CN108053478B - Particle-reinforced composite finite element modeling method based on pixel theory - Google Patents
Particle-reinforced composite finite element modeling method based on pixel theory Download PDFInfo
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Abstract
The invention discloses a particle reinforced composite finite element modeling method based on a pixel theory, relates to a particle reinforced composite finite element modeling method considering a microstructure based on the pixel theory, and belongs to the technical field of particle reinforced composite finite element modeling. According to the invention, on the basis of the definition of the traditional particle reinforced composite finite element model material, the geometric model of the reinforced phase particles is established based on the pixel theory method, so that the contradiction between the profile of the reinforced phase, the balance efficiency and the accuracy of the simulation result can be accurately reflected, the simulation accuracy and reliability of the particle reinforced composite finite element model are improved, and further the engineering problem in the field of particle reinforced composite materials is solved. Furthermore, the invention has general versatility, being applicable to, but not limited to, finite element modeling of particle-reinforced composites; the method can improve the accuracy of the model and greatly simplify the modeling operation of the finite element, and has the advantages of simplifying the complexity of digital image processing and finite element software modeling.
Description
Technical Field
The invention relates to a particle reinforced composite finite element modeling method considering a microstructure, in particular to a particle reinforced composite finite element modeling method considering the microstructure based on a pixel theory, and belongs to the technical field of finite element modeling of particle reinforced composites.
Background
The particle reinforced metal matrix composite material has the comprehensive properties of metal and nonmetal, has the characteristics of high strength, high elastic modulus, wear resistance, good electric conduction and heat conduction performance and the like, and has specific strength, specific modulus and heat resistance which exceed those of matrix metal, so that the particle reinforced metal matrix composite material is widely applied to the industries of aerospace, electronics, automobiles, buildings and the like and is also a research hotspot in the field of current engineering materials. When the performance of a material is researched, finite element simulation is an effective and common method, aiming at a particle reinforced metal matrix composite, modeling is called as a key difficult point of simulation, and the microstructure of the metal matrix composite, such as particle morphology, size, distribution, content and the like, has very important influence on the overall performance of the composite, so that how to establish a finite element model based on reality is very important.
The particle reinforced metal matrix composite material is obviously different from the traditional homogeneous metal material, and the material properties of the reinforced phase particles and the matrix are obviously different, so that the internal structure of the material cannot be ignored, and the simulation result obtained by a finite element model established like the traditional homogeneous material is difficult to convince.
The current finite element modeling technology for particle reinforced composite materials mainly focuses on statistical analysis, and modeling is carried out by counting information such as particle morphology, size and position distribution in the materials and utilizing random numbers. However, fine differences often exist between materials of different batches and between the same materials produced by different manufacturers due to various reasons, and at this time, the finite element model established based on the statistical information often cannot accurately reflect the real internal structure of the material.
The scanning electron microscope and the high-magnification optical scanning microscope can enable people to clearly observe the real microstructure of the material, and the specific distribution condition of the enhanced phase particles in the composite material can be clearly seen by scanning the inner section of the material. With the development of digital image processing technology, various image processing software such as Photoshop, Matlab and the like is applied, pixel points of a picture can be connected with grid (quadrilateral grid) units of a simulation model through a series of processing, a simulation model based on a real outline is established in ABAQUS software by utilizing Python language, and a convenient way is provided for simulating a real microstructure.
Disclosure of Invention
In order to more accurately establish a finite element model of a particle reinforced composite material and change the problems of low efficiency, complicated operation, incapability of accurately reflecting the real microstructure structure of the material and the like of the traditional modeling method based on statistics, the invention discloses a particle reinforced composite material finite element modeling method based on a pixel theory, which aims to solve the technical problems that: the method for modeling the finite element by combining a plurality of digital image processing software and finite element simulation software is provided, the influence of the microstructure on the finite element model of the particle reinforced composite material is considered, the particle item outline can be accurately represented, the complex operations of digital image processing and finite element software modeling are simplified, and the accuracy and the reliability of finite element simulation are improved.
The purpose of the invention is realized by the following technical scheme.
The invention discloses a finite element modeling method of a particle reinforced composite material based on a pixel theory, which comprises the following steps:
step one, extracting a microstructure morphology digital image of the particle reinforced composite material, and preparing for subsequent digital image processing work.
The method comprises the steps of carrying out linear cutting, grinding, polishing and ultrasonic vibration cleaning on a used specific composite material to obtain a material section, extracting a microstructure appearance image of the specific composite material, wherein the oxidation and corrosion areas on the surface of the material are required to be completely removed before shooting, selecting a typical area with uniformly distributed enhanced phase particles during shooting, and the shot image is clear and meets the requirement of preset contrast, can clearly show the edges of the particles and is prepared for the subsequent digital image processing work.
Preferably, the microstructure morphology image of the specific composite material extracted in the step one is extracted by using an SEM scanning electron microscope or an OM optical microscope.
And secondly, preprocessing the microstructure morphology digital image by using image processing software.
The digital image of the microstructure morphology of the particle-reinforced composite material extracted in the step one is difficult to automatically process through image processing software due to impurities and shadows, so in order to more accurately and rapidly identify the reinforced phase particles, the image processing software is required to be used for preprocessing the reinforced phase particles.
Step two, preprocessing the microstructure morphology digital image by using image processing software, preferably by a manual method
The concrete implementation method of the second step is as follows:
step 2.1: and (5) image cutting.
And extracting a rectangular region with uniformly distributed reinforcing phase particles in the microstructure morphology digital image of the particle reinforced composite material by using image processing software, wherein the length and the width of the rectangular region are required to meet the specific size requirement of the finite element simulation or are in equal proportion to the specific size requirement of the finite element simulation.
Step 2.2: particle segmentation and background removal.
And (3) processing the microstructure morphology digital image obtained in the step 2.1 by using image processing software to remove the characteristics of impurities, defects and the like which are similar to the color of the enhanced phase particles and affect two-phase segmentation and the enhanced phase characteristics with a small area, so that the matrix and the particles have obvious contrast, the edges of the particles are clear, the adjacent particles are segmented, the distance of more than three pixel points is required between the two adjacent particles, the microstructure morphology digital image after the particle segmentation and background removal is obtained, and the pretreatment of the microstructure morphology digital image is completed.
And thirdly, segmenting the reinforced phase and the matrix, and extracting the characteristics of the particles and the matrix of the reinforced phase of the composite material.
And (4) introducing the microstructure morphology digital image obtained in the step two into Matlab for digital image processing, clearly representing the outline of the particles, removing the fine particles and features ignored in finite element analysis, and finally extracting the outline features of the required enhanced phase particles to prepare for subsequent calculation and fitting. The digital image processing comprises gray processing, binarization processing and hole filling.
The concrete implementation method of the third step is as follows:
step 3.1: and (5) carrying out gray scale processing.
Introducing the microstructure morphology image preprocessed in the step two into Matlab, carrying out gray processing on the microstructure morphology image, converting the microstructure morphology image into a gray image with pixel point values between 0 and 255, enhancing the contrast value of the gray image to further improve the definition of the outline of the microstructure morphology image, and removing noise points by using a median filtering method;
step 3.2: and (6) carrying out binarization processing.
After the outline of the enhancement phase particles is clearer, carrying out binarization processing on a microstructure morphology digital image, automatically identifying a threshold value of the microstructure morphology digital image by utilizing Matlab, and dividing the enhancement phase particles in the image from a matrix material to obtain the enhancement phase particles with the gray value of 0 which is represented as black and the matrix with the gray value of 1 which is represented as white;
step 3.3: and removing the small-area features to finish extracting the profile features of the composite material reinforced phase particles.
And (3) for the binary image obtained in the step (3.2), repairing the small-area defect inside the enhancement phase by using a hole filling instruction, removing the small-area enhancement phase characteristic by using a method of opening and closing operation combined with expansion and corrosion, further simplifying the finite element model on the premise of not influencing the calculation precision, facilitating the division of grids, reducing the calculation cost and finishing the extraction of the composite material enhancement phase particles and the matrix characteristic.
The small area stated in step 3.3 depends on the mesh division precision requirement.
And step four, adjusting the pixels of the digital image after the composite material enhanced phase particles and the matrix characteristics are extracted in the step three based on the pixel theory, and preparing for extracting the coordinate information of the finite element geometric model.
The larger the digital image pixel is, the more the pixel points are, the more the corresponding finite element simulation model units are, the more the node and unit coordinate information is, the more the model can accurately reflect the actual workpiece, the more accurate the simulation result is, but the lower the digital image processing and subsequent simulation efficiency is; the smaller the digital image pixel is, the fewer the pixel points are, the fewer the corresponding finite element simulation model units are, the fewer the node and unit coordinate information is, the higher the digital image processing and subsequent simulation efficiency is, but the larger the difference between the model and the actual workpiece is, the more inaccurate the simulation result is. Therefore, the method needs to comprehensively consider factors of efficiency and accuracy according to actual working conditions and precision requirements to adjust pixels of the digital image, balance contradictions between efficiency and accuracy of simulation results until the requirements of preset efficiency and accuracy of the simulation results are met, and prepares for extracting coordinate information of the finite element geometric model.
And step five, extracting the coordinate information of the finite element geometric model and preparing for establishing the finite element geometric model.
The concrete implementation method of the fifth step is as follows:
and 5.1, calculating the size of the pixel points to ensure that the size of the finite element geometric model is consistent with the size of the digital image of the microstructure morphology of the particle reinforced composite material obtained in the first step.
And (3) each pixel point in the digital picture is a square with the side length of L, each pixel point corresponds to a grid unit in the simulation model, wherein L is the length (or width) of the actual model/the number of corresponding side length pixel points, and the corresponding L value can be changed after the pixels are adjusted, so that the size of the geometric model of the finite element is ensured to be consistent with the size of the microstructure morphology digital image of the particle reinforced composite material extracted in the step one.
Therefore, coordinate information of the enhanced phase particles and the matrix is respectively stored in a text file according to a specified format so as to compile a corresponding finite element geometric model, wherein the coordinate information mainly comprises node coordinates and serial numbers required by modeling and grid unit coordinates and serial numbers; and dividing the grid cells into two sets of an enhancement phase and a matrix according to the difference of pixel values, and extracting an enhancement phase cell number set and a matrix cell number set. Namely finishing extracting the coordinate information of the finite element geometric model and preparing for establishing the finite element geometric model.
And 5.2, storing the node coordinate information and the serial numbers of the reinforced phase particles and the matrix.
Taking the leftmost point of the digital image as the origin of coordinates, the right as the X axis, and the downward as the Y axis, the number of model nodes corresponding to the digital image expressed as P × T in Matlab is (P +1) × (T +1), the node coordinates are (X, Y), X is an integer from 1 to P +1 multiplied by L, Y is an integer from 1 to T +1 multiplied by L, the node numbers are stored as 1 to (P +1) × (T +1) in the order from left to right and from top to bottom, and the coordinate numbers and all the node coordinates are stored.
And 5.3, storing the grid unit coordinates and the serial numbers of the reinforced phase particles and the matrix.
Taking the leftmost point of the digital image as the origin of coordinates, the right as the X axis, and the downward as the Y axis, the number of model units corresponding to the digital image expressed as P × T in Matlab is P × T, and the coordinate numbers of four nodes of the square grid unit corresponding to any pixel point (m, n) are expressed according to the number of step 5.2 and the counterclockwise sequence with the upper left node as the first point:
(m-1)×(T+1)+n，m×(T+1)+n，
m×(T+1)+n+1，(m+1)×(T+1)+n+1
and 5.4, extracting the enhanced phase unit number set and the matrix unit number set.
And respectively storing the enhanced phase unit number with the pixel point value of 0 and the matrix unit number with the pixel point value of 1 in two files, wherein the unit numbers in the two files are not repeated mutually and the total number of the numbers is the total number of image pixel points.
And step six, establishing a particle reinforced composite finite element model based on the coordinate information of the finite element geometric model extracted in the step five.
And compiling a finite element geometric model file by using a modeling script based on the node coordinate information and the serial number of the enhanced phase particles and the matrix extracted in the step five, the grid unit coordinates and the serial number of the enhanced phase particles and the matrix, the enhanced phase unit serial number set and the matrix unit serial number set information, establishing a geometric model in finite element simulation software, completing assembly, respectively giving corresponding material properties to the enhanced phase set and the matrix set in the finite element simulation software to define the matrix and the enhanced phase, and finally completing finite element modeling.
Step 6.1: and establishing a finite element geometric model.
And compiling a finite element geometric model file by utilizing a modeling script based on the node coordinate information and the serial number of the enhanced phase particles and the matrix extracted in the step five, the grid unit coordinates and the serial number of the enhanced phase particles and the matrix, the enhanced phase unit serial number set and the matrix unit serial number set information
Step 6.2: and (5) assembling and setting.
Assembling the matrix material, the enhanced phase particles and the enhanced phase particle interface according to the coordinate positions.
Step 6.3: and (4) setting materials.
According to the condition that the properties of two parts of the materials of the matrix and the reinforcing phase particles are different, two corresponding materials are arranged and are respectively endowed to the matrix and the reinforcing phase particles.
The setting of the two corresponding materials as described in step 6.3 is preferably based on experiments, relevant literature data or empirical data.
And establishing a particle reinforced composite finite element model based on the pixel theory.
The method also comprises the seventh step: combining the particle reinforced composite finite element model established in the step six with the related engineering problem in the field of particle reinforced composite, setting simulation parameters of the model established in the step six according to actual working conditions in finite element software, and carrying out simulation analysis on the particle reinforced composite so as to solve the engineering problem in the field of particle reinforced composite.
The establishing of the particle reinforced composite finite element model is preferably realized by ABAQUS finite element software, and the automatic modeling of the composite material in the ABAQUS finite element software is preferably realized by adopting Python script.
Has the advantages that:
1. compared with the traditional particle reinforced composite finite element modeling method, the particle reinforced composite finite element modeling method based on the pixel theory disclosed by the invention has the advantages that on the basis of the definition of the traditional particle reinforced composite finite element model material, the geometric model of the particle reinforced phase is established based on the pixel theory, the contradiction between the profile of the particle reinforced phase, the balance efficiency and the accuracy of the simulation result can be accurately reflected, the simulation accuracy and the reliability of the particle reinforced composite finite element model are improved, and further the engineering problem in the field of particle reinforced composite materials is solved.
2. The particle reinforced composite finite element modeling method based on the pixel theory has general universality and is suitable for, but not limited to, finite element modeling of the particle reinforced composite.
3. The invention discloses a particle reinforced composite finite element modeling method based on a pixel theory, which adopts a Python script method to realize the composite material automatic modeling method in ABAQUS finite element software, improves the accuracy of a model, greatly simplifies the modeling operation of the finite element, and has the advantages of simplifying the complexity of digital image processing and finite element software modeling.
Drawings
FIG. 1 is a flow chart of a finite element modeling method for a particle-reinforced composite material based on a pixel theory, which is disclosed by the invention;
FIG. 2 is an optical microscope image of the microstructure of the particle-reinforced composite;
FIG. 3 is a processed image with Photoshop background removal and contouring;
FIG. 4 is an image after grayscale processing and contrast enhancement;
FIG. 5 is a histogram of gray scale distribution of a digital image;
FIG. 6 is a binary image of a digital image;
FIG. 7 is a binary image of the reinforced phase particles after void filling;
FIG. 8 is an enlarged view of the digital image with the pixel adjusted to be 0.25 times of the binary image pixel of the microstructure topography in the third step;
FIG. 9 is a finite element geometric model diagram corresponding to an unadjusted pixel digitized image;
FIG. 10 is a diagram of a finite element geometric model corresponding to a digitized image with pixels adjusted to 0.5 times that of an image taken by an SEM;
FIG. 11 is a diagram of a finite element geometric model corresponding to a digitized image with pixels adjusted to 0.25 times that of an image taken by an SEM;
FIG. 12 is an enlarged view of a portion of a finite element geometric model corresponding to an unadjusted pixilated digitized image;
FIG. 13 is a partial enlarged view of a finite element geometric model corresponding to a digitized image having pixels adjusted to 0.5 times the image taken by the SEM;
FIG. 14 is a partial enlarged view of a finite element geometric model corresponding to a digitized image with pixels adjusted to 0.25 times that of an image taken by an SEM;
Detailed Description
For a better understanding of the objects and advantages of the present invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples.
Example 1:
the embodiment discloses a finite element modeling method of a particle reinforced composite material based on a pixel theory, which comprises the following concrete implementation steps:
observing and extracting a specific microstructure morphology image of the particle reinforced composite material by using methods such as SEM, OM and the like, storing the image into a JPEG format, and preparing subsequent digital image processing work.
Aiming at the specific material used, the smooth and clean material section is obtained after the specific material is subjected to the working procedures of linear cutting, grinding, polishing, ultrasonic vibration cleaning and the like, the surface oxidation and corrosion areas of the material are ensured to be removed completely, the specific microstructure appearance of the material is observed by utilizing an SEM (scanning electron microscope), an OM (scanning object model) optical microscope and the like, a typical area with least stains and scratches and the clearest material microstructure appearance is selected, the specific microstructure photo of the researched particle-reinforced composite material is stored and obtained in a JPEG (joint photographic experts group) format, the shot photo is clear and has higher contrast, particles can be clearly shown, the preparation is made for the subsequent digital image processing work, and the actually shot photo is shown in figure 2.
And secondly, preprocessing the microstructure morphology number image by using Photoshop image processing software.
The microstructure photos of the materials obtained by using methods such as OM, SEM and the like are mixed with a plurality of impurities and shadows, so that the microstructure photos are difficult to be automatically processed directly through digital image processing software, and therefore, in order to more accurately and rapidly identify the enhanced phase particles, the microstructure morphology digital images are required to be preprocessed through a manual method, and Photoshop image processing software is selected for preprocessing.
Step 2.1: and (5) image cutting.
And extracting a rectangular region with uniformly distributed reinforcing phase particles in the microstructure morphology digital image of the particle reinforced composite material by using Photoshop, wherein the length and the width of the rectangular region are required to meet the specific size requirement of the finite element simulation or are in equal proportion to the specific size requirement of the finite element simulation.
Step 2.2: particle segmentation and background removal.
Processing the microstructure digital image obtained in the step 2.1 by using tools such as a Photoshop eraser and the like, removing the characteristics of impurities, defects and the like which are similar to the color of the enhanced phase particles and affect two-phase segmentation and the enhanced phase characteristics with a small area, so that the matrix has obvious contrast with the particles, the edges of the particles are clear, the adjacent particles are segmented, the distance between two adjacent particles is required to be more than three pixel points, the microstructure digital image after the particle segmentation and background removal is obtained, the pretreatment of the microstructure digital image is completed, and the image after the pretreatment in Photoshop is shown in FIG. 3.
And thirdly, utilizing the digital image processing software Matlab to segment the reinforced phase and the matrix, and extracting the characteristics of the composite material reinforced phase particles and the matrix.
And (4) introducing the microstructure morphology digital image obtained in the step two into Matlab for digital image processing, clearly representing the outline of the particles, removing the fine particles and features ignored in finite element analysis, and finally extracting the outline features of the required enhanced phase particles to prepare for subsequent calculation and fitting. The digital image processing comprises gray processing, binarization processing and hole filling.
Step 3.1: and (5) carrying out gray scale processing.
Importing the microstructure morphology digital image after Photoshop pretreatment into Matlab, carrying out gray processing on the image by utilizing an rgb2gray function in Matlab software, and converting the microstructure morphology digital image into a gray image with pixel point values between 0 and 255; the contrast value of the gray image is enhanced by utilizing the imadjust function in the Matlab software to further improve the definition of the microstructure enhanced phase particle outline, noise is removed by utilizing the median filtering function medfilt2 in the Matlab software, and the obtained microstructure morphology gray image is shown in FIG. 4.
Step 3.2: and (6) carrying out binarization processing.
And (3) carrying out binarization processing on the microstructure morphology gray-scale image processed in the step (3.1), wherein the pixel gray-scale values of the processed microstructure morphology gray-scale image are mainly concentrated in two ranges, and the gray-scale value distribution histogram of the microstructure morphology gray-scale image is displayed by utilizing an imhist function in Matlab, as shown in FIG. 5. The method comprises the steps of automatically identifying a threshold value of a microstructure morphology gray scale image by using a threshold value identification function graythresh in Matlab, segmenting enhancement phase particles in the image from a base material according to the threshold value, processing the enhancement phase particles into a binary image with pixel point values of only 0 and 1 by using a binary image processing function im2bw, wherein the base and the enhancement phase in the digital image are obviously distinguished, the gray value of the enhancement particles is 0 and is represented as black, the gray value of the base is 1 and is represented as white, and the microstructure morphology gray scale image is shown as figure 6.
And 3.3, removing the small-area features to finish extracting the contour features of the composite material reinforced phase particles.
For the obtained microstructure morphology binary image, a small-area defect inside the enhancement phase is repaired by using a hole filling function imfill in Matlab to ensure that no hole exists in the enhancement item particle, an object with an area below 2 pixel points in the microstructure morphology binary image is removed by using a bweareaopen function, and the influence of minimum enhancement relative simulation is ignored, so that a finite element model is further simplified on the premise of not influencing the calculation precision, the calculation cost is reduced, small-area features are removed, and the image after extracting the contour features of the composite material enhancement phase particle is shown in FIG. 7.
And step four, adjusting the pixels of the digital image after the composite material enhanced phase particles and the matrix characteristics are extracted in the step three based on the pixel theory, and preparing for extracting the coordinate information of the finite element geometric model.
The larger the digital image pixel is, the more the pixel points are, the more the corresponding finite element simulation model units are, the more the node and unit coordinate information is, the more the model can accurately reflect the actual workpiece, the more accurate the simulation result is, but the lower the digital image processing and subsequent simulation efficiency is; the smaller the digital image pixel is, the fewer the pixel points are, the fewer the corresponding finite element simulation model units are, the fewer the node and unit coordinate information is, the higher the digital image processing and subsequent simulation efficiency is, but the larger the difference between the model and the actual workpiece is, the more inaccurate the simulation result is. Therefore, the method needs to comprehensively consider factors of efficiency and accuracy according to actual working conditions and precision requirements to adjust pixels of the digital image, balance contradictions between efficiency and accuracy of simulation results until the requirements of preset efficiency and accuracy of the simulation results are met, and prepares for extracting coordinate information of the finite element geometric model.
The specific operation is as follows: the pixel of the microstructure morphology binary image is adjusted by utilizing an imbesize pixel adjusting function in Matlab and comprehensively considering factors of efficiency and accuracy according to actual working conditions and precision requirements, when the pixel of the digital image is smaller than a certain value, the image is seriously distorted due to overlarge pixel area, generally speaking, in order to ensure that the microstructure morphology of the digital image is clear and accurate, the minimum value of the pixel is about 0.25 to 0.3 times of the pixel of the microstructure morphology binary image in the three steps. Compared with the digital image of fig. 7, the digital image after adjusting the pixels has different sizes and slightly different details, and fig. 8 is a digital image in which the adjusted pixels are 0.25 times of the binary image pixels of the microstructure topography in step three.
And step five, extracting the coordinate information of the finite element geometric model and preparing for establishing the finite element geometric model.
And (3) each pixel point in the digital picture is a square with the side length of L, each pixel point corresponds to a grid unit in the simulation model, wherein L is the length (or width) of the actual model/the number of corresponding side length pixel points, and the corresponding L value can be changed after the pixels are adjusted, so that the size of the geometric model of the finite element is ensured to be consistent with the size of the digital image of the microstructure morphology of the particle reinforced composite material obtained in the step one.
Therefore, coordinate information of the enhanced phase particles and the matrix is respectively stored in a text file according to a specified format so as to compile a corresponding finite element geometric model, wherein the coordinate information mainly comprises node coordinates and serial numbers required by modeling and grid unit coordinates and serial numbers; dividing grid units into two sets of an enhancement phase and a matrix according to different pixel values, extracting a number set of the enhancement phase unit and a number set of the matrix unit, and realizing the functions used by the data storage function, wherein the functions mainly comprise a function fopen for opening a specified file in Matlab and a function fprintf for formatting output of data. Namely finishing extracting the coordinate information of the finite element geometric model and preparing for establishing the finite element geometric model.
Step 5.1: and calculating the size of the pixel point.
And (4) calculating the size of the pixel points to ensure that the size of the finite element geometric model is consistent with the size of the digital image of the microstructure morphology of the particle reinforced composite material obtained in the step one.
Each pixel point in the digital picture is a square with the side length of L, each pixel point corresponds to a grid unit in the simulation model, wherein L is the length (or width) of the actual model/the number of the corresponding side length pixel points, and the corresponding L value is also changed after the pixels are adjusted, so that the size of the geometric model of the finite element is ensured to be consistent with the size of the digital image of the microstructure morphology of the particle reinforced composite material obtained in the first step, and the size L of the pixels after the pixels of the microstructure morphology of the particle reinforced composite material are adjusted is 0.00056696mm in the example.
And 5.2, storing the node coordinate information and the serial numbers of the reinforced phase particles and the matrix.
Taking the leftmost point of the digital image as the origin of coordinates, the rightmost point as the X axis and the downwards point as the Y axis, wherein the total number of 191X 255 pixel points are arranged on the digital image after the microstructure morphology pixels of the particle reinforced composite material are adjusted in the Matlab, the number of finite element geometric model nodes corresponding to the digital image is 49152, the coordinates of the nodes are (X, Y), X is an integer from 1 to 256 multiplied by L, Y is an integer from 1 to 192 multiplied by L, the node numbers are stored as 1 to 49152 from top to bottom according to the sequence from the left to the right, and the node coordinate numbers and all the node coordinates are stored in a TXT text file by utilizing an open appointed file function fopen in the Matlab and a formatted output function fprintf of data.
And 5.3, storing the grid unit coordinates and the serial numbers of the reinforced phase particles and the matrix.
Taking the leftmost point of the digital image as the origin of coordinates, the rightmost point as the X axis and the downwards point as the Y axis, wherein the total number of 191X 255 pixel points of the digital image after the microstructure morphology image pixels of the particle reinforced composite material are adjusted in Matlab, and the number of finite element geometric model units corresponding to the digital image is 48705, then the coordinate numbers of four nodes of the square grid unit corresponding to any pixel point (m, n) are expressed according to the number of the step 5.2 and the anticlockwise sequence by taking the upper left node as the first point:
（m-1)×(T+1)+n，m＝(T+1)+n，
m×(T+1)+n+1，(m+1)×(T+1)+n+1
the unit coordinate number and all unit coordinates are stored in a TXT text file by utilizing an open specified file function fopen in Matlab and a formatting output function fprintf of data.
And 5.4, extracting the enhanced phase unit number set and the matrix unit number set.
An enhancement phase unit number with a pixel point value of 0 and a matrix unit number with a pixel point value of 1 are respectively stored in two TXT text files by utilizing an open specified file function fopen function and a data formatting output function fprintf function in Matlab, the unit numbers in the two TXT text files are not repeated, and the total number of the numbers is the total number of image pixel points.
And step six, establishing a particle reinforced composite finite element model based on the coordinate information of the finite element geometric model extracted in the step five.
And compiling an INP file of the ABAQUS finite element simulation model based on the node coordinate information and the number of the enhanced phase particles and the matrix extracted in the fifth step, the grid unit coordinate and the number of the enhanced phase particles and the matrix, the enhanced phase unit number set and the matrix unit number set information by using a modeled Python script, establishing a geometric model in ABAQUS finite element simulation software, completing assembly, respectively endowing the enhanced phase set and the matrix set with corresponding material properties on an ABAQUS finite element software operation interface to define the matrix and the enhanced phase, and finally completing finite element modeling.
Step 6.1: and establishing a finite element geometric model.
And compiling an INP file of the ABAQUS finite element simulation model by utilizing the modeling Python script based on the node coordinate information and the serial number of the enhanced phase particles and the matrix extracted in the step five, the grid unit coordinate and the serial number of the enhanced phase particles and the matrix, and the enhanced phase unit serial number set and the matrix unit serial number set information.
Step 6.2: and (5) assembling and setting.
Assembling the matrix material, the enhanced phase particles and the enhanced phase particle interface according to the coordinate positions.
Step 6.3: and (4) setting materials.
According to the condition that the properties of two parts of the materials of the matrix and the reinforcing phase particles are different, two corresponding materials are arranged on an ABAQUS finite element software operation interface and are respectively endowed to the matrix and the reinforcing phase particles, wherein the matrix material is Al6063, and the reinforcing phase material is SiC.
The finite element geometric models created in ABAQUS are shown in fig. 9-14.
The setting of the two corresponding materials as described in step 6.3 is preferably based on experiments, relevant literature data or
Empirical data.
And establishing a particle reinforced composite finite element model based on the pixel theory.
Step seven: combining the particle reinforced composite finite element model established in the step six with the related engineering problem in the field of particle reinforced composite, setting simulation parameters of the model established in the step six according to actual working conditions in finite element software, and carrying out simulation analysis on the particle reinforced composite so as to solve the engineering problem in the field of particle reinforced composite.
The above detailed description is intended to illustrate the objects, aspects and advantages of the present invention, and it should be understood that the above detailed description is only exemplary of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A particle reinforced composite finite element modeling method based on pixel theory is characterized in that: the method comprises the following steps:
firstly, extracting a microstructure morphology digital image of the particle reinforced composite material to prepare for subsequent digital image processing work;
aiming at the specific composite material, carrying out related processing procedures of linear cutting, grinding, polishing and ultrasonic vibration cleaning on the specific composite material to obtain a material section, extracting a microstructure morphology image of the specific composite material, wherein the oxidation and corrosion areas on the surface of the material are required to be completely removed before shooting, selecting a typical area with uniformly distributed enhanced phase particles during shooting, and the shot image is clear and meets the requirement of preset contrast, can clearly show the edges of the particles and is prepared for the subsequent digital image processing work;
secondly, preprocessing the microstructure morphology digital image by using image processing software;
the microstructure morphology digital image of the particle reinforced composite material extracted in the step one is difficult to automatically process through image processing software due to impurities and shadows, so in order to more accurately and rapidly identify reinforced phase particles, the reinforced phase particles must be preprocessed through the image processing software;
thirdly, segmenting the reinforced phase and the matrix, and extracting the characteristics of the particles and the matrix of the reinforced phase of the composite material;
importing the microstructure morphology digital image obtained in the step two into Matlab for digital image processing, clearly representing the outline of the particles, removing the fine particles and features ignored in finite element analysis, and finally extracting the outline features of the required enhanced phase particles to prepare for subsequent calculation and fitting; the digital image processing comprises gray level processing, binarization processing and hole filling;
adjusting the pixels of the digital image after the composite material enhanced phase particles and the matrix characteristics are extracted in the step three based on a pixel theory, and preparing for extracting the coordinate information of the finite element geometric model;
adjusting pixels of the digital image according to the actual working condition and the precision requirement by comprehensively considering the factors of efficiency and accuracy, balancing the contradiction between the efficiency and the accuracy of the simulation result until the requirements of preset efficiency and the accuracy of the simulation result are met, and preparing for extracting the coordinate information of the finite element geometric model;
extracting coordinate information of the finite element geometric model to prepare for establishing the finite element geometric model;
each pixel point in the digital picture is a square with the side length of L, each pixel point corresponds to a grid unit in the simulation model, wherein L = the length or width of the actual model/the number of corresponding variable-length pixel points, and the corresponding L value is changed after the pixels are adjusted, so that the size of the geometric model of the finite element is ensured to be consistent with the size of the digital image of the microstructure morphology of the particle reinforced composite material extracted in the first step;
therefore, coordinate information of the enhanced phase particles and the matrix is respectively stored in a text file according to a specified format so as to compile a corresponding finite element geometric model, wherein the coordinate information mainly comprises node coordinates and serial numbers required by modeling and grid unit coordinates and serial numbers; dividing grid cells into two sets of an enhancement phase and a matrix according to different pixel values, and extracting an enhancement phase cell number set and a matrix cell number set; finishing extracting the coordinate information of the finite element geometric model and preparing for establishing the finite element geometric model;
step six, establishing a particle reinforced composite finite element model based on the coordinate information of the finite element geometric model extracted in the step five;
and compiling a finite element geometric model file by using a modeling script based on the node coordinate information and the serial number of the enhanced phase particles and the matrix extracted in the step five, the grid unit coordinates and the serial number of the enhanced phase particles and the matrix, the enhanced phase unit serial number set and the matrix unit serial number set information, establishing a geometric model in finite element simulation software, completing assembly, respectively giving corresponding material properties to the enhanced phase set and the matrix set in the finite element simulation software to define the matrix and the enhanced phase, and finally completing finite element modeling.
2. The finite element modeling method for particle reinforced composite material based on pixel theory as claimed in claim 1, wherein: the method also comprises the seventh step: combining the particle reinforced composite finite element model established in the step six with the related engineering problem in the field of particle reinforced composite, setting simulation parameters of the model established in the step six according to actual working conditions in finite element software, and carrying out simulation analysis on the particle reinforced composite so as to solve the engineering problem in the field of particle reinforced composite.
3. A particle-reinforced composite finite element modeling method based on pixel theory as claimed in claim 1 or 2, characterized in that: step two, the image processing software is utilized to carry out preprocessing on the microstructure morphology digital image and gate a manual method, the specific realization method of the step two is that,
step 2.1: image cutting;
extracting a rectangular area with uniformly distributed reinforcing phase particles in a microstructure morphology digital image of the particle reinforced composite material by using image processing software, wherein the length and the width of the rectangular area meet the specific size requirement of finite element simulation or are in equal proportion to the specific size requirement of the finite element simulation;
step 2.2: particle segmentation and background removal;
and (3) processing the microstructure morphology digital image obtained in the step 2.1 by using image processing software to remove impurities, defect characteristics and small-area enhancement phase characteristics which are similar to the color of enhancement phase particles and affect two-phase segmentation, so that the matrix and the particles have obvious contrast, the edges of the particles are clear, the adjacent particles are segmented, the distance of more than three pixel points is required between the two adjacent particles, the microstructure morphology digital image after particle segmentation and background removal is obtained, and the pretreatment of the microstructure morphology digital image is completed.
4. A particle-reinforced composite finite element modeling method based on pixel theory as claimed in claim 3, wherein: the concrete implementation method of the third step is that,
step 3.1: carrying out gray level processing;
introducing the microstructure morphology image preprocessed in the step two into Matlab, carrying out gray processing on the microstructure morphology image, converting the microstructure morphology image into a gray image with pixel point values between 0 and 255, enhancing the contrast value of the gray image to further improve the definition of the outline of the microstructure morphology image, and removing noise points by using a median filtering method;
step 3.2: carrying out binarization processing;
after the outline of the enhancement phase particles is clearer, carrying out binarization processing on a microstructure morphology digital image, automatically identifying a threshold value of the microstructure morphology digital image by utilizing Matlab, and dividing the enhancement phase particles in the image from a matrix material to obtain the enhancement phase particles with the gray value of 0 which is represented as black and the matrix with the gray value of 1 which is represented as white;
step 3.3: removing the small-area features to finish extracting the contour features of the composite material reinforced phase particles;
for the binary image obtained in the step 3.2, a small-area defect inside the enhancement phase is repaired by using a hole filling instruction, and a small-area enhancement phase characteristic is removed by using a method of opening and closing operation combined with expansion and corrosion, so that the finite element model is further simplified on the premise of not influencing the calculation precision, the grid division and the calculation cost are conveniently reduced, and the extraction of the composite material enhancement phase particles and the matrix characteristic is completed;
the small area stated in step 3.3 depends on the mesh division precision requirement.
5. The finite element modeling method for particle reinforced composite material based on pixel theory as claimed in claim 4, wherein: the concrete implementation method of the step five is that,
step 5.1, calculating the size of the pixel points to ensure that the size of the finite element geometric model is consistent with the size of the digital image of the microstructure morphology of the particle reinforced composite material obtained in the step one;
each pixel point in the digital picture is a square with the side length of L, each pixel point corresponds to a grid unit in the simulation model, wherein L = the length or width of the actual model/the number of corresponding variable-length pixel points, and the corresponding L value is changed after the pixels are adjusted, so that the size of the geometric model of the finite element is ensured to be consistent with the size of the digital image of the microstructure morphology of the particle reinforced composite material extracted in the first step;
therefore, coordinate information of the enhanced phase particles and the matrix is respectively stored in a text file according to a specified format so as to compile a corresponding finite element geometric model, wherein the coordinate information mainly comprises node coordinates and serial numbers required by modeling and grid unit coordinates and serial numbers; dividing grid cells into two sets of an enhancement phase and a matrix according to different pixel values, and extracting an enhancement phase cell number set and a matrix cell number set; finishing extracting the coordinate information of the finite element geometric model and preparing for establishing the finite element geometric model;
step 5.2, storing node coordinate information and serial numbers of the enhanced phase particles and the matrix;
the digital image is represented as the coordinate origin at the leftmost point, the X axis toward the right and the Y axis toward the bottom in Matlab
The number of model nodes corresponding to the digital image is
The node coordinates are (x, y), x is an integer from 1 to P +1 multiplied by L, y is an integer from 1 to T +1 multiplied by L, the node numbers are stored from 1 to 1 in the order from left to right and from top to bottom
Storing the coordinate numbers and all the node coordinates;
step 5.3, storing grid unit coordinates and serial numbers of the reinforced phase particles and the matrix;
the digital image is represented as the coordinate origin at the leftmost point, the X axis toward the right and the Y axis toward the bottom in Matlab
The number of model units corresponding to the digital image is
And if the number of the coordinates of the four nodes of the square grid unit corresponding to any pixel point (m, n) is the number in the step 5.2, the upper left node is taken as the first point, and the coordinates are expressed according to the anticlockwise sequence as follows:
step 5.4, extracting an enhanced phase unit number set and a matrix unit number set;
and respectively storing the enhanced phase unit number with the pixel point value of 0 and the matrix unit number with the pixel point value of 1 in two files, wherein the unit numbers in the two files are not repeated mutually and the total number of the numbers is the total number of image pixel points.
6. The finite element modeling method for particle reinforced composite material based on pixel theory as claimed in claim 5, wherein: the concrete realization method of the sixth step is that,
step 6.1: establishing a finite element geometric model;
and compiling a finite element geometric model file by utilizing a modeling script based on the node coordinate information and the serial number of the enhanced phase particles and the matrix extracted in the step five, the grid unit coordinates and the serial number of the enhanced phase particles and the matrix, the enhanced phase unit serial number set and the matrix unit serial number set information
Step 6.2: assembling and setting;
assembling the matrix material, the enhanced phase particles and the enhanced phase particle interface according to the coordinate positions;
step 6.3: setting materials;
according to the condition that the properties of two parts of the matrix and the reinforced phase particles are different, two corresponding materials are arranged and are respectively endowed to the matrix and the reinforced phase particles;
the setting of the two corresponding materials described in step 6.3 is preferably based on experiments, relevant literature data or empirical data;
and establishing a particle reinforced composite finite element model based on the pixel theory.
7. The finite element modeling method for particle reinforced composite material based on pixel theory as claimed in claim 6, wherein: the establishing of the particle reinforced composite finite element model is realized by ABAQUS finite element software, and the automatic modeling of the composite material in the ABAQUS finite element software is realized by adopting Python script.
8. The finite element modeling method for particle reinforced composite material based on pixel theory as claimed in claim 7, wherein: and step one, extracting the microstructure morphology image of the specific composite material by using an SEM scanning electron microscope or an OM optical microscope.
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