CN108038325A - A kind of porous framework structure macroscopic view elastic performance Reliability Prediction Method of 3D printing technique manufacture - Google Patents
A kind of porous framework structure macroscopic view elastic performance Reliability Prediction Method of 3D printing technique manufacture Download PDFInfo
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Abstract
The present invention discloses a kind of porous framework structure macroscopic view elastic performance Reliability Prediction Method of 3D printing technique manufacture, comprising：(1) cube porous framework structure design step；(2) sample customization step；(3) with CT scanner demixing scan observation microstructural flaws the step of；(4) the step of quantitatively characterizing microstructural flaws represent size；(5) the step of defect represents the size statistic regularity of distribution is found；(6) the step of defect characteristic size is with Print direction, supporting type relation is found；(7) the step of establishing stochastic numerical simulation model, carrying out macroscopical elastic performance and microstress numerical analysis；(8) macroscopical compression experiment is carried out, above fail-safe analysis result is verified and corrected.The present invention is suitable for microdefect evaluation, the credit rating certification of 3D metallic print structures, for the Reliability Prediction Method for providing a kind of numerical simulation using existing 3D printing technique and experiment combines, metal 3D printing reliability proof cycle is shortened, improves work efficiency, saves the production time.
Description
Technical field
The present invention relates to a kind of 3D printing technique manufacture porous framework structure macroscopic view elastic performance Reliability Prediction Method,
Suitable for microdefect evaluation, the credit rating certification of 3D metallic print structures.
Background technology
3D printing technique can directly carry out sample raw basin, have production without traditional fixture, mould, cutter etc.
Cycle is short, stock utilization is high, can many advantages such as processed complex part, received very big concern since last century comes out,
Achieve and develop rapidly.The fields such as industrial manufacture, aerospace, composite material and biologic medical are had an important influence on.The U.S.
《Epoch》Weekly is classified as one of " industry fastest-rising greatly of the U.S. ten ", Britain《Economist》Magazine think it will " with
Other Digitalisation Manufacture Modes promote together realizes the third time industrial revolution ".
Main selective laser sintered (the Selective Laser of metal 3D printing technique of current most mainstream
Sintering, SLS) technology, selective laser melt (Selective Laser Melting, SLM) technology, electron beam constituency
(Selective Electron Beam Melting, the SEBM) technology of melt-forming etc., it has used heat source and powder used
It is different.However, during above 3D printing, workpiece is both needed to constantly undergo thermal cycle effect, this will cause inside workpiece to produce
Raw complicated temperature field.Due to there is higher temperature gradient, non-uniform temperature field can cause non-uniform thermal deformation, and produce
Heat stress.Can be there are thermal residual strain when workpiece is cooled to room temperature, inside it, while produce residual deformation.This will significantly
The mechanical property and precision of 3D printing workpiece are influenced, while limits development of the 3D printing technique in high-grade, precision and advanced manufacturing field.It is right
In the workpiece of some geometries complexity, stress concentration can occur in some positions inside it, when stress value is excessive, will cause
Crackle.In RP technique, a most important technological problems are shaping warpage issues, and so-called " warpage ", is remaining
Deformation.Residual deformation can seriously affect the precision of workpiece.
Although Current Domestic is outer in equipment, gold such as existing devices (such as Germany EOS, Siemens, U.S. 3D Systems)
The hardware condition such as category or polycaprolactone (PCL) powder, and configure existing process optimization software and exper ienced operation
On the premise of personnel, the stent performance printed is still unstable, and basic reason is that there are internal flaw for stent power transmission rod piece.I
State in the 3D printing area research time due to not growing, rule, influential effect to internal flaw formation, and control defect level
Reliability Optimum Design method, the basic research of these three key issues and hold still inadequate, be current to restrict laser choosing
Area sinters stent, or even the main problem that 3D printing further develops.
Traditional reliability engineering flow is to carry out reliability assessment to its performance by the way that experiment is repeated several times, and not meeting can
Modification design is then returned by property requirement, optimizes manufacturing process.Such reliability estimation method with the huge time and it is economical into
Originally reliability growth is got in return.
In the metal 3D printing field, how by adjusting machine process parameter interior porosity is eliminated, be in recent years
Hot issue.Consider that possible internal void changes and utilizes computer-assisted analysis, the reliability of product be designed,
Become an important method to solve the above problems.
The content of the invention
The present invention overcomes the deficiencies in the prior art, there is provided a kind of porous framework structure macroscopic view bullet of 3D printing technique manufacture
Property performance reliability Forecasting Methodology, shortens metal 3D printing reliability proof cycle, improves work efficiency, save production
Time.
To achieve the above object, this invention takes following technical solution：3D metallic print micro-structure macro-mechanical properties
Reliability Prediction Method, comprise the following steps：
1st, a kind of porous framework structure macroscopic view elastic performance Reliability Prediction Method of 3D printing technique manufacture, its feature exist
In comprising the following steps：
The first step, design one kind meet mechanical property and Functional Requirement structure；
Second step, the design to the first step uses 3D metallic print fabrication techniques samples, and requires using identical
Dusty material, the powder has identical average grain diameter, and the powder meets that particle diameter distribution dispersiveness requires, and sets printing
Direction, the printing strategy of supporting type；
3rd step, the printing frame microstructure characterizations of the second step is completed using X-ray CT scanner, to obtain
State Three-dimension Reconstruction Model of the frame based on scan image；
4th step, records all samples, to each micro- list of the frame on the basis of the 3rd step
Born of the same parents are observed, the characteristic size regularity of distribution of statistical shortcomings；
5th step, institute's statistics in the 4th step is fitted using Normal Distribution Characteristics, obtains Parameters of Normal Distribution
Value；
6th step, establishes random number simulation model, meets the probability statistical distribution feature in the 5th step, geometrical model into
Row finite element discretization, carries out macro equivalent elastic performance analysis；Microstress analysis is carried out at the same time, respectively in X, Y, Z-direction
Upper progress simple tension analog approach obtains stress field；
7th step, carries out printed sample in second step macroscopical compression experiment, is obtained from compression stress strain curve
The macro equivalent Young's modulus of test, verifies the correctness of the 6th step reliability values analysis result.
Preferably, in the 3rd step, printing frame microstructure characterizations are completed using X-ray CT scanner, with
The Three-dimension Reconstruction Model based on image scanning is obtained, the process of realizing of the three-dimensional reconstruction is：CT scanner is complete every 0.05mm
Into once to the cross-sectional scans of frame, all scan images are sequentially introduced into open source software ImageJ, select segmentation threshold, until
Image middle frame after thresholding is highlighted to obtain the bianry image of frame, and image imports software for calculation after noise reduction process
In VOXELCON.
Preferably, in the 4th step, the statistical shortcomings characteristic size regularity of distribution, scale-up model to local defect position
Put, observation carried out to defect and is summarized as warpage, notch, hole three classes, and define, mark the characteristic size measured per class defect,
Carry out the statistics of sample.
Preferably, in the 6th step, establish random number simulation model and solve calculating macro-mechanical property, its
Stochastic modeling realizes that process is with numerical solution：Equally distributed random number is generated by computer, using Box-Muller algorithms
Realize the generation of normal distribution random number, geometric properties and ruler are defined in the script generation using the CAD software FreeCAD that increases income
It is very little .stl form geometrical model files are exported, finally import grid division in VOXELCON softwares, generate the node letter of pre-treatment
Breath, unit information, export finite element scheme model file, and asymptotic homogenization is unfolded using first order perturbation in computational methods, by
Fortran programmings are realized, are connected with finite element scheme model file interface, and solving the microcosmic of macroscopical elastic matrix and unit should
Force vector.
Preferably, in the 7th step, macroscopical compression stiffness is measured by compression test device, to numerical simulation result
Verified, the process of test amount is in fact：Test sample is positioned between underbeam and upper beam, adjusts the ball pad of underbeam
Circle, applies load by Oldham's coupling, is connected above upper beam with displacement sensor, finally obtains load-displacement curves.
The present invention compared with prior art the advantages of be：
(1) present invention provides a kind of Reliability Prediction Method of 3D metallic prints micro-structure macro-mechanical property, this method
A large amount of independent laboratory sample random geometrical imperfection statistical forms are provided, are realized based on actual physical model foundation simulation model
Rapid data basis, provide with optimization design and determine from experience to data for the prediction of 3D metallic print structural mechanical properties
The accumulation of quantificational description, there is provided simple and feasible method, shortens the design cycle, improve work efficiency, and saves design
Cost.
(2) present invention provides a kind of Reliability Prediction Method of 3D metallic prints micro-structure macro-mechanical property, this method
It enormously simplify and up to ten million time experiments are repeated measure reliability of structure.It is progressive uniform using being unfolded based on first order perturbation
Change Computational Mechanics method, geometry in microstructure and material properties randomness are expanded to by numerical simulation macro-mechanical property into
Row analysis, and experimental verification has been carried out to macroscopical compression stiffness therein, avoid repetition it is cumbersome have a fling at and test process.
Brief description of the drawings
Fig. 1 is Forecasting Methodology flow chart.
Fig. 2 is cubic lattice structure design drawing.
Fig. 3 is warping characteristics size ak definition and distribution schematic diagram.
Fig. 4 is warping characteristics size bk definition and distribution schematic diagram.
Fig. 5 is warping characteristics size θ k definition and distribution schematic diagram.
Fig. 6 is notch characteristic size an definition and distribution schematic diagram.
Fig. 7 notch characteristic sizes bn is defined and distribution schematic diagram.
Fig. 8 Porous Characteristic sizes ah is defined and distribution schematic diagram.
Fig. 9 Porous Characteristic sizes bh is defined and distribution schematic diagram.
The frame partial schematic diagram of Figure 10 defect characteristics containing three classes.
Figure 11 establishes porous framework illustraton of model based on random defect feature.
The horizontal positioned scheme stochastic model I of Figure 12 are in X-direction Stress Map.
45 ° of placement schemes stochastic model II of Figure 13 are in X-direction Stress Map.
Figure 14 checking experiments test pictorial diagram.
Figure 15 checking experiment part description figures.
Embodiment
Forecasting Methodology flow according to figure 1, is detailed further below specific implementation process.
The first step, is designing a model after structure simplifies as shown in Figure 2 according to practical application request.Intend selecting using laser
Area's sintering technology prints this metal micro structure.
Second step, to the design in the first step, according to establish flaw evaluation, quality authentication principle, select different three
Print service company of family, using identical martensite steel powder, has identical average grain diameter, meets that particle diameter distribution dispersiveness will
Ask.Linking up Print direction has two kinds, and one kind is horizontal direction (0 °), and another kind is in (45 °) of 45 directions printing strategy with level.
6 printed samples are observed and classified by the 3rd step, and the printing of the second step is completed using X-ray CT scanner
Frame microstructure characterizations.Segmentation threshold is selected using open source software ImageJ, the image middle frame after thresholding highlights
Display obtains the bianry image of frame.Import in VOXELCON softwares, obtain Three-dimension Reconstruction Model .stl files.
4th step, on the basis of the 3rd step, observes each thin bar of general frame, the feature ruler of statistical shortcomings
The very little regularity of distribution.It is first by following the trail of single bar actual in total, acquisition structure whole defect information, its result such as Fig. 3-
Shown in Fig. 9.
5th step, handles institute's statistics in the 4th step.It is fitted using Normal Distribution Characteristics, obtains normal state
Distributed constant value.
Here is Defect Distribution Statistics geometric parameter lookup table and fitting normal distribution attached drawing：
1 Defect Distribution Statistics geometric parameter statistical form of table
Subscript n, the mark that k, H are defect characteristic notch, warpage and hole, a in formulan,bnThe respectively feature ruler of notch
It is very little, shown in its statistical distribution Fig. 3-5.ak,bk,θkThe respectively characteristic size of warpage, shown in its statistical distribution Fig. 6-7.aH,bHPoint
Not Wei hole characteristic size, shown in its statistical distribution Fig. 8-9.
6th step, establishes random number simulation model, meets the probability statistical distribution feature shown in table 1 in the 5th step.Adopt
The generation of normal distribution random number is realized with Box-Muller algorithms, its process is：
U1, U2 are [0,1] section uniformly distributed random variable,θ=2* π * U2；
So Z0=R*cos (θ) or Z1=R*sin (θ) is the independent random numeral for meeting standardized normal distribution.
Since positive state value Z0 and Z1 has the average value equal to 0 and a standard deviation equal to 1, can be used such as the following
Z (Z0 or Z1) is mapped to the statistic X that an average value is m, standard deviation is sd by formula：
X=m+ (Z*sd) (1)
By model X of the above method generation geometry containing random defectj(j=1Jtot) as shown in figs. 10-11.Solve material etc.
The reliability of the elastic engineering constant of effect, in addition to above geometry randomness, also needs to consider the randomness of material property attribute, it is asked
Solution method is using the asymptotic homogenization theory as follows based on first order perturbation expansion.
Assuming that contain stochastic variable α, Normal Distribution, using first order perturbation in the selection of material elastic constitutive model matrix [D]
It is unfolded as follows：
[D]≈[D]0+[D]1α (2)
In formula [D]0For the zeroth order item of [D], [D]1For the single order item of [D].Material macro equivalent elastic matrix [D]HAlso use
First order perturbation is assumed to be unfolded as follows：
X in formulajFor microstructure label,For model XjMacroscopical elastic matrix.For zeroth order item,For one
Rank.Macro equivalent elastic matrix [DH] average value Exp [] and variance Var [] it is as follows by Definitions On Integration：
According to asymptotic homogenization, macro equivalent elastic matrix is equivalent by microstructure and asks, using following expression
To be averaging performance
[I] is unit matrix in formula, and [B] is strain differential matrix, and [χ] is characterized transposed matrix, is equally taken the photograph using single order
Dynamic to be unfolded as follows, substituting into above formula can obtain：
The zeroth order item and single order item of characteristic displacement matrix { χ } in above formula (7-8), can be asked by equation below (9-10)
Solution：
[B] is strain-displacement relation matrix in formula,To be single just integrated total firm,TDOFFor total number of degrees of freedom, by the above
Formula tries to achieve equivalent elastic constant, and microstress distribution can be solved by equation below in X-direction simple tension：
In formula, { ε } is strain vector, and { σ } is stress vector.
Similarly, stress field can be obtained in the hope of carrying out simple tension analog approach in Y, Z-direction.
The solving result of stress is as shown in Figure 12-Figure 13.And its reliability index is as shown in table 2：
2 equivalent performance of microstructure containing random defect of table and its reliability index value
7th step, in order to verify the correctness of the 6th step reliability values analysis result, using such as Figure 14-15
Shown experimental provision is verified, carries out macroscopical compression experiment, above fail-safe analysis result is verified and corrected.It is real
Experiment device is the omnipotent experiment instruments of Instron.It is loading unit component above workbench, it is upward successively that parts put order
For spherical washer, underbeam, sample, upper beam, Oldham's coupling component.The process of test amount is in fact：Test sample is positioned over
Between underbeam and upper beam, the spherical washer for adjusting underbeam clamps workpiece, applies load by Oldham's coupling in experimentation, on
It is connected above beam with displacement sensor, finally obtains load-displacement curves, thus it is firm calculates macroscopic view compression for slope of a curve
Degree.
What the present invention did not elaborated partly belongs to techniques well known.
The above, is only the part embodiment in the present invention, but protection scope of the present invention is not limited to
This, the equivalent change or modification or equal proportion that every design spirit according in the present invention is made zoom in or out, and all should
It is included within the scope of the present invention.
Claims (5)
- A kind of 1. porous framework structure macroscopic view elastic performance Reliability Prediction Method of 3D printing technique manufacture, it is characterised in that Comprise the following steps：The first step, design meet mechanical property and Functional Requirement structure；Second step, the design to the first step use 3D metallic print fabrication techniques samples, and require to use identical powder Material, the powder has identical average grain diameter, and the powder meets that particle diameter distribution dispersiveness requires, and sets printing side To the printing strategy of, supporting type；3rd step, the printing frame microstructure characterizations of the second step is completed using X-ray CT scanner, to obtain the frame Three-dimension Reconstruction Model of the frame based on scan image；4th step, records all samples on the basis of the 3rd step, to each micro- unit cell of the frame into Row observation, the characteristic size regularity of distribution of statistical shortcomings；5th step, institute's statistics in the 4th step is fitted using Normal Distribution Characteristics, obtains Parameters of Normal Distribution value；6th step, establishes random number simulation model, meets the probability statistical distribution feature in the 5th step, and geometrical model is had The first discretization of limit, carries out macro equivalent elastic performance analysis；Be carried out at the same time microstress analysis, respectively in the X, Y, Z direction into Row simple tension analog approach obtains stress field；7th step, carries out printed sample in second step macroscopical compression experiment, is tested from compression stress strain curve Macro equivalent Young's modulus, verify the correctness of the 6th step reliability values analysis result.
- 2. the porous framework structure macroscopic view elastic performance reliability prediction side of 3D printing technique manufacture according to claim 1 Method, it is characterised in that in the 3rd step, printing frame microstructure characterizations are completed using X-ray CT scanner, to obtain Three-dimension Reconstruction Model based on image scanning, the process of realizing of the three-dimensional reconstruction are：CT scanner completes one every 0.05mm All scan images are sequentially introduced into open source software ImageJ, segmentation threshold are selected, until threshold value by the secondary cross-sectional scans to frame Image middle frame after change is highlighted to obtain the bianry image of frame, and image imports software for calculation VOXELCON after noise reduction process In.
- 3. the porous framework structure macroscopic view elastic performance reliability prediction side of 3D printing technique manufacture according to claim 1 Method, it is characterised in that in the 4th step, the statistical shortcomings characteristic size regularity of distribution, scale-up model to local defect position, Observation is carried out to defect and is summarized as warpage, notch, hole three classes, and defines, mark the characteristic size measured per class defect, is carried out The statistics of sample.
- 4. the porous framework structure macroscopic view elastic performance reliability prediction side of 3D printing technique manufacture according to claim 1 Method, it is characterised in that in the 6th step, establish random number simulation model and solve calculating macro-mechanical property, its with Machine modeling realizes that process is with numerical solution：Equally distributed random number is generated by computer, it is real using Box-Muller algorithms The generation of existing normal distribution random number, geometric properties and size are defined in the script generation using the CAD software FreeCAD that increases income, Export .stl form geometrical model files, finally import VOXELCON softwares in grid division, generate pre-treatment nodal information, Unit information, exports finite element scheme model file, and asymptotic homogenization is unfolded using first order perturbation in computational methods, by Fortran programmings are realized, are connected with finite element scheme model file interface, and solving the microcosmic of macroscopical elastic matrix and unit should Force vector.
- 5. the porous framework structure macroscopic view elastic performance reliability prediction side of 3D printing technique manufacture according to claim 1 Method, it is characterised in that in the 7th step, macroscopical compression stiffness is measured by compression test device, to numerical simulation result into Row verification, the process of test amount is in fact：Test sample is positioned between underbeam and upper beam, adjusts the spherical washer of underbeam, Load is applied by Oldham's coupling, is connected above upper beam with displacement sensor, finally obtains load-displacement curves.
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| CN111203537A (en) * | 2020-04-22 | 2020-05-29 | 中国航发上海商用航空发动机制造有限责任公司 | Method for controlling defect of poor fusion of LMD process prefabrication |
| CN111203537B (en) * | 2020-04-22 | 2020-07-28 | 中国航发上海商用航空发动机制造有限责任公司 | Method for prefabricating fusion defect by controlling L MD process |
| CN111203538A (en) * | 2020-04-22 | 2020-05-29 | 中国航发上海商用航空发动机制造有限责任公司 | Prefabricated crack defect, preparation method of built-in crack defect and prefabricated part |
| WO2021212888A1 (en) * | 2020-04-22 | 2021-10-28 | 中国航发上海商用航空发动机制造有限责任公司 | Method for prefabricating poor fusion defects by controlling lmd process |
| RU2805914C1 (en) * | 2020-04-22 | 2023-10-24 | Аесс Шанхай Кемешл Эйркрафт Энджин Мэньюфэкчуринг Ко., Лтд. | Method for preliminary formation of non-formation defect by controlling lmd process in additive production of metal parts |
| CN111521600A (en) * | 2020-04-26 | 2020-08-11 | 长春工业大学 | 3D printing metal component defect online monitoring and analyzing device and control method thereof |
| CN112059178A (en) * | 2020-07-31 | 2020-12-11 | 长沙新材料产业研究院有限公司 | Method for adjusting printing process parameters through microstructure arrangement form |
| CN112059178B (en) * | 2020-07-31 | 2023-04-14 | 航天科工(长沙)新材料研究院有限公司 | Method for adjusting printing process parameters through microstructure arrangement form |
| CN113722942A (en) * | 2021-07-08 | 2021-11-30 | 北京理工大学 | Finite element calculation model considering 3D printing geometric defects |
| CN116051562A (en) * | 2023-03-31 | 2023-05-02 | 北京大学 | Metal 3D printing quality prediction method based on depth generation network |
| CN116905819A (en) * | 2023-07-21 | 2023-10-20 | 重庆大学溧阳智慧城市研究院 | Modularized building unit manufacturing method by utilizing 3D printing technology |
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