CN110781615A - CAE simulation accuracy evaluation method - Google Patents
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- 238000004458 analytical method Methods 0.000 claims abstract description 69
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- 238000004512 die casting Methods 0.000 description 1
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Abstract
The scheme relates to an evaluation method of CAE simulation accuracy, which aims to improve the CAE simulation accuracy and facilitate the improvement of CAE analysis accuracy. It includes: performing high-pressure casting and mold filling on the casting through the mold, and shooting the high-pressure mold filling process of the casting through a visual window to obtain an actual mold filling view; simulating the high-pressure mold filling of the casting by using CAE software, carrying out simulation analysis on the high-pressure mold filling process of the casting, and intercepting a simulation analysis view at the same position corresponding to the actual mold filling view from a simulation analysis result diagram; performing meshing on an actual filling view shot at the same moment and a simulation analysis view obtained through simulation analysis according to the same meshing mode; determining a first filling ratio of the solution in each grid cell in the actual filling view; determining a second fill ratio of the solution within each grid cell in the simulation analysis view; and determining the CAE simulation accuracy according to the first filling ratio and the second filling ratio.
Description
Technical Field
The invention relates to the field of casting and casting simulation, in particular to a CAE simulation accuracy rate evaluation method.
Background
With the advanced ideas of computer virtual design, virtual manufacturing, virtual verification and the like in the manufacturing industry field related to digital factories, the deep development of computer simulation technology (hereinafter referred to as CAE technology) and casting process technology increasingly requires that the casting CAE analysis technology is verified by the current auxiliary casting process, and the development is carried out to the structural design of castings, the design and optimization of the casting process and the intervention of the whole production process of the castings. All of them require the CAE software to have higher analysis precision and efficiency, thereby effectively improving the design level of casting technology and the development efficiency of new products. However, quantitative calculation of the CAE filling precision cannot be carried out in the industry, and only fuzzy words such as 'basically same', 'basically different', 'mostly same', 'mostly different', and the like can be adopted for evaluation when the CAE filling precision is compared with a visual filling result of precision improvement, so that great trouble is caused to the development of precision improvement work.
Disclosure of Invention
The invention aims to provide an evaluation method of CAE simulation accuracy, so as to improve the determination precision of the CAE simulation accuracy and facilitate the improvement of CAE analysis precision in the high-pressure casting and mold filling process.
The technical scheme of the invention is as follows:
the invention provides a CAE simulation accuracy evaluation method, which comprises the following steps:
step S1, performing high-pressure casting and mold filling on the casting through the mold, and shooting the high-pressure mold filling process of the casting through a visual window to obtain an actual mold filling view;
s2, simulating high-pressure mold filling of the casting by using CAE software, carrying out simulation analysis on the high-pressure mold filling process of the casting, and intercepting a simulation analysis view at the same position corresponding to the actual mold filling view from a simulation analysis result diagram;
step S3, performing mesh division on the actual filling view shot at the same time and the simulation analysis view obtained by simulation analysis according to the same mesh division mode;
step S4, determining the solventFirst filling ratio X of liquid in each grid cell in actual filling view
i.j；
Step S5, determining a second filling ratio Y of the solution in each grid cell in the simulation analysis view
i.j；
Step S6, according to the first filling ratio X
i.jAnd the second filling ratio Y
i.jAnd determining the CAE simulation accuracy.
Preferably, step S6 includes:
step S61, according to the first filling ratio X
i.jAnd the second filling ratio Y
i.jDetermining simulation accuracy N for each grid cell in a simulation analysis view
ij；
Step S62, according to simulation accuracy rate N of each grid cell in simulation analysis view
ijDetermining the simulation accuracy rate N of the simulation analysis view, wherein the simulation accuracy rate N is the simulation accuracy rate N of each grid unit
ijThe ratio of the sum to the total number of grid cells;
and step S63, performing mean value solving according to the simulation accuracy rate N of the simulation analysis view corresponding to each moment to obtain the CAE simulation accuracy rate.
Preferably, step S61 includes: if X
i.j≠0，
When Y is
i.j＜X
i.jAnd then, satisfy: n is a radical of
ij=Y
i.j/X
i.j(ii) a When Y is
i.j≥X
i.jAnd then, satisfy: n is a radical of
ij=X
i.j/Y
i.j；
If X
i.j=0, then: n is a radical of
ij=（1-Y
i.j）/（1-X
i.j）。
Preferably, the solution selected during the mold filling is an aluminum alloy solution, the adopted aluminum alloy material is AlSi9Cu3, and during the mold filling process: the lowest speed of the mold filling is 0.2m/s, the highest speed of the mold filling is 2m/s, and the pouring temperature is 700 ℃.
Preferably, in steps S4 and S5, the filling ratio of the solution in each grid cell in the view is determined by an image recognition technique.
Preferably, in step S3, the actual filling view and the simulation analysis view are gridded according to the same scale and density gridding method.
Preferably, in step S1, the high-pressure casting and filling of the casting is performed through the visualization mold, and the high-pressure casting and filling process of the casting is photographed from the visualization window of the visualization mold through the high-speed camera.
The invention has the beneficial effects that:
the method can quantitatively calculate the casting mold filling rate, and can efficiently, accurately and quantitatively judge the CAE mold filling accuracy rate by comparing the CAE analysis mold filling result with the visual mold filling result, thereby providing convenient and powerful support for subsequent precision improvement work.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Referring to fig. 1, the invention provides a method for evaluating CAE simulation accuracy, comprising:
and step S1, performing high-pressure casting and mold filling on the casting through the mold, and shooting the high-pressure mold filling process of the casting through the visual window to obtain an actual mold filling view.
The high-pressure casting and mold filling of the casting are carried out through the visual mold, and the high-pressure mold filling process of the casting is shot from a visual window of the visual mold through a high-speed camera.
And step S2, simulating the high-pressure mold filling of the casting by using CAE software, carrying out simulation analysis on the high-pressure mold filling process of the casting, and intercepting a simulation analysis view at the same position corresponding to the actual mold filling view from a simulation analysis result diagram.
The method for simulating the high-pressure mold filling of the casting by adopting CAE software specifically comprises the following steps: and introducing a three-dimensional model and material attributes of the casting into CAE software, setting CAE analysis parameters according to the process of the die casting, and carrying out CAE simulation in the high-pressure casting process.
The simulation analysis view is intercepted in the simulation analysis result graph mainly by means of manual identification and interception. In the CAE analysis process, during simulation, CAE simulation analysis is carried out at the same time as the high-speed camera capture, and the CAE simulation software adopts Magmasoft5.3 to obtain a simulation analysis filling result at the same time.
And step S3, meshing the actual filling view shot at the same time and the simulation analysis view obtained by simulation analysis according to the same meshing mode.
Specifically, analysis accuracy and work efficiency required in the matrix are comprehensively considered, the number of grids is determined, the actual filling view and the simulation analysis view are subjected to grid division according to a grid division mode with the same proportion and density, areas corresponding to each divided grid are the same area on the casting, and therefore accuracy of CAE simulation accuracy is improved.
Step S4, determining the first filling ratio X of the solution in each grid cell in the actual filling view
i.j. Wherein the filling ratio of the solution in the view at the start of filling is 0%, and the filling ratio at the time of complete filling is 100%
Step S5, determining a second filling ratio Y of the solution in each grid cell in the simulation analysis view
i.j。
The filling ratio of the solution in each grid cell in the view is determined by an image recognition technology, each grid cell comprises a blank part and a painted part during image recognition, and the ratio of the area of the painted part to the area of the grid cell is the filling ratio of the solution in the grid cell.
Step S6, according to the first filling ratio X
i.jAnd the second filling ratio Y
i.jAnd determining the CAE simulation accuracy.
Specifically, step S6 includes:
step S61, according to the first filling ratio X
i.jAnd the second filling ratio Y
i.jDetermining simulation accuracy N for each grid cell in a simulation analysis view
ij；
Step S62, according to simulation accuracy rate N of each grid cell in simulation analysis view
ijDetermining the simulation accuracy rate N of the simulation analysis view, wherein the simulation accuracy rate N is the simulation accuracy rate N of each grid unit
ijThe ratio of the sum to the total number of grid cells, i.e. N = (N)
1.1+N
1.2+…N
ij) The number of grid cells;
and step S63, performing mean value solving according to the simulation accuracy rate N of the simulation analysis view corresponding to each moment to obtain the CAE simulation accuracy rate.
Preferably, step S61 includes: if X
i.j≠0，
When Y is
i.j＜X
i.jAnd then, satisfy: n is a radical of
ij=Y
i.j/X
i.j(ii) a When Y is
i.j≥X
i.jAnd then, satisfy: n is a radical of
ij=X
i.j/Y
i.j；
If X
i.j=0, then: n is a radical of
ij=（1-Y
i.j）/（1-X
i.j）。
After the simulation accuracy N of the simulation analysis view is calculated, if the calculated simulation accuracy N is lower than a set threshold (e.g., 95%, 99%, 96%, 98%, or 80%), it indicates that the CAE simulation analysis accuracy is not high, and it is necessary to perform accuracy improvement work, and it is necessary to find out main factors affecting the CAE analysis model filling accuracy, modify CAE analysis input parameters, and improve the CAE analysis accuracy, for example, compare and adjust the mold temperature, the model filling state, the model filling sequence, the trend of bubbles and slag inclusions, and the high-pressure position in the CAE analysis, so as to improve the CAECAE simulation analysis accuracy, specifically, refer to the application number: 201710083171.9 to override the CAE simulation analysis accuracy; otherwise, if the calculated simulation accuracy rate N is higher than the set threshold, it indicates that the CAE simulation analysis accuracy is high.
The method of the present invention is described below with a specific example, wherein the method specifically includes the following steps:
①, completing the high-pressure casting aluminum alloy mold filling process through a visual mold, wherein the aluminum alloy material is AlSi9Cu3, the pouring temperature is 700 ℃, the mold filling speed is low, the speed is 0.2m/s, the speed is 2m/s, and the mold filling view shot by a high-speed camera in 1532ms is obtained through a visual window;
② CAE simulation software used Magmasoft5.3 to obtain the filling result of the simulation analysis at the same time (e.g. 1532ms in this example).
③ the two views are gridded at the same scale and density.
④ sample the 1532ms time fill-type view "grid cell fill-rate" taken by the high-speed camera, X
4.7=25%；X
4.8=0；X
5.6=80%；X
5.7=97%；X
5.8=80%；X
5.9=0；X
5.10=0；X
6.4=100%；X
6.5=0；X
6.6=0；X
6.7=0；X
6.8=60%；X
6.9=40%；X
6.10=0；X
6.11=0；X
7.4=100%；X
7.5=70%；X
7.6=100%；X
7.7=96%；X
7.8=65%；X
7.9=97%；X
7.10=0；X
8.3=100%；X
8.4=100%；X
8.5=100%；X
8.6=100%；X
8.7=100%；X
8.8=100%,；X
8.9=65%；X
8.10=0%；X
8.11=0%；X
8.12=0%；X
8.13=0%；X
9.2=100%；X
9.3=100%；X
9.4=100%；X
9.5=100%；X
9.6=100%；X
9.7=98%；X
9.8=15%；X
9.9=5%；X
9.10=0%；X
10.2=100%；X
10.3=100%；X
10.4=100%；X
10.5=100%；X
10.6=45%；X
10.7=0%；X
10.8=0%；X
10.9=0%；X
11.1=100%；X
11.2=100%；X
11.3=100%；X
11.4=100%；X
11.5=92%；X
11.6=3%；X
11.7=0%；X
11.8=0%；X
11.9=0%；X
12.1=100%；X
12.2=100%；X
12.3=100%；X
12.4=100%；X
12.5=65%；X
12.6=95%；X
12.7=0%；X
12.8=0%；X
12.9=0%；X
12.10=0%；X
12.11=0%；X
13.1=20%；X
13.2=100%；X
13.3=100%；X
13.4=50%；X
13.5=99%；X
13.6=2%；X
13.7=0%；X
13.8=0%；X
13.9=0%；X
13.10=0%；X
13.11=0%。
⑤ sample the CAE analysis filling view 'grid cell filling rate' at the same time
4.7=95%；Y
4.8=65%；Y
5.6=100%；Y
5.7=100%；Y
5.8=80%；Y
5.9=20%；Y
5.10=15%；Y
6.4=100%；Y
6.5=100%；Y
6.6=100%；Y
6.7=99%；Y
6.8=90%；Y
6.9=50%；Y
6.10=80%；Y
6.11=10%；Y
7.4=100%；Y
7.5=100%；Y
7.6=85%；Y
7.7=100%；Y
7.8=100%；Y
7.9=100%；Y
7.10=100%；Y
8.3=100%；Y
8.4=100%；Y
8.5=100%；Y
8.6=90%；Y
8.7=70%；Y
8.8=15%；Y
8.9=5%；Y
8.10=3%；Y
8.11=95%；Y
8.12=100%；Y
8.13=90%；Y
9.2=100%；Y
9.3=100%；Y
9.4=100%；Y
9.5=100%；Y
9.6=60%；Y
9.7=0%；Y
9.8=0%；Y
9.9=0%；Y
9.10=0%；Y
10.2=100%；Y
10.3=100%；Y
10.4=100%；Y
10.5=35%；Y
10.6=10%；Y
10.7=15%；Y
10.8=0%；Y
10.9=70%；Y
11.1=100%；Y
11.2=100%；Y
11.3=100%；Y
11.4=100%；Y
11.5=4%；Y
11.6=7%；Y
11.7=0%；Y
11.8=3%；Y
11.9=40%；Y
12.1=100%；Y
12.2=100%；Y
12.3=100%；Y
12.4=100%；Y
12.5=55%；Y
12.6=45%；Y
12.7=20%；Y
12.8=90%；Y
12.9=100%；Y
12.10=100%；Y
12.11=40%；Y
13.1=50%；Y
13.2=45%；Y
13.3=92%；Y
13.4=2%；Y
13.5=1%；Y
13.6=30%；Y
13.7=55%；Y
13.8=85%；Y
13.9=95%；Y
13.10=100%；Y
13.11=80%。
⑥ CAE analysis grid cell filling accuracy N when calculating 1532ms
i.jIf X is
i.jNot equal to 0: when Y is
i.j＜X
i.jTime N
ij=Y
i.j/X
i.jWhen Y is
i.j≥X
i.jTime N
ij=X
i.j/Y
i.j(ii) a If X
i.j=0：N
ij=（1-Y
i.j）/（1-X
i.j）：
N
4.7=26.3%；N
4.8=35%；N
5.6=80%；N
5.7=97%；N
5.8=100%；N
5.9=80%；N
5.10=75%；N
6.4=100%；N
6.5=0%；N
6.6=0%；N
6.7=1%；N
6.8=66.7%；N
6.9=80%；N
6.10=20%；N
6.11=90%；N
7.4=100%；N
7.5=70%；N
7.6=85%；N
7.7=96%；N
7.8=65%；N
7.9=97%；N
7.10=0%；N
8.3=100%；N
8.4=100%；N
8.5=100%；N
8.6=90%；N
8.7=70%；N
8.8=15%；N
8.9=7.7%；N
8.10=97%；N
8.11=5%；N
8.12=0%；N
8.13=10%；N
9.2=100%；N
9.3=100%；N
9.4=100%；N
9.5=100%；N
9.6=60%；N
9.7=0%；N
9.8=0%；N
9.9=95%；N
9.10=100%；N
10.2=100%；N
10.3=100%；N
10.4=100%；N
10.5=35%；N
10.6=22.2%；N
10.7=75%；N
10.8=100%；N
10.9=30%；N
11.1=100%；N
11.2=100%；N
11.3=100%；N
11.4=100%；N
11.5=4.3%；N
11.6=42.9%；N
11.7=100%；N
11.8=97%；N
11.9=60%；N
12.1=100%；N
12.2=100%；N
12.3=100%；N
12.4=100%；N
12.5=84.6%；N
12.6=47.4%；N
12.7=80%；N
12.8=10%；N
12.9=0%；N
12.10=0%；N
12.11=60%；N
13.1=40%；N
13.2=45%；N
13.3=92%；N
13.4=4%；N
13.5=1%；N
13.6=6.7%；N
13.7=45%；N
13.8=15%；N
13.9=5%；N
13.10=0%；N
13.11=20%；
⑦ CAE analysis filling accuracy rate at 1532ms
1.1+N
1.2+…N
i.j) Number of grid cells, N =4701.1%/81= 58.03%.
⑧ the CAE analysis filling accuracy rate N at each time can be obtained by the method, and the average value is the CAE analysis filling accuracy rate.
The method of the embodiment has the following technical effects:
①, the CAE analysis filling accuracy can be calculated quantitatively, and the filling accuracy calculation has higher accuracy;
② the method is simple, efficient and accurate, and the grid cell filling rate of the division number can be calculated as quantitative data of the solution filling rate;
③ the structure of the part with lower accuracy can be intuitively known by comparing the CAE analysis filling rate with the visual filling result, the technological parameters of the part can be optimized in a targeted manner, and the CAE analysis filling accuracy is improved.
④ can compare the filling accuracy of multiple CAE analysis software at the same time, and provide data basis for the preferred selection software.
The embodiments described above describe only some of the one or more embodiments of the present invention, but those skilled in the art will recognize that the invention can be embodied in many other forms without departing from the spirit or scope thereof. Accordingly, the present examples and embodiments are to be considered as illustrative and not restrictive, and various modifications and substitutions may be made therein without departing from the spirit and scope of the present invention as defined by the appended claims.
Claims (7)
1. A CAE simulation accuracy evaluation method is characterized by comprising the following steps:
step S1, performing high-pressure casting and mold filling on the casting through the mold, and shooting the high-pressure mold filling process of the casting through a visual window to obtain an actual mold filling view;
s2, simulating high-pressure mold filling of the casting by using CAE software, carrying out simulation analysis on the high-pressure mold filling process of the casting, and intercepting a simulation analysis view at the same position corresponding to the actual mold filling view from a simulation analysis result diagram;
step S3, performing mesh division on the actual filling view shot at the same time and the simulation analysis view obtained by simulation analysis according to the same mesh division mode;
step S4, determining the first filling ratio X of the solution in each grid cell in the actual filling view
i.j；
Step S5, determining a second filling ratio Y of the solution in each grid cell in the simulation analysis view
i.j；
Step S6, according to the first filling ratio X
i.jAnd the second filling ratio Y
i.jAnd determining the CAE simulation accuracy.
2. The method according to claim 1, wherein step S6 includes:
step S61, according to the first filling ratio X
i.jAnd the second filling ratio Y
i.jDetermining simulation accuracy N for each grid cell in a simulation analysis view
ij；
Step S62, according to the simulation standard of each grid cell in the simulation analysis viewRate of determination N
ijDetermining the simulation accuracy rate N of the simulation analysis view, wherein the simulation accuracy rate N is the simulation accuracy rate N of each grid unit
ijThe ratio of the sum to the total number of grid cells;
and step S63, performing mean value solving according to the simulation accuracy rate N of the simulation analysis view corresponding to each moment to obtain the CAE simulation accuracy rate.
3. The method according to claim 2, wherein step S61 includes: if X
i.j≠0，
When Y is
i.j＜X
i.jAnd then, satisfy: n is a radical of
ij=Y
i.j/X
i.j(ii) a When Y is
i.j≥X
i.jAnd then, satisfy: n is a radical of
ij=X
i.j/Y
i.j；
If X
i.j=0, then: n is a radical of
ij=（1-Y
i.j）/（1-X
i.j）。
4. The method of claim 1, wherein the solution selected during the filling is an aluminum alloy solution, the aluminum alloy material used is AlSi9Cu3, and during the filling: the lowest speed of the mold filling is 0.2m/s, the highest speed of the mold filling is 2m/s, and the pouring temperature is 700 ℃.
5. The method of claim 1, wherein the filling ratio of the solution in each grid cell in the view is determined by image recognition technology in steps S4 and S5.
6. The method of claim 1, wherein in step S3, the actual filling view and the simulation analysis view are gridded in a gridding manner with the same scale and density.
7. The method according to claim 1, wherein in step S1, the high pressure casting filling of the casting is performed by a visualization mold, and the high pressure filling of the casting is photographed by a high speed camera from a visualization window of the visualization mold.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7277841B1 (en) * | 2002-02-11 | 2007-10-02 | Sun Microsystems, Inc. | Method for adaptive sub-gridding for power/ground plane simulations |
| CN103678749A (en) * | 2012-09-24 | 2014-03-26 | 重庆长安汽车股份有限公司 | Finite element analysis method of automobile pull rod type suspension |
| CN104573237A (en) * | 2015-01-08 | 2015-04-29 | 湖南大学 | Frictional wear CAE (Computer Aided Engineering) analysis-based mold optimization method |
| CN105170935A (en) * | 2015-08-31 | 2015-12-23 | 重庆长安汽车股份有限公司 | High-pressure casting mold filling visualization device |
| CN106000007A (en) * | 2016-07-06 | 2016-10-12 | 福建龙净环保股份有限公司 | Simulation system and method for wet desulphurization flow fields |
| CN106528972A (en) * | 2016-10-31 | 2017-03-22 | 山西新华化工有限责任公司 | Canister gas flow simulation test method |
| CN106644499A (en) * | 2016-09-09 | 2017-05-10 | 北京新能源汽车股份有限公司 | Electric automobile power assembly intensity test method and system |
| CN107145692A (en) * | 2017-06-22 | 2017-09-08 | 山东农业大学 | A kind of dispenser mist droplet deposition Forecasting Methodology based on CFD and crop porosity similitude |
| CN107292016A (en) * | 2017-06-15 | 2017-10-24 | 中车唐山机车车辆有限公司 | Emulate data processing method and device |
| CN108256239A (en) * | 2018-01-23 | 2018-07-06 | 华中科技大学 | A kind of Forecasting Methodology suitable for Mold Filling Process cold shut defect |
| CN109902326A (en) * | 2018-06-14 | 2019-06-18 | 中国煤炭科工集团太原研究院有限公司 | A kind of finite element simulation experiment effect assessment method |
| CN110244023A (en) * | 2019-07-02 | 2019-09-17 | 西南石油大学 | The measuring method that the physical analogy of seam full-hole core is combined with numerical simulation |
-
2019
- 2019-12-11 CN CN201911264380.9A patent/CN110781615A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7277841B1 (en) * | 2002-02-11 | 2007-10-02 | Sun Microsystems, Inc. | Method for adaptive sub-gridding for power/ground plane simulations |
| CN103678749A (en) * | 2012-09-24 | 2014-03-26 | 重庆长安汽车股份有限公司 | Finite element analysis method of automobile pull rod type suspension |
| CN104573237A (en) * | 2015-01-08 | 2015-04-29 | 湖南大学 | Frictional wear CAE (Computer Aided Engineering) analysis-based mold optimization method |
| CN105170935A (en) * | 2015-08-31 | 2015-12-23 | 重庆长安汽车股份有限公司 | High-pressure casting mold filling visualization device |
| CN106000007A (en) * | 2016-07-06 | 2016-10-12 | 福建龙净环保股份有限公司 | Simulation system and method for wet desulphurization flow fields |
| CN106644499A (en) * | 2016-09-09 | 2017-05-10 | 北京新能源汽车股份有限公司 | Electric automobile power assembly intensity test method and system |
| CN106528972A (en) * | 2016-10-31 | 2017-03-22 | 山西新华化工有限责任公司 | Canister gas flow simulation test method |
| CN107292016A (en) * | 2017-06-15 | 2017-10-24 | 中车唐山机车车辆有限公司 | Emulate data processing method and device |
| CN107145692A (en) * | 2017-06-22 | 2017-09-08 | 山东农业大学 | A kind of dispenser mist droplet deposition Forecasting Methodology based on CFD and crop porosity similitude |
| CN108256239A (en) * | 2018-01-23 | 2018-07-06 | 华中科技大学 | A kind of Forecasting Methodology suitable for Mold Filling Process cold shut defect |
| CN109902326A (en) * | 2018-06-14 | 2019-06-18 | 中国煤炭科工集团太原研究院有限公司 | A kind of finite element simulation experiment effect assessment method |
| CN110244023A (en) * | 2019-07-02 | 2019-09-17 | 西南石油大学 | The measuring method that the physical analogy of seam full-hole core is combined with numerical simulation |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116910984B (en) * | 2023-06-15 | 2024-02-06 | 小米汽车科技有限公司 | Injection curve parameter optimization method and device, electronic equipment and storage medium |
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