A simulation method and system for metal additive manufacturing grain structure solidification and coarsening

Abstract

The present invention relates to a method and system for simulating the solidification and coarsening of grain structure in metal additive manufacturing. The method comprises: meshing the computational domain of the metal material; calculating the current temperature distribution to determine the region in which each unit of the metal material resides; and then simulating the solidification and coarsening of the melt pool and heat-affected zone units using a cellular automaton method and a Monte Carlo method, respectively. In this invention, the cellular automaton method and the Monte Carlo method are strongly coupled, not only simultaneously considering the solidification and coarsening of the grain structure, resulting in more accurate simulation results, but also combining the advantages of both algorithms, namely improving computational efficiency while also providing a more comprehensive physical foundation.

Description

Technical Field

[0001] The present invention relates to the technical field of metal additive manufacturing, and in particular to a method and system for simulating solidification and coarsening of grain structure in metal additive manufacturing. Background Art

[0002] Metal additive manufacturing (also known as metal 3D printing) uses a high-power heat beam (laser or electron beam) to melt an applied metal material into a liquid state and form a molten pool. After the molten material solidifies, a deposited layer is formed. By depositing the molten material layer by layer, it is possible to manufacture three-dimensional parts of any shape. Therefore, metal additive manufacturing is a transformative manufacturing technology that combines low cost, short cycle times, and integrated design and manufacturing. Compared with traditional manufacturing technologies, its most significant feature is that high temperature gradients and cyclical thermal histories lead to complex metallurgical changes, forming a complex grain structure, and ultimately affecting the mechanical properties of the molded part. This is the well-known "manufacturing process-structure-mechanical properties" relationship, and the grain structure serves as a bridge between "process and performance," so accurate prediction and modeling are of great significance.

[0003] The entire additive manufacturing process involves many complex metallurgical phenomena, common metallurgical phenomena such as Figure 1 As shown in the figure, the nucleation of equiaxed crystals and the epitaxial and competitive growth at the melt pool boundary during the solidification of the material in the molten pool, as well as the grain coarsening in the heat-affected zone around the molten pool, occur simultaneously in two adjacent areas. They influence each other and together lead to the final complex grain morphology.

[0004] Existing simulation technologies include cellular automata and Monte Carlo methods. Existing technologies each have advantages and disadvantages when modeling and simulating melt pool solidification and heat-affected zone grain coarsening in additive manufacturing: the cellular automata method, based on solidification theory, has unique advantages in simulating melt pool solidification structure, but its computational complexity is relatively large, while the Monte Carlo method, based on mathematical probability, has high computational efficiency in simulating heat-affected zone coarsening, but lacks a physical basis, such as a lack of crystal orientation information. Physically, these two metallurgical phenomena will occur simultaneously in adjacent spaces and influence each other. Therefore, in order to more accurately simulate the grain structure of additive manufacturing, the present invention proposes a simulation method and system for the solidification and coarsening of metal grain structure in additive manufacturing. More specifically, this method couples the cellular automaton method with the Monte Carlo method. This method not only simultaneously considers the solidification and coarsening of the grain structure, making the simulation results more accurate, but also combines the advantages of the two algorithms, namely, improving computational efficiency while also having a more complete physical basis. Summary of the Invention

[0005] The purpose of the present invention is to provide a simulation method and system for the solidification and coarsening of grain structure in metal additive manufacturing, which can simultaneously simulate the solidification in the molten pool and the grain coarsening in the heat-affected zone based on more physical foundations (such as considering the influence of crystal orientation on competitive growth and coarsening, and considering physical time evolution), and more accurately simulate the grain structure of additive manufacturing.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for simulating solidification and coarsening of grain structure in metal additive manufacturing, comprising:

[0008] Meshing the calculation domain of the metal material, assigning an initial grain identifier and crystal orientation to the unit metal material of each mesh unit, selecting a preset number of the unit metal materials as potential nucleation points based on the nucleation density, and assigning a critical undercooling degree to each of the potential nucleation points based on a Gaussian distribution;

[0009] Calculate the temperature field distribution of the calculation domain at the current time t;

[0010] Based on the current temperature field distribution, determining the region where the state of each unit metal material is located; the region is a molten pool region, a heat-affected zone, or a non-affected zone; the non-affected zone is a region outside the heat-affected zone and the molten pool region;

[0011] For all the unit metal materials in the molten pool area, each of the unit metal materials is selected one by one, and a cellular automation method is used to simulate the growth and capture process according to the state and temperature of the unit metal material and its neighboring unit metal materials, and a nucleation process is simulated according to whether the unit metal material is the potential nucleation point and its critical undercooling;

[0012] For all the unit metal materials in the heat-affected zone, each of the unit metal materials is selected one by one, and a Monte Carlo method is used to simulate a roughening process;

[0013] When the unit metal material is in the non-affected area, there is no need to perform simulation processing on the current unit metal material;

[0014] Determine whether the temperature of each of the unit metal materials is less than the lower limit temperature of the heat affected zone, and obtain a first determination result;

[0015] If the first judgment result is no, set t=t+Δt, and return to the step of "calculating the temperature field distribution of the calculation domain at time t";

[0016] If the first judgment result is yes, the solidification and coarsening simulation is completed.

[0017] A simulation system for metal additive manufacturing grain structure solidification and coarsening, including:

[0018] a computational domain initialization module, configured to mesh the computational domain of the metal material, assign an initial grain identifier and crystal orientation to the unit metal material of each mesh unit, select a preset number of the unit metal materials as potential nucleation points based on the nucleation density, and assign a critical undercooling degree to each of the potential nucleation points based on a Gaussian distribution;

[0019] a state region determination module, configured to calculate the temperature field distribution of the calculation domain at time t; determine the region where the state of each unit metal material is located based on the current temperature field distribution; the region being a molten pool region, a heat-affected zone, or a non-affected zone; the non-affected zone being an area outside the heat-affected zone and the molten pool region;

[0020] a solidification process simulation module, configured to select each of the unit metal materials in the molten pool area one by one, and simulate the growth and capture process of the unit metal material and its neighboring unit metal materials using a cellular automation method according to the state and temperature of the unit metal material, and simulate the nucleation process using whether the unit metal material is the potential nucleation point and the critical undercooling;

[0021] a roughening process simulation module, configured to select each of the unit metal materials in the heat-affected zone one by one and simulate the roughening process using a Monte Carlo method;

[0022] a judgment module, configured to judge whether the temperature of each of the unit metal materials is less than the lower limit temperature of the heat-affected zone, and obtain a first judgment result;

[0023] If the first judgment result is no, set t=t+Δt, and return to the step of "calculating the temperature field distribution of the calculation domain at the current time t";

[0024] If the first judgment result is yes, the solidification and coarsening simulation is completed.

[0025] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0026] The present invention relates to a method and system for simulating the solidification and coarsening of grain structure in metal additive manufacturing, aiming to solve the problem that existing methods cannot simultaneously consider the solidification and coarsening of the molten pool when simulating the grain structure in metal additive manufacturing. This simulation includes: meshing the calculation domain of the metal material; determining the area where the state of each unit metal material is located based on the current temperature field distribution; performing solidification simulation using the cellular automaton method and coarsening simulation using the Monte Carlo method according to the area where the unit metal material is located; until it is determined that the temperature of each unit metal material is less than the lower limit temperature of the heat affected zone. Coupling the cellular automaton method with the Monte Carlo method can not only simultaneously consider the solidification and coarsening of the grain structure, making the simulation results more accurate, but also integrate the advantages of the two algorithms, that is, it improves the calculation efficiency while also having a more complete physical basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 Schematic diagram of the microstructure evolution process of the additive manufacturing process provided by the present invention;

[0029] Figure 2 A two-dimensional schematic diagram of envelope growth and capture using the cellular automaton method provided by the present invention;

[0030] Figure 3 Schematic diagram of randomly modifying the central unit grain identifier using the Monte Carlo method provided by the present invention;

[0031] Figure 4 A flow chart of a method for simulating solidification and coarsening of grain structure in metal additive manufacturing provided in Example 1 of the present invention;

[0032] Figure 5 Schematic diagram of the octahedron envelope provided in Example 1 of the present invention;

[0033] Figure 6 A block diagram of a simulation system for metal additive manufacturing grain structure solidification and coarsening provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] The cellular automaton method, based on solidification theory and a stochastic nucleation model, has a sound physical foundation and can accurately simulate the solidification process within the molten pool. The kinetic Monte Carlo method, which minimizes local grain boundary energy by modifying grain identifiers using a random algorithm, can simulate grain coarsening in the heat-affected zone. The following describes both methods:

[0036] Cellular automaton method: The cellular automaton method discretizes the computational domain into many tiny units, each unit representing a local material point at the unit's location, which contains a series of variables: unit state, grain identifier, crystal orientation, unit temperature, octahedral envelope, etc. The algorithm generates randomly distributed new nuclei based on the nucleation model, and determines the growth rate of the nucleus tip based on the dendrite tip growth dynamics. The nucleation model generally adopts a continuous nucleation model, which randomly selects potential nucleation points of a given density in the computational domain and assigns a critical undercooling to each nucleation point. When there is no envelope at the center of the nucleation point unit and its self-generated undercooling reaches the critical undercooling criterion, the unit nucleates and generates a randomly oriented octahedral envelope at the center of the unit. Subsequently, the envelope of the nucleation unit (called the parent unit) continues to grow according to its own undercooling. When the envelope exceeds the center of the neighboring unit, the neighboring unit (called the child unit) is captured, and a child envelope that inherits the parent unit envelope orientation is generated in the neighboring unit based on the eccentric growth algorithm. The growth and capture of the envelope are as follows Figure 2 As shown, Figure 2 (a) shows the envelope growth, Figure 2 (b) shows the capture of neighboring cells. The continuous growth and capture of the cell envelope represents the solidification process, in which the solid nucleus continuously consumes the surrounding liquid cells. This algorithm demonstrates that its model includes both a nucleation model and a dendrite tip dynamics model, thus possessing complete physical meaning. However, this algorithm only simulates the solidification process and ignores the grain coarsening process in the heat-affected zone surrounding the melt pool.

[0037] Dynamic Monte Carlo method: Monte Carlo method is a random algorithm based on minimizing interface energy. The algorithm discretizes the computational domain into small cells and assigns a grain identifier to each cell. Two adjacent cells with different grain identifiers form a grain boundary pair. For a three-dimensional model, a cell has 26 neighboring cells, so there are at most 26 grain boundary pairs. The Monte Carlo method randomly selects a cell on the grain boundary each time and randomly changes its grain identifier to the grain identifier of its neighboring cell, such as Figure 3 As shown in the figure, the thick black solid line represents the modified grain boundary. Assuming that the number of grain boundary pairs in the central unit before and after the modification is m and n respectively, the change in the system grain boundary energy caused by this modification is ΔE = (mn)E, (where E is the grain boundary energy of a grain boundary pair). Since the driving force of grain coarsening is to minimize the system grain boundary energy, the probability that the Monte Carlo method accepts this modification is When ΔE≥0, the modification is fully accepted. When ΔE<0, the modification is accepted with a certain probability, and the larger ΔE is, the lower the probability of acceptance. Therefore, as the algorithm continues, the grain boundary energy can gradually decrease, achieving the effect of simulating grain coarsening. The Monte Carlo method modifies the grain boundary based on probability, which can well simulate the grain coarsening process, but the algorithm cannot simulate the solidification process. In response to this problem, although some scholars have tried to improve the Monte Carlo method so that it can simulate solidification, the Monte Carlo method still lacks a physical basis. For example: the Monte Carlo method ignores the crystal orientation, so it is difficult to simulate the competitive growth process of grains with different crystal orientations during solidification; the basic time unit in the Monte Carlo method is the Monte Carlo time step, which lacks physical meaning, rather than physical time.

[0038] The purpose of the present invention is to provide a simulation method and system for the solidification and coarsening of grain structure in metal additive manufacturing, which can simultaneously simulate the solidification in the molten pool and the grain coarsening in the heat-affected zone based on more physical foundations (such as considering the influence of crystal orientation on competitive growth and coarsening, and considering physical time evolution), and more accurately simulate the grain structure of additive manufacturing.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Example 1

[0041] like Figure 4 As shown, this embodiment provides a method for simulating solidification and coarsening of grain structure in metal additive manufacturing, including:

[0042] S1: Divide the calculation domain of the metal material into grids, assign the initial grain identifier and crystal orientation to the unit metal material of each grid unit, select a preset number of the unit metal materials as potential nucleation points according to the nucleation density, and assign a critical undercooling ΔT to each potential nucleation point according to the Gaussian distribution. c .

[0043] S2: Calculate the temperature field distribution of the calculation domain at the current time t; based on the current temperature field distribution, determine the area where the state of each unit metal material is located; the area is the molten pool area, the heat-affected zone or the non-affected zone; the non-affected zone is the area outside the range of the heat-affected zone.

[0044] The expression for calculating the temperature field distribution of the calculation domain at time t in step S2 is:

[0045]

[0046] Where T0 is the starting temperature; q and v are the power and velocity of the heat source; r(y,z) is the distance from the grid cell to the heat source path; λ is the thermal conductivity; α is the thermal diffusivity; ξ = x-vt is the distance from the grid cell to the center of the heat source in the scanning direction; x, y, and z are the coordinate values of the center of the grid cell.

[0047] In this embodiment, the temperature field may also be calculated using other methods, such as the finite element method, the finite volume method, the finite difference method, the lattice Boltzmann method, and the like.

[0048] The state of the metal material in each unit in step S2 is in the molten pool area, and the criteria for determining the heat-affected zone and the non-affected zone are:

[0049] If T>=T s , then the unit metal material is located in the molten pool, where T s is the solid phase limit temperature of the material.

[0050] If T s >T>=T HAZ , then the unit metal material is located in the heat affected zone, where T HAZ It is the lower limit temperature of the heat affected zone of the material.

[0051] If T < T HAZ , the unit metal material is located in the non-affected area, and there is no need to perform simulation processing on the current unit metal material.

[0052] The specific judgment process is:

[0053] The determining, based on the temperature field distribution, the region where the state of each of the unit metal materials is located specifically includes:

[0054] For each of the unit metal materials, determine whether the temperature T of the unit metal material is greater than or equal to the solid phase limit temperature T s , obtain the second judgment result;

[0055] When the second judgment result is yes, determining that the state of the unit metal material is in the molten pool area;

[0056] When the second judgment result is no, it is determined whether the temperature T of the unit metal material is greater than or equal to the lower limit temperature T of the heat affected zone of the material. HAZ If yes, it is determined that the current state of the unit metal material is in the heat-affected zone. If no, it is determined that the current state of the unit metal material is in an area outside the heat-affected zone, that is, a non-affected zone.

[0057] S3: For all the unit metal materials in the molten pool, each unit metal material is selected one by one and a cellular automation method is used to simulate the growth and capture process based on the state and temperature of the unit metal material and its neighboring unit metal materials. The nucleation process is simulated based on whether the unit metal material is a potential nucleation point and its critical undercooling. This step ends when all unit metal materials have been selected once.

[0058] Wherein, step S3 specifically includes:

[0059] Step S31: Calculate the supercooling degree of the unit metal material according to the current temperature of the unit metal material and the liquid phase limit temperature of the material. ΔT=T l -T,T l is the liquidus limit temperature of the material.

[0060] Step S32: for each of the unit metal materials, determine the state of the unit metal material according to the degree of undercooling.

[0061] (1) When the degree of supercooling ΔT is less than 0, the unit metal material is overheated, the central envelope of the unit metal material is deleted, and the state of the unit metal material is changed to liquid.

[0062] (2) When the degree of supercooling ΔT is greater than or equal to 0, the unit metal material is supercooled, and the nucleation process simulation and the growth and capture process simulation are performed according to the state of the unit metal material and whether the unit metal material is the potential nucleation point.

[0063] Specifically, when the unit metal material is in a liquid state (i.e., no envelope exists in the unit metal material) and the unit metal material is the potential nucleation point, a determination is made as to whether the undercooling of the unit metal material is greater than or equal to the critical undercooling, thereby obtaining a third determination result. When the condition that the unit metal material is the potential nucleation point is not met, a nucleation process simulation is performed on other unit metal materials.

[0064] If the third judgment result is yes, then an octahedral envelope with a random orientation is generated at the center of the current unit metal material, such as Figure 5 As shown in Figure 3, the envelope orientation represents the orientation of the grains, and the envelope size is controlled by its half-diagonal length, which initially has a length of 0. The generation of the envelope here is the process of forming a crystal nucleus.

[0065] If the third judgment result is no, the current unit metal material will not nucleate, that is, the nucleation process simulation will not be performed.

[0066] When the state of the unit metal material is solid (i.e., an envelope exists in the unit metal material), determining whether the state of the neighboring unit metal materials of the unit metal material is liquid (whether there is one or more liquid units in the neighboring unit metal materials), and obtaining a fourth determination result;

[0067] If the fourth judgment result is yes, the envelope in the unit metal material begins to grow, and the size of the envelope in the unit metal material is updated; the update formula is:

[0068] L t+Δt =L t +v(ΔT)Δt

[0069] v(ΔT)=a1ΔT+a2ΔT 2 +a3ΔT 3

[0070] Among them, L t With L t+Δt are the envelope size modules at time t and t+Δt respectively; v(ΔT) is the envelope growth rate; a1, a2, a3 are fitting parameters; ΔT is the supercooling degree; Δt is the time increment of the current time iteration step of the cellular automaton model.

[0071] When the center of the liquid neighboring unit metal material is within the envelope of the unit metal material, the liquid neighboring unit metal material is captured by the envelope of the unit metal material. An envelope with the same orientation as the envelope is generated at the center of the neighboring unit metal material to inherit its growth direction.

[0072] If the fourth judgment result is no, the size of the envelope in the unit metal material is not updated, and the envelope in the unit metal material stops growing.

[0073] S4: For all the unit metal materials in the heat-affected zone, each unit metal material is selected one by one and a Monte Carlo method is used to simulate the roughening process. The Monte Carlo method used in this embodiment associates the roughening time step in the traditional Monte Carlo method with real physical time and introduces the grain boundary migration probability into the probability calculation of whether the grain identifier accepts the modification.

[0074] Step S4 specifically includes:

[0075] Step S41: Calculate the coarsening time step t C , and calculate the coarsening time step increment Δt within the time increment Δt according to the coarsening time step C (t) and its maximum value Δt C max (t). Coarsening time step t C is the iterative time step in the coarsening simulation, which means the number of iterations and has no physical meaning. Δt is the time increment in the cellular automaton, which is the real physical time.

[0076] The calculation formula for the coarsening time step is:

[0077] Among them, K1 and n1 are model constants; K and n are the pre-exponential factor and grain coarsening index respectively (which can be measured experimentally or calculated theoretically); R is the gas constant; T(t) is the current temperature, L0 is the initial grain size, d cell is the grid size, and Q is the activation energy.

[0078] Then the coarsening time step increment corresponding to the time increment Δt is:

[0079] Δt C (t) = t C (t)-t C (t-Δt).

[0080] In the time increment Δt, for each unit metal material, it is necessary to iterate Δt C (t) times, for any time, since additive manufacturing is a non-isothermal process, the Δt of different unit metal materials C (t) values are not the same, therefore, for all the unit metal materials in the heat affected zone, each unit metal material is selected one by one and the coarsening time step increment Δt is calculated. C (t), and obtain Δt C The maximum value of (t)Δt C max(t). This time increment Δt traverses each unit metal material in the heat affected zone and performs roughening simulation calculations. When each unit is traversed and calculated once, it is recorded as a roughening time step. The total roughening time step is Δt C max (t).

[0081] Step S42: For all the unit metal materials in the heat-affected zone, each unit metal material is selected one by one to determine whether the current unit metal material is located at a grain boundary (i.e., whether the grain identifier of the current unit metal material is consistent with the grain identifiers of all corresponding neighboring unit metal materials), thereby obtaining a fifth determination result. Once all the unit metal materials in the heat-affected zone have been selected, the coarsening time step is increased by one, and the process returns to step S46, "Determining whether the number of coarsening time steps in the heat-affected zone within the time increment Δt has reached the maximum number of iterations."

[0082] Step S43: When the fifth judgment result is yes, the unit metal material is not at the grain boundary, the grain identifier of the current unit metal material is not modified, and the next metal material is traversed and the process returns to step S42 to "determine whether the current unit metal material is at the grain boundary (that is, whether the grain identifier of the current unit metal material is consistent with the grain identifiers of all corresponding neighboring unit metal materials)".

[0083] Step S44: If the fifth determination result is negative, the unit metal material is located at a grain boundary, and the grain identifier of the unit metal material is pre-modified to a target grain identifier; the target grain identifier is the grain identifier of any neighboring unit metal material that is inconsistent with the grain identifier of the current unit metal material. The pre-modification here can be understood as a preliminary modification, not a final modification. The final modification result is determined only after the acceptance probability is determined to determine whether the pre-modification is accepted.

[0084] Step S45: calculating the acceptance probability according to the boundary energy difference, temperature influence probability and grain boundary migration probability before and after the grain identifier is modified, and determining whether the pre-modification of the grain identifier is accepted according to the acceptance probability.

[0085] The calculation formula for the acceptance probability is:

[0086]

[0087] Among them, P is the acceptance probability; p m is the probability of grain boundary migration related to grain boundary misorientation, θ ij is the grain boundary misorientation; θ mis the boundary value between high-angle grain boundaries and low-angle grain boundaries; ΔE is the boundary energy difference before and after the grain identifier is modified; k is the model constant used to reduce the anisotropy introduced by the cubic mesh. T is the temperature influence probability, which is used to consider the influence of different temperatures at different spatial positions on tissue coarsening. T The calculation formula is: Where, Δt C (t) is the coarsening time step increment; Δt C max (t) is the maximum value of the coarsening time step increment.

[0088] Since the pre-modified grain identifier is random, this modification may not conform to the laws of physics (i.e., grain coarsening proceeds in the direction of reducing the free energy of the system). Therefore, it is necessary to judge the rationality of the modification based on the energy change of the pre-modification and correct the modification result based on the acceptance probability (i.e., a reasonable pre-modification has a greater probability of being accepted, and an unreasonable pre-modification has a greater probability of being rejected). If the pre-modification is rejected, the modification is canceled.

[0089] Step S46: Determine whether the number of coarsening time steps of the heat-affected zone within the time increment Δt is the maximum number of iterations, and obtain a sixth judgment result; the maximum number of iterations is the maximum value of the coarsening time step increment Δt C max (t).

[0090] Step S47: If the sixth judgment result is no, return to step S42 of "selecting each unit metal material one by one for all the unit metal materials in the heat-affected zone."

[0091] Step S48: If the sixth judgment result is yes, the grain coarsening simulation at the current moment is completed.

[0092] S5: Determine whether the temperature of each of the unit metal materials is lower than the lower limit temperature of the heat affected zone, and obtain a first determination result.

[0093] If the first judgment result is no, set t=t+Δt and return to step S2.

[0094] If the first judgment result is yes, the solidification and coarsening simulation is completed.

[0095] The solidification process of metal materials occurs between the solidus and liquidus temperatures, while the coarsening process occurs between the lower limit temperature of the heat-affected zone and the solidus temperature. Therefore, when the temperature of all units in the calculation domain cools below the lower limit temperature of the heat-affected zone, the solidification and coarsening processes are completed and the simulation process ends.

[0096] In this embodiment, (1) the cellular automaton method and the Monte Carlo method are coupled. The coupled algorithm can simultaneously consider the solidification in the molten pool and the grain coarsening in the heat-affected zone, that is, solidification simulation and coarsening simulation can be performed simultaneously in one method flow. (2) The iterative time step of grain coarsening, which has no physical meaning, is coupled with the physical time of the cellular automaton. The solidification simulation and the coarsening simulation are performed on the same physical time scale, and the simulation results are more accurate. (3) The advantages of the two algorithms are integrated, that is, the computational efficiency is improved while also having a more complete physical basis.

[0097] Example 2

[0098] like Figure 6 As shown, this embodiment provides a simulation system for metal additive manufacturing grain structure solidification and coarsening, including:

[0099] The computational domain initialization module M1 is used to mesh the computational domain of the metal material, assign an initial grain identifier and crystal orientation to the unit metal material of each grid unit, select a preset number of the unit metal materials as potential nucleation points based on the nucleation density, and assign a critical undercooling degree to each potential nucleation point based on a Gaussian distribution;

[0100] The state region determination module M2 is configured to calculate the temperature field distribution of the calculation domain at the current time t; based on the current temperature field distribution, determine the region where the state of each unit metal material is located; the region is a molten pool region, a heat-affected zone, or a non-affected zone; the non-affected zone is an area outside the heat-affected zone;

[0101] a solidification process simulation module M3, configured to select each of the unit metal materials in the molten pool area one by one, and simulate the growth and capture process of the unit metal material and its neighboring unit metal materials using a cellular automation method according to the state and temperature of the unit metal material, and simulate the nucleation process based on whether the unit metal material is the potential nucleation point and its critical undercooling;

[0102] A roughening process simulation module M4 is used to select each unit metal material in the heat-affected zone one by one and simulate the roughening process using a Monte Carlo method;

[0103] A judgment module M5 is used to judge whether the temperature of each of the unit metal materials is lower than the lower limit temperature of the heat affected zone, and obtain a first judgment result;

[0104] If the first judgment result is no, set t=t+Δt, and return to the step of "calculating the temperature field distribution of the calculation domain at the current time t";

[0105] If the first judgment result is yes, the solidification and coarsening simulation is completed.

[0106] As for the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0107] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for simulating solidification and coarsening of grain structure in metal additive manufacturing, characterized in that: include: Meshing the calculation domain of the metal material, assigning an initial grain identifier and crystal orientation to the unit metal material of each mesh unit, selecting a preset number of the unit metal materials as potential nucleation points based on the nucleation density, and assigning a critical undercooling degree to each of the potential nucleation points based on a Gaussian distribution; Calculate the temperature field distribution of the calculation domain at the current time t; Determining the region where the state of each of the unit metal materials is located based on the current temperature field distribution; The area is a molten pool area, a heat-affected zone or a non-affected zone; the non-affected zone is an area outside the heat-affected zone; For all the unit metal materials in the molten pool area, each of the unit metal materials is selected one by one, and a cellular automation method is used to simulate the growth and capture process according to the state and temperature of the unit metal material and its neighboring unit metal materials, and a nucleation process is simulated according to whether the unit metal material is the potential nucleation point and its critical undercooling; For all the unit metal materials in the heat-affected zone, each of the unit metal materials is selected one by one, and a roughening process is simulated using a Monte Carlo method; When the unit metal material is in the non-affected area, the current unit metal material does not need to be simulated; Determine whether the temperature of each of the unit metal materials is less than the lower limit temperature of the heat affected zone, and obtain a first determination result; If the first judgment result is no, set t=t+Δt, and return to the step of "calculating the temperature field distribution of the calculation domain at time t"; If the first judgment result is yes, the solidification and coarsening simulation is completed.

2. The method according to claim 1, characterized in that The expression for calculating the temperature field distribution of the calculation domain at the current time t is: Where T0 is the starting temperature; q and v are the power and velocity of the heat source; r(y, z) is the distance from the grid unit to the heat source path; λ is the thermal conductivity; α is the thermal diffusivity; ξ = x-vt is the distance from the grid unit to the center of the heat source in the scanning direction; x, y, and z are the coordinate values of the center of the grid unit.

3. The method according to claim 2, characterized in that The determining, based on the temperature field distribution, the region where the state of each of the unit metal materials is located specifically includes: For each of the unit metal materials, determining whether the temperature of the unit metal material is greater than or equal to the solidus limit temperature of the material, and obtaining a second determination result; When the second judgment result is yes, determining that the state of the unit metal material is in the molten pool area; When the second judgment result is no, it is determined whether the temperature of the unit metal material is greater than or equal to the lower limit temperature of the heat-affected zone of the material; if so, it is determined that the current state of the unit metal material is in the heat-affected zone; if not, it is determined that the current state of the unit metal material is in the non-affected zone.

4. The method according to claim 1, wherein The cellular automation method is used to simulate the growth and capture process according to the state and temperature of the unit metal material and its neighboring unit metal materials, and the nucleation process is simulated according to whether the unit metal material is the potential nucleation point and its critical undercooling, specifically including: Calculating the undercooling degree of the unit metal material according to the current temperature of the unit metal material and the liquidus limit temperature of the material; For each of the unit metal materials, determining the state of the unit metal material according to the degree of undercooling; When the degree of supercooling is less than 0, the central envelope of the unit metal material is deleted and the state of the unit metal material is changed to liquid; When the degree of supercooling is greater than or equal to 0, the nucleation process simulation and the growth and capture process simulation are performed according to the state of the unit metal material and whether the unit metal material is the potential nucleation point.

5. The method according to claim 4, characterized in that The performing of nucleation process simulation and growth and capture process simulation according to the state of the unit metal material and whether the unit metal material is the potential nucleation point specifically includes: When the state of the unit metal material is liquid and the unit metal material is the potential nucleation point, determining whether the undercooling degree of the unit metal material is greater than or equal to the critical undercooling degree to obtain a third determination result; If the third judgment result is yes, then generating an octahedral envelope with a random orientation at the center of the current unit metal material; If the third judgment result is no, the nucleation process simulation is not performed on the current unit metal material; When the state of the unit metal material is solid, determining whether there is a liquid state in the state of a neighboring unit metal material of the unit metal material to obtain a fourth determination result; If the fourth judgment result is yes, the size of the envelope in the unit metal material is updated; when the center of the liquid neighboring unit metal material is within the envelope in the unit metal material, the liquid neighboring unit metal material is captured by the envelope in the unit metal material; If the fourth judgment result is no, the size of the envelope in the unit metal material is not updated, and the envelope in the unit metal material stops growing.

6. The method according to claim 5, characterized in that The updating of the size of the envelope in the unit metal material specifically includes: L t+Δt =L t +u(ΔT)Δt u(ΔT)=a1ΔT+a2ΔT 2 +a3ΔT 3 Among them, L t With L t+Δt are the envelope size modules at time t and t+Δt respectively; v(ΔT) is the envelope growth rate; a1, a2, a3 are fitting parameters; ΔT is the supercooling degree; Δt is the time increment of the current time iteration step of the cellular automaton model.

7. The method according to claim 1, characterized in that The simulation of the roughening process using the Monte Carlo method specifically includes: Calculating a coarsening time step, and calculating a coarsening time step increment within a time increment in a cellular automation method based on the coarsening time step; For all the unit metal materials in the heat-affected zone, each of the unit metal materials is selected one by one; for each of the unit metal materials, it is determined whether the grain identifier of the current unit metal material is consistent with the grain identifier of the corresponding neighboring unit metal material, and a fifth determination result 2 is obtained. When the fifth judgment result is yes, the unit metal material is not located at the grain boundary, and the grain identifier of the current unit metal material is not modified; If the fifth judgment result is no, the unit metal material is at a grain boundary, and the grain identifier of the unit metal material is pre-modified to a target grain identifier; the target grain identifier is the grain identifier of any neighboring unit metal material that is inconsistent with the grain identifier of the current unit metal material; Calculating an acceptance probability based on the boundary energy difference, temperature influence probability, and grain boundary migration probability before and after the grain identifier is modified, and determining whether the pre-modification of the grain identifier is accepted based on the acceptance probability; Determine whether the number of coarsening traversals of the heat-affected zone within the time increment Δt is a maximum number of iterations, to obtain a sixth determination result; the maximum number of iterations is the maximum value of the coarsening time step increment; If the sixth judgment result is no, return to step "selecting each unit metal material one by one for all the unit metal materials in the heat-affected zone"; If the sixth judgment result is yes, the grain coarsening simulation at the current moment is completed.

8. The method according to claim 7, characterized in that The calculation formula of the acceptance probability is: Among them, P is the acceptance probability; p m is the probability of grain boundary migration related to grain boundary misorientation, θ ij is the grain boundary misorientation; θ m is the boundary value between high-angle grain boundaries and low-angle grain boundaries; ΔE is the boundary energy difference before and after the grain identifier is modified; k is the model constant.

9. The method according to claim 7, characterized in that The calculation formula for the temperature influence probability is: Where Δt C (t) is the coarsening time step increment; Δt C max (t) is the maximum value of the coarsening time step increment.

10. A system based on the method according to any one of claims 1 to 9, characterized in that: include: a computational domain initialization module, configured to mesh the computational domain of the metal material, assign an initial grain identifier and crystal orientation to the unit metal material of each mesh unit, select a preset number of the unit metal materials as potential nucleation points based on the nucleation density, and assign a critical undercooling degree to each of the potential nucleation points based on a Gaussian distribution; a state region determination module, configured to calculate the temperature field distribution of the calculation domain at a current time t; determine the region where the state of each unit metal material is located based on the current temperature field distribution; the region being a molten pool region, a heat-affected zone, or a non-affected zone; the non-affected zone being an area outside the heat-affected zone; a solidification process simulation module, configured to use a cellular automation method to simulate the growth and capture process and the nucleation process when the unit metal material is in the molten pool area according to the state and temperature of the unit metal material and its neighboring unit metal materials, as well as whether the unit metal material is the potential nucleation point and its critical undercooling; a roughening process simulation module, configured to simulate the roughening process using a Monte Carlo method when the unit metal material is in the heat-affected zone; a judgment module, configured to judge whether the temperature of each of the unit metal materials is less than the lower limit temperature of the heat-affected zone, and obtain a first judgment result; If the first judgment result is no, set t=t+Δt, and return to the step of "calculating the temperature field distribution of the calculation domain at the current time t"; If the first judgment result is yes, the solidification and coarsening simulation is completed.

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