Laser additive manufacturing process-organization-performance cross-scale modeling method and system

Abstract

The application belongs to the technical field of material deformation analysis, and provides a laser additive manufacturing process-structure-performance cross-scale modeling method and system, which comprises establishing a discrete dislocation dynamics model, simulating the movement behavior of dislocations under external load under different temperature field-strain rate field conditions, establishing a quantitative relationship between dislocation flux and temperature field and strain rate field, establishing a cyclic crystal plasticity constitutive model based on dislocation density, calling the dislocation flux generation model to obtain dislocation flux in each time increment step, updating dislocation density and material state variables according to the dislocation flux, and coupling phase field damage evolution; through cross-scale mapping of dislocation flux between the discrete dislocation dynamics model and the cyclic crystal plasticity constitutive model coupled with phase field damage evolution, the evolution of material mechanical properties is predicted, and the process parameters are fed back and optimized. The problem of space-time discontinuity from discrete dislocation dynamics to cyclic crystal plasticity model is solved.

Description

Technical Field

[0001] This invention belongs to the field of material deformation analysis technology, and in particular relates to a cross-scale modeling method and system for laser additive manufacturing processes, microstructures and properties. Background Technology

[0002] As an advanced digital manufacturing technology, laser additive manufacturing determines the final performance of its formed parts based on defects, grain structure, phase composition, and their evolution behavior generated during the process. These phenomena span multiple scales, from microscopic dislocations and fine grains to macroscopic components. Therefore, establishing a cross-scale model that connects "process-structure-performance" is crucial for achieving integrated design and performance control of materials and components.

[0003] At both macroscopic and microscopic scales, various modeling methods have been developed for phase and microstructure evolution during laser additive manufacturing. Thermodynamic (CALPHAD)-based models are widely used to predict phase formation in non-equilibrium solidification processes of multi-component alloy systems. For example, they have successfully predicted the evolution of Laves phases and intermetallic compounds and their impact on hardness in Ni / Fe, Ti / Ni gradient materials, or dual alloy systems. Regarding defect formation, researchers have developed powder-scale melt pool dynamics models to reveal the correlation between melt flow, heat and mass transfer, and the formation of defects such as porosity and lack of fusion. Continuum-scale melt pool models are further used to analyze the influence of process parameters on the forming stability and defects of thin-walled parts. For microstructure simulation, the phase-field method offers high accuracy but incurs significant computational costs. Cellular automata and Monte Carlo methods each have their own advantages in terms of computational efficiency and simulation capabilities, and have been used to simulate grain growth and texture evolution.

[0004] However, existing research largely focuses on predicting the relationship between "process" and "microstructure." A mechanical model that truly integrates "process," "microstructure," and "performance" remains a significant challenge in the integrated fabrication of materials, microstructure, and properties. Crystal plasticity theory, due to its ability to correlate microstructural characteristics with macroscopic mechanical responses, has become a crucial microscopic link for achieving this integration. For example, by coupling the phase-field method with crystal plasticity models, the influence of grain structure on the mechanical properties and anisotropy of laser-added titanium alloys can be predicted. Of particular note is the recent research revealing the dominant role of unique dislocation structures (such as dislocation cells) in the thermal stability and mechanical behavior of laser-added metals. This indicates that a deeper understanding of the origin of properties requires delving into the dislocation scale. Discrete dislocation dynamics can precisely describe the short-range interactions of dislocations. Combining this with higher-scale models provides a new approach to revealing the correlation mechanism between dislocation evolution and mechanical properties in laser-added heterogeneous materials (such as Cu / Ni alloys).

[0005] To achieve efficient cross-scale computation, indirect coupling strategies have shown advantages. By mapping or transferring hardening patterns or dislocation density evolution information obtained from dislocation dynamics simulations to crystal plasticity models, a predictive bridge can be built from dislocation motion to macroscopic stress-strain response. This has been successfully applied to reveal the influence of dislocation multiplication and annihilation processes on the strong plastic behavior of materials. However, laser additive manufacturing processes are accompanied by complex rapid temperature changes and thermo-mechanical cycles. Under specific conditions, the short-range reaction mechanism of dislocations is even more complex, and how it affects the cyclic plastic deformation and damage accumulation of materials still lacks cross-scale mechanical characterization and understanding.

[0006] This challenge is particularly pronounced under extreme conditions. For example, for rocket engine components that require long lifespan and reusability, the fatigue performance of materials under cyclic loading is crucial. Although various fatigue performance prediction methods exist, ranging from engineering stress-life models and fracture mechanics-based fatigue crack propagation models to microscopic mechanism-based phase-field fracture models, crystal plastic fatigue models, and data-driven machine learning models, none of them fully consider the cross-scale intrinsic correlation between the microstructure of laser additive-manufactured heterogeneous materials in thermo-mechanical cyclic plastic deformation and damage, and it is difficult to reveal the correlation mechanism between short-range dislocation responses and low-cycle fatigue performance. Summary of the Invention

[0007] To address at least one of the technical problems mentioned in the background, this invention provides a cross-scale modeling method and system for laser additive manufacturing processes, microstructures, and properties. It utilizes "dislocation flux," which is related to strain rate and temperature, to describe the short-range velocity of dislocations under varying temperatures, and constructs a quantitative functional relationship between this flux and dislocation density evolution. This achieves a transfer mapping of "dislocation short-range response - dislocation flux - dislocation density," resolving the spatiotemporal discontinuity problem in the transition from discrete dislocation dynamics to cyclic crystal plasticity models. Compared to direct coupling, this method avoids the insufficiency of real-time synchronous updates of spatiotemporally relevant physical quantities at the micro and mesoscale, exhibiting high efficiency. Furthermore, it easily couples more complex dislocation short-range responses, demonstrating strong scalability.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A first aspect of the present invention provides a method for cross-scale modeling of laser additive manufacturing processes, microstructures, and properties, comprising the following steps:

[0010] To obtain crystallographic information of target metallic materials for laser additive manufacturing and temperature-strain rate field data experienced by the materials during the laser additive manufacturing process;

[0011] Based on crystallographic information, a discrete dislocation dynamics model is established. Under different temperature and strain rate conditions, the motion behavior of dislocations under external load is simulated. A quantitative relationship between dislocation flux and temperature and strain rate fields is established, and a dislocation flux generation model is constructed.

[0012] A cyclic crystal plastic constitutive model based on dislocation density is established. In each time increment step, the dislocation flux generation model is called to obtain the dislocation flux. The dislocation density and material state variables are updated according to the dislocation flux, and the phase field damage evolution is coupled.

[0013] Laser additive manufacturing process parameters are input into the dislocation flux generation model through temperature and strain rate fields. By mapping the dislocation flux across scales between the discrete dislocation dynamics model and the cyclic crystal plastic constitutive model of coupled phase field damage evolution, the evolution of material mechanical properties is predicted, and the process parameters are optimized.

[0014] Furthermore, the crystallographic information of the target metallic material for laser additive manufacturing includes crystal structure type, slip system type, Burgers vector, dislocation line direction, and typical dislocation type, which are used to determine the basic geometric constraints for dislocation motion and plastic deformation.

[0015] Furthermore, the establishment of the discrete dislocation dynamics model based on crystallographic information includes determining the dislocation segment type based on the angle between the long straight dislocation segment near the grain boundary and the Burgers vector, determining the dislocation reaction type, and determining the slip plane where the dislocation is generated based on the crystal structure of the metallic material.

[0016] Furthermore, the dislocation segment types include screw dislocations and mixed dislocations. The Burgers vector b is determined by the extinction law of TEM, and the dislocation segment type is determined by combining the orientation of the Burgers vector b and the dislocation line direction u. If the Burgers vector b is parallel to the dislocation line direction u, the dislocation segment type is a screw dislocation; if the Burgers vector b is perpendicular to the dislocation line direction u, the dislocation segment type is an edge dislocation; if the Burgers vector b is neither parallel nor perpendicular to the dislocation line direction u, the dislocation segment type is a mixed dislocation, which has characteristics of both screw and edge dislocations.

[0017] Furthermore, when simulating the motion behavior of dislocations under external loads under different temperature-strain rate field conditions, the kinematic equations of dislocations under variable temperature and tensile-shear cyclic loading are constructed and the distorted stress field during the motion of the dislocation segment under tensile-shear cyclic loading is obtained. Combining the kinematic and stress field descriptions of dislocations under variable temperature and tensile-shear cyclic loading, the short-range response of dislocations is reconstructed, and the discrete dislocation dynamics simulation results are obtained, including the average motion velocity of dislocations on each slip system and the evolution characteristics of dislocation line length.

[0018] Furthermore, dislocation flux Defined as the length of an effective dislocation line passing through a unit area per unit time, its mathematical expression is:

[0019] ,

[0020] in, For local equivalent dislocation density, For temperature, To divide shear stress, For strain rate, The mean slip velocity of dislocations. , The characteristic velocity constant of dislocations, For dislocation thermal activation energy, The stress coefficient is... It is Burgers vector modulus.

[0021] Furthermore, the increase in dislocation density caused by dislocation flux It is a function of dislocation flux and time increment, expressed as:

[0022] ,

[0023] in, Δt is the dislocation flux, Δt is the time increment, and C is a scaling factor related to material type and interface characteristics, which can be determined through experimental calibration or discrete dislocation dynamics simulation.

[0024] A second aspect of the present invention provides a cross-scale modeling system for laser additive manufacturing processes, microstructures, and properties, comprising:

[0025] The data acquisition module is used to acquire crystallographic information of the target metallic material in laser additive manufacturing and temperature-strain rate field data experienced by the material during the laser additive manufacturing process;

[0026] The microscopic model building module is used to establish a discrete dislocation dynamics model based on crystallographic information. It simulates the motion behavior of dislocations under external loads under different temperature and strain rate field conditions, establishes a quantitative relationship between dislocation flux and temperature and strain rate fields, and constructs a dislocation flux generation model.

[0027] The macroscopic model building module is used to establish a cyclic crystal plastic constitutive model based on dislocation density. In each time increment step, the dislocation flux generation model is called to obtain the dislocation flux. The dislocation density and material state variables are updated according to the dislocation flux, and the phase field damage evolution is coupled.

[0028] The cross-scale mapping module is used to input laser additive manufacturing process parameters into the dislocation flux generation model through temperature and strain rate fields. By mapping the dislocation flux across scales between the discrete dislocation dynamics model and the cyclic crystal plastic constitutive model of coupled phase field damage evolution, the module predicts the evolution of material mechanical properties and feeds back to optimize process parameters.

[0029] A third aspect of the present invention provides a computer-readable storage medium.

[0030] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps in the cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described above.

[0031] A fourth aspect of the present invention provides a computer device.

[0032] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps in the cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described above.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] This invention utilizes "dislocation flux," which is related to strain rate and temperature, to describe the short-range velocity of dislocations under varying temperatures. It also constructs a quantitative functional relationship between this flux and the evolution of dislocation density, achieving a transfer mapping from "dislocation short-range reaction to dislocation flux to dislocation density." This solves the problem of spatiotemporal discontinuity in the transition from discrete dislocation dynamics to a cyclic crystal plasticity model. Compared to direct coupling, this method avoids the insufficiency of real-time synchronous updates of spatiotemporally relevant physical quantities at the micro and mesoscale, thus exhibiting high efficiency. Furthermore, it easily couples more complex dislocation short-range reactions, demonstrating strong scalability.

[0035] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0036] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0037] Figure 1 This is a flowchart of the cross-scale modeling method for laser additive manufacturing process-microstructure-properties provided in this embodiment of the invention;

[0038] Figure 2 This is the coupling process of cyclic crystal plasticity and phase field damage provided in the embodiments of the present invention. Detailed Implementation

[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0040] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0041] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0042] Example 1

[0043] like Figure 1 As shown, this embodiment provides a cross-scale modeling method for laser additive manufacturing processes, microstructures, and properties, including the following steps:

[0044] Step 1: Obtain crystallographic information of the target metallic material for laser additive manufacturing and temperature-strain rate field data experienced by the material during the laser additive manufacturing process;

[0045] Among them, crystallographic information can be obtained through transmission electron microscopy, electron backscatter diffraction or existing material databases; temperature field and strain rate field can be obtained through numerical thermo-mechanical simulation of laser additive manufacturing process, or equivalent characterization of temperature field and strain rate field can be performed through in-situ temperature measurement and inversion methods.

[0046] Specifically, the crystallographic information of the target metallic material in laser additive manufacturing includes crystal structure type, slip system type, Burgers vector, dislocation line direction and typical dislocation type, which are used to determine the basic geometric constraints for dislocation motion and plastic deformation.

[0047] Specifically, the thermo-mechanical history of materials during laser additive manufacturing is equivalent to the temperature field at a point in the material, representing the process characteristics such as rapid heating, cooling, and cyclic heat input formed during laser scanning. With strain rate field ,in, This indicates the spatial coordinates of a material point within the component. This represents the time variable in the laser additive manufacturing process;

[0048] Step 2: Based on crystallographic information, establish a discrete dislocation dynamics model, simulate the short-range dislocation response under different temperature field-strain rate field conditions, establish the quantitative relationship between dislocation flux and temperature field and strain rate field, and construct a dislocation flux generation model;

[0049] Specifically, the steps include the following:

[0050] Step 201: Based on the obtained crystallographic information, establish a discrete dislocation dynamics model describing the movement and interaction of dislocations within the grain and near grain boundaries / phase boundaries, and clarify the short-range reaction mechanisms such as dislocation slip, cross-slip, annihilation, and interface reactions.

[0051] The construction of the discrete dislocation dynamics model includes determining the type of dislocation segment based on the angle between the long straight dislocation segment near the grain boundary and the Burgers vector, determining the reaction type of the dislocation, and determining the slip plane where the dislocation is generated based on the crystal structure of the metallic material.

[0052] In this embodiment, the dislocation segment types include screw dislocations and mixed dislocations. The Burgers vector b is determined by the extinction law of TEM. The dislocation segment type is determined by combining the orientation of the Burgers vector b and the dislocation line direction u. If the Burgers vector b is parallel to the dislocation line direction u, the dislocation segment type is a screw dislocation; if the Burgers vector b is perpendicular to the dislocation line direction u, the dislocation segment type is an edge dislocation; if the Burgers vector b is neither parallel nor perpendicular to the dislocation line direction u, the dislocation segment type is a mixed dislocation, which has characteristics of both screw and edge dislocations.

[0053] In this embodiment, the reaction type of dislocations is determined based on the energy minimization criterion and the Burgers vector conservation principle. The energy minimization criterion states that the total energy of the dislocations after the reaction should be less than the total energy of the dislocations before the reaction, because this is the energy-advantageous aspect of the reaction. The specific determination process is prior art and is not the focus of this application, so it will not be described in detail.

[0054] In this embodiment, the principle of maximizing partial shear stress is adopted when determining the slip plane where the newly generated dislocation is located based on the crystal structure of the metallic material, including:

[0055] Based on the crystal structure type, determine the set of all possible slip systems for the crystal, where each slip system is defined by the slip plane and the slip direction located on the slip plane;

[0056] When the crystal structure is face-centered cubic, the slip system set is {111}. <110> ;

[0057] When the crystal structure is body-centered cubic, the slip system set is {110}. <111> ;

[0058] When the crystal structure is a close-packed hexagonal structure, the slip system set is {0001}. <1120> .

[0059] For each slip system in the set of slip systems, the corresponding partial shear stress is calculated based on the applied load stress. The calculation formula is as follows:

[0060] , ,

[0061] in, For the first i Partial shear stress of a slip system For external load stress, For the first i Schmid factor of a slip system The normal unit vector of the slip surface. The unit vector in the direction of slip. This is the unit vector in the direction of the load.

[0062] Compare the partial shear stresses corresponding to all slip systems, and select the target slip system with the maximum partial shear stress; determine the slip plane defined by the target slip system as the slip plane for the newly generated dislocation.

[0063] In this embodiment, the short-range reaction mechanisms such as dislocation slip, cross-slip, annihilation, and interface reaction are clearly defined, including the reaction criteria of dislocations at grain boundaries / phase boundaries and the topological evolution criteria of short-range dislocation reactions at grain boundaries and phase boundaries.

[0064] Among them, when establishing the reaction criterion of dislocations at grain boundaries / phase boundaries, the phase boundary is characterized as a large-angle grain boundary by considering the relationship between phase boundaries and traditional grain boundaries in terms of energy and orientation difference between adjacent grains, thus realizing a unified description from phase boundaries to traditional grain boundaries. Based on the dislocation-grain boundary reaction criterion, the parameters of the dislocation-phase boundary reaction criterion are determined.

[0065] The short-range dislocation reaction process and dislocation formation are modified to determine the topological evolution criteria for short-range dislocation reactions at grain boundaries and phase boundaries. In this embodiment, considering the influence of temperature effects on different dislocation motion velocities, the initial activation energy of dislocations is introduced into the energy criterion calculation to modify the above-mentioned short-range dislocation reaction process and dislocation formation. The modification principle is that the dislocation reaction must satisfy the geometric condition (Burse's vector conservation) and the static energy condition (total energy decrease after the reaction). Temperature and velocity are modified, with temperature affecting the thermodynamic barrier of dislocation motion through the activation energy, and velocity reflecting the kinetic resistance through the correction coefficient. The effective energy is calculated by combining the type of dislocation and the modified temperature and velocity, making the reaction criterion more consistent with actual working conditions.

[0066] Step 202: Simulate the motion behavior of dislocations under external loads under different temperature-strain rate field conditions to obtain discrete dislocation dynamics simulation results;

[0067] In this embodiment, during the specific simulation, the kinematic equations of dislocations under variable temperature and tensile-shear cyclic loads are constructed, and the distorted stress field during the movement of the dislocation segment under tensile-shear cyclic loads is obtained by solving them. Combined with the kinematic and stress field descriptions of dislocations under variable temperature and tensile-shear cyclic loads, the short-range response of dislocations is reconstructed to obtain the discrete dislocation dynamics simulation results.

[0068] The process of constructing the kinematic equations of dislocations under variable temperature and tensile-shear cyclic loading is as follows:

[0069] Based on the dislocation motion equation with coupled inertial terms, the superposition method with the dislocation segment length as the weight is used to calculate the force state of the node, and then the node motion velocity is calculated. The discrete dislocation kinematic equation under variable temperature and tension-shear cycle is proposed.

[0070] Finally, the average motion velocity of dislocations in each slip system and the evolution characteristics of dislocation line length were calculated. Through numerical simulation of dislocation slip, cross-slip and dislocation-interface reaction processes, the effective dislocation line length and the corresponding average motion velocity of dislocations per unit time were statistically analyzed.

[0071] Step 203: Based on the results of discrete dislocation dynamics simulation, construct a quantitative relationship between dislocation flux, dislocation density, and dislocation velocity;

[0072] Dislocation flux is obtained by dividing the effective dislocation line length by the characteristic area of ​​the statistical volume element, and further correlated with temperature and strain rate under corresponding conditions. To improve computational efficiency, the relationship between dislocation flux and temperature and strain rate can be obtained in advance through multiple sets of discrete dislocation dynamics simulations. The relationship between dislocation flux and temperature and strain rate is stored in function form or lookup table form to form a dislocation flux generation model for subsequent macroscopic model calls.

[0073] Specifically, dislocation flux is the product of local equivalent dislocation density and average dislocation velocity, and is a continuous physical quantity related to temperature and strain rate; the average dislocation velocity is determined by the thermodynamic behavior of dislocations under the combined action of partial shear stress and thermal activation, and is expressed as:

[0074] Dislocation flux Defined as the length of an effective dislocation line passing through a unit area per unit time, its mathematical expression is:

[0075]

[0076] in, For local equivalent dislocation density, For temperature, To divide shear stress, For strain rate, The mean slip velocity of dislocations. , The characteristic velocity constant of dislocations, For dislocation thermal activation energy, The stress coefficient is... It is Burgers vector modulus.

[0077] Step 3: Establish a cyclic crystal plastic constitutive model based on dislocation density. In each time increment step, call the dislocation flux generation model to obtain the dislocation flux, update the dislocation density and material state variables accordingly, and couple the phase field damage evolution.

[0078] Specifically, the steps include the following:

[0079] Step 301: Establish a cyclic crystal plastic constitutive model based on dislocation density, and use dislocation density as an internal state variable to describe the hardening, softening and anisotropic evolution of the material;

[0080] It should be noted that the construction process of the cyclic crystal plastic constitutive model based on dislocation density is existing technology. This embodiment is based on the construction and analysis, but does not provide a detailed description of the specific construction process.

[0081] Step 302: In each time increment step of the cyclic crystal plastic constitutive model, the constructed dislocation flux generation model is invoked according to the current temperature field and stress state to obtain the dislocation flux under the corresponding conditions;

[0082] In each time increment step of the cyclic crystal plastic constitutive model, the partial shear stress on each slip system is first calculated based on the current stress state; then, combined with the current temperature and strain rate information of the material point, the dislocation flux generation model is called to obtain the dislocation flux value under the corresponding conditions. The call to the dislocation flux is performed in each time increment step of the cyclic crystal plastic model.

[0083] Step 303: Based on dislocation flux, calculate the dislocation density increment caused by short-range dislocation response, and superimpose this increment into the dislocation density evolution equation of each slip system in the crystal plasticity model to update internal state variables such as displacement resistance and back stress.

[0084] Dislocation flux is used to calculate the increase in dislocation density caused by short-range dislocation reactions. It can be expressed as a function of dislocation flux and time increment, and its form can be written as:

[0085] ,

[0086] in, Let be the dislocation flux, Δt be the time increment, and C be a scaling factor related to material type and interface characteristics. This scaling factor can be determined through experimental calibration or discrete dislocation dynamics simulation.

[0087] After completing the dislocation density update, the phase field damage model is driven based on the updated crystal plastic deformation results to achieve coupled calculation of plastic deformation and damage evolution.

[0088] Step 304: Based on the updated cyclic crystal plastic constitutive model, couple the phase field damage evolution;

[0089] In this embodiment, the cyclic crystal plastic constitutive model introduces phase field damage variables to describe the crack initiation and propagation process during coupled phase field damage evolution, thereby achieving coupled modeling of plastic deformation and damage evolution. During coupling, the fast Fourier method is used to solve the deformation gradient of elastic and inelastic deformation occurring at material points under instantaneous boundary conditions, which is more computationally efficient than the traditional Newton-Raphson iterative method.

[0090] Step 4: Input the laser additive manufacturing process parameters into the dislocation flux generation model through the temperature field and strain rate field. By mapping the dislocation flux across scales between the discrete dislocation dynamics model and the cyclic crystal plastic constitutive model of coupled phase field damage evolution, the evolution of material mechanical properties is predicted, and the process parameters are optimized.

[0091] In this embodiment, the laser additive manufacturing process parameters indirectly regulate the magnitude and distribution of dislocation flux by influencing the temperature field and strain rate field, thereby affecting the dislocation density evolution, crystal plastic response, and damage behavior.

[0092] By mapping dislocation flux across scales between the discrete dislocation dynamics model and the cyclic crystal plastic constitutive model of coupled phase field damage evolution, a chain relationship between short-range dislocation response and dislocation density evolution is established through the dislocation flux function. The chain relationship is then used to update the dislocation density and its related internal variables in the crystal plastic model.

[0093] Specifically, the deformation gradient of the established cyclic crystal plasticity model of coupled damage is described, and the velocity gradient under large deformation is decomposed into the deformation gradient configuration of the matrix region and the heterogeneous region. The deformation of the matrix region is described by the deformation gradient of the cyclic crystal plasticity model, while the deformation of the heterogeneous region is described by the short-range response of discrete dislocations and interface interactions.

[0094] For any slip system, based on the evolution equation of geometrically necessary dislocation density in the matrix region, the dislocation density evolution caused by the short-range reaction of discrete dislocations in the heterogeneous region is further introduced to update the geometrically necessary dislocation density in the matrix region. At the same time, the internal variables of the hardening criteria for partial shear stress, slip resistance, and back stress in the matrix region are updated, thus completing the construction of a cross-scale model from the dynamics of discrete dislocations in the heterogeneous region to the cyclic crystal plasticity in the matrix region.

[0095] The prediction results are fed back to optimize the laser additive manufacturing process parameters, thereby enabling the control of microstructure and properties. By changing process parameters such as laser power, scanning speed, or interlayer dwell time, the dislocation flux and the resulting microstructure evolution behavior can be adjusted, thereby enabling the prediction and optimization of the macroscopic mechanical properties of additively manufactured components.

[0096] This invention constructs a chain-like transmission relationship of "dislocation short-range reaction - dislocation flux - dislocation density evolution" and maps it to the dislocation density internal variable in the cyclic crystal plasticity model in a non-direct coupling manner, thereby achieving efficient cross-scale prediction.

[0097] Example 2

[0098] This embodiment provides a cross-scale modeling system for laser additive manufacturing processes, microstructures, and properties, including:

[0099] The data acquisition module is used to acquire crystallographic information of the target metallic material in laser additive manufacturing and temperature-strain rate field data experienced by the material during the laser additive manufacturing process;

[0100] The microscopic model building module is used to establish a discrete dislocation dynamics model based on crystallographic information. It simulates the motion behavior of dislocations under external loads under different temperature and strain rate field conditions, establishes a quantitative relationship between dislocation flux and temperature and strain rate fields, and constructs a dislocation flux generation model.

[0101] The macroscopic model building module is used to establish a cyclic crystal plastic constitutive model based on dislocation density. In each time increment step, the dislocation flux generation model is called to obtain the dislocation flux. The dislocation density and material state variables are updated according to the dislocation flux, and the phase field damage evolution is coupled.

[0102] The cross-scale mapping module is used to input laser additive manufacturing process parameters into the dislocation flux generation model through temperature and strain rate fields. By mapping the dislocation flux across scales between the discrete dislocation dynamics model and the cyclic crystal plastic constitutive model of coupled phase field damage evolution, the module predicts the evolution of material mechanical properties and feeds back to optimize process parameters.

[0103] It should be noted that the specific implementation of the cross-scale modeling system for laser additive manufacturing process-structure-property in this embodiment of the invention is similar to the specific implementation of the cross-scale modeling method for laser additive manufacturing process-structure-property in this embodiment of the invention. For details, please refer to the description in the method section. To reduce redundancy, it will not be repeated here.

[0104] Example 3

[0105] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described above.

[0106] Example 4

[0107] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described above.

[0108] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0109] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0110] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0111] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0112] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cross-scale modeling method for laser additive manufacturing processes, microstructures, and properties, characterized in that, Includes the following steps: To obtain crystallographic information of target metallic materials for laser additive manufacturing and temperature-strain rate field data experienced by the materials during the laser additive manufacturing process; Based on crystallographic information, a discrete dislocation dynamics model is established. Under different temperature and strain rate conditions, the motion behavior of dislocations under external load is simulated. A quantitative relationship between dislocation flux and temperature and strain rate fields is established, and a dislocation flux generation model is constructed. A cyclic crystal plastic constitutive model based on dislocation density is established. In each time increment step, the dislocation flux generation model is called to obtain the dislocation flux. The dislocation density and material state variables are updated according to the dislocation flux, and the phase field damage evolution is coupled. Laser additive manufacturing process parameters are input into the dislocation flux generation model through temperature and strain rate fields. By mapping the dislocation flux across scales between the discrete dislocation dynamics model and the cyclic crystal plastic constitutive model of coupled phase field damage evolution, the evolution of material mechanical properties is predicted and the process parameters are optimized. Among them, dislocation flux Defined as the length of an effective dislocation line passing through a unit area per unit time, its mathematical expression is: in, For local equivalent dislocation density, For temperature, To divide shear stress, For strain rate, The mean slip velocity of dislocations. , The characteristic velocity constant of dislocations, For dislocation thermal activation energy, The stress coefficient is... It is Burgers vector modulus.

2. The cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described in claim 1, characterized in that, The crystallographic information of the target metallic material for laser additive manufacturing includes crystal structure type, slip system type, Burgers vector, dislocation line direction, and typical dislocation type, which are used to determine the basic geometric constraints for dislocation motion and plastic deformation.

3. The cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described in claim 1, characterized in that, The establishment of a discrete dislocation dynamics model based on crystallographic information includes determining the type of dislocation segment and the reaction type of the dislocation based on the angle between the long straight dislocation segment near the grain boundary and phase boundary and the Burgers vector, and determining the slip plane where the dislocation is generated based on the crystal structure of the metallic material.

4. The cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described in claim 3, characterized in that, The dislocation segment types include screw dislocations and mixed dislocations. The Burgers vector b is determined by the extinction law of TEM. The type of dislocation segment is determined by combining the orientation of the Burgers vector b and the dislocation line direction u. If the Burgers vector b is parallel to the dislocation line direction u, the dislocation segment type is a screw dislocation; if the Burgers vector b is perpendicular to the dislocation line direction u, the dislocation segment type is an edge dislocation; if the Burgers vector b is neither parallel nor perpendicular to the dislocation line direction u, the dislocation segment type is a mixed dislocation, which has characteristics of both screw and edge dislocations.

5. The cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described in claim 1, characterized in that, When simulating the motion behavior of dislocations under external loads under different temperature-strain rate conditions, the kinematic equations of dislocations under variable temperature and tensile-shear cyclic loading are constructed and the distorted stress field during the motion of the dislocation segment under tensile-shear cyclic loading is obtained. By combining the kinematic and stress field descriptions of dislocations under variable temperature and tensile-shear cyclic loading, the short-range response of dislocations is reconstructed, and the discrete dislocation dynamics simulation results are obtained, including the average motion velocity of dislocations on each slip system and the evolution characteristics of dislocation line length.

6. The cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described in claim 1, characterized in that, Dislocation density increment caused by dislocation flux It is a function of dislocation flux and time increment, expressed as: ， in, Δt is the dislocation flux, Δt is the time increment, and C is a scaling factor related to material type and interface characteristics, which can be determined through experimental calibration or discrete dislocation dynamics simulation.

7. A cross-scale modeling system for laser additive manufacturing processes, microstructures, and properties, characterized in that, A cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described in any one of claims 1-6, comprising: The data acquisition module is used to acquire crystallographic information of the target metallic material in laser additive manufacturing and temperature-strain rate field data experienced by the material during the laser additive manufacturing process; The microscopic model building module is used to establish a discrete dislocation dynamics model based on crystallographic information. It simulates the motion behavior of dislocations under external loads under different temperature and strain rate field conditions, establishes a quantitative relationship between dislocation flux and temperature and strain rate fields, and constructs a dislocation flux generation model. The macroscopic model building module is used to establish a cyclic crystal plastic constitutive model based on dislocation density. In each time increment step, the dislocation flux generation model is called to obtain the dislocation flux. The dislocation density and material state variables are updated according to the dislocation flux, and the phase field damage evolution is coupled. The cross-scale mapping module is used to input laser additive manufacturing process parameters into the dislocation flux generation model through temperature and strain rate fields. By mapping the dislocation flux across scales between the discrete dislocation dynamics model and the cyclic crystal plastic constitutive model of coupled phase field damage evolution, the module predicts the evolution of material mechanical properties and feeds back to optimize process parameters.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps in the cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described in any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the cross-scale modeling method for laser additive manufacturing process-microstructure-properties as described in any one of claims 1-6.

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