CN116275125A - Method for predicting formation and distribution characteristics of additive manufacturing molten pool - Google Patents

Method for predicting formation and distribution characteristics of additive manufacturing molten pool Download PDF

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CN116275125A
CN116275125A CN202310565544.1A CN202310565544A CN116275125A CN 116275125 A CN116275125 A CN 116275125A CN 202310565544 A CN202310565544 A CN 202310565544A CN 116275125 A CN116275125 A CN 116275125A
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王灵杰
邢辉
李玉泽
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Northwestern Polytechnical University
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Abstract

The invention discloses a method for predicting formation and distribution characteristics of an additive manufacturing molten pool, which comprises the following steps: 1. constructing a three-dimensional fluid heat transfer, laminar fluid and dynamic grid transient mathematical model; 2. setting process parameters of a simulation process; 3. setting a function relation of thermodynamic parameters along with temperature change; 4. setting a parameter-containing quantity related to heat; 5. constructing a geometric model of a substrate and a powder bed, and determining initial conditions and boundary conditions; 6. multiple physical field coupling; 7. setting a laser scanning track function; 8. setting a domain probe and a global variable probe; 9. introducing a dynamic grid technology to realize an apparent cladding layer or a melting concave region; 10. grid division is carried out on the geometric models corresponding to the substrate and the powder bed, and the temperature, the fluid flow rate and the phase composition of each node in the molten pool are determined through finite difference calculation; 11. predicting the temperature field, flow field, gradient size and phase distribution in a molten pool in the processing process. The invention can guide actual processing production and has obvious effect.

Description

Method for predicting formation and distribution characteristics of additive manufacturing molten pool
Technical Field
The invention belongs to the technical field of additive manufacturing, and particularly relates to a method for predicting formation and distribution characteristics of an additive manufacturing molten pool.
Background
Selective area laser melting (SLM) is a near net shape laser additive manufacturing technology developed based on prototype fabrication, and the main characteristics are represented by material dispersion and gradual accumulation. The technology comprises the steps of slicing, layering and analyzing a three-dimensional mould of a preformed piece through special software to obtain forming profile data of each section, selectively melting a metal powder area corresponding to the profile data layer by utilizing high-energy beam laser, and manufacturing a three-dimensional solid part in a mode of rapidly melting, solidifying and accumulating layer by laying powder layer. Similar to the above, the coaxial powder feeding type laser solid forming technology (LSF) uses the synchronous material feeding of the laser melting base material as the main technical characteristics, namely, the coaxial coupling output of laser beams, protective atmosphere and metal powder. The laser builds the actual path plan and performs a scan according to the slicing parameters, causing the substrate to melt locally. And meanwhile, the nozzle sprays out metal powder to fuse with a substrate molten pool, and is combined with a matrix after cooling and solidification to form a cladding layer consistent with the slice shape simulation model.
In the prior art, during SLM & LSF processing, powders and local substrates are considered to undergo rapid heating, solidification and cooling processes; the existence of the temperature gradient of the pasty area at the tail part of the molten pool ensures that the solidification form of the liquid metal in the area is mainly shown as directional solidification, but the solidification form is only empirical, and the problems of low quality of formed parts, complex process, insufficient production efficiency and the like can be caused. Therefore, it is needed to perform multi-physical field (temperature field, flow field, phase field, gradient magnitude, stress field, etc.) characteristic research on the micro-area of the SLM & LSF process by establishing a mathematical physical model, so as to realize theoretical guidance and practical production, and finally achieve the purpose of improving the comprehensive performance of the three-dimensional formed part.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the method for predicting the formation and distribution characteristics of the additive manufacturing molten pool, which has the advantages of simple steps, reasonable design and convenient realization, can help scientific researchers or technical engineering staff predict and understand various physical phenomena which are difficult to explain and are generated in the SLM & LSF simulation processing process, avoid various processing phenomena which influence the processing quality of the SLM & LSF three-dimensional forming part, guide actual processing production, have obvious effect and are convenient to popularize.
In order to solve the technical problems, the invention adopts the following technical scheme: a method of additive manufacturing puddle formation and distribution feature prediction comprising the steps of:
step one, constructing a mathematical model;
step two, constructing a three-dimensional fluid heat transfer, laminar fluid and dynamic grid transient model;
step three, determining the technological parameters of the simulation process by combining actual production;
determining a functional relation of thermodynamic parameters of the powder material in the melting process along with the change of temperature;
step five, determining parameters related to heat;
step six, constructing a geometric model of the substrate and the powder bed, and determining initial conditions and boundary conditions;
step seven, coupling multiple physical fields;
step eight, setting a laser scanning track function;
step nine, setting a domain probe and a global variable probe;
step ten, introducing a dynamic grid technology to realize an apparent cladding layer or a melting concave region;
step eleven, carrying out grid division on the geometric models corresponding to the substrate and the powder bed, and determining the temperature, the fluid flow rate and the phase composition of each node in the molten pool through finite difference calculation;
step twelve, predicting the temperature field, flow field, gradient size and phase distribution in the molten pool in the processing process.
The method for predicting the formation and distribution characteristics of the molten pool for additive manufacturing, in the first step, the mathematical model includes: the liquid alloy in the molten pool is incompressible and uniform Newtonian laminar fluid and does not contain reduced pressure; the substrate and the powder bed are made of uniform and isotropic materials; the thermophysical parameters of the alloy are only temperature dependent; the influence of the air flow in the cavity on the surface morphology of the molten pool is zero; the absorption of the laser radiation by the material surface is a specified value, independent of the incident laser and the normal angle of the surface; the influence of the focusing depth and the defocusing amount on the processing part is zero, and the boundary heat source directly acts on the surface of the powder bed; the heat transfer equation acts on the entire liquid region and the momentum equation only affects the bath region.
According to the method for predicting the formation and distribution characteristics of the additive manufacturing molten pool, for an SLM process, the first step further comprises preprocessing the shape of a powder bed to be melted, and controlling the shape of the powder bed by referring to random surface generation; the random surface adopts three global definition coupling geometric models of Gaussian random, uniform random and geometric parts.
In the above method for predicting formation and distribution characteristics of an additive manufacturing molten pool, the process parameters in the third step include scanning interval, laser scanning speed, laser power, spot radius, surface emissivity, diffraction rate, powder radius, ambient pressure, ambient temperature, melting temperature, solid-liquid phase transition region, latent heat of solidification (melting), expansion coefficient, surface tension coefficient, boltzmann constant, pasty region constant, substrate density, powder density, processing time, single-pass length, total track width, total number of rows, powder stacking rate and powder layer thickness.
In the above method for predicting formation and distribution characteristics of an additive manufacturing molten pool, in the fifth step, the parameter-containing quantity includes a gaussian moving heat source, a heat flux, heat radiation and heat insulation, and the gaussian moving heat source includes a laser beam flow direction, a beam profile, a deposited beam power, a beam origin and a range standard deviation; the heat flux comprises convection heat transfer generated by contact of a system with air and contact of a heat source with a model; the thermal radiation includes surface to ambient radiation.
The formation of the additive manufacturing molten poolA distribution feature prediction method, wherein the Gaussian moving heat source is as follows:
Figure SMS_2
wherein->
Figure SMS_7
For the normal direction of the upper surface of the system, < >>
Figure SMS_10
For the origin of the beam, i.e.
Figure SMS_4
，/>
Figure SMS_6
The laser beam flow direction is: />
Figure SMS_9
The method comprises the steps of carrying out a first treatment on the surface of the The deposition beam power:
Figure SMS_12
，/>
Figure SMS_1
，/>
Figure SMS_5
wherein->
Figure SMS_8
For surface emissivity, +.>
Figure SMS_11
The value representing the standard deviation of the light spot is the powder radius +.>
Figure SMS_3
；
The heat flux:
Figure SMS_13
wherein->
Figure SMS_14
For heat transfer coefficient>
Figure SMS_15
For the external temperature->
Figure SMS_16
Temperature values at the micro-element lattice points;
the heat radiation:
Figure SMS_17
wherein->
Figure SMS_18
For ambient temperature->
Figure SMS_19
For surface emissivity, +.>
Figure SMS_20
Is Stefan-Boltzmann constant.
In the above method for predicting the formation and distribution characteristics of an additive manufacturing molten pool, the boundary conditions in the sixth step include a heat transfer equation, a mass transfer equation, a momentum transfer equation and a phase change material assumption equation;
the heat transfer equation is:
Figure SMS_21
wherein,,
Figure SMS_23
is the specific heat capacity of the material->
Figure SMS_26
Is of heat conductivity>
Figure SMS_28
Indicating the temperature difference>
Figure SMS_24
Representing gradient operators +_>
Figure SMS_27
For energy power, +.>
Figure SMS_29
For speed field +.>
Figure SMS_30
Indicating the laser heat source->
Figure SMS_22
Indicating loss of heat of evaporation, +.>
Figure SMS_25
Indicating radiation heat dissipation loss;
considering the powder bed and the melt pool as continuous medium models, the continuity equation is satisfied:
Figure SMS_31
。
the method for predicting the formation and distribution characteristics of the additive manufacturing molten pool comprises the following steps of:
Figure SMS_32
wherein,,
Figure SMS_33
respectively indicate->
Figure SMS_34
Corresponding volume fraction, density, heat conductivity coefficient and constant pressure heat capacity,
Figure SMS_35
respectively represents the density, the heat conductivity coefficient and the constant pressure heat capacity of the system>
Figure SMS_36
Represents the latent heat from phase 1 to phase 2; the solid metal material is phase 1, the liquid metal material is phase 2, and the phase transition temperature between phase 1 and phase 2 is +.>
Figure SMS_37
Indicating that the transition interval between phase 1 and phase 2 is +.>
Figure SMS_38
Indicating (I)>
Figure SMS_39
Is a function of temperature and represents the specific heat rate of the material.
In the above method for predicting formation and distribution characteristics of an additive manufacturing molten pool, in the seventh step, the multiple physical field coupling includes non-isothermal flow coupling and marangoni effect coupling, where the non-isothermal flow coupling and the marangoni effect coupling are both from laminar flow and fluid heat transfer.
In the method for predicting the formation and distribution characteristics of the additive manufacturing molten pool, in the eleventh step, the grid division adopts hydrodynamic approximate calibration, and a free tetrahedral grid is constructed.
Compared with the prior art, the invention has the following advantages:
1. the method has simple steps, reasonable design and convenient realization.
2. The invention is based on multi-field coupling software COMSOL Multiphysics, utilizes a laminar flow-phase change-dynamic grid method to couple fluid heat transfer to carry out hydrodynamic simulation, is embodied in carrying out numerical simulation reproduction on an SLM & LSF process under a variable track scanning strategy, has controllability and fidelity, can predict molten pool distribution in real time, and can characterize a system (including a molten pool) temperature field, a flow field, a gradient size, a phase field distribution and characteristics thereof.
3. The invention can help scientific researchers or technical engineering personnel predict and understand various difficult-to-interpret physical phenomena generated in the SLM & LSF simulation processing process, avoid various processing phenomena affecting the processing quality of the SLM & LSF three-dimensional formed part, guide actual processing production, have obvious effect and are convenient to popularize.
In conclusion, the method has the advantages of simple steps, reasonable design and convenient realization, can help scientific researchers or technical engineering staff predict and understand various difficult-to-interpret physical phenomena generated in the SLM & LSF simulation processing process, avoids various processing phenomena affecting the processing quality of the SLM & LSF three-dimensional formed part, guides actual processing production, has obvious effect and is convenient to popularize.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a model diagram of the SLM process of the present invention;
FIG. 3 is a schematic diagram of a geometric model constructed based on a uniform material model instead of a powder bed and a grid cut-away diagram according to the present invention;
FIG. 4 is a schematic diagram of a geometric model structure and a grid cut-away diagram of a powder bed constructed based on random surface generation theory according to the present invention;
FIG. 5 is a graph showing the top profile distribution of powder particles on a powder bed generated based on random surface theory according to the present invention;
FIG. 6 is a cloud plot of the spatial scale of the surface relief generated based on random surface theory in accordance with the present invention;
FIG. 7 is a schematic diagram of an SLM & LSF laser processing track and scanning strategy of the present invention;
fig. 8 shows a single-pass, multi-pass bath phase field distribution (left:
Figure SMS_40
right: />
Figure SMS_41
) Cloud graphics;
FIG. 9 shows a single pass, multi-pass bath temperature field distribution (left:
Figure SMS_42
right: />
Figure SMS_43
) Cloud graphics;
FIG. 10 shows the single-pass, multi-pass bath temperature gradient size distribution (left:
Figure SMS_44
right:
Figure SMS_45
) Cloud graphics;
FIG. 11 shows a single-pass, multi-pass bath liquid phase velocity field profile (left:
Figure SMS_46
right: />
Figure SMS_47
) Cloud graphics;
FIG. 12 shows the single pass bath phase field of the present invention
Figure SMS_48
) Symmetric boundary slice distribution cloud pictures;
FIG. 13 shows the temperature field of the single pass molten pool of the present invention
Figure SMS_49
) Symmetric boundary slice distribution cloud pictures;
FIG. 14 shows the temperature gradient of the single-pass molten pool
Figure SMS_50
) Symmetric boundary slice distribution cloud pictures;
FIG. 15 shows the liquid phase velocity field of the single pass molten pool of the present invention
Figure SMS_51
) Center slice distribution cloud.
Detailed Description
As shown in fig. 1 to 15, the method for predicting the formation and distribution characteristics of an additive manufacturing molten pool according to the present invention comprises the steps of:
step one, constructing a three-dimensional fluid heat transfer, laminar fluid and dynamic grid transient mathematical model;
in specific implementation, a three-dimensional fluid heat transfer, laminar fluid and dynamic grid transient mathematical model is constructed based on COMSOL Multiphysics.
The construction conditions were as follows:
the liquid alloy in the molten pool is incompressible and uniform Newtonian laminar fluid and does not contain reduced pressure;
the substrate and the powder bed are made of uniform and isotropic materials;
the thermophysical parameters of the alloy are only temperature dependent;
the influence of the gas molecular flow on the surface morphology of the molten pool is zero;
the absorption of the laser radiation by the material surface is a specified value, independent of the incident laser and the normal angle of the surface;
in specific implementation, the absorption of the laser radiation on the material surface is 0.3-0.8;
the influence of the focusing depth and the defocusing amount on the melting process of the molten pool is zero, and the boundary heat source directly acts on the surface of the powder bed;
in this embodiment, for the SLM process, step one further includes preprocessing the shape of the powder bed to be fused, and implementing control of the powder bed morphology by referencing three-dimensional random surface generation; the three-dimensional random surface is established by adopting a Gaussian random method and a uniform random method.
In specific implementation, the parameters are set as follows:
gaussian random: number of arguments: 2, average value: 0, standard deviation: 1, random seed number: 3, a step of;
and (3) uniformly and randomly: number of arguments: 2, average value: 0, range: pi;
the amplitude scaling factor (curved film) is specified: rectangular width: 1.15mm, rectangular height: 0.55mm, scale factor: 0.01, spectral index: 0.18, specified surface RMS height: 0.01, spatial frequency resolution: 25.
step two, setting process parameters of a simulation process by combining actual production;
the process parameters include scan interval, laser scan speed, laser power, spot radius, surface emissivity, diffraction rate, powder radius, ambient pressure, ambient temperature, melting temperature, solid-liquid phase transition zone, latent heat of solidification (melting), expansion coefficient, surface tension coefficient, boltzmann constant, paste zone constant, substrate density, powder density, processing time, single pass length, total track width, total number of rows, powder packing rate, and powder layer thickness.
Setting thermodynamic parameters of the powder material melting process;
in particular embodiments, thermodynamic parameters include thermal conductivity, constant pressure heat capacity, kinetic viscosity, specific heat rate, poisson's ratio, material density, coefficient of thermal expansion, young's modulus, and poisson's ratio.
316L steel is selected as a base material, and Inconel 718 is selected as an additive.
In specific implementation, the setting is constant or function.
Setting a parameter-containing quantity related to heat;
the parameter-containing variables comprise Gaussian moving heat sources, heat flux, heat radiation and heat insulation, wherein the Gaussian moving heat sources comprise laser beam flow directions, beam profiles, deposited beam power, beam origins and range standard deviations; the heat flux comprises convection heat transfer generated by contact of a system with air and contact of a heat source with a model; the thermal radiation includes surface to ambient radiation.
The Gaussian moving heat source expression is
Figure SMS_56
Wherein->
Figure SMS_55
For the normal direction of the upper surface of the system, < >>
Figure SMS_62
For Gaussian movement of heat source, +.>
Figure SMS_60
For depositing the beam power and->
Figure SMS_66
，/>
Figure SMS_57
For laser power +.>
Figure SMS_65
For surface absorption rate->
Figure SMS_54
Is Gaussian and->
Figure SMS_63
，/>
Figure SMS_52
Is the origin of the beam>
Figure SMS_61
Is the negative direction of the z-axis of the three-dimensional space coordinate system, perpendicular to the surface of the powder bed, for the laser beam flow direction>
Figure SMS_58
Is the standard deviation of light spots, and->
Figure SMS_68
,/>
Figure SMS_59
For the powder radius>
Figure SMS_67
Point location of Gaussian distribution and +.>
Figure SMS_53
，/>
Figure SMS_64
Moving an origin of the light beam in the x-axis direction;
the heat flux is
Figure SMS_69
Wherein->
Figure SMS_70
For heat transfer coefficient>
Figure SMS_71
For the external temperature->
Figure SMS_72
Temperature values at the micro-element lattice points;
the heat radiation expressionIs that
Figure SMS_73
Wherein->
Figure SMS_74
For ambient temperature->
Figure SMS_75
For surface emissivity, +.>
Figure SMS_76
Is Stefan-Boltzmann constant.
In practice, a set of boundary conditions is introduced to simulate heat loss caused by material ablation, the material reaches vaporization temperature, and the material is removed in the modeling domain. Therefore, the temperature of the solid material cannot be higher than the ablation temperature, and when the material temperature reaches its ablation temperature, the surface loses a certain quality, depending on the material density and the heat of sublimation. To model the material to be ablated, thermal boundary conditions are set, and material removal is modeled.
The thermal boundary conditions introduced for ablation modeling are ablation heat flux conditions, expressed as:
Figure SMS_77
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_78
Indicating the heat flux absorbed by ablation of the material, +.>
Figure SMS_79
The ablation temperature is indicated as such,
Figure SMS_80
representing the temperature-dependent heat transfer coefficient, +.>
Figure SMS_81
Zero (zero) in case of->
Figure SMS_82
Is linearly increased, and the steep relation curve ensures that the solid temperature cannot be increasedSignificantly exceeding the ablation temperature; in addition to thermal boundary conditions, material removal is introduced. In addition, the introduced symmetrical boundary conditions can well simplify the model and save the computing resources.
The erosion rate of the material is expressed as:
Figure SMS_83
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_84
Indicating the material ablation rate, +.>
Figure SMS_85
Representing equivalent material density, +.>
Figure SMS_86
Representing sublimation heat.
Presetting all domains as fluids, setting material properties and setting phase change material data; the boundary conditions around the material are set to be thermal insulation, the upper surface and the air are set to have heat exchange, and the heat transfer coefficient is
Figure SMS_87
The method comprises the steps of carrying out a first treatment on the surface of the The thermal flux boundary condition is used for replacing the thermal insulation condition to be set in the previous step, and the boundary condition around the material is set as the thermal flux based on the heat transfer coefficient in the metal, so that the model part is assumed to be in the material, and the observation and analysis of the simulation result are facilitated.
Step five, constructing a geometric model of the substrate and the powder bed, and determining initial conditions and boundary conditions;
in specific implementation, two cuboids are set, based on the corners
Figure SMS_88
And
Figure SMS_89
the sizes are respectively +.>
Figure SMS_90
And
Figure SMS_91
the difference set is taken to obtain a model, and the effect is shown in fig. 5 and 6.
The boundary conditions comprise a heat transfer equation, a mass transfer equation, a momentum transfer equation and a phase change material hypothesis equation;
the heat transfer equation is:
Figure SMS_92
wherein,,
Figure SMS_94
is equivalent material density->
Figure SMS_99
Is the equivalent specific heat capacity of the material->
Figure SMS_102
For the temperature change with time at the microcell lattice, +.>
Figure SMS_96
For time (I)>
Figure SMS_98
For speed field +.>
Figure SMS_101
For gradient operator->
Figure SMS_104
Is the temperature value at the micro-element lattice, < +.>
Figure SMS_93
Is the temperature difference at the microcell lattice, +.>
Figure SMS_97
Is of heat conductivity>
Figure SMS_100
Indicating the laser heat source->
Figure SMS_103
Indicating loss of heat of evaporation, +.>
Figure SMS_95
Indicating radiation heat dissipation loss;
regarding the powder bed and the melt pool as continuous medium models, the continuity equation is satisfied
Figure SMS_105
，/>
Figure SMS_106
Is the change in equivalent material density over time.
In practice, the heat transfer equation works for the entire liquid region. The momentum equations only affect the bath area.
Because the heat transfer equation involves the effect of pressure, gravity, surface tension, and viscous forces required to define the Reynolds number on the flow of liquid in the bath, in combination with alloy liquid in the bath, a momentum transfer equation is established as follows:
Figure SMS_107
wherein,,
Figure SMS_108
for the bulk forces of buoyancy and gravity of the alloy liquid located in the bath, +.>
Figure SMS_109
Is a volumetric force.
Figure SMS_110
Figure SMS_111
Wherein,,
Figure SMS_114
is the pressure term, in particular the internal pressure of the bath, < + >>
Figure SMS_117
Is a unitary matrix->
Figure SMS_120
For speed->
Figure SMS_113
For the movement speed of the mesh in an arbitrary Lagrangian-Euler (ALE) model, and defined by the time derivative of the deformed mesh; />
Figure SMS_116
Is the dynamic viscosity of liquid metal, and generates mutation along with phase change; />
Figure SMS_119
For reference temperature->
Figure SMS_122
Is the reference density of the fluid->
Figure SMS_112
Is the thermal diffusivity of the material, +.>
Figure SMS_118
Acceleration of gravity, ++>
Figure SMS_121
As a density function->
Figure SMS_123
For the material or system expansion coefficient, +.>
Figure SMS_115
Is ambient temperature.
The phase change material hypothesis equation is:
Figure SMS_124
wherein,,
Figure SMS_127
for the volume fraction of phase 1, +.>
Figure SMS_129
Density of phase 1>
Figure SMS_132
For the volume fraction of phase 2, +.>
Figure SMS_128
For the density of phase 2, the solid metal material is phase 1, the liquid metal material is phase 2,/->
Figure SMS_130
Is constant pressure heat capacity of phase 1 +.>
Figure SMS_133
Is constant pressure heat capacity of phase 2 +.>
Figure SMS_135
Represents the latent heat from phase 1 to phase 2, < >>
Figure SMS_125
For the change of the liquid phase ratio of the material with the temperature at the micro-lattice point +.>
Figure SMS_131
For the liquid phase ratio of the material, +.>
Figure SMS_134
Is of heat conductivity>
Figure SMS_136
For the thermal conductivity of phase 1 +.>
Figure SMS_126
Is the thermal conductivity of phase 2.
In particular, the erosion rate of a material is expressed as
Figure SMS_137
，/>
Figure SMS_138
Representing normal grid speed in a proxy grid technique, < +.>
Figure SMS_139
Taking 0.3 for coupling parameters corresponding to surface growth (applicable to LSF processing technology) or a concave time scale (applicable to SLM processing technology); at the same time, the motion boundary smoothing technique is enabled, and the smoothing parameter is set to +.>
Figure SMS_140
0.5; in addition, the spatial dimensions of the surface growth (applicable to LSF processes) or the dishing (applicable to SLM processes) can be expressed in the form of:
Figure SMS_141
or->
Figure SMS_142
Wherein,,
Figure SMS_143
is the spot radius.
Step six, coupling multiple physical fields;
the multiple physical field coupling includes a non-isothermal flow coupling and a marangoni effect coupling, both from laminar and fluidic heat transfer.
In specific implementation, the non-isothermal flow coupling adopts a Buchnical approximation technique for the material properties, and the specified density and the specified reference temperature are both from a heat transfer interface; the marangoni effect coupling sets a surface tension coefficient, and the surface tension coefficient expression is:
Figure SMS_144
wherein,,
Figure SMS_145
the surface tension temperature derivative, also called Marangoni coefficient, is the value of the model
Figure SMS_146
。
Step seven, setting a laser scanning track function;
in specific implementation, the variable track scanning strategy is determined through the laser scanning track function, so that the SLM is realized&Layer-by-layer stacking of LSFs; such as the track-change (e.g., 10 rows) related parameters are shown in table 1. Wherein,,
Figure SMS_147
the laser scanning speed is represented by T1, the total time of single-pass scanning is represented by T, the real time point of laser scanning is represented by S, the path of laser scanning is represented by S, and the pass interval of laser scanning is represented by D_s:
TABLE 1 laser scanning track path function
Figure SMS_148
Step eight, setting a domain probe and a global variable probe;
in specific implementation, the highest temperature, the lowest temperature, the average temperature and the highest and lowest temperature difference of a processing system are monitored through a domain probe and a global variable probe.
Step nine, introducing a dynamic grid technology to realize an apparent cladding layer or a melting concave region;
in practice, the apparent cladding layer or molten recessed region is achieved by introducing an arbitrary Lagrangian-Euler method (ALE).
Step ten, carrying out grid division on the geometric models corresponding to the substrate and the powder bed, and determining the temperature, the fluid flow rate and the phase composition of each node in the molten pool through finite difference calculation;
the meshing takes hydrodynamic approximate calibration and builds a free tetrahedral mesh.
In the implementation, according to the size parameters of the unit required by precision, particularly, the unit distribution at the track where the laser scanning is positioned is subjected to refinement treatment, so that the accuracy of a calculation result is ensured, wherein the space scale parameters of the system unit cells are set based on the principle of 'precise simulation', the grid distribution of the scanning track is realized, and the refinement is realized.
Step eleven, predicting the temperature field, the flow field, the gradient size and the phase distribution in a molten pool in the processing process.
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and any simple modification, variation and equivalent structural changes made to the above embodiment according to the technical substance of the present invention still fall within the scope of the technical solution of the present invention.

Claims (10)

1. A method for predicting formation and distribution characteristics of an additive manufacturing molten pool, comprising the steps of:
step one, constructing a three-dimensional fluid heat transfer, laminar fluid and dynamic grid transient mathematical model;
step two, setting process parameters of a simulation process by combining actual production;
setting a functional relation of thermodynamic parameters of the powder material in the melting process along with the change of temperature;
setting a parameter-containing quantity related to heat;
step five, constructing a geometric model of the substrate and the powder bed, and determining initial conditions and boundary conditions;
step six, coupling multiple physical fields;
step seven, setting a laser scanning track function;
step eight, setting a domain probe and a global variable probe;
step nine, introducing a dynamic grid technology to realize an apparent cladding layer or a melting concave region;
step ten, carrying out grid division on the geometric models corresponding to the substrate and the powder bed, and determining the temperature, the fluid flow rate and the phase composition of each node in the molten pool through finite difference calculation;
step eleven, predicting the temperature field, the flow field, the gradient size and the phase distribution in a molten pool in the processing process.
2. A method of predicting bath formation and distribution characteristics for additive manufacturing as set forth in claim 1, wherein the constructing conditions for constructing three-dimensional fluid heat transfer, laminar fluid and moving grid transient mathematical models in step one include:
the liquid alloy in the molten pool is incompressible and uniform Newtonian laminar fluid and does not contain reduced pressure;
the substrate and the powder bed are made of uniform and isotropic materials;
the thermophysical parameters of the alloy are only temperature dependent;
the influence of the gas molecular flow on the surface morphology of the molten pool is zero;
the absorption of the laser radiation by the material surface is a specified value, independent of the incident laser and the normal angle of the surface;
the influence of the focusing depth and the defocusing amount on the melting process of the molten pool is zero, and the boundary heat source directly acts on the surface of the powder bed.
3. The method for predicting the formation and distribution characteristics of an additive manufacturing molten pool according to claim 2, wherein for the SLM process, step one further comprises preprocessing the shape of the powder bed to be melted, and controlling the morphology of the powder bed by referencing three-dimensional random surface generation; the three-dimensional random surface is established by adopting a Gaussian random method and a uniform random method.
4. An additive manufacturing molten pool forming and distribution characteristic predicting method according to claim 1, wherein the process parameters in step three include scanning interval, laser scanning speed, laser power, spot radius, surface emissivity, diffraction rate, powder radius, ambient pressure, ambient temperature, melting temperature, solid-liquid phase transition zone, latent heat of solidification, expansion coefficient, surface tension coefficient, boltzmann constant, mushy zone constant, substrate density, powder density, processing time, single pass length, total track width, total number of rows, powder packing rate, and powder layer thickness.
5. A method of predicting bath formation and distribution characteristics for additive manufacturing according to claim 1, wherein in step four, the parameters include gaussian moving heat source, heat flux, heat radiation and thermal insulation, the gaussian moving heat source including laser beam flow direction, beam profile, deposited beam power, beam origin and range standard deviation; the heat flux comprises convection heat transfer generated by contact of a system with air and contact of a heat source with a model; the thermal radiation includes surface to ambient radiation.
6. The method for predicting pool formation and distribution characteristics of an additive manufacturing process of claim 5, wherein said gaussian moving heat source expression is
Figure QLYQS_1
Wherein->
Figure QLYQS_8
For the normal direction of the upper surface of the system, < >>
Figure QLYQS_15
For Gaussian movement of heat source, +.>
Figure QLYQS_6
For depositing the beam power and->
Figure QLYQS_12
，/>
Figure QLYQS_7
For laser power +.>
Figure QLYQS_11
For the surface absorption rate,
Figure QLYQS_3
is Gaussian and->
Figure QLYQS_10
，/>
Figure QLYQS_5
Is the origin of the beam>
Figure QLYQS_16
For the direction of the laser beam flow>
Figure QLYQS_9
Is the standard deviation of light spots, and->
Figure QLYQS_17
,/>
Figure QLYQS_4
For the powder radius>
Figure QLYQS_14
Point location of Gaussian distribution and +.>
Figure QLYQS_2
，/>
Figure QLYQS_13
Moving an origin of the light beam in the x-axis direction;
the heat flux is
Figure QLYQS_18
Wherein->
Figure QLYQS_19
For heat transfer coefficient>
Figure QLYQS_20
For the external temperature->
Figure QLYQS_21
Temperature values at the micro-element lattice points;
the heat radiation expressionIs that
Figure QLYQS_22
Wherein->
Figure QLYQS_23
For ambient temperature->
Figure QLYQS_24
For surface emissivity, +.>
Figure QLYQS_25
Is Stefan-Boltzmann constant.
7. A method of predicting bath formation and distribution characteristics for additive manufacturing as set forth in claim 1, wherein said boundary conditions in step five include heat transfer equations, momentum transfer equations, and phase change material hypothesis equations;
the heat transfer equation is:
Figure QLYQS_27
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure QLYQS_31
Is equivalent material density->
Figure QLYQS_35
Is the equivalent specific heat capacity of the material->
Figure QLYQS_29
For the temperature change with time at the microcell lattice, +.>
Figure QLYQS_33
For time (I)>
Figure QLYQS_36
For speed field +.>
Figure QLYQS_38
For gradient operator->
Figure QLYQS_26
Is the temperature value at the micro-element lattice, < +.>
Figure QLYQS_30
Is the temperature difference at the microcell lattice, +.>
Figure QLYQS_34
Is of heat conductivity>
Figure QLYQS_37
Indicating the laser heat source->
Figure QLYQS_28
Indicating loss of heat of evaporation, +.>
Figure QLYQS_32
Indicating radiation heat dissipation loss;
regarding the powder bed and the melt pool as continuous medium models, the continuity equation is satisfied
Figure QLYQS_39
，/>
Figure QLYQS_40
Is the change in equivalent material density over time.
8. A method of predicting bath formation and distribution characteristics for additive manufacturing as set forth in claim 7, wherein said phase change material hypothesis equation is:
Figure QLYQS_43
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure QLYQS_48
Volume fraction of phase 1，/>
Figure QLYQS_51
Density of phase 1>
Figure QLYQS_42
For the volume fraction of phase 2, +.>
Figure QLYQS_46
For the density of phase 2, the solid metal material is phase 1, the liquid metal material is phase 2,/->
Figure QLYQS_50
Is constant pressure heat capacity of phase 1 +.>
Figure QLYQS_53
Is constant pressure heat capacity of phase 2 +.>
Figure QLYQS_41
Represents the latent heat from phase 1 to phase 2, < >>
Figure QLYQS_45
For the change of the liquid phase ratio of the material with the temperature at the micro-lattice point +.>
Figure QLYQS_49
For the liquid phase ratio of the material, +.>
Figure QLYQS_52
Is of heat conductivity>
Figure QLYQS_44
As a thermal conductivity of phase 1,
Figure QLYQS_47
is the thermal conductivity of phase 2.
9. An additive manufacturing puddle formation and distribution characterization method according to claim 1 wherein in step six the multiple physical field coupling comprises non-isothermal flow coupling and marangoni effect coupling, both from laminar and fluid heat transfer.
10. An additive manufacturing puddle formation and distribution characterization method according to claim 1 wherein in step ten the meshing takes a hydrodynamic approximation calibration and a free tetrahedral mesh is constructed.
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