CN111508075B - Three-dimensional real finite element model modeling method for closed-cell foamed aluminum - Google Patents
Three-dimensional real finite element model modeling method for closed-cell foamed aluminum Download PDFInfo
- Publication number
- CN111508075B CN111508075B CN202010271362.XA CN202010271362A CN111508075B CN 111508075 B CN111508075 B CN 111508075B CN 202010271362 A CN202010271362 A CN 202010271362A CN 111508075 B CN111508075 B CN 111508075B
- Authority
- CN
- China
- Prior art keywords
- matrix
- node
- gray
- closed
- threshold
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Graphics (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The invention provides a method for modeling a three-dimensional real finite element model of closed-cell foamed aluminum, which comprises the steps of firstly scanning a closed-cell foamed aluminum sample based on a CT electronic computer tomography to obtain a plurality of gray images, then calculating the distance between the closed-cell foamed aluminum sample and each position point of an aluminum substrate based on the center of each cubic region obtained by dividing a virtual space, and eliminating redundant cubic regions through the minimum distance to obtain effective unit and node information so as to establish a three-dimensional real finite element model of the closed-cell foamed aluminum; therefore, the method can effectively solve the problem that three-dimensional reconstruction is difficult to perform due to the complex structure of the closed-cell foamed aluminum, the error between the model and a real foamed aluminum sample is small, the calculation efficiency can be greatly improved under the condition of ensuring the precision by the model established by the method, and the method is particularly suitable for various porous materials with large differences between X-ray attenuation values and air.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a method for modeling a three-dimensional real finite element model of closed-cell foamed aluminum.
Background
In the research process of closed-cell foamed aluminum model and numerical simulation, in order to more intuitively research and analyze the relationship between the complicated and irregular structure of the foamed metal and the macroscopic mechanical property, gradually scholars propose to establish a three-dimensional real model based on the closed-cell foamed aluminum structure. Zhu Kai et al obtained a true three-dimensional structure of closed-cell aluminum foam by using synchrotron radiation X-ray microtomography, and the model had a problem that the sample size was small and the size-to-aperture ratio was less than 7, and errors in measuring the mechanical properties thereof due to boundary effects could not be avoided. The inst Jeon et al also established a three-dimensional finite element model using the commercial mesh generation program PATRAN of MSC Software corp, after two-dimensional meshing of the hole surface using a microfocus X-ray CT system, but the volume obtained by this model was relatively large compared to the actual sample error.
Disclosure of Invention
In order to solve the problems, the invention provides a method for modeling a three-dimensional real finite element model of closed-cell foamed aluminum, which can greatly improve the operation efficiency under the condition of ensuring the precision and is particularly suitable for various porous materials with larger differences between X-ray attenuation values and air.
A method for modeling a three-dimensional real finite element model of closed-cell foamed aluminum comprises the following steps:
s1: scanning a closed-cell foamed aluminum sample by a CT (computed tomography) electronic computer to obtain more than two gray images, and numbering the gray images in sequence, wherein the attribute of each pixel point of the gray images is an aluminum matrix or air;
s2: obtaining a gray value matrix according to the gray image, wherein the serial number of the gray value matrix corresponds to the serial number of the gray image one by one, and meanwhile, the gray value of the gray value matrix corresponds to the pixel points of the gray image one by one;
s3: performing threshold segmentation on the gray value matrix according to a set threshold to obtain gray values of all pixel points with the attribute of the aluminum matrix;
s4: determining the position coordinates of the gray value of the aluminum matrix in the three-dimensional space according to the row number, the column number and the sequence number of the gray value matrix;
s5: uniformly dividing a virtual space into a plurality of same cubic areas to obtain a matrix of the center points of the cubic areas, wherein the length, the width and the height of the virtual space are respectively the maximum values of the gray value of an aluminum matrix in the three-axis direction of the position coordinate of the aluminum matrix in the three-dimensional space;
s6: sequentially calculating the distance between each position coordinate with the gray value of the aluminum matrix and the center point of each cubic area, and reserving the cubic area corresponding to the minimum distance;
s7: for each cubic area obtained in step S6, generating a unit with its center point, each unit including eight nodes, and then numbering each unit and each node in sequence to obtain a unit matrix and a node matrix, wherein the unit matrix is composed of a unit sequence number and a node sequence number, and the node matrix is composed of a node sequence number and a node triaxial coordinate;
s8: for the shared nodes of the adjacent units, replacing the serial numbers of the subsequent nodes arranged by the shared nodes and the triaxial coordinates of the nodes corresponding to the serial numbers of the subsequent nodes by the serial numbers of the minimum nodes arranged by the shared nodes and the triaxial coordinates of the nodes corresponding to the serial numbers of the minimum nodes, and finishing the updating of the unit matrix and the node matrix;
s9: and constructing a three-dimensional real finite element model of the closed-cell foamed aluminum sample according to the updated unit matrix and the updated node matrix.
Further, the method for acquiring the set threshold specifically includes:
s31: dividing the grey values into two sets G 1 And G 2 Wherein G is 1 Is greater than an initial threshold T0, G 2 Is not greater than the initial threshold T0;
s32: obtain set G 1 And G 2 The number of middle gray scale values, respectively denoted as n 1 And n 2 ；
S33: respectively obtain the sets G 1 And G 2 Weighted average u of all gray values in 1 And u 2 ：
S34: according to a weighted average u 1 And u 2 Updating the threshold value:
wherein T is the updated threshold;
s35: judging whether the absolute value of the difference between the updated threshold T and the initial threshold T0 is smaller than a set iteration cutoff parameter delta T or not, and if so, taking the updated threshold T as the final set threshold; if not, go to step S36;
s36: and replacing the initial threshold T0 in the step S31 with the updated threshold T to divide the gray value again, and then repeating the steps S32 to S35 until the absolute value of the difference is smaller than the set iteration cut-off parameter delta T.
Further, the weighted average u 1 And u 2 The calculation method comprises the following steps:
where x represents the gray value and y represents the number of gray values with the value x.
Has the advantages that:
the invention provides a method for modeling a three-dimensional real finite element model of closed-cell foamed aluminum, which comprises the steps of firstly scanning a closed-cell foamed aluminum sample based on a CT electronic computer tomography to obtain a plurality of gray images, then calculating the distance between the closed-cell foamed aluminum sample and each aluminum matrix position point based on the center of each cubic region obtained by dividing a virtual space, and eliminating redundant cubic regions through the minimum distance to obtain effective unit and node information, thereby establishing a three-dimensional real finite element model of the closed-cell foamed aluminum; therefore, the method can effectively solve the problem that three-dimensional reconstruction is difficult to perform due to the complex structure of the closed-cell foamed aluminum, the error between the model and a real foamed aluminum sample is small, the calculation efficiency can be greatly improved under the condition of ensuring the precision through the model established by the method, and the method is particularly suitable for various porous materials with large differences between X-ray attenuation values and air.
Drawings
FIG. 1 is a flow chart of a method for modeling a three-dimensional real finite element model of closed-cell foamed aluminum according to the present invention;
FIG. 2 is a schematic diagram showing the distance between the cubic region and the gray level value of the aluminum substrate according to the present invention;
FIG. 3 is a schematic diagram of a gray scale image with a porosity of 81.2% according to the present invention;
FIG. 4 is a cross-sectional view of a three-dimensional model of the present invention providing a porosity of 80%;
FIG. 5 is a schematic diagram of a gray scale image with a porosity of 82.8% according to the present invention;
FIG. 6 is a cross-sectional view of a three-dimensional model of the present invention with a porosity of 81.2%;
FIG. 7 is a schematic diagram of a three-dimensional real finite element model of a closed-cell aluminum foam sample with a diameter of 20mm multiplied by 10mm provided by the invention;
FIG. 8 shows the dynamic compression simulation conditions provided by the present invention as
Strain cloud of closed cell foam aluminum model corresponding to epsilon 0.1;
FIG. 9 shows the dynamic compression simulation conditions provided by the present invention as
Strain clouds of a closed-cell aluminum foam model corresponding to epsilon 0.2;
FIG. 10 shows the dynamic compression simulation conditions provided by the present invention as
Strain clouds of closed cell aluminum foam models corresponding to 0.3;
FIG. 11 is a graph of experimental and simulated stress-strain curves under dynamic compression provided by the present invention;
FIG. 12 is a schematic diagram of a three-dimensional real finite element model of a closed-cell aluminum foam sample with a diameter of 20mm and a diameter of 30mm provided by the invention;
fig. 13 is a strain cloud of a closed-cell aluminum foam model corresponding to a quasi-static compression simulation condition provided by the present invention, where ∈ 0.1;
fig. 14 is a strain cloud of a closed-cell aluminum foam model corresponding to a quasi-static compression simulation condition provided by the present invention, where ∈ 0.2;
fig. 15 is a strain cloud of a closed-cell aluminum foam model corresponding to a quasi-static compression simulation condition provided by the present invention, where ∈ 0.3;
fig. 16 is a strain cloud of a closed-cell aluminum foam model corresponding to a quasi-static compression simulation condition provided by the present invention, where ∈ 0.4;
fig. 17 is a graph of experimental and simulated stress-strain curves under quasi-static compression provided by the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
Example one
As shown in fig. 1, a method for modeling a three-dimensional real finite element model of closed-cell aluminum foam comprises the following steps:
s1: and scanning the closed-cell foamed aluminum sample by CT (computed tomography) to obtain more than two gray images, and numbering the gray images in sequence, wherein the attribute of each pixel point of the gray image is an aluminum matrix or air.
It should be noted that the grayscale image is obtained by CT tomography, and is in a dicom image format. The pixel values (gray values) of the image typically range from 0 to 4096.
S2: and obtaining a gray value matrix according to the gray image, wherein the serial number of the gray value matrix corresponds to the serial number of the gray image one by one, and meanwhile, the gray value of the gray value matrix corresponds to the pixel point of the gray image one by one.
It should be noted that the grayscale image may be read layer by layer through a round-robin algorithm, and the grayscale matrix is sequentially led to step S3.
S3: and performing threshold segmentation on the gray value matrix according to a set threshold to obtain the gray values of all the pixels with the attributes of the aluminum matrix.
Furthermore, the calculation method for dividing the threshold can adopt the simplest threshold processing method- "single global threshold", this method is suitable for the situation that the gray level difference of the target and the background is large; specifically, the method for acquiring the set threshold specifically includes:
s31: dividing the gray values into two sets G 1 And G 2 Wherein G is 1 Is greater than the initial threshold T0, G 2 Is not greater than the initial threshold T0;
s32: obtain set G 1 And G 2 The number of middle gray scale values, respectively, is denoted as n 1 And n 2 ；
S33: respectively obtain the sets G 1 And G 2 Weighted average u of all gray values in 1 And u 2 ：
Wherein x is a gray value, and y is the number of gray values with the numerical value of x;
s34: according to a weighted average u 1 And u 2 And (3) updating the threshold value:
wherein T is the updated threshold;
s35: judging whether the absolute value of the difference between the updated threshold T and the initial threshold T0 is smaller than a set iteration cutoff parameter delta T or not, and if so, taking the updated threshold T as the final set threshold; if not, go to step S36;
s36: and replacing the initial threshold T0 in the step S31 with the updated threshold T to divide the gray scale value again, and then repeating the steps S32 to S35 until whether the absolute value of the difference value is smaller than the set iteration cutoff parameter delta T.
S4: and determining the position coordinates of the gray value of the aluminum matrix in the three-dimensional space according to the row number, the column number and the sequence number of the gray value matrix.
It should be noted that, the accuracy of the general CT scan is 16-25 μm, the number of cell walls to be screened out, i.e. the number of position points with the attribute of aluminum matrix is too large, if the direct mapping method is adopted to generate cells and nodes, the number of cells and nodes is too large, and the calculation time is too long; therefore, the number of cells, nodes is simplified by steps S5 and S6; step S6 may also be understood as mapping the location points with the attribute of aluminum matrix according to an algorithm, as shown in fig. 2, and the obtained new location points are a series of regularly distributed cube centers with the same size.
S5: uniformly dividing a virtual space into a plurality of same cubic areas to obtain a matrix of the center points of the cubic areas, wherein the length, the width and the height of the virtual space are respectively the maximum values of the gray value of the aluminum matrix in the three-axis direction of the position coordinate of the aluminum matrix in the three-dimensional space.
S6: for each position coordinate of the gray value with the attribute of the aluminum matrix, the distance between the position coordinate and the center point of each cubic region is calculated in turn, and the cubic region corresponding to the minimum distance is reserved, as shown in fig. 2.
S7: for each cube region obtained in step S6, a unit is generated with its center point, and each unit includes eight nodes, and then each unit and each node are numbered in sequence, so as to obtain a unit matrix and a node matrix, where the unit matrix is formed by a unit number and a node number, and the node matrix is formed by a node number and a node triaxial coordinate.
S8: and for the shared node of the adjacent unit, replacing the node triaxial coordinates corresponding to the minimum node serial number and the minimum node serial number of the arranged shared node with the node triaxial coordinates corresponding to the subsequent node serial number and the arranged subsequent node serial number of the shared node, and finishing the updating of the unit matrix and the node matrix.
S9: and constructing a three-dimensional real finite element model of the closed-cell foamed aluminum sample according to the updated unit matrix and the updated node matrix.
As shown in fig. 3 to 4, a gray scale image with a porosity of 81.2% and a three-dimensional model with a porosity of 80%, respectively, are cross-sectional views; fig. 5 to 6 are respectively a gray scale image with a porosity of 82.8% and a three-dimensional model cross-sectional view with a porosity of 81.2%.
It should be noted that, taking CT scanning of a 30mm side length cubic closed-cell foamed aluminum test piece as an example, the closed-cell foamed aluminum porosity is about 85%, and the scanning precision is 30 μm. If a cubic cell is generated centering on each cell wall, i.e., a location point having an aluminum matrix as a property, there will be one hundred million or fifty million cells, which is extremely inefficient and difficult to accomplish. In the prior art, a point is taken at intervals on a row, a column and a layer of a gray value matrix every three rows, three columns and three layers, so that the number of finite element model units of the user meets the calculation capability; however, this approach has two drawbacks: firstly, the cell walls of closed-cell foamed aluminum have different thicknesses (0.1-1 mm), and points are taken at intervals, so that the cell walls of the cells are likely to be omitted, and defects on a model are caused. In the finite element mechanical analysis of the closed-cell foamed aluminum, structural defects can generate stress concentration, and influence is caused on the authenticity of a simulation result. Secondly, after point taking is carried out on a cube type part with the side length of 30mm at intervals, the obtained model still has more than five hundred and ten thousand units; however, if the number of intervals is increased and the number of cells is reduced, the accuracy of the obtained model is difficult to ensure, and therefore, the conventional method cannot be used for a test piece having a larger volume; according to the invention, based on slice data obtained by CT electron computer tomography scanning of closed-cell foamed aluminum samples, the MATLAB program is used for self-programming processing to obtain unit and node information, and then through the steps S5-S6, a three-dimensional real finite element model of closed-cell foamed aluminum is established on the premise of reducing the number of units, so that defects of the generated model can be effectively avoided.
Therefore, the method solves three problems, namely the problem that only regular closed-cell foamed aluminum with small size can be processed by using a common three-dimensional reconstruction method due to the complex structure of the closed-cell foamed aluminum, the problem that the error between a three-dimensional reconstruction model and the actual volume is large, the problem that data is too large in the modeling process is solved through an algorithm, and the operation efficiency is improved well under the condition of ensuring the precision.
Example two
A gray scale image in dicom format was generated by CT electron computed tomography of a closed cell aluminum foam sample (sample phi 20mm x 10mm, porosity 83.0%) with a scanning accuracy of 24.5 μm. And reading all images through a dicommead function in MATLAB to obtain a gray value matrix. The division gray threshold 1218 is obtained by calculation, and the gray value matrix is judged, and the value higher than the threshold belongs to the aluminum matrix. Cube regions with regular distribution and the same size are established in the space, and the side length of each cube region is 0.14 mm. The distance between the position point of each cell wall and the center of each region is calculated, the center of the region closest to the position point of each cell wall is left, and the rest points are deleted. For the remaining region center points, each point generates 1 cell and 8 nodes centered on it. A set of cell matrices and node matrices is obtained. For nodes with the same location information contained in different units, only the first node is reserved. The final model had 220260 cells and 1762079 nodes. And compiling the node information and the unit information into a K file according to an APDL language of ANSYS, and importing LS-Prepost to obtain the three-dimensional real finite element model shown in the figure 7, wherein the porosity of the closed-cell foamed aluminum three-dimensional real finite element model is 79.8%.
The closed-cell foamed aluminum sample was subjected to a dynamic compression experiment using a split hopkinson ram, and the corresponding finite element model was numerically simulated using LSDYNA. Fig. 8 to 10 are calculated strain clouds at different times, respectively, and fig. 11 is an experimental and calculated stress-strain curve, and the calculated results are well matched with the experimental results.
EXAMPLE III
In accordance with the two steps of the example, the sample was transformed to 20 mm. times.30 mm, the porosity was 83.0%, and the scanning precision was 33 μm. And scanning a closed-cell foamed aluminum sample through a CT (computed tomography) electronic computer to generate a gray image in a dicom format, and reading all the images to obtain a gray value matrix. And judging the gray value matrix, and dividing a gray threshold value into 815. And establishing regularly distributed cube regions with the same size, wherein the side length of each cube region is 0.16 mm. And generating unit nodes according to the algorithm of the second embodiment, wherein the model has 389485 units and 3115879 nodes. And writing the obtained three-dimensional real finite element model into a K file, and importing LS-Prepost to obtain the three-dimensional real finite element model shown in figure 12, wherein the porosity of the closed-cell foamed aluminum three-dimensional real finite element model is 82.1%.
A material testing machine is used for carrying out a quasi-static compression experiment on the closed-cell foamed aluminum sample, and LSDYNA is adopted to carry out numerical simulation on a corresponding finite element model. Fig. 13 to 16 are calculated strain clouds at different times, and fig. 17 is an experimental and calculated stress-strain curve, and the calculated results are well matched with the experimental results.
The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it will be understood by those skilled in the art that various changes and modifications may be made herein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (3)
1. A method for modeling a three-dimensional real finite element model of closed-cell foamed aluminum is characterized by comprising the following steps:
s1: scanning a closed-cell foamed aluminum sample by a CT (computed tomography) electronic computer to obtain more than two gray images, and numbering the gray images in sequence, wherein the attribute of each pixel point of the gray images is an aluminum matrix or air;
s2: obtaining a gray value matrix according to the gray image, wherein the serial number of the gray value matrix corresponds to the serial number of the gray image one by one, and meanwhile, the gray value of the gray value matrix corresponds to the pixel points of the gray image one by one;
s3: performing threshold segmentation on the gray value matrix according to a set threshold to obtain gray values of all pixel points with the attribute of the aluminum matrix;
s4: determining the position coordinates of the gray value of the aluminum matrix in the three-dimensional space according to the row number, the column number and the sequence number of the gray value matrix;
s5: uniformly dividing a virtual space into a plurality of same cubic areas to obtain a matrix of cubic area center points, wherein the length, the width and the height of the virtual space are respectively the maximum values of the gray value of an aluminum matrix in the three-axis direction of the position coordinate of the aluminum matrix in the three-dimensional space;
s6: sequentially calculating the distance between each position coordinate with the gray value of the aluminum matrix and the center point of each cubic region, and reserving the cubic region corresponding to the minimum distance;
s7: for each cube region obtained in step S6, generating a unit with its center point, where each unit includes eight nodes, and then numbering each unit and each node in sequence to obtain a unit matrix and a node matrix, where the unit matrix is composed of a unit number and a node number, and the node matrix is composed of a node number and a node triaxial coordinate;
s8: for the shared node of the adjacent unit, replacing the node three-axis coordinate corresponding to the minimum node serial number and the minimum node serial number of the arranged shared node with the node three-axis coordinate corresponding to the subsequent node serial number and the arranged subsequent node serial number of the shared node to complete the updating of the unit matrix and the node matrix;
s9: and constructing a three-dimensional real finite element model of the closed-cell foamed aluminum sample according to the updated unit matrix and the updated node matrix.
2. The method for modeling a three-dimensional real finite element model of closed-cell aluminum foam according to claim 1, wherein the threshold is obtained by:
s31: dividing the gray values into two sets G 1 And G 2 Wherein G is 1 Is greater than the initial threshold T0, G 2 Is not greater than the initial threshold T0;
s32: obtain set G 1 And G 2 The number of middle gray scale values, respectively denoted as n 1 And n 2 ；
S33: respectively obtain the sets G 1 And G 2 Weighted average u of all gray values in 1 And u 2 ：
S34: according to a weighted average u 1 And u 2 And (3) updating the threshold value:
wherein T is the updated threshold;
s35: judging whether the absolute value of the difference between the updated threshold T and the initial threshold T0 is smaller than a set iteration cutoff parameter delta T or not, and if so, taking the updated threshold T as the final set threshold; if not, go to step S36;
s36: and replacing the initial threshold T0 in the step S31 with the updated threshold T to divide the gray value again, and then repeating the steps S32 to S35 until the absolute value of the difference is smaller than the set iteration cut-off parameter DeltaT.
3. A three dimensional true finite element of closed cell aluminum foam as claimed in claim 2Method for modeling a model, characterized in that said weighted mean u 1 And u 2 The calculating method comprises the following steps:
where x represents a gray value and y represents the number of gray values with the value x.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010271362.XA CN111508075B (en) | 2020-04-09 | 2020-04-09 | Three-dimensional real finite element model modeling method for closed-cell foamed aluminum |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010271362.XA CN111508075B (en) | 2020-04-09 | 2020-04-09 | Three-dimensional real finite element model modeling method for closed-cell foamed aluminum |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111508075A CN111508075A (en) | 2020-08-07 |
| CN111508075B true CN111508075B (en) | 2022-08-19 |
Family
ID=71878702
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010271362.XA Active CN111508075B (en) | 2020-04-09 | 2020-04-09 | Three-dimensional real finite element model modeling method for closed-cell foamed aluminum |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111508075B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113312814B (en) * | 2021-05-12 | 2022-04-22 | 华南理工大学 | Insertion method of directional inherent cohesion unit of local model |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102768699A (en) * | 2012-06-14 | 2012-11-07 | 西安交通大学 | Method for accurately reconstructing dissimilar material microcosmic finite element grid model on basis of CT (computed tomography) images |
| CN108648263A (en) * | 2018-04-24 | 2018-10-12 | 中国人民解放军陆军工程大学 | The three-dimensional meso-mechanical model modeling algorithm of closed-cell aluminum foam |
| CN108765390A (en) * | 2018-05-18 | 2018-11-06 | 青岛理工大学 | A method of obtaining the true microscopical structure of closed-cell aluminum foam two dimension |
| CN109087396A (en) * | 2018-07-26 | 2018-12-25 | 东南大学 | A kind of microscopical structure reconstructing method based on CT images of concrete pixel characteristic |
-
2020
- 2020-04-09 CN CN202010271362.XA patent/CN111508075B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102768699A (en) * | 2012-06-14 | 2012-11-07 | 西安交通大学 | Method for accurately reconstructing dissimilar material microcosmic finite element grid model on basis of CT (computed tomography) images |
| CN108648263A (en) * | 2018-04-24 | 2018-10-12 | 中国人民解放军陆军工程大学 | The three-dimensional meso-mechanical model modeling algorithm of closed-cell aluminum foam |
| CN108765390A (en) * | 2018-05-18 | 2018-11-06 | 青岛理工大学 | A method of obtaining the true microscopical structure of closed-cell aluminum foam two dimension |
| CN109087396A (en) * | 2018-07-26 | 2018-12-25 | 东南大学 | A kind of microscopical structure reconstructing method based on CT images of concrete pixel characteristic |
Non-Patent Citations (3)
| Title |
|---|
| Finite element simulation of the plastic collapse of closed-cell aluminum foams with X-ray computed tomography;Insu Jeon 等;《Mechanics of Materials》;20101231;全文 * |
| Investigation of strain-rate effect on the compressive behaviour of closed-cell aluminium foam by 3D image-based modelling;Yongle Sun 等;《Materials and Design》;20150921;全文 * |
| 基于CT的泡沫铝三维细观模型重建及应用;李侯贞强 等;《北京航空航天大学学报》;20180131;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111508075A (en) | 2020-08-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5371221B2 (en) | Slice data structure for particle method simulation, and method for implementing particle method simulation on GPU using slice data structure | |
| Korshunova et al. | Image-based material characterization of complex microarchitectured additively manufactured structures | |
| CN112992294B (en) | Porous medium LBM calculation grid generation method | |
| CN112363236B (en) | Gravity field data equivalent source continuation and data type conversion method based on PDE | |
| CN111710023B (en) | Three-dimensional point cloud data feature point extraction method and application | |
| JP4349562B2 (en) | Spatial identification method and program | |
| CN113722942B (en) | Finite element calculation model considering 3D printing geometric defects | |
| CN111508075B (en) | Three-dimensional real finite element model modeling method for closed-cell foamed aluminum | |
| WO2021088099A1 (en) | Total matrix computation-based discrete element neighbor search and solution finding method and system | |
| De Pastre et al. | Shape defect analysis from volumetric data-Application to lattice struts in additive manufacturing | |
| CN112346139B (en) | Gravity data multilayer equivalent source continuation and data conversion method | |
| CN117408167A (en) | Debris flow disaster vulnerability prediction method based on deep neural network | |
| CN112598792A (en) | Multi-fractal quantification method and system for terrain complexity in three-dimensional scene | |
| CN109509220B (en) | Method for simulating fluid flow in porous medium solid phase converter | |
| CN113806951A (en) | Elastic simulation method for natural adjacent point search based on half-edge data structure | |
| CN115272594A (en) | Iso-surface generation method based on geotools | |
| CN114781207A (en) | Heat source layout temperature field prediction method based on uncertainty and semi-supervised learning | |
| Klaas et al. | Enabling 3D FEA solvers perform mesoscale simulations | |
| CN113936116B (en) | Complex space curved surface mapping method for transparent A column | |
| de Pastre et al. | A novel approach for geometrical deviations determination of lattice structures from volumetric data | |
| CN118037995B (en) | Grid model generation method, device and storage medium | |
| JP2005259043A (en) | Three-dimensional mesh generation method | |
| CN107577899A (en) | A kind of Three Dimensional Discrete Element characterizing method of rock mass random structure plane | |
| Pirzadeh et al. | Domain decomposition by the advancing-partition method for parallel unstructured grid generation | |
| Ferreira et al. | Data Processing of L-PBF Manufactured Lattice Structures for Geometric Defects Characterization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |