Simulation method for determining laser powder bed fusion printing process for single-crystal austenitic stainless steel

Abstract

The present application relates to the field of additive manufacturing, and discloses a simulation method for determining a laser powder bed fusion printing process for single-crystal austenitic stainless steel. The simulation method comprises the following steps: S1, establishing a three-dimensional polycrystalline powder bed microstructure model; S2, establishing a three-dimensional temperature field prediction model on the basis of a finite element method; S3, establishing a three-dimensional polycrystalline microstructure evolution model by using a phase-field method; and, S4, coupling the models from steps S1, S2 and S3, and simulating a multi-layer, multi-pass laser powder bed fusion process by using different process parameters, so as to determine appropriate single-crystal process parameters. The present invention replaces conventional trial-and-error experimental methods with numerical simulation methods to determine single-crystal process parameters, thereby greatly reducing the time and economic costs of process exploration, and assisting experiments in revealing the microstructure evolution mechanism of single crystal preparation via laser powder bed fusion additive manufacturing.

Description

A simulation method for determining a laser powder bed fusion single-crystal austenitic stainless steel printing process TECHNICAL FIELD

[0001] The present application relates to the technical field of additive manufacturing, in particular to a simulation method for determining a laser powder bed fusion single-crystal austenitic stainless steel printing process. BACKGROUND

[0002] Compared with polycrystalline materials with many grain boundaries, single-crystal materials have excellent mechanical properties at high temperatures due to the lack of grain boundaries and grain boundary strengthening elements. In addition, due to the elimination of grain boundaries that cause segregation, the oxidation and hot corrosion resistance of single-crystal materials has also been significantly improved. Therefore, the development of single-crystal 316L austenitic stainless steel manufacturing technology not only can realize the lightweight of 316L engineering structure and tap the potential of materials, but also can prolong the service life of related parts in the field of nuclear power and chemical industry. At the same time, this technology is also one of the important ways to realize the intelligent manufacturing of functional driven high-end materials.

[0003] However, traditional single-crystal preparation methods such as investment casting method have some problems, such as strict temperature control and complex process flow, resulting in high cost and low yield. Therefore, it is of great significance to find a more efficient and economical preparation method for the application of single-crystal materials.

[0004] Laser powder bed fusion technology can precisely control the microstructure by adjusting process parameters to manufacture products with unique characteristics. However, a large number of process parameters hinder the understanding of microstructure evolution, thereby limiting the ability to customize microstructure. Although this technology has been proven to be useful for preparing single-crystal 316L austenitic stainless steel materials, the current process parameters are still determined by trial and error experiments. In this research field, there is a lack of theoretical support, and there is no report on using simulation to predict the manufacturing of single-crystal materials. There is a lack of reports on using phase field simulation to predict the manufacturing of single-crystal materials.

[0005] SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a simulation method for determining a laser powder bed fusion single-crystal austenitic stainless steel printing process, which can predict the formation of single-crystal microstructure and help deepen the understanding of the microstructure evolution mechanism of laser powder bed fusion technology.

[0007] To achieve the above purpose, the present application is implemented by the following technical scheme: a simulation method for determining a laser powder bed fusion single-crystal austenitic stainless steel printing process, comprising the following steps:

[0008] S1, establishing a microstructure model of a three-dimensional polycrystalline powder bed;

[0009] S2, establishing a three-dimensional temperature field prediction model based on the finite element method;

[0010] S3, establishing a three-dimensional polycrystalline microstructure evolution model using the phase field method;

[0011] S4, coupling the models of steps S1, S2 and S3, simulating the multi-layer and multi-pass laser powder bed fusion process using different process parameters to determine suitable single crystal process parameters.

[0012] Preferably, the step S1 comprises the following steps:

[0013] S11, constructing a three-dimensional spatial network distribution of microstructure morphology based on the Voronoi tessellation method;

[0014] S12, generating two sizes of Voronoi unit organizations to simulate the process of powder laying on the substrate;

[0015] S13, generating a series of crystallographic orientations in three-dimensional space and assigning these crystallographic orientation information to the Voronoi unit.

[0016] Preferably, the heat transfer equation in the finite element model in the step S2 follows the formula:

[0017] Where, p is the density of the material, c is the specific heat, T is the temperature, Q(x, y, z, t) is the heat generation per unit volume, and k is the thermal conductivity.

[0018] Preferably, the step S2 includes setting the temperature boundary condition of the simulation region at the initial time, which includes uniformly setting the temperature of all points in the simulation region to a predetermined initial temperature value at the beginning of the simulation.

[0019] Preferably, the phase field equation in the step S3 satisfies the definition of the local free energy density function:

[0020] Where, a, b and c are constants satisfying the conditions a = b > 0 and c > b / 2.

[0021] Preferably, the step S3 further includes using the Ginzburg-Landau equation to describe the numerical evolution of the order parameter.

[0022] Preferably, the process parameters in the step S4 include laser power, scanning speed and powder layer thickness.

[0023] The present application also provides a simulation device for determining the printing process of laser powder bed fusion single crystal austenitic stainless steel, comprising:

[0024] A micro-model construction unit is configured to establish a micro-model of a three-dimensional polycrystalline powder bed;

[0025] A temperature field prediction unit is configured to establish a three-dimensional temperature field prediction model based on a finite element method;

[0026] A microstructure evolution simulation unit is configured to establish a three-dimensional polycrystalline microstructure evolution model by using a phase field method;

[0027] A coupling control unit is configured to couple the micro-model construction unit, the temperature field prediction unit and the microstructure evolution simulation unit, simulate a multi-layer and multi-pass laser powder bed melting process by using different process parameters, and determine suitable single crystal process parameters.

[0028] The application further provides a computer device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method described above is realized.

[0029] The application further provides a storage medium having a computer program stored thereon, and when the processor executes the computer program, the method described above is realized.

[0030] The application provides a simulation method for determining a laser powder bed melting single crystal austenitic stainless steel printing process.

[0031] 1、The application uses a numerical simulation method instead of a traditional experimental trial and error method to determine single crystal process parameters, which greatly reduces the time and economic cost of process exploration, and assists in revealing the microstructure evolution mechanism of laser powder bed melting additive manufacturing for preparing single crystals. This provides an effective technical means for obtaining better performance structural parts and improving the design efficiency of laser powder bed melting additive manufacturing.

[0032] 2、The application uses a three-dimensional environment to simulate the temperature field and microstructure evolution process, compared with a two-dimensional simulation scheme, the method can explicitly consider the influence of the interaction between multiple layers and multiple passes on the microstructure evolution, and can more effectively predict the formation process of single crystal microstructure. BRIEF DESCRIPTION OF DRAWINGS

[0033] Fig. 1 is a flowchart of the prediction method of the application;

[0034] Fig. 2 is a schematic diagram of the micro-model of the three-dimensional polycrystalline powder bed of the application;

[0035] Fig. 3 is a result diagram of the finite element simulation temperature field of the application;

[0036] Fig. 4 is a result diagram of the laser powder bed melting simulation microstructure under different process parameters of the application and a corresponding experimental result diagram;

[0037] Fig. 5 is a schematic diagram of the device structure of the present application;

[0038] Fig. 6 is a schematic diagram of the computer device structure of the present application.

[0039] Wherein, 100, microstructure construction unit; 200, temperature field prediction unit; 300, microstructure evolution simulation unit; 400, coupling control unit; 40, computer device; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the specification of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0041] Please refer to Figs. 1-4, the embodiment of the present application provides a simulation method for determining the printing process of laser powder bed fused single crystal austenitic stainless steel, which comprises the following steps:

[0042] S1, establishing a microstructure model of a three-dimensional polycrystalline powder bed;

[0043] The core of this step is to accurately simulate the microstructure of the powder bed. For this purpose, the Voronoi tessellation method is used to construct the three-dimensional network distribution of the microstructure morphology in space. This method can effectively simulate the random arrangement and size distribution of powder particles, and provide an accurate basis for subsequent temperature field and microstructure evolution simulation.

[0044] In this embodiment, step S1 specifically comprises the following steps:

[0045] S1.1, based on the Voronoi tessellation method, a three-dimensional network distribution of microstructure morphology in space is constructed, and is divided into eight-node hexahedral elements. The specific implementation of Voronoi tessellation is completed by calling the open source software library Neper.

[0046] S1.2, according to the grain size of the substrate and the powder, two sizes of Voronoi unit structures are generated, which are stacked to simulate the process of powder laying on the substrate.

[0047] S1.3, a series of crystallographic orientations in three-dimensional space are generated, and these crystallographic orientation information is assigned to the Voronoi unit, and the finally formed powder bed microstructure model is shown in Fig. 2.

[0048] S2, a three-dimensional temperature field prediction model is established based on the finite element method, and different process parameters are used for multi-layer and multi-pass simulation;

[0049] This step utilizes finite element analysis techniques to simulate the temperature changes during the interaction of the laser with the powder bed. By adjusting different process parameters such as laser power, scanning speed, and powder layer thickness and scanning spacing, a multi-layer and multi-pass simulation in a three-dimensional environment can be performed, accurately predicting the temperature distribution inside each region and on the interface. This is crucial for understanding and controlling the thermal effects during laser melting.

[0050] In this embodiment, the heat transfer equation based on thermal conduction during laser powder bed melting can be defined as:

[0051] where ρ is the density of the material, c is the specific heat, T is the temperature, Q(x, y, z, t) is the heat generation per unit volume, and k is the thermal conductivity.

[0052] The temperature field during laser powder bed melting, in addition to the main heat transfer equation based on thermal conduction, refines the model to include initial boundary conditions for temperature and consider the effects of thermal radiation and heat convection.

[0053] At the beginning of the simulation, the temperature of all points in the simulation area is uniformly set to a predetermined initial temperature value T0. This setting is crucial for the accuracy of the simulation, as it provides a benchmark for subsequent temperature changes. The mathematical expression of the initial temperature boundary condition is: t＝0 T(x, y, z, 0) = T0(x, y, z) ∈ D

[0054] Here, D represents the simulation area, and T0 is the given initial temperature, which can be set according to actual conditions.

[0055] To further improve the accuracy and realistic approximation of the simulation, this embodiment also considers the effects of thermal radiation and heat convection on temperature distribution. The addition of these factors makes the temperature model more complex but also more realistic. The corresponding heat balance equation is:

[0056] where T amb is the ambient temperature, q is the heat flux, ε is the thermal radiation coefficient, h is the heat convection coefficient, and σ is the Stephen-Boltzmann constant. The above finite element equations are implemented based on the commercial software ABAQUS.

[0057] Further, this embodiment describes the simulation method of laser input during laser powder bed melting. By using a Gaussian distribution to describe the laser input, this embodiment can more accurately simulate the thermal effects during the interaction of the laser with the powder bed, and thus more accurately predict the temperature field and microstructure evolution during the melting process.

[0058] The laser input model adopts a Gaussian distribution to describe, the specific expression is as follows:

[0059] Where A represents the powder heat absorption rate, p is the laser power, r is the laser beam radius, and η is the laser penetration depth. X, Y and Z represent the coordinates of the center region of the Gaussian heat source.

[0060] In order to realize the simulation of the whole printing process, the embodiment adopts a multi-layer multi-channel laser scanning simulation method. In this method, laser scanning is carried out layer by layer, and each layer may contain multiple scanning channels. The implementation details of laser scanning are as follows:

[0061] In the multi-layer multi-channel model, when the laser scans along the x direction, the parameter X is defined as X=x-vt, where v is the scanning speed and t is the current time.

[0062] Y is defined as the Y-axis coordinate value on different scanning tracks. In addition, Z is defined as the product of n and h, where n represents the current layer and h represents the thickness of each powder layer.

[0063] The implementation of moving heat source uses the user subroutine DFLUX of ABAQUS.

[0064] In order to simulate the printing process of multi-layer structure, the embodiment adopts the birth and death element method. In this method, when the laser scans the current layer n, only the simulation area of the previous n layers is activated, and other layers are set to inactive state. This method can effectively simulate the step-by-step addition and solidification process of each layer of material. The temperature field result of the simulation is shown in Figure 3.

[0065] Through the above-mentioned Gaussian distribution laser input model and multi-layer multi-channel simulation method, the embodiment can accurately simulate the heat input distribution in the laser melting process, improve the accuracy of the simulation. At the same time, effectively simulate the temperature change and microstructure evolution in the printing process of multi-layer structure. And provide a theoretical basis and simulation tool for optimizing the laser powder bed melting process parameters, and then improve the quality and performance of the printed parts.

[0066] In summary, the simulation method provided by the embodiment not only can provide important technical support for the research and optimization of laser powder bed melting single crystal austenitic stainless steel printing process, but also provides valuable reference for the research and application of other related additive manufacturing technologies.

[0067] S3, a three-dimensional polycrystalline microstructure evolution model is established by using a phase field method;

[0068] The phase field method is a powerful mathematical model based on thermodynamic theory that can describe and predict the microstructure evolution during material phase transformation. Through this method, the grain growth and orientation selection of single crystal materials during laser melting can be simulated, providing a theoretical basis for optimizing single crystal growth conditions.

[0069] In the phase field model part of this embodiment, the microstructure changes during the phase transformation within the simulation region are described by introducing an order parameter φ. The introduction of the order parameter allows the model to express the existence of different phases in a continuous medium and the evolution of phase interfaces, which is crucial for understanding and predicting the evolution of material microstructure.

[0070] In the model, a continuous order parameter φ is used to represent the information of different phases in the material. For each point r and time t, φ i indicates the degree of existence of the i-th crystallographic orientation at that point. When φ i = 1, it means that the position is completely a solid phase of the i-th crystallographic orientation; while φ i = 0 indicates that the position is a liquid phase.

[0071] The local free energy density function f0 is expressed by the order parameter φ, which is defined as follows:

[0072] Here, a, b and c are normal numbers that satisfy the conditions a = b > 0 and c > b / 2 to ensure the stability and reasonableness of the system energy.

[0073] The total free energy functional F(t) is the integral of the local free energy density function f0 over the entire simulation region, and also includes the interface energy term caused by the gradient of the order parameter, which is expressed as:

[0074] where κ q represents the interface energy coefficient related to the q-th crystallographic orientation, and w is the weight coefficient to balance the contributions between different terms.

[0075] Through the precise description of the local free energy and its functional, the phase field model can capture the subtle changes in the material phase transformation process, providing an accurate method for simulating the evolution of microstructure. And by introducing the gradient term of the order parameter, the model can describe and predict the behavior of the phase interface, including the migration and evolution of the interface, which is crucial for understanding the micro-mechanism of material performance.

[0076] In the further refinement of the phase field model, this embodiment introduces a series of key equations and definitions to describe the evolution of the microstructure of the material, especially the simulation of grain growth and grain boundary behavior.

[0077] The coefficient k qUsed to describe the energy barrier height associated with the grain boundary energy, its dependence on crystallographic orientation is given by the following equation:

[0078] Here, k q0 is the base interfacial energy coefficient, α is a constant describing the strength of material anisotropy, and denotes the angle between the specific orientation of the grain and its growth direction. In this way, the model can take into account the effect of grain orientation on interfacial energy and phase interface behavior.

[0079] The time evolution of the order parameter is described by the Ginzburg-Landau equation, which is the core of the phase field model, defined as:

[0080] Here, L q (T) is a temperature-dependent kinetic constant that enables the model to capture the effect of temperature changes on the evolution of the microstructure.

[0081] The kinetic constant L q (T) is defined as follows:

[0082] where, and m are constants, ΔE represents the activation energy for grain growth, T a is the reference temperature, R g is the gas constant. This definition reflects the complex dependence of grain growth rate on temperature.

[0083] The numerical solution of the phase field equation is implemented through Python programming, using the forward Euler algorithm for time discretization and the central second-order difference algorithm for spatial discretization. This numerical method enables the model to simulate the evolution of the microstructure in an efficient and accurate manner.

[0084] Through these advanced models and numerical methods, this embodiment can accurately simulate grain boundary behavior and grain growth: considering the effect of grain orientation and temperature on grain boundary behavior and grain growth, providing a powerful tool for understanding and controlling these processes. At the same time, by introducing a temperature-dependent kinetic constant, the model can accurately predict the evolution of the microstructure under different heat treatment conditions. Finally, using Python programming and appropriate numerical algorithms, the phase field equation can be effectively solved, providing a feasible simulation tool for engineering applications.

[0085] The comprehensive application of these methods and techniques enables this embodiment not only to provide in-depth theoretical support for materials science research, but also to provide practical solutions for material design and process optimization in industrial applications.

[0086] S4, coupling the models of steps S1, S2 and S3, simulating with different process parameters to determine the appropriate single crystal process parameters.

[0087] Step S4 is the core of this embodiment, which involves the comprehensive coupling of the microstructure model, temperature field prediction model and microstructure evolution model established in the previous steps, and the evaluation of their impact on the formation of single crystal microstructure by simulating different process parameter combinations, so as to determine the optimal single crystal process parameters.

[0088] In this embodiment, the laser power is fixed at 200 W, the scanning speed is 1000 mm / s, and the powder layer thickness is 35 μm as the basic process parameters, while the influence of three different scanning spacings (50 μm, 70 μm and 90 μm) on the printing process is investigated. These parameters are selected based on the finite element model established in step S2 to calculate the corresponding temperature history.

[0089] By interpolating the temperature history calculated in step S2 in time and space, the temperature data is ensured to match the grid size of the microstructure model established in step S1 and the time increment step in the calculation of the phase field equation in step S3. This processing ensures the consistency and comparability of data between different models.

[0090] Using the three-dimensional powder bed microstructure model generated in step S1 as the simulation area, and using the temperature field calculated in step S2 as the input, the phase field equation of step S3 is used to simulate the evolution of the microstructure. This comprehensive simulation method can reflect the specific influence of different process parameters on the formation of single crystal microstructure.

[0091] As shown in Figure 4, the simulation results show that the scanning spacing is one of the key factors affecting the evolution of the microstructure. This embodiment found that a scanning spacing of 70 μm is conducive to the formation of single crystal microstructure, while increasing or decreasing the scanning spacing leads to the microstructure showing polycrystalline characteristics. This finding is verified by corresponding experiments, and the experimental results show similar trends to the simulation, further confirming the effectiveness and practicality of this method.

[0092] This embodiment couples different models and simulates the influence of different process parameters, not only providing precise process parameter guidance for the preparation of single crystal austenitic stainless steel, but also providing a theoretical basis and experimental verification for the preparation of similar materials in the field of additive manufacturing. This method can help engineers and researchers optimize the printing process and improve the quality and performance of printed parts, especially in the fields of aerospace and nuclear energy where high-performance single crystal materials are needed.

[0093] The simulation device for determining a laser powder bed fusion single-crystal austenitic stainless steel printing process described below can be correspondingly referred to the simulation method for determining a laser powder bed fusion single-crystal austenitic stainless steel printing process described above.

[0094] Referring to FIG. 5, the present application further provides a simulation device for determining a laser powder bed fusion single-crystal austenitic stainless steel printing process, comprising:

[0095] A micro-model construction unit 100 is configured to establish a micro-model of a three-dimensional polycrystal powder bed;

[0096] A temperature field prediction unit 200 is configured to establish a three-dimensional temperature field prediction model based on a finite element method, and perform multi-layer and multi-pass simulation using different process parameters;

[0097] A microstructure evolution simulation unit 300 is configured to establish a three-dimensional polycrystal microstructure evolution model using a phase field method;

[0098] A coupling control unit 400 is configured to couple the models of the micro-model construction unit 100, the temperature field prediction unit 200 and the microstructure evolution simulation unit 300, and perform simulation using different process parameters to determine suitable single-crystal process parameters.

[0099] The device of the present embodiment can be used to perform the above-mentioned method embodiments, and has similar principles and technical effects, which will not be described here again.

[0100] Referring to FIG. 6, the present application further provides a computer device 40, comprising a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor 41, and the computer program is executed by the processor 41 to perform the above-mentioned method.

[0101] The present application further provides a storage medium 43, wherein the storage medium 43 stores a computer program, and the computer program is executed by the processor 41 to perform the above-mentioned method.

[0102] The storage medium 43 can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic or optical disk.

[0103] While embodiments of the present application have been shown and described with reference to particular embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the application. The scope of the application is defined by the appended claims and their equivalents.

Claims

1. A method of determining a simulation of a laser powder bed fusion single crystal austenitic stainless steel printing process, characterized by, The method comprises the following steps: S1, establishing a microstructure model of a three-dimensional polycrystalline powder bed; S2, establishing a three-dimensional temperature field prediction model based on a finite element method; S3, establishing a three-dimensional polycrystalline microstructure evolution model by using a phase field method; S4, coupling the models of steps S1, S2 and S3, and simulating a multi-layer and multi-pass laser powder bed melting process by using different process parameters to determine suitable single crystal process parameters.

2. The method of claim 1, wherein the method is characterized by: The step S1 comprises the following steps: S11, constructing a three-dimensional network distribution microstructure morphology based on a Voronoi tessellation method; S12, generating two sizes of Voronoi unit organizations for simulating powders and substrates; S13, generating a series of crystallographic orientations in a three-dimensional space, and assigning the crystallographic orientation information to the Voronoi units.

3. The method of claim 1, wherein: The heat transfer equation in the finite element model in the step S2 follows the formula: Wherein, ρ is the density of the material, c is the specific heat, T is the temperature, Q(x, y, z, t) is the heat generation per unit volume, and k is the thermal conductivity.

4. The method of claim 3, wherein the method is characterized by: The step S2 comprises setting a temperature boundary condition of a simulation region at an initial time, and the temperature boundary condition comprises uniformly setting the temperature of all points in the simulation region to a predetermined initial temperature value at the beginning of the simulation.

5. The method of claim 1, wherein: The phase field equation in the step S3 satisfies the definition of a local free energy density function: Wherein, a, b and c are constants satisfying the conditions of a = b > 0 and c > b / 2.

6. The method of claim 5, wherein: The step S3 further comprises using a Ginzburg-Landau equation to describe the numerical evolution of the order parameter.

7. The method of claim 1, wherein: The process parameters in the step S4 include laser power, scanning speed, powder layer thickness and scanning spacing.

8. A simulation device for determining a laser powder bed fusion single crystal austenitic stainless steel printing process for carrying out the method according to any one of claims 1 to 7, characterized in that It comprises: a microstructure model construction unit for establishing a microstructure model of a three-dimensional polycrystalline powder bed; a temperature field prediction unit for establishing a three-dimensional temperature field prediction model based on a finite element method; a microstructure evolution simulation unit for establishing a three-dimensional polycrystalline microstructure evolution model by using a phase field method; a coupling control unit for coupling the models of the microstructure model construction unit, the temperature field prediction unit and the microstructure evolution simulation unit, and simulating a multi-layer and multi-pass laser powder bed melting process by using different process parameters to determine suitable single crystal process parameters.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method of any one of claims 1-7.

10. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the method of any one of claims 1-7.

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