CN109684716B - Periodic interval optimization method for metal droplet multilayer deposition process - Google Patents
Periodic interval optimization method for metal droplet multilayer deposition process Download PDFInfo
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- CN109684716B CN109684716B CN201811573132.8A CN201811573132A CN109684716B CN 109684716 B CN109684716 B CN 109684716B CN 201811573132 A CN201811573132 A CN 201811573132A CN 109684716 B CN109684716 B CN 109684716B
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- 238000000034 method Methods 0.000 title claims abstract description 44
- 239000002184 metal Substances 0.000 title claims abstract description 31
- 238000005457 optimization Methods 0.000 title claims abstract description 12
- 230000000737 periodic effect Effects 0.000 title claims abstract description 8
- 238000005137 deposition process Methods 0.000 title abstract description 10
- 238000000151 deposition Methods 0.000 claims abstract description 30
- 238000007711 solidification Methods 0.000 claims abstract description 30
- 230000008023 solidification Effects 0.000 claims abstract description 30
- 230000008021 deposition Effects 0.000 claims abstract description 29
- 238000009826 distribution Methods 0.000 claims abstract description 21
- 230000008569 process Effects 0.000 claims abstract description 18
- 238000004519 manufacturing process Methods 0.000 claims abstract description 16
- 230000008859 change Effects 0.000 claims abstract description 10
- 239000007788 liquid Substances 0.000 claims abstract description 10
- 239000000463 material Substances 0.000 claims abstract description 5
- 238000004088 simulation Methods 0.000 claims abstract description 5
- 238000003892 spreading Methods 0.000 claims abstract description 5
- 230000007480 spreading Effects 0.000 claims abstract description 5
- 238000005516 engineering process Methods 0.000 claims description 10
- 238000012546 transfer Methods 0.000 claims description 6
- 238000002474 experimental method Methods 0.000 claims description 2
- 239000012530 fluid Substances 0.000 claims description 2
- 238000007654 immersion Methods 0.000 claims description 2
- 239000011159 matrix material Substances 0.000 claims description 2
- 238000012795 verification Methods 0.000 claims description 2
- 238000012805 post-processing Methods 0.000 abstract description 2
- 230000008646 thermal stress Effects 0.000 abstract description 2
- 239000000654 additive Substances 0.000 description 3
- 230000000996 additive effect Effects 0.000 description 3
- 238000011960 computer-aided design Methods 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 2
- 238000000465 moulding Methods 0.000 description 2
- 230000015271 coagulation Effects 0.000 description 1
- 238000005345 coagulation Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000007639 printing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
- Y02P10/25—Process efficiency
Abstract
The invention discloses a periodic interval optimization method for a multilayer deposition process of metal liquid drops, which can reduce the problem of uneven thermal stress distribution caused by discrete solidification time distribution and avoid deformation or failure of a sample after production. The method firstly adopts a dead unit method and a lattice Boltzmann equation to establish a numerical model of a multilayer spreading process of the metal liquid drops. And simulating the phase change process of the metal unit in the sample according to the geometric structure of the sample and the thermophysical parameters of the material. And (3) carrying out post-processing on the data to obtain the solidification time of each unit, and calculating the standard deviation of the solidification time under the deposition period scheme. According to the sample geometry and the obtained setting time distribution, the original deposition cycle mode is adjusted, the simulation and post-treatment steps are repeated, and the obtained setting time standard deviation is compared with the original scheme. And repeatedly adjusting the deposition period scheme to obtain an optimized period interval scheme according to actual production requirements.
Description
Technical Field
The invention relates to a deposition period interval optimization method in a metal liquid drop multilayer deposition process, which can reduce the discrete degree of solidification time distribution of a metal unit. The invention belongs to the field of metal additive manufacturing.
Background
The metal additive manufacturing technology is a direct molding manufacturing technology, has the advantages of simple molding process, high speed, low loss and the like, and is attractive in aviation, aerospace and automobile industries. The technology can combine Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) technologies to finish the manufacture of complex three-dimensional structures by adopting a layer-by-layer deposition mode.
The Drop-on-Demand (Drop-on-Demand) based metal Drop deposition fabrication technique (Metal Droplet Deposition Method) not only inherits the advantages of metal additive fabrication techniques, but also does not require expensive energy sources or special dies. The technology heats the printing material to a liquid state, forms heat source liquid drops through nozzles, deposits the heat source liquid drops on a substrate drop by drop, and rapidly cools and solidifies. The solidified metal surface is locally melted by the heat source droplets and forms metallurgical bonds with the heat source droplets.
In the process of carrying out multilayer spreading on a sample by adopting a metal droplet deposition manufacturing technology, the droplet solidification time is influenced by factors such as deposition positions, coagulation periods and the like, and uniform distribution is difficult to realize. Too high degree of discretization of the solidification time can lead to uneven thermal stress distribution and even the deformation of the sample, the reduction of mechanical properties and the like. To improve the quality of metal droplet technology production, attempts have been made to reduce the degree of dispersion of the solidification time distribution by varying the droplet deposition cycle interval.
Disclosure of Invention
The invention is a method for reducing the dispersion degree of solidification time by improving the deposition cycle interval in the multilayer deposition process based on the metal droplet deposition manufacturing technology. And respectively realizing the optimization of the deposition cycle interval of the metal droplet deposition manufacturing technology by three steps of establishing a numerical model, calculating the solidification time distribution and optimizing the deposition cycle interval.
The technical scheme adopted by the invention is a periodic interval optimization method of a metal liquid drop multilayer deposition process, which mainly comprises the following steps:
1) Establishing a numerical model: and establishing a numerical model of the multilayer spreading process of the metal liquid drop by combining a dead unit method with a lattice Boltzmann method. The numerical model simulates a heat transfer process in a system using an enthalpy-based heat transfer lattice boltzmann equation, and a density-based movement lattice boltzmann equation simulates a fluid movement process. And setting boundary conditions on the phase change interface by adopting an immersion boundary method. The numerical model heat transfer boundary condition adopts a first-order differential rebound format, and the motion boundary condition adopts an unbalanced rebound format with second-order precision. And establishing a geometric model according to the actual structure and the dimension of the sample, inputting the thermal physical parameters of the actual material for simulation, and verifying the reliability and the accuracy of the model through experiments. And simulating different deposition cycle intervals by using the obtained numerical model, and respectively storing the three-dimensional coordinates, the temperature and the phase change rate of each metal unit.
2) Calculating the solidification time distribution: classifying the data according to the coordinates, counting the number of data with the phase change rate larger than 0 under each coordinate, and converting the data into actual time according to the time step. And respectively storing the results to corresponding coordinates to obtain a three-dimensional matrix related to the solidification time. And calculating the standard deviation of the solidification time of all units in the sample piece to obtain the discretization degree of the solidification time distribution.
3) Optimizing the deposition cycle interval: and modifying the deposition period according to the solidification time distribution rule, and carrying out simulation and post-treatment processes again to obtain new solidification time distribution and discretization degree. And obtaining a scheme with smaller discrete degree of the solidification time distribution through comparison until the actual production requirement is met.
The invention can predict the solidification time distribution in the multilayer spreading process of the metal liquid drop, can obtain the optimal deposition period interval for reducing the discrete degree, and the related simulation and verification method is mature, thereby not only reducing the cost, but also ensuring the reliability and having simple operation process.
Drawings
FIG. 1 is a flow chart of the operational steps of a method for periodic interval optimization of a metal droplet multilayer deposition process of the present invention.
Fig. 2 is a simulated process diagram of a method for periodic interval optimization of a metal droplet multilayer deposition process of the present invention.
Fig. 3 is a deposition cycle interval scheme that demonstrates the cycle interval optimization method of the metal droplet multilayer deposition process of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the drawings and examples.
FIG. 1 is a flow chart showing the steps of a method for optimizing the periodic interval of a metal droplet multilayer deposition process according to the present invention.
1) Numerical model modeling process:
and establishing a geometric model according to the actual structure and the size of the sample by adopting a numerical model combining a dead unit method and a lattice Boltzmann method, and inputting the thermal physical parameters of the actual materials. The phase change process of each unit in the sample (as shown in fig. 2) was simulated, and the temperature and the degree of phase change of each unit were determined. And monitoring the temperature and the phase change degree of each metal unit, and respectively storing according to the actual time and the space coordinates of the metal units.
2) The data post-processing process comprises the following steps:
according to the degree of thinking of the metal unit at each coordinate position at each time point, the total time from the beginning of deposition to the complete solidification is calculated, and the solidification time is stored in the corresponding coordinate. And (5) solving standard deviation of the solidification time in all coordinates to obtain the discretization degree of the solidification time distribution.
3) Period interval optimization process:
according to the actual shape of the sample and the obtained solidification time distribution rule, a deposition period interval improvement scheme is designed (shown in figure 3). And repeating the previous two steps by adopting a new scheme, and comparing the discretization degree obtained by the original deposition scheme to obtain the deposition cycle interval with the lowest discretization degree.
TABLE 1 scheme optimization Effect comparison
Claims (1)
1. The method realizes the optimization of the deposition cycle interval of the metal droplet deposition manufacturing technology by establishing a numerical model, calculating the solidification time distribution and optimizing the deposition cycle interval;
the method is characterized in that: the method comprises the steps of,
1) Establishing a numerical model: establishing a numerical model of a multilayer spreading process of the metal liquid drops by combining a dead unit method with a lattice Boltzmann method; the numerical model adopts an enthalpy-based heat transfer lattice Boltzmann equation to simulate a heat transfer process in a system, and a density-based movement lattice Boltzmann equation to simulate a movement process of fluid; setting boundary conditions on a phase change interface by adopting an immersion boundary method; the numerical model heat transfer boundary condition adopts a first-order differential rebound format, and the motion boundary condition adopts an unbalanced rebound format with second-order precision; establishing a geometric model according to the actual structure and the dimension of the sample, inputting the thermal physical parameters of the actual material for simulation, and verifying the reliability and the accuracy of the geometric model through experiments; simulating different deposition periodic intervals by using the numerical model after experimental verification, and respectively storing the three-dimensional coordinates, the temperature and the phase change rate of each metal unit in the numerical model;
2) Calculating the solidification time distribution: classifying the data according to coordinates, counting the number of data with the phase change rate larger than 0 under each coordinate, and converting the data into actual time according to time step; respectively storing the results to corresponding coordinates to obtain a three-dimensional matrix about solidification time; calculating the standard deviation of the solidification time of all units in the sample piece to obtain the discretization degree of the solidification time distribution;
3) Optimizing the deposition cycle interval: modifying and re-simulating the deposition period according to the solidification time distribution rule to obtain new solidification time distribution and discretization degree; and obtaining a scheme with smaller discrete degree of the solidification time distribution through comparison until the actual production requirement is met.
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| CN201811573132.8A CN109684716B (en) | 2018-12-21 | 2018-12-21 | Periodic interval optimization method for metal droplet multilayer deposition process |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811573132.8A CN109684716B (en) | 2018-12-21 | 2018-12-21 | Periodic interval optimization method for metal droplet multilayer deposition process |
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| Publication Number | Publication Date |
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| CN109684716A CN109684716A (en) | 2019-04-26 |
| CN109684716B true CN109684716B (en) | 2023-11-24 |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105711102A (en) * | 2016-04-19 | 2016-06-29 | 山东大学 | 3D printing path programming method based on Fermat spiral |
| CN107962772A (en) * | 2017-11-16 | 2018-04-27 | 闫晰尧 | A kind of support optimization Method of printing based on 3D printing path |
| CN108038266A (en) * | 2017-11-17 | 2018-05-15 | 西安铂力特增材技术股份有限公司 | A kind of method for numerical simulation of selective laser repair process |
| CN108326301A (en) * | 2018-02-24 | 2018-07-27 | 深圳意动航空科技有限公司 | A kind of printing path generation method of metal increasing material manufacturing |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9895845B2 (en) * | 2015-02-16 | 2018-02-20 | Arevo Inc. | Method and a system to optimize printing parameters in additive manufacturing process |
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2018
- 2018-12-21 CN CN201811573132.8A patent/CN109684716B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105711102A (en) * | 2016-04-19 | 2016-06-29 | 山东大学 | 3D printing path programming method based on Fermat spiral |
| CN107962772A (en) * | 2017-11-16 | 2018-04-27 | 闫晰尧 | A kind of support optimization Method of printing based on 3D printing path |
| CN108038266A (en) * | 2017-11-17 | 2018-05-15 | 西安铂力特增材技术股份有限公司 | A kind of method for numerical simulation of selective laser repair process |
| CN108326301A (en) * | 2018-02-24 | 2018-07-27 | 深圳意动航空科技有限公司 | A kind of printing path generation method of metal increasing material manufacturing |
Non-Patent Citations (1)
| Title |
|---|
| Numerical simulation of droplet detachment from solid walls under gravity force using lattice Boltzmann method;S.E. Mousavi Tilehboni 等;《Journal of Molecular Liquids》;全文 * |
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