG. 7 is a diagram illustrating a fitting ellipse of a die according to an embodiment of the present invention;
FIG. 8 is a fitted ellipse distribution plot of a die of an embodiment of the present invention;
FIG. 9 is a diagram of databases E-R, according to an embodiment of the present invention;
FIG. 10 is a database structure diagram according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
the terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the present application. As used in the examples of this application and the appended claims, the singular forms "a", "an", "the" and "the" include plural referents
"the" is also intended to include the plural unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims.
In the description of the present application, it is to be understood that the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not necessarily used to describe a particular order or sequence, nor are they to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
In addition, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or",
describing the association relationship of the associated object, indicating that there may be three relationships, e.g., a and/or B, which may indicate: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. The invention is further illustrated below with reference to the figures and examples.
In order to solve the limitation of the prior art, the present embodiment provides a technical solution, and the technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.
A method for constructing a material microstructure information database is characterized by comprising the following steps:
s01, acquiring material microstructure characteristic data by adopting an EBSD technology to obtain a pixel visual field information table and a crystal grain information table;
s02, adopting an image-based region growing algorithm to classify data, and establishing the pixel visual field information table and the crystal grains
Mapping relation of the information table;
s03, importing the pixel visual field information table, the crystal grain information table and the mapping relation thereof into a database, and constructing a material micro-structure
And viewing and organizing the information database.
EBSD (Electron Back scattered Diffraction) is currently popular
A material microstructure characterization technique is characterized in that two data tables, namely a pixel visual field information table and a crystal grain information table, can be obtained after EBSD scans a low-alloy high-strength steel microstructure. The pixel visual field information table reflects point-by-point microstructure characteristic data during EBSD scanning, and the grain information table reflects grain characteristic data counted after scanning is completed.
From the data obtained by EBSD processing, it can be observed that the BC (Band Contrast) Band Contrast represents the diffraction
The quality of the shot pattern is that according to material metallography knowledge, the change of BC data can reflect the light and shade information of grains, when the BC value is mutated in the EBSD equal-step-length continuous scanning process, the mutated point is the edge point of the grains, and the data needs to be clustered from the image level, and the original grain image needs to be reconstructed from the data. Since the scanning step length of the EBSD is 0.15 μm, the EBSD is reflected as one-dimensional data with a length of 441025 in the database, the EBSD is reconstructed into a two-dimensional matrix with a size of 767 × 575, the gray value of each pixel corresponds to BC and the BS value, the BC can be reconstructed into a gray image, and after comparison, the grain image restored by BC is found to be clearer, so that the gray image with the BC value is adopted as the initial image used by all subsequent image classification algorithms, and the reconstructed image is shown in fig. 1.
In the EBSD post-processing data, there is a data as the statistics of the center of gravity of the grains, so that the seed points are located inside the grains to be extracted, theoretically, each center of gravity point corresponds to one grain, the center of gravity point is selected as the seed point, the whole grain region can be efficiently grown, and the distribution of the center of gravity points in the grain map is shown in fig. 2.
From the image reconstructed by BC, there is interference in the gray value of some pixel points in the image, for example, the gray value of this pixel point is small, that is, the color is dark, and is greatly different from other pixel points in the crystal grain itself, but as shown in fig. 2, the gravity center of some crystal grains exactly falls on these interference points, which results in that the crystal grains cannot grow outward normally in the process of region growth. The reason for this may be that in the process of grinding and preparing the steel material, the sample is damaged, so that there are many abnormal pixel positions on the crystal grains, even large areas are disturbed, and finally these areas cannot grow normally. Besides obvious large grain boundaries, subboundary conditions exist in the grains, and if the pixel points just fall in the subboundary in the grains, the growth of the pixel points can be hindered more or less. Because the number of the problems is few, the method indicates that most of the gravity points of the crystal grains can be normally grown to the correct size as the seed points, and the probability that the gravity points fall on the interference points is reduced.
For the merging criterion of the pixel points in the growing process, the difference value of the gray values is used as the growing criterion, when the difference value between the gray value of the pixel where the seed point is located and the gray values of the pixel points in the four neighborhoods of the seed point does not exceed a set threshold, the pixel points meeting the conditions in the four neighborhoods of the seed point are absorbed and marked, and then the seed point grows outwards from the points meeting the conditions. In this way, the four neighborhood points outside the set which meet the conditions are continuously searched until no point which meets the threshold condition and the marking condition is obtained, and the growth is stopped, so that the growth from the seed point inside the crystal grain to the boundary of the crystal grain is realized.
Further, the step S02 includes the following steps:
s021, reconstructing a gray image of the original crystal grain by adopting a BC value;
s022, reading the gravity center points of the gray level image to generate seed points;
s023, initializing a mark matrix seedmark with the size of 767 × 575 of the image size, and initializing an empty seed column
The seed is used for storing the seed points;
s024, taking out a first element in the seed as an initial seed point for the next search;
s025, searching four neighborhood points of the initial seed point, and judging gray values of the four neighborhood points and the initial seed point
Whether the difference exceeds a set threshold value or not and whether the four adjacent domain points are marked or not is judged, if the gray value difference does not exceed the threshold value and the four adjacent domain points are not marked, the four adjacent domain points are added to the position of the first element in the seed;
s026, if the seedlist length is not empty, then returning to S024, if the seedlist length is empty, then stopping
And (5) stopping growing.
By using the above growth algorithm, the number of the grain images equal to the number of the final gravity points is finally grown, and the grown single grain image is shown in fig. 3, for example, compared with the original grain image in fig. 1, the region growth of the gray scale image better realizes the segmentation of the single grain at the corresponding position.
Because the selection mode of the seed points leads the final growth result to exceed the actual number of the crystal grains contained in the picture, in other words, one crystal grain necessarily contains a plurality of seed points, and finally the seed points in one crystal grain inevitably grow the same two pictures, so firstly, the invention needs to delete the repeated pictures in batches with higher efficiency.
The MD5 is Message-Digest Algorithm 5 (information-summarization Algorithm 5), because the picture obtained by the region growing itself is built from the pixel level, the picture MD5 value in the folder is traversed, and the picture with the same MD5 value is deleted, i.e. the repeated picture can be removed at a faster speed.
After removing the repeated pictures, further screening is needed. Because the size of some grains is small, the grains may belong to small-angle grain boundaries, the subsequent utilization value is small, the small space is occupied, the subsequent data processing efficiency is influenced, and the statistical value is not large, the grains with small sizes need to be deleted. The result picture grown from the region is a binary picture, so that when the grown crystal grain region is smaller, the size of the picture is correspondingly smaller, a proper threshold value is selected, and the picture smaller than the threshold value is deleted, so that the crystal grains smaller in size are removed.
According to the picture effect generated by the region growing, the invention observes that more black isolated points exist in the picture, and can be found by comparing with an original picture, the points are impurity points possibly existing in crystal grains, the pixel gray value of the impurity points is greatly different from the gray value of the surrounding normal crystal grains, therefore, the impurity points need to be filtered in a certain mode, and in morphology, an expansion corrosion operation is provided, so that the isolated points can be filtered.
The invention combines two operations of expansion and corrosion, namely a closed operation mode, wherein the closed operation mode comprises the steps of expansion operation and corrosion operation, black isolated points or small holes in a processing area are removed by the method, the size and the shape of the original area are not changed, the requirement of the image after the growth of the processing area is met, and the growth result graph of the post-processed area is shown in figure 4.
Careful inspection of fig. 1 the present invention has found that there are significant scratches, noise and blurred regions in the reconstructed original image. When such a region is grown, taking a crystal grain with scratches as an example, when the crystal grain is divided into two by the scratches, the difference between the internal gray levels of the scratched portion and the crystal grain is much larger than a predetermined threshold value by the gray level difference rule of the region growth, and the scratched portion cannot grow from one divided side to the other divided side. Thus even optimizing the algorithm for region growth is difficult to solve this problem, and the present invention therefore contemplates optimizing the grain image for region growth.
The original grain image is reconstructed using the BC value reflecting the quality of the diffraction pattern as the grey value. The value of BC reflects to some extent the edge and internal information of the grains. In addition, the grains have a structural characteristic of Euler angle, namely Euler1, euler2 and Euler3 in a pixel visual field information table. Under the same crystal grain, the values of the three Euler angles of each point are very close, so that the crystal grain image can be reconstructed. Since the euler angle is three-dimensional, it needs to be mapped to the RGB space. According to the metallography knowledge of the material, the value range of the Euler angle is between 0 and 360 degrees, but the euler angle is symmetrical, when the Euler angle exceeds 180 degrees, the value obtained by subtracting the current Euler angle from 360 degrees can be used for substitution, so that the value range of the Euler angle is between 0 and 180 degrees, the Euler angle can be mapped to an RGB three-dimensional space, and the reconstructed RGB is shown in figure 5.
Compared with a gray scale image reconstructed by BC value, an RGB image reconstructed by Euler angles has the same crystal grain represented by the same color, only has slight difference in RGB three-channel values, and solves the problems of scratch and noise in the gray scale image (the black point in the RGB image is the problem that data is empty due to sampling precision limitation when EBSD processing is carried out). Such an RGB map is more advantageous for improving the accuracy of the region growing algorithm. FIG. 6 is a graph showing the result of the RGB map after region growing.
Numbering the white areas grown from each seed point, marking the labels of all white pixel points in the graph as 1,
and recording a different label in the growing area of each other seed point, and establishing a mapping relation between the pixel view information table and the crystal grain information table.
The step S03 includes the steps of:
s031, carry on the standardized parameter to the fitting ellipse, circularity and convexity of the crystalline grain and process;
s032, creating a database and a corresponding table in MySQL, and importing the material microstructure characteristic data into the table
Forming a pixel visual field data table;
s033, creating a new die data table in the database, which is responsible for storing upper die information, and mainly includes
Granid, roundness of the grains, fitting ellipse information of the grains, perimeter of the grains, area of the grains and convexity information of the grains.
The information of the fitted ellipse of the crystal grain mainly comprises two information, wherein one information is the angle of the fitted ellipse, and the other information is the aspect ratio of the fitted ellipse, and the aspect ratio of the fitted ellipse refers to the ratio of the long axis to the short axis of the ellipse fitting. This value is greater than 1, and a larger value indicates a larger degree of elongation of the crystal grains. The approximate representation is shown in fig. 7 by a graph, and the distribution of all crystal grains in a bitmap can be drawn by fitting the angles of the ellipse, the major axis and the wide axis of the ellipse and the position information of the crystal grains, as shown in fig. 8.
It is necessary to obtain the ratio of the longest length of the crystal grain in the rolling direction to the longest length in the thickness direction. The larger this value represents the larger the degree to which the grains are elongated in the rolling direction. The parameter can be calculated by two parameters of the aspect ratio of the fitting ellipse of the crystal grain and the angle of the fitting ellipse, wherein the angle of the fitting ellipse refers to the included angle between the long axis of the fitting ellipse and the X direction, therefore, the parameter can be calculated by a trigonometric function of the aspect ratio of the fitting ellipse and the angle of the fitting ellipse, the aspect ratio of the fitting ellipse is represented by beta, and the angle is represented by theta, so that the calculation formula of the parameter is represented by the following formula.
The roundness of a grain represents how close a measured grain is to a mathematically perfect circle. The calculation method is to divide the perimeter of the equivalent circle by the perimeter of the grain. The roundness can be described as "deviation from perfect circle" and the range of 0 to 1,1 represents a circular grain. Indicating complete roundness. When processing the image characteristics of the microstructure, the roundness characteristics are expected to be more sensitive, and the sensitivity of the data characteristics is generally improved by adopting a square method, so that the concept of high sensitivity roundness is introduced and formulatedThe formula is shown below. Wherein the content of the first and second substances,Ais the area of the crystal grains,Pis the grain perimeter.
The convexity of a grain represents the measured curvature, describing the degree to which the grain profile bulges outward. The calculation method is to divide the area of the grain by the area of the convex hull. Ranging from 0 to 1 with a maximum of 1 indicating complete convexity. The ratio of the perimeter of the convex hull to the perimeter of the grains can also be used. Also ranging from 0 to 1. In the microstructure characteristic of the crystal grains, in order to represent the structural parameters of the crystal grain surfaces, a new parameter is introduced to represent the difference between convex shells and the areas of the crystal grains, and the larger the value is, the larger the degree of undulation of the crystal grain surfaces is represented. The calculation formula of the parameter is shown in the following formula. Wherein the content of the first and second substances,A convex is the minimum convex hull area surrounding the die.
The data source is EBSD post-processed data, and the data of a bitmap comprises Index, X, Y, phases, euler1, euler2, euler3, band contrast, BS, MAD and the like for a total of 40 or more than ten thousand. These are partial tanacetum band information obtained after EBSD processing. The data in the pixel view table mean as follows: index: an index entry for a pixel field of view point; x, Y: coordinates of each pixel field of view point; phases: phase category. The basic analysis here is ferrite, i.e. bcc (body centered cubic) phase; euler1, 2, 3: euler angle, representing the spatial orientation of each scanned point, i.e. the angle of rotation in three-dimensional space with respect to a reference coordinate system; band Contrast: the band contrast shows the quality of the diffraction pattern, and the greater the grain deformation, the worse the pattern quality, and the smaller the BC value. Band Slope: the band slope, meaning is the same as the band contrast. Are often used as the main parameters for distinguishing bcc structures. MAD: the average band deviation angle indicates the degree of matching between the diffraction pattern and the standard diffraction pattern.
The DATA are summarized into a table, a database and a corresponding table are firstly created in MySQL, and as the DATA volume is huge and the use of a common DATA import method is slow, more than forty thousand pieces of DATA are imported into the table in batches by using a LOAD DATA INFILE command in a command line page of the MySQL database to form a pixel view DATA table.
In addition, a crystal grain data table is newly established in the database and is responsible for storing upper-layer crystal grain information, wherein the upper-layer crystal grain information mainly comprises GrainID, crystal grain roundness, fitting ellipse information of crystal grains, crystal grain perimeter, crystal grain area and convexity of the crystal grains. Thus, a bitmap corresponds to two data tables, one is a pixel view data table and the other is a crystal grain information data table. And other point maps are also used for establishing a data table corresponding to the point maps according to the method, so that the initial establishment of the whole database is completed. FIG. 9 is an E-R diagram of a database.
When pixel visual field data is established, an Index of a pixel visual field point is set as a main key, a row of foreign key names are added as GrainID, and a mapping relation is established with another crystal grain information table, so that crystal grain information corresponds to bottom layer pixel visual field point information in subsequent data query, and selected crystal grains and bottom layer tissue characteristic information matched with the crystal grain information can be queried simultaneously. And the data source of the GranlD is the label result finally obtained by region growing. FIG. 10 is a block diagram of the structure of the database of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. A method for constructing a material microstructure information database is characterized by comprising the following steps:
s01, acquiring material microstructure characteristic data by adopting an EBSD technology to obtain a pixel visual field information table and a crystal grain information table;
s02, carrying out data classification by adopting an image-based region growing algorithm, and establishing a mapping relation between the pixel visual field information table and the crystal grain information table;
s03, importing the pixel visual field information table, the crystal grain information table and the mapping relation thereof into a database, and constructing a material microstructure information database;
the step S02 includes the steps of:
s021, reconstructing a gray image of the original crystal grain by adopting a BC value;
s022, reading the gravity center point of the gray level image to generate a seed point;
s023, initializing a mark matrix seed, initializing an empty seed list seed for storing seed points;
s024, taking out a first element in the seed as an initial seed point for the next search;
s025, searching four neighborhood points of the initial seed point, judging whether the gray value difference between the four neighborhood points and the initial seed point exceeds a set threshold value, judging whether the four neighborhood points are marked, and if the gray value difference does not exceed the threshold value and the four neighborhood points are not marked, adding the four neighborhood points to the position of a first element in the seed;
s026, if the length of the seedlist is not empty, then returning to the step S024, if the length of the seedlist is empty, then stopping growing.
2. The method according to claim 1, wherein the step S02 further comprises at least one of the following steps:
deleting duplicate pictures by deleting pictures having the same MD5 value;
removing the crystal grains with smaller sizes by deleting the pictures smaller than the set size threshold;
and removing black isolated points or small holes in the processing area by adopting a mode of firstly performing expansion operation and then performing corrosion operation.
3. The method of claim 1, wherein the step S021 is to reconstruct an RGB image of an original grain by using Euler angles.
4. The method for constructing a database of material microstructure information according to any one of claims 1 to 3,
and numbering the white areas grown by each seed point, recording the labels of all the white pixel points in the graph as 1, recording different labels in the growing areas of other seed points, and establishing a mapping relation between the pixel visual field information table and the crystal grain information table.
5. The method according to claim 4, wherein the step S03 comprises the steps of:
s031, carry on the standardized parameter to the fitting ellipse, circularity and convexity of the crystalline grain and process;
s032, creating a database and a corresponding table in MySQL, and importing the material microstructure characteristic data into the table to form a pixel field data table;
and S033, newly building a crystal grain data table in the database, for storing upper-layer crystal grain information, including GrainID, crystal grain roundness, fitting ellipse information of the crystal grains, crystal grain perimeter, crystal grain area and convexity information of the crystal grains.
6. The method for constructing the material microstructure information database according to claim 5, wherein when the pixel view data table is established, an Index of a pixel view point is set as a main key, a list of foreign key names is added as graninid, and a mapping relation is established with another grain data table.
7. The method as claimed in claim 6, wherein the data source of the GranlD is the information corresponding to the label.
8. A storage medium having a computer program stored thereon, characterized in that: the computer program when executed by a processor implements the steps of the method of constructing a database of material microstructure information according to any one of claims 1 to 7.
9. A computer device, characterized by: comprising a storage medium, a processor and a computer program stored in the storage medium and executable by the processor, the computer program, when executed by the processor, implementing the steps of the method of constructing a database of material microstructure information according to any one of claims 1 to 7.
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| CN108564564A (en) * | 2018-03-09 | 2018-09-21 | 华南理工大学 | Based on the medical image cutting method for improving fuzzy connectedness and more seed points |
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| CN108564564A (en) * | 2018-03-09 | 2018-09-21 | 华南理工大学 | Based on the medical image cutting method for improving fuzzy connectedness and more seed points |
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