CN116108713A - Microscopic finite element modeling method, system, equipment and medium for fiber reinforced composite material - Google Patents
Microscopic finite element modeling method, system, equipment and medium for fiber reinforced composite material Download PDFInfo
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Abstract
The invention provides a microscopic finite element modeling method, a microscopic finite element modeling system, microscopic finite element modeling equipment and a microscopic finite element modeling medium for a fiber reinforced composite material, which comprise the following steps: step S1: preparing a tensile sample, and performing X-ray CT tomography to obtain a CT scanning image; step S2: performing median filtering processing and threshold segmentation processing on the CT scanning image; step S3: separating the adhesion fibers in the processed CT scanning image through a template matching image algorithm to obtain the endpoint coordinates of the center line of the fibers; step S4: deriving the end point coordinates, reconstructing a fiber geometric model, and adding a matrix geometric model wrapping the fibers; step S5: programming a program to define material properties, grid types and grid sizes, and dividing grids of the geometric model and the matrix geometric model respectively; step S6: and (3) inserting a program into the cohesive force unit, establishing force transmission between the fiber geometric model and the matrix geometric model by using embedded constraint, adding boundary conditions, and submitting calculation. The invention can improve the grid dividing speed and the quality of grids.
Description
Technical Field
The invention relates to the technical field of fiber reinforced composite materials, in particular to a microscopic finite element modeling method for a fiber reinforced composite material injection molding piece based on CT scanning images and Abaqus python secondary development, and particularly relates to a microscopic finite element modeling method, a microscopic finite element modeling system, microscopic finite element modeling equipment and medium for the fiber reinforced composite material.
Background
The fiber reinforced composite material can make up for the advantages of the component materials in performance, generate a synergistic effect, obtain the performance which is incomparable with the single component material, and realize the effect of 1+1> 2. In terms of material selection, people can obtain composite materials with different microstructures and excellent mechanical properties through selection and matching of component materials according to specific requirements. Injection molding is widely used with low production cost and high production efficiency, and the different phases of the injection molded article are uniform on a macroscopic scale, non-uniform on a microscopic scale, and exhibit anisotropy as a whole. Since the nature of composite design is such that the macroscopic properties of the material are altered by the combination of materials on a microscopic scale. Therefore, how to predict macro-scale material information from micro-scale design information is one of the key issues in composite design. The method of calculating the mesomechanics finite element is one of the most effective methods for researching the relationship between the microstructure and the macroscopic property of the material, and the method firstly needs to build a model capable of reflecting the real microstructure of the material. However, because of the complexity of the microstructure of fiber reinforced injection molding, building a model of its microstructure is a major challenge in material design.
In the study of the properties of fiber reinforced composite injection molded parts, the single cell model method is more traditional. This method idealizes its microstructure into a model with simple geometric features based on the macroscopic properties (modulus, strength) that the material exhibits macroscopically. This method can effectively predict the impact of the composite's component properties, volume fraction, shape and distribution on macroscopic properties, but ignores the true microscopic geometry of the composite, such as the interface between the matrix and the fibers, and is difficult to use for accurate analysis of the material.
The geometric reconstruction based on the image is a method based on digital image processing, and the image is divided into different components through processing the digital image, and boundary information of each component is further extracted, so that the geometric information of an enhancement phase and a matrix phase is extracted and reconstructed. This approach allows for complete restoration of the geometry of the mesostructure and the reconstruction process is not limited by factors such as high volume fraction. With the progress of computer digital image processing technology in acquiring high-resolution images and image processing algorithms, the method has been rapidly developed.
Although geometric reconstruction methods have tended to be sophisticated in recent years, the development of new microstructure reconstruction methods for the finite element prediction of macroscopic properties of composite materials is still needed. Because the model established by adopting the existing geometric reconstruction method needs to be further divided into grids, the grids divided based on complex surfaces or entities are mostly tetrahedral units, and the finite element analysis precision is not high; in addition, complex composite microstructures such as a large number of fibers, with complex geometric features such as fiber length distribution and fiber orientation distribution, reduce the quality of the grid, and tend to lose relatively fine information in the microstructure of the material during the meshing process. These drawbacks make it difficult to apply the geometric reconstruction method more widely to the research design of materials, and it is therefore important to develop new reconstruction methods that can overcome these drawbacks.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a microscopic finite element modeling method, a microscopic finite element modeling system, microscopic finite element modeling equipment and a microscopic finite element modeling medium for fiber reinforced composite materials.
According to the method, the system, the equipment and the medium for microscopic finite element modeling of the fiber reinforced composite material, the scheme is as follows:
in a first aspect, there is provided a method of microscopic finite element modeling of a fiber reinforced composite material, the method comprising:
step S1: preparing a tensile sample, and performing X-ray CT tomography to obtain a CT scanning image;
step S2: performing median filtering processing and threshold segmentation processing on the CT scanning image;
step S3: separating the adhesion fibers in the processed CT scanning image through a template matching image algorithm to obtain end point coordinates of a fiber center line;
step S4: deriving the end point coordinates, reconstructing a fiber geometric model, and adding a matrix geometric model wrapping the fibers;
step S5: programming to define material properties, grid types and grid sizes, and dividing grids of the geometric model and the matrix geometric model respectively;
step S6: and programming and inserting a cohesive force unit, establishing force transmission between the fiber geometric model and the matrix geometric model by using embedded constraint, adding boundary conditions, and submitting calculation.
Preferably, the step S1 includes: the fiber in the fiber reinforced composite material and the matrix material must have different attenuation coefficients for CT scanning rays; the scanned CT equipment is industrial CT equipment, and the resolution is not smaller than the diameter of the fiber.
Preferably, the mesh dividing the fibers and the matrix in step S5 is a hexahedral mesh, the mesh size should be 2.5% of the fiber diameter at the maximum, and the mesh size of the interface remains consistent with the fibers.
Preferably, the constraint type is Embedded Element contact constraint in the step S6, and the cohesive force unit is inserted, the direction of the cohesive force unit is set, the boundary condition is applied, and the operation is submitted through language programming.
In a second aspect, there is provided a fiber reinforced composite microscopic finite element modeling system, the system comprising:
module M1: preparing a tensile sample, and performing X-ray CT tomography to obtain a CT scanning image;
module M2: performing median filtering processing and threshold segmentation processing on the CT scanning image;
module M3: separating the adhesion fibers in the processed CT scanning image through a template matching image algorithm to obtain end point coordinates of a fiber center line;
module M4: deriving the end point coordinates, reconstructing a fiber geometric model, and adding a matrix geometric model wrapping the fibers;
module M5: programming to define material properties, grid types and grid sizes, and dividing grids of the geometric model and the matrix geometric model respectively;
module M6: and programming and inserting a cohesive force unit, establishing force transmission between the fiber geometric model and the matrix geometric model by using embedded constraint, adding boundary conditions, and submitting calculation.
Preferably, the module M1 comprises: the fiber in the fiber reinforced composite material and the matrix material must have different attenuation coefficients for CT scanning rays; the scanned CT equipment is industrial CT equipment, and the resolution is not smaller than the diameter of the fiber.
Preferably, the mesh dividing the fibers and the matrix in the module M5 is a hexahedral mesh, the mesh size should be 2.5% of the fiber diameter at maximum, and the mesh size of the interface remains consistent with the fibers.
Preferably, the module M6 is Embedded with constraint type of Embedded Element contact constraint, and inserts cohesive force unit, sets direction of cohesive force unit, applies boundary condition and submits operation work through language programming.
In a third aspect, a computer readable storage medium storing a computer program is provided, which when executed by a processor implements the steps of the method for microscopic finite element modeling of a fiber reinforced composite material.
In a fourth aspect, an electronic device is provided comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the method for modeling microscopic finite elements of a fiber reinforced composite material.
Compared with the prior art, the invention has the following beneficial effects:
1. the finite element grid model of the real microstructure of the fiber reinforced composite material is directly and accurately reconstructed, so that the joint requirements of the existing geometric method in reconstruction and grid division are avoided, the grid quality is improved by using hexahedral units instead of tetrahedral grids of the existing method, the reconstruction time is saved, and the reconstruction efficiency is improved;
2. the invention carries out fiber geometry reconstruction based on CT images, adopts an image processing method which can lead the tomographic images to reflect the real microstructure of the material more accurately, and can reflect the real microstructure;
3. the invention can well transfer the load between the fiber and the matrix by adopting the embedded constraint, and can independently divide the grids of the fiber and the matrix, thereby effectively balancing the contradiction between the finite element analysis precision and the preprocessing time;
4. the reconstructed finite element mesh model can be conveniently used for analyzing the relationship between microstructure and macroscopic performance such as aggregation distribution characteristics of fibers in the fiber reinforced composite material, thereby providing basis for development and design of the material.
Other advantages of the present invention will be set forth in the description of specific technical features and solutions, by which those skilled in the art should understand the advantages that the technical features and solutions bring.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a modeling flow chart of the present invention;
FIG. 2 is a schematic view of a CT scan sample;
FIG. 3 is a view of the acquisition of sequential tomographic images;
FIG. 4 is a flow chart of a CT scan image filtering process;
FIG. 5 is a graph of the centerline fitting effect of the binder fibers;
FIG. 6 is a calculated fiber diameter;
FIG. 7 is a grid demarcation of individual fibers;
FIG. 8 is a geometric model map based on CT scan images;
FIG. 9 is a diagram of the effect of applying boundary conditions to a simulation model.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
The embodiment of the invention provides a microscopic finite element modeling method of a fiber reinforced composite material, which is characterized in that a sequence tomogram is acquired through industrial CT, endpoint coordinate information of a central line of a fiber geometric structure is extracted by using a template matching image algorithm on the basis of a digital gray image and median filtering and threshold segmentation, an Abaqus python secondary development is utilized to reconstruct the fiber geometric structure, automatic unit meshing is performed, boundary conditions are added, cohesive force units are inserted, and a matrix and a fiber separate meshing mesh improve the meshing speed and the mesh quality. The invention can be widely applied to the fields of performance prediction, optimization design and the like of fiber reinforced composite materials. Referring to fig. 1, the method is specifically as follows:
step S1: and performing X-ray CT tomography on the center of the tensile sample to obtain a CT scanning image. The fiber in the fiber reinforced composite material and the matrix material must have different attenuation coefficients for CT scanning rays; the scanned CT equipment is industrial CT equipment, and the resolution is not smaller than the diameter of the fiber.
Step S2: median filtering and thresholding are performed on the CT scan image with Avizo software.
Step S3: the adhered fibers in the processed CT scan images were separated using a Cylinder Correlation and Trace Correlation Lines template-matched image algorithm to obtain the end point coordinates of the fiber center line.
Step S4: and (3) deriving the end point coordinates of the central line of the fiber, secondarily developing by using Abaqus python, reconstructing a fiber geometric model, and adding a matrix geometric model wrapping the fiber.
Step S5: and defining material properties, grid types and grid sizes by using an Abaqus python secondary development programming program, and respectively dividing grids of the geometric model and the matrix geometric model. The mesh is hexahedral, the mesh size should be 2.5% of the fiber diameter at maximum, and the mesh size of the interface remains consistent with the fiber.
Step S6: the cohesive force unit is inserted by using an Abaqus python secondary development programming program, force transmission between the fiber geometric model and the matrix geometric model is established by using embedded constraint, boundary conditions are added, and calculation is submitted in Abaqus software. The constraint type is an assembled Element contact constraint, and the insertion of an automatic cohesive unit, the direction setting of the cohesive unit and the automatic application of boundary conditions are realized in Abaqus software through Python programming, and the submission of Abaqus operation work is realized.
The invention will be described in more detail below with reference to one embodiment in conjunction with the accompanying drawings.
Referring to fig. 1, a flow chart is established for a representative volume unit model developed based on CT scan images and Abaqus Python secondarily, and the basic principle of the present invention is as follows: obtaining the real internal structure of the fiber reinforced composite material through CT scanning, obtaining the endpoint coordinates of the fiber center line by means of Avizo image processing software, then carrying out secondary development based on Abaqus Python, reconstructing the geometric structures of the fiber and the matrix according to the obtained endpoint coordinates of the fiber center line, characterizing the interface between the fiber and the matrix by using a cohesive force unit, and carrying out automatic high-quality meshing, cohesive force unit insertion, embedding constraint addition and boundary condition addition. Modeling of a representative volume element of a fiber reinforced composite material taking into account microstructure is achieved. The specific technical problems are described in detail below based on this flow.
The glass fiber polyether-ether-ketone injection molding plate shown in the figure 2 is prepared through an injection molding process, and is subjected to machining cutting to obtain a proper composite material standard stretching sample piece, wherein the arrow direction in the figure indicates that the fiber flow direction is consistent with the stretching direction of a sample, the central area of the sampling piece is subjected to fault scanning, the voxel resolution is 2um, the scanning voltage is 80kV, and the composite material sample rotates in the horizontal direction by taking a Z axis vertical to the horizontal plane of a sample table as the center, so that a series of ordered two-dimensional gray images are obtained. It should be noted that, the fibers and the matrix material in the fiber reinforced composite material must have different attenuation coefficients for the CT scan rays, the attenuation coefficients are related to the density, the two-dimensional gray scale image obtained with a larger density difference has a better contrast, and the subsequent image processing algorithm will more easily distinguish the two phases of the fibers and the matrix.
Referring to fig. 3, a two-dimensional gray scale image slice of a partial YZ plane is obtained.
Referring to fig. 4, in order to perform an image preprocessing process on a CT scan image, an obtained two-dimensional gray image is subjected to a nonlinear filtering technology median filter to eliminate isolated noise points and enhance boundaries, and finally, a gray value distinguishing point of ROI (region of interest) and a background area is defined by determining a gray threshold value by using threshold segmentation to perform binarization segmentation, and a binarized image g is obtained by dividing an input image f by a threshold value T to distinguish different phases, wherein 1 is an ROI fiber phase and 0 is a background matrix phase because a geometric microstructure of fibers is required to be obtained, and the expression is as follows:
where (i, j) is a point on the two-dimensional digital image plane and f (i, j) is a gray value of each point of the image.
The sub-volume region of interest of the scanned region is selected for reconstruction, the X-Fiber module in Avizo commercial software is used for quantitative analysis of Fiber orientation and Fiber length according to the Fiber continuity after threshold segmentation, the Cylinder Correlation module determines the proper Fiber template length and radius of the visual cylindrical template by selecting the Fiber section in the slice for Fiber geometry detection, and the Fiber tracking process begins by calculating the correlation between the cylindrical template and the cylindrical correlation module. The Trace Correlation Lines module is used for tracking the central line of the tubular structure to reconstruct the fiber, and avoiding abrupt changes of values in the boundary area. The extracted fiber center effect is shown in fig. 5, and the geometric coordinates of two end points of the fiber center line are obtained by performing data post-processing.
Referring to fig. 6, the cross section of the fiber can be seen in the XY plane perpendicular to the fiber flow direction, after thresholding, pixel units non-parallel to the fiber cross section are screened out, label analysis is performed, and the equivalent diameter of the fiber is calculated to be 10.0um, which matches the data provided by the supplier.
Considering the interface between the collocation fiber and the matrix and the transmission of load between the fiber and the matrix, a single cell model comprising the fiber, the matrix and the interface was created as shown in fig. 7, wherein the mesh thickness of the interface and the matrix was 2.5% of the fiber diameter.
All fiber unit cell models are generated in batches based on secondary development of Abaqus Python according to the geometric coordinates of two end points of the center line of the fiber, and the fibers are not interfered with each other as shown in figure 8. The geometry model of the matrix was generated using the getbase box () function built in by Abaqus, with all fibers just embedded in the matrix.
Referring to fig. 9, material properties were set based on the secondary development of Abaqus Python, cohesion units were inserted and cohesion thickness unit directions were set to characterize interface properties, embedding constraints were established between the unit cell and substrate contact surfaces, boundary conditions were added, and calculations were submitted in Abaqus software. The calculation result can accurately reflect the mechanical properties of the fiber reinforced composite material under the set working condition.
The invention also provides a fiber reinforced composite microscopic finite element modeling system which can be realized by executing the flow steps of the fiber reinforced composite microscopic finite element modeling method, namely, the fiber reinforced composite microscopic finite element modeling method can be understood as a preferred implementation mode of the fiber reinforced composite microscopic finite element modeling system by a person skilled in the art. The system specifically comprises:
module M1: preparing a tensile sample, and performing X-ray CT tomography to obtain a CT scanning image; the fiber in the fiber reinforced composite material and the matrix material must have different attenuation coefficients for CT scanning rays; the scanned CT equipment is industrial CT equipment, and the resolution is not smaller than the diameter of the fiber.
Module M2: performing median filtering processing and threshold segmentation processing on the CT scanning image;
module M3: separating the adhesion fibers in the processed CT scanning image through a template matching image algorithm to obtain end point coordinates of a fiber center line;
module M4: deriving the end point coordinates, reconstructing a fiber geometric model, and adding a matrix geometric model wrapping the fibers;
module M5: programming to define material properties, grid types and grid sizes, and dividing grids of the geometric model and the matrix geometric model respectively; the mesh is hexahedral, the mesh size should be 2.5% of the fiber diameter at maximum, and the mesh size of the interface remains consistent with the fiber.
Module M6: and programming and inserting a cohesive force unit, establishing force transmission between the fiber geometric model and the matrix geometric model by using embedded constraint, adding boundary conditions, and submitting calculation. The contact type referred to in module M6 is an assembled Element contact constraint that works by programming in language to insert cohesive units, orient cohesive units, apply boundary conditions, and submit operations.
The embodiment of the invention provides a microscopic finite element modeling method, a microscopic finite element modeling system, microscopic finite element modeling equipment and a microscopic finite element modeling medium for fiber reinforced composite materials, which are characterized in that sequential tomographic images of materials are acquired through industrial CT, endpoint coordinate information of a fiber geometric structure is extracted by using a template-matched image algorithm on the basis of Avizo software median filtering and threshold segmentation image preprocessing, an Abaqus Python secondary development is utilized to reconstruct the fiber geometric structure, automatic unit meshing is performed, cohesive force units are inserted, the thickness direction of the cohesive force units is set, embedding constraint is added, boundary conditions are added, and the meshing speed and the mesh quality are improved. So that any detailed structural information in the fiber-reinforced composite material can be reproduced in the model.
Those skilled in the art will appreciate that the invention provides a system and its individual devices, modules, units, etc. that can be implemented entirely by logic programming of method steps, in addition to being implemented as pure computer readable program code, in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units for realizing various functions included in the system can also be regarded as structures in the hardware component; means, modules, and units for implementing the various functions may also be considered as either software modules for implementing the methods or structures within hardware components.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the invention. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily without conflict.
Claims (10)
1. A method for modeling microscopic finite elements of a fiber reinforced composite material, comprising:
step S1: preparing a tensile sample, and performing X-ray CT tomography to obtain a CT scanning image;
step S2: performing median filtering processing and threshold segmentation processing on the CT scanning image;
step S3: separating the adhesion fibers in the processed CT scanning image through a template matching image algorithm to obtain end point coordinates of a fiber center line;
step S4: deriving the end point coordinates, reconstructing a fiber geometric model, and adding a matrix geometric model wrapping the fibers;
step S5: programming to define material properties, grid types and grid sizes, and dividing grids of the geometric model and the matrix geometric model respectively;
step S6: and programming and inserting a cohesive force unit, establishing force transmission between the fiber geometric model and the matrix geometric model by using embedded constraint, adding boundary conditions, and submitting calculation.
2. The method of modeling microscopic finite elements of a fiber reinforced composite material according to claim 1, wherein the step S1 includes: the fiber in the fiber reinforced composite material and the matrix material must have different attenuation coefficients for CT scanning rays; the scanned CT equipment is industrial CT equipment, and the resolution is not smaller than the diameter of the fiber.
3. The method according to claim 1, wherein the mesh dividing the fibers and the matrix in step S5 is a hexahedral mesh, the mesh size is 2.5% of the fiber diameter at the maximum, and the mesh size of the interface is kept consistent with the fibers.
4. The method according to claim 1, wherein the embedding constraint type in step S6 is an Embedded Element contact constraint, and the cohesive force unit is inserted, the direction of the cohesive force unit is set, the boundary condition is applied, and the operation is submitted by language programming.
5. A fiber reinforced composite microscopic finite element modeling system, comprising:
module M1: preparing a tensile sample, and performing X-ray CT tomography to obtain a CT scanning image;
module M2: performing median filtering processing and threshold segmentation processing on the CT scanning image;
module M3: separating the adhesion fibers in the processed CT scanning image through a template matching image algorithm to obtain end point coordinates of a fiber center line;
module M4: deriving the end point coordinates, reconstructing a fiber geometric model, and adding a matrix geometric model wrapping the fibers;
module M5: programming to define material properties, grid types and grid sizes, and dividing grids of the geometric model and the matrix geometric model respectively;
module M6: and programming and inserting a cohesive force unit, establishing force transmission between the fiber geometric model and the matrix geometric model by using embedded constraint, adding boundary conditions, and submitting calculation.
6. The fiber reinforced composite microscopic finite element modeling system of claim 5, wherein the module M1 comprises: the fiber in the fiber reinforced composite material and the matrix material must have different attenuation coefficients for CT scanning rays; the scanned CT equipment is industrial CT equipment, and the resolution is not smaller than the diameter of the fiber.
7. The system of claim 5, wherein the mesh of the module M5 dividing the fibers and the matrix is a hexahedral mesh, the mesh size should be at most 2.5% of the fiber diameter, and the mesh size of the interface remains consistent with the fibers.
8. The system of claim 5, wherein the module M6 is Embedded with constraint type of Embedded Element contact constraint, inserting cohesive units by language programming, setting the direction of cohesive units, applying boundary conditions, and submitting an operation.
9. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method for microscopic finite element modeling of a fiber reinforced composite material according to any one of claims 1 to 4.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program when executed by the processor implements the steps of the method for microscopic finite element modeling of a fiber reinforced composite material according to any of claims 1 to 4.
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| CN117648846B (en) * | 2024-01-30 | 2024-04-26 | 中国空气动力研究与发展中心计算空气动力研究所 | Image sample generation method for composite material performance prediction modeling |
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