阅读说明：本技术 一种基于晶粒形貌预测确定增材制造成形工艺参数的方法 (Method for determining additive manufacturing forming process parameters based on grain morphology prediction ) 是由 吴倩茹 唐文来 夏俊 于 2021-09-16 设计创作，主要内容包括：本发明公开了一种基于晶粒形貌预测确定增材制造成形工艺参数的方法,通过建立有限元热模型；仿真增材制造过程,获得成形零件的瞬态温度场分布结果；基于零件的瞬态温度场分布结果,提取观测点瞬态温度变化结果；计算观测点的冷却速度、温度梯度和凝固速度；将计算得到的观测点的温度梯度和凝固速度值绘制到凝固图上,预测晶粒形貌,最后根据预测晶粒形貌结果确定增材制造成形的最佳工艺参数。本发明相比于其他增材制造晶粒形貌模拟方法计算量较小,更为快捷方便,从而实现增材制造工艺参数优化,细化成形零件晶粒具有重要意义。(The invention discloses a method for determining additive manufacturing forming process parameters based on grain morphology prediction, which comprises the steps of establishing a finite element thermal model; simulating an additive manufacturing process to obtain a transient temperature field distribution result of a formed part; extracting an observation point transient temperature change result based on the transient temperature field distribution result of the part; calculating the cooling speed, the temperature gradient and the solidification speed of the observation point; and drawing the temperature gradient and the solidification speed value of the observation point obtained by calculation on a solidification diagram, predicting the morphology of the crystal grain, and finally determining the optimal process parameter of additive manufacturing and forming according to the result of predicting the morphology of the crystal grain. Compared with other additive manufacturing crystal grain shape simulation methods, the method has the advantages that the calculated amount is small, the method is faster and more convenient, the additive manufacturing process parameter optimization is realized, and the method has important significance in refining the formed part crystal grains.)

1. A method for determining additive manufacturing forming process parameters based on grain morphology prediction is characterized by comprising the following steps:

(1) establishing a finite element thermal model based on the initial process parameters of additive manufacturing forming and the sizes of the formed part and the substrate;

(2) simulating an additive manufacturing process to obtain a transient temperature field distribution result of a formed part;

(3) extracting a transient temperature change result of an observation point based on the transient temperature field distribution result of the part in the step (2);

(4) calculating the cooling speed, the temperature gradient and the solidification speed of the observation point;

(5) drawing the temperature gradient and the solidification speed value of the observation point obtained by calculation in the step (4) on a solidification diagram to realize the prediction of the shape of the formed part crystal grain under the group of process parameters;

(6) and judging whether the grain morphology of the formed part under the set of process parameters meets the design requirements, and continuously adjusting the process parameters to enable the formed part under the optimized process parameters to reach the expected grain morphology, thereby finally determining the optimal process parameters.

2. The method for determining the parameters of the additive manufacturing forming process based on the grain morphology prediction according to claim 1, wherein in the step (1), the finite element thermal model is a three-dimensional geometric model of an additive manufacturing part, and comprises a substrate and a forming part, wherein the substrate is of a three-dimensional block structure, and the part is a structural part formed by stacking a single-channel multilayer structure or multiple-channel multilayer structure from bottom to top; carrying out network division on the three-dimensional geometric model, wherein the model is divided by adopting full hexahedral meshes, and the areas close to the parts on the substrate are divided into more compact meshes, and the areas far away from the parts are divided into more sparse meshes; determining thermophysical performance parameters and forming process parameters of the substrate and the formed part; and setting heat source model parameters, and setting an initial temperature condition and a heat exchange boundary condition.

3. The method for determining the parameters of the additive manufacturing forming process based on the grain morphology prediction as claimed in claim 1, wherein in the step (2), the simulated additive manufacturing process simulates the additive manufacturing process by a 'dead cell' method, and a group of discrete cells are sequentially activated according to the moving path of the high energy beam, so as to realize the point-by-point cladding of the metal material; after the model parameters are set, the transient temperature field distribution result of the formed part is obtained by solving a thermal analysis control equation.

4. The method for determining the parameters of the additive manufacturing forming process based on the grain morphology prediction as recited in claim 1, wherein in the step (3), the observation point selects the key position in the forming part, and the transient temperature change result of the observation point is extracted; the key position is more than one, including the position of the center of the body of the part and the center of the upper surface of the part.

5. The method for determining the parameters of the additive manufacturing forming process based on the grain morphology prediction as claimed in claim 1, wherein in the step (4), based on the transient temperature change result of the observation point, the solidification cooling speed, the temperature gradient and the solidification speed of the metal material at the observation point are obtained;

wherein the solidification cooling rate of the metal material is defined by the following formula:

wherein, T_SAnd T_LRespectively representing the solid phase temperature and the liquid phase temperature of the metal material; t is t_SAnd t_LRespectively representing the time for the metal material to reach the solid phase temperature and the liquid phase temperature; when the metal material undergoes a plurality of thermal cycles, selecting the last thermal cycle curve data which is higher than the solidus temperature to calculate the solidification cooling speed of the metal material;

the shaped part being at t ═ t_LThe temperature gradient G at time is given by Fourier's law:

wherein q represents a heat flow vector, which is calculated from finite element simulation results; k is the liquidus temperature T_LThe thermal conductivity of the lower metal material; the solidification rate R is calculated from the cooling rate and the temperature gradient at the time of solidification:

6. the method for determining the parameters of the additive manufacturing forming process based on the grain shape prediction as claimed in claim 1, wherein in the step (5), the temperature gradient and the solidification speed value calculated at the observation point are drawn on a metal solidification diagram, and the grain shape prediction of the part is realized according to the microstructure area on the solidification diagram where the point is located.

7. The method for determining additive manufacturing forming process parameters based on grain morphology prediction as claimed in claim 1, wherein in step (6), if the part formed under the initial process parameters reaches the expected grain morphology, the forming of the part is completed using the set of process parameters; otherwise, adjusting the technological parameters during part forming, returning to the step (1) to perform modeling and grain morphology prediction again until the expected grain morphology is achieved, and finally determining the optimal technological parameters.

Technical Field

The invention belongs to the technical field of additive manufacturing, and particularly relates to a method for determining additive manufacturing forming process parameters based on grain morphology prediction.

Background

Additive manufacturing (also known as 3D printing) is a technology for realizing direct manufacturing of parts by adopting a material layer-by-layer accumulation mode according to a three-dimensional model based on a discrete and accumulation principle. Compared with traditional material reduction manufacturing (such as turning, milling and the like), the technology is a principle change and has the advantages of no need of a die, quick response, high material utilization rate, capability of forming any complex component and the like, wherein the metal material additive manufacturing technology has huge development potential and application prospect, is an important strategic direction of the development of the material increase manufacturing technology in China, and is widely applied to the fields of aerospace, automobiles, medical treatment and the like.

Metal additive manufacturing is largely classified into laser, electron beam, and arc additive manufacturing techniques according to the heat source used. The electric arc additive manufacturing adopts electric arcs as heat sources, metal wires which are synchronously fed are melted according to a set track, and the layer-by-layer manufacturing of metal parts is realized. Compared with laser and electron beam additive manufacturing technologies, arc additive manufacturing has the technical advantages of high forming efficiency, low cost, large size and the like, however, a large molten pool exists during the forming of the metal component manufactured by the arc additive manufacturing, the formed part usually has a coarse grain structure, and the growth morphology of a solid-liquid interface is an important factor influencing the performance of the alloy structure and the formation of defects in the alloy solidification process. Therefore, accurate prediction of the grain morphology of the additive manufacturing part is realized, and the method is beneficial to manufacturing of high-performance metal structural parts.

In metal additive manufacturing, the temperature distribution and the cooling speed distribution of a part are extremely uneven, so that the microstructure distribution of the part has great difference. At present, the method for acquiring the crystal grain morphology of the additive manufacturing part mainly comprises a physical experiment method and a numerical simulation method.

The experimental method is to perform three-dimensional Electron Back Scattering Diffraction (EBSD) analysis on the microstructure of the additive manufacturing formed part, which consumes a lot of time and manpower, and is based on destructive analysis of the formed part. The material microstructure numerical simulation method mainly comprises a phase field model, a cellular automata method, a dynamic Monte Carlo method and the like. The phase field model can be used for simulating a dendritic crystal formation process, a coarsening phenomenon, a solid phase transition and other microstructure evolution processes in a material solidification process, and has certain advantages in the aspect of quantitatively describing the morphology of the dendritic crystal. However, because of its large computational complexity, it is mainly used to simulate solving two-dimensional problems and can only simulate a small area scale. The cellular automata method can directly simulate the growth process of the dendritic crystal morphology outline, is a mesoscopic scale simulation method of a material microstructure with wider application, and is a method for solving and calculating on the basis of the known transient temperature field distribution result, and meanwhile, the solution grid for simulating the material microstructure is finer, so that longer calculation time is still needed. The dynamic Monte Carlo method is a random algorithm based on the principle of minimum free energy of grain boundaries, and a model is additionally introduced to establish the correlation between the temperature field and time in the simulation process and the real temperature information and time. The microstructure simulation method for the additive manufacturing part generally has the problems of large calculation amount, long calculation time and the like.

Disclosure of Invention

The invention aims to solve the technical problem of the prior art and provides a method for determining additive manufacturing forming process parameters based on a temperature field distribution-based additive manufacturing part crystal grain morphology prediction result.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

a method for determining additive manufacturing forming process parameters based on grain morphology prediction comprises the following steps:

(1) establishing a finite element thermal model based on the initial process parameters of additive manufacturing forming and the sizes of the formed part and the substrate;

(2) simulating an additive manufacturing process to obtain a transient temperature field distribution result of a formed part;

(3) extracting a transient temperature change result of an observation point based on the transient temperature field distribution result of the part in the step (2);

(4) calculating the cooling speed, the temperature gradient and the solidification speed of the observation point;

(5) drawing the temperature gradient and the solidification speed value of the observation point obtained by calculation in the step (4) on a solidification diagram to realize the prediction of the shape of the formed part crystal grain under the group of process parameters;

(6) and judging whether the grain morphology of the formed part under the set of process parameters meets the design requirements, and continuously adjusting the process parameters to enable the formed part under the optimized process parameters to reach the expected grain morphology, thereby finally determining the optimal process parameters.

Specifically, in the step (1), the finite element thermal model is a three-dimensional geometric model of an additive manufacturing part, and comprises a substrate and a forming part, wherein the substrate is of a three-dimensional block structure, and the part is a structural member formed by sequentially stacking a single-channel multilayer or multiple-channel multilayer from bottom to top; carrying out network division on the three-dimensional geometric model, wherein the model is divided by adopting full hexahedral meshes, and the areas close to the parts on the substrate are divided into more compact meshes, and the areas far away from the parts are divided into more sparse meshes; determining thermophysical performance parameters and forming process parameters of the substrate and the formed part, and calculating the required thermophysical performance parameters including density, latent heat of fusion, solid-phase temperature and liquid-phase temperature, thermal conductivity and specific heat which change along with the temperature, and the like; and setting heat source model parameters, and setting an initial temperature condition and a heat exchange boundary condition.

Specifically, in the step (2), in the additive manufacturing experiment, the high-energy beam continuously melts the fed metal wire according to a set path to realize the point-by-point forming of the part. In the simulation additive manufacturing process, a 'living and dead unit' method is adopted to simulate the additive manufacturing process, and a group of discrete units are sequentially activated according to the moving path of a high-energy beam, so that point-by-point cladding of the metal material is realized; after the model parameters are set, the transient temperature field distribution result of the formed part is obtained by solving a thermal analysis control equation.

Specifically, in the step (3), the temperature field distribution result of the formed part is analyzed, the key position in the formed part is selected as an observation point, and the transient temperature change result of the observation point is extracted. Selecting key positions in the formed part by the observation points, and extracting transient temperature change results of the observation points; the key positions are more than one, including the positions of the center of the body of the part, the center point of the upper surface of the part and the like.

Specifically, in the step (4), based on the transient temperature change result of the observation point, the solidification cooling speed, the temperature gradient and the solidification speed of the metal material at the observation point are obtained;

wherein the solidification cooling rate of the metal material is defined by the following formula:

wherein, T_SAnd T_LRespectively representing the solid phase temperature and the liquid phase temperature of the metal material; t is t_SAnd t_LRespectively representing the time for the metal material to reach the solid phase temperature and the liquid phase temperature; when the metal material undergoes a plurality of thermal cycles, selecting the last thermal cycle curve data which is higher than the solidus temperature to calculate the solidification cooling speed of the metal material;

the shaped part being at t ═ t_LThe temperature gradient G at time is given by Fourier's law:

wherein q represents a heat flow vector, i.e., heat energy per unit time per unit area, calculated from finite element simulation results; k is the liquidus temperature T_LThe thermal conductivity of the lower metal material; the solidification rate R is calculated from the cooling rate and the temperature gradient at the time of solidification:

specifically, in the step (5), the solidification map of the metal material is calculated and drawn by referring to a criterion formula of the transformation of the crystal grain morphology of the metal material from columnar crystal to isometric crystal, which is a method known in the art.

Specifically, in the step (5), the temperature gradient and the solidification speed value calculated at the observation point are drawn on the metal solidification diagram, and the shape of the part crystal grain is predicted according to the microstructure area on the solidification diagram where the point is located.

Specifically, in the step (6), if the part formed under the initial process parameters reaches the expected crystal grain appearance, the group of process parameters is adopted to complete the forming of the part; otherwise, adjusting the process parameters such as power, scanning speed and the like during part forming, returning to the step (1) to perform modeling and grain morphology prediction again until the expected grain morphology is achieved, and finally determining the optimal process parameters.

Has the advantages that:

the method is based on a finite element model technology, adopts a 'living and dead unit' method to simulate the part additive manufacturing process, obtains the temperature field distribution result of the formed part, calculates the solidification cooling speed, the temperature gradient and the solidification speed of the metal material at the observation point by analyzing the transient temperature change result of the observation point, realizes the prediction of the part crystal grain appearance after comparing and analyzing the metal solidification diagram, realizes the optimization of process parameters during the part forming, and obtains the part with the expected crystal grain appearance. Compared with other additive manufacturing crystal grain shape simulation methods, the method is smaller in calculation amount and is quicker and more convenient, so that the method has important significance for realizing optimization of additive manufacturing process parameters and refining formed part crystal grains.

Drawings

The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.

FIG. 1 is a schematic flow diagram of the present invention.

FIG. 2 is a schematic illustration of an additive manufactured three-pass three-layer part in an embodiment of the invention.

Fig. 3 is a schematic diagram of a full hexahedral mesh partition model for additive manufacturing of three-layer parts according to an embodiment of the present invention.

Fig. 4 is a schematic view of cladding of a metallic material.

Figure 5 is a plot of temperature versus time at point a of three-layer formed parts.

FIG. 6 is a solidification diagram of Ti-6Al-4V titanium alloy.

Detailed Description

The invention will be better understood from the following examples.

It should be noted that the method for predicting the grain morphology of the additive manufactured part provided by the present invention is applicable to various high-energy beam metal additive manufacturing technologies, including laser, electron beam, or arc additive manufacturing technologies, and the present embodiment takes a simulated arc additive manufacturing technology as an example.

The grain morphology prediction method can be suitable for simulating various metal materials, including titanium alloy, nickel-based alloy, stainless steel, aluminum alloy, magnesium alloy, iron-based alloy or high-entropy alloy and the like. In the embodiment, the substrate material is Ti-6Al-4V titanium alloy, the forming part material is Ti-6Al-4V titanium alloy, and an electric arc additive manufacturing process is simulated by utilizing Abaqus finite element software.

The method for predicting the grain morphology of the multilayer multi-channel arc additive manufacturing metal part provided by the embodiment is mainly as shown in fig. 1, and comprises the following steps:

step one, establishing a finite element thermal model:

the method comprises the steps of establishing a three-dimensional geometric model of the additive manufacturing part, as shown in figure 2, wherein the three-dimensional geometric model comprises a substrate and a forming part, the substrate is a Ti-6Al-4V titanium alloy plate, the length of the substrate is 200mm, the width of the substrate is 100mm, the height of the substrate is 5mm, the forming part is a Ti-6Al-4V titanium alloy part formed by sequentially stacking three layers, and each melting channel is 80mm in length.

The method comprises the steps of carrying out network division on a three-dimensional geometric model, wherein the model adopts an eight-node hexahedron linear heat conduction unit (DC3D8), and as a part and a surrounding area thereof have higher temperature gradient and cooling speed in the additive manufacturing process, in order to give consideration to model precision and calculation efficiency, a gradually-changed grid is adopted, a more compact grid is divided in an area close to the part on a substrate, and a less dense grid is divided in an area far away from the part, as shown in figure 3. Thermophysical performance parameters of the substrate and the shaped part were determined including density, latent heat of fusion, solid phase temperature (1878K) and liquid phase temperature (1928K), and thermal conductivity and specific heat as a function of temperature. Meanwhile, determining the forming process parameters of the part, as shown in the table 1; and selecting a double-ellipsoid heat source model as a heat source model for simulating the electric arc additive manufacturing process, and setting an initial temperature of 25 ℃ and convection and radiation boundary conditions for the substrate.

TABLE 1 part Forming Process parameters

Step two, simulating an additive manufacturing process:

in order to realize the point-by-point increasing process of the metal material during additive forming, a 'dead unit' method is adopted in simulation to simulate the additive manufacturing process, namely a group of discrete units are orderly activated according to the moving path of a high-energy beam, so that the point-by-point cladding of the metal is realized. Fig. 4 is a simulated schematic of a metal additive manufacturing process, with the bottom grey cell being the substrate, the top transparent cell representing the "killed" cell, and the blue cell being the deposited metal. As the heat source is moved gradually, the corresponding cell where the heat source arrives is gradually "activated", as shown by the red cell (Set-1) indicating that the arc is cladding the metal there. After the model parameters are set, a thermal analysis model file for numerical calculation can be generated, calculation is executed by adopting an Abaqus command prompt window, and a transient temperature field distribution result and a heat flow vector distribution result of the formed part are obtained by solving a thermal analysis control equation.

Extracting the transient temperature change result of the observation point:

and selecting the end position of the first layer middle melting channel as an observation point, such as a point A in fig. 3, based on the distribution result of the transient temperature field of the part. Analyzing the temperature change curve with time at the point, wherein the point undergoes eight thermal cycles as shown in figure 5, and the point undergoes 1 st to 2 nd peak temperatures when a first layer melting channel is deposited; when a second layer of melting channel is deposited, the point experiences the peak temperature of 3-5 times; and when the third layer of melting channel is deposited, the point experiences 6 th-8 th peak temperature. The first peak temperature is higher than the melting point and is the highest point of the eight peak temperatures, which indicates that when the heat source passes through the point, the metal is completely melted into liquid; when the fourth peak temperature is reached, the heat source is moved to the second layer intermediate melt path, the temperature at that point exceeds the solidus temperature, the metal remelts, and the subsequent peak temperatures are all less than the solidus temperature, indicating that the point will only experience post-heat effects when the subsequent melt path is deposited.

Step four, calculating the cooling speed, the temperature gradient and the solidification speed of the observation point:

and (4) calculating the cooling speed, the temperature gradient and the solidification speed of the point A by selecting a fourth peak temperature falling edge section through analyzing the temperature change curve of the point A at the observation point along with the time. To explain the calculation process of the cooling rate, the temperature gradient and the solidification rate at point a more clearly, the fourth peak temperature curve is separately extracted for explanation, as shown in fig. 5 b. Solid phase temperature T of Ti-6Al-4V titanium alloy_S1878K, liquidus temperature T_L1928K, point A is cooled to liquidus temperature T after the fourth peak temperature_LAnd temperature T of solid phase_SThe time taken was 480.252s (t) respectively_L) And 480.422s (t)_S). Therefore, the solidification cooling rate of the metallic material at the observation point a is:

based on the heat flow vector distribution result obtained by solving the thermal model in the step two, extracting the point A of the observation point at t-t_LA heat flow vector q of 10081510W/m²Therefore, the point A metal material is t ═ t_LThe temperature gradient G is:

k is the liquidus temperature T_LThermal conductivity of lower metallic material, for Ti-6Al-4V titanium alloyIn other words, the value is 34W m^{- 1}K^-1. According to the cooling rate and the temperature gradient result when the observation point A is solidified, the solidification speed R can be solved as follows:

step five, drawing the temperature gradient and the solidification speed value of the observation point on a solidification diagram, and predicting the appearance of the crystal grain:

according to the calculation results in the fourth step, the temperature gradient G of the observation point A is 2965K/cm, the solidification speed R is 0.08226cm/s, the temperature gradient and the solidification speed value of the observation point A are drawn on the solidification diagram of the Ti-6Al-4V titanium alloy, and as shown in FIG. 6, the observation point A is located in a columnar crystal area, which shows that the crystal grain appearance of the arc additive manufacturing forming part at the observation point A is columnar crystal under the process parameters shown in Table 1. Through the temperature field simulation, parameter calculation and solidification map drawing processes, the prediction of the appearance of the crystal grains of the electric arc additive manufacturing part is realized.

Step six, judging whether the grain morphology of the formed part under the set of process parameters meets the design requirements, and finishing a part forming experiment:

according to the drawing result of the solidification diagram of the Ti-6Al-4V titanium alloy in the fifth step, the part formed under the process parameters shown in the table 1 has a columnar crystal morphology and meets the expected crystal grain morphology requirement of the columnar crystal grains, so that the forming of the Ti-6Al-4V titanium alloy part is completed by adopting the process parameters shown in the table 1.

The present invention provides a method and a concept for determining additive manufacturing forming process parameters based on grain morphology prediction, and a method and a way for implementing the technical solution are many, and the above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, a plurality of improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. All the components not specified in the present embodiment can be realized by the prior art.