Discrete element modeling method and system for compaction behavior of printing material

Abstract

The invention belongs to the related technical field of printing material mechanics research, and discloses a discrete element modeling method and system for the compaction behavior of a printing material, and the method comprises the steps: S1, building a discrete element model of the compaction behavior of the printing material, and determining to-be-optimized parameters in the model and the range of the to-be-optimized parameters; establishing an objective function according to a deviation between a numerical calculation result obtained by discrete element model simulation and a test measurement result; s2, establishing a distributed reinforcement learning model: designing a state space according to the target function value and each parameter, and setting a reward structure and an action space of each to-be-optimized parameter, the reward structure being a reciprocal of the target function value; and S3, parameter inversion is carried out through the distributed reinforcement learning model, each optimized parameter is obtained, and discrete element modeling is completed. According to the method, the model parameters are optimized and determined through parameter inversion in the discrete element model based on the distributed reinforcement learning algorithm, the speed and efficiency of parameter inversion are improved, and the parameter inversion is more accurate.

Description

Technical Field

[0001] The present invention belongs to the technical field related to the study of printing material mechanics, and more specifically, relates to a discrete element modeling method and system for the compaction behavior of printing materials. Background Art

[0002] Sintering and melting lunar soil powder to make lunar soil bricks is an important technology for preparing lunar base building materials. This method can reduce the cost of transportation between the Earth and the Moon and maximize the use of extraterrestrial materials to achieve in-situ manufacturing. To ensure the smooth progress of this technology, relevant research experiments have been carried out in an orderly manner on the ground, including printing lunar soil bricks with simulated lunar soil powder and high-energy lasers. However, due to the limitations of printing technology and the unknown properties of lunar soil printing materials, the research on the various mechanical properties of lunar soil bricks is still in the initial exploration stage.

[0003] Discrete element method is a numerical simulation technology based on discrete particle material modeling. It can analyze the mechanical behavior of the entire system by simulating the collision, friction, adhesion and other related effects between particles. This method has been widely used in the fields of civil engineering and material forming. Accurately reflecting the mechanical behavior of materials during compression and destruction is the primary purpose of discrete element modeling. In order for the discrete element simulation model to more realistically and accurately reflect the mechanical behavior of materials, it is first necessary to determine the particle contact properties and model parameters. Model-related parameter calibration is an important part of discrete element modeling.

[0004] There are currently two methods for parameter calibration: experimental-manual calibration and automatic calibration based on optimization algorithms. The method of manually comparing experimental results to find patterns consumes a lot of time and manpower, and the interrelated parameters of molten printing materials are usually difficult to decouple, making debugging difficult and the accuracy poor. The automatic calibration method based on optimization algorithms often relies on heuristic algorithms, such as particle swarm algorithms, genetic algorithms, and simulated annealing methods. These methods usually solve problems through guessing and searching, have a large search space, and are prone to falling into local optimality. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a discrete element modeling method and system for the compaction behavior of printing materials, which is used to solve the problem of low efficiency and accuracy of parameter calibration algorithms in current discrete element modeling.

[0006] To achieve the above object, according to one aspect of the present invention, a discrete element modeling method for compaction behavior of printing materials is provided, comprising:

[0007] S1, establishing a discrete element model of the compaction behavior of printing materials by discrete element method, determining the parameters to be optimized in the discrete element model and their ranges; obtaining the compaction behavior test measurement results of the printing material sample, and establishing an objective function with the goal of minimizing the deviation between the numerical calculation results obtained by simulating the compaction behavior of the printing material by discrete element model and the test measurement results;

[0008] S2, establish a distributed reinforcement learning model: use the objective function value and the parameters to be optimized as the state space elements of the distributed reinforcement learning model, set the reward structure and the action space of the parameters to be optimized, where the reward structure is the inverse of the objective function value;

[0009] S3, solve the distributed reinforcement learning model, obtain the optimized parameters, and complete the discrete element modeling of the printing material compression behavior; wherein, in each iteration of the distributed reinforcement learning model solution process, the action combination of the parameters to be optimized is substituted into the discrete element model to simulate the printing material compression behavior to obtain the objective function value of each action combination.

[0010] According to the discrete element modeling method for the compaction behavior of printing materials provided by the present invention, establishing the discrete element model for the compaction behavior of printing materials in S1 specifically includes:

[0011] According to the printed material sample, a sample model in a discrete element model is established, and the particle type and particle contact properties in the sample model are determined;

[0012] Construct parameter adjustment model in discrete element model based on Hertz contact model;

[0013] The loading model in the discrete element model is established based on the compaction behavior test of the printed material sample;

[0014] The parameters in the parameter adjustment model are the parameters to be optimized in the discrete element model.

[0015] According to the discrete element modeling method for the compaction behavior of printing materials provided by the present invention, a sample model in the discrete element model is established according to the printing material sample, and the particle type and particle contact properties in the sample model are determined, which specifically includes:

[0016] According to the appearance size and morphological characteristics of the printed material sample, a three-dimensional sample model is established; wherein the appearance size and morphological characteristics of the printed material sample include: sample length, width, height, number of printing layers, melt channel height, melt channel width and printing route;

[0017] The sample model was determined to include three different types of particles, namely, particles between melt layers, particles between melt channels, and melt channel particles;

[0018] Six types of particle contact properties are created, namely: melt particle-melt particle contact, particle-melt particle contact between melt channels, particle-melt layer particle contact, melt particle-melt layer particle contact, melt particle-melt layer particle contact, and melt layer particle-melt particle contact.

[0019] According to the discrete element modeling method for the compaction behavior of printing materials provided by the present invention, establishing a sample model in the discrete element model according to the printing material sample also includes:

[0020] According to the CT scanning results of the printed material sample, a three-dimensional model of cracks in the sample is created, and the three-dimensional model of cracks is imported into the sample model by deleting particles.

[0021] According to the discrete element modeling method for the compaction behavior of printing materials provided by the present invention, the parameter adjustment model in the discrete element model is constructed based on the Hertz contact model and specifically includes:

[0022] A nonlinear contact parameter adjustment model is constructed based on the Hertz contact model. The particle stiffness calculation formula in the nonlinear contact parameter adjustment model is:

[0023]

[0024] Among them, K n is the contact stiffness between particles, E * is the equivalent elastic modulus of the contact particles to be optimized, R * is the equivalent radius of the contact particles, δ is the overlap displacement between the contact particles, and α is the exponential parameter to be optimized; δ=βε+c, where ε is the compressive strain, β and c are the coefficients to be optimized related to the overlap amount;

[0025] The elastic modulus E used in the calculation update is converted using the following formula:

[0026] E=K n L / A;

[0027] Where A = πr 2 , L=R (1) +R (2) , r = min(R (1) ,R (2) ), R (1) With R (2) To find the diameters of two particles in contact, the average radius of the particles in the group was taken in the calculation.

[0028] According to the discrete element modeling method for the compaction behavior of printing materials provided by the present invention, establishing a loading model in the discrete element model according to the compaction behavior test of the printing material sample specifically includes: establishing a loading plate in the loading model according to the test parameters and determining the loading speed of the loading plate;

[0029] Correspondingly, the compaction behavior of the printed materials in S1 and S3 is simulated by the discrete element model as follows:

[0030] The sample model is loaded and simulated according to the loading model. The real-time stress-strain relationship of the sample model is calculated during the simulation process, and the contact fracture process is recorded. At the same time, the real-time strain is taken at each calculation step of the loading simulation process, and the model is adjusted according to the real-time strain and nonlinear contact parameters to update and record the elastic modulus of the particles in the sample model, thereby realizing nonlinear update of the elastic modulus of the particles.

[0031] According to the discrete element modeling method for the compaction behavior of printing materials provided by the present invention, establishing the objective function in S1 specifically includes:

[0032] Obtaining a stress-strain curve of a printed material sample measured under a uniaxial compression test, extracting a plurality of strain characteristic points, and obtaining a plurality of strain characteristic points measured in the test and a plurality of corresponding stress values;

[0033] Obtaining a stress-strain curve obtained by numerical calculation when the printed material is subjected to uniaxial compression simulation through a discrete element model, and obtaining a plurality of stress values ​​corresponding to a plurality of strain characteristic points obtained by numerical calculation;

[0034] An objective function is established with the goal of minimizing the deviation between multiple stress values ​​calculated numerically and multiple stress values ​​measured experimentally.

[0035] According to the discrete element modeling method for the compaction behavior of printing materials provided by the present invention, the objective function F is specifically:

[0036]

[0037] Among them, α, β, E * , … are the parameters to be optimized in the discrete element model, λ k To optimize the weight, σ k ′ is the stress value of the characteristic point calculated numerically, σ k is the stress value of the characteristic point measured in the test.

[0038] According to the discrete element modeling method for the compaction behavior of printing materials provided by the present invention, the distributed reinforcement learning model in S2 is specifically: the state space S t =[F,α,β,E * , ...], where F is the objective function value; the range of the parameter to be optimized follows 1.5<α<9.5, 1<β<9, E * min <E * <E * max ; The action space of each parameter to be optimized is specifically:

[0039]

[0040] Among them, n is the action update step length; the reward structure R is set to the inverse of the objective function, that is,

[0041] According to another aspect of the present invention, a discrete element modeling system for the compression behavior of printing materials is provided, the system comprising a memory and a processor, the memory storing a computer program, and the processor executing any of the discrete element modeling methods for the compression behavior of printing materials described above when executing the computer program.

[0042] In general, compared with the prior art, the above technical solutions conceived by the present invention provide a discrete element modeling method and system for the compaction behavior of printing materials:

[0043] 1. It is proposed to optimize and determine the model parameters through parameter inversion in the discrete element model based on the distributed reinforcement learning algorithm. The distributed reinforcement learning algorithm defines the action space of each parameter and sets the reward structure. On the one hand, it can save time costs, reduce manual intervention, and significantly improve the speed and efficiency of parameter inversion; on the other hand, the reinforcement learning method can continuously optimize the decision-making strategy through interaction with the environment, making the parameter inversion more accurate, which is conducive to improving the accuracy of parameter determination in the discrete element model and enhancing the prediction ability of the model;

[0044] 2. The distributed reinforcement learning algorithm (D4PG) used can efficiently utilize multiple agents to sample in parallel by collecting experience in a distributed environment, accelerate the learning process, and improve the stability and robustness of the strategy;

[0045] 3. Based on the Hertz contact theory, a nonlinear Hertzian simulation material modulus adjustment scheme, namely the nonlinear contact parameter adjustment model, which can be used in discrete element programs, is proposed. This makes the update of the particle elastic modulus during the compression behavior simulation process closer to reality. This scheme can not only more reasonably show the compression behavior of printed materials such as lunar soil printing samples before uniaxial compression failure, but also more accurately simulate the deformation and failure characteristics of materials under different stress conditions, thereby achieving a high degree of consistency with actual experimental conditions; at the same time, it also provides a more reliable theoretical basis for the design and application of lunar soil printing materials, which is helpful to develop building materials with better performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention provides a total process of discrete element modeling and parameter inversion of printing material compaction behavior;

[0047] Figure 2 It is a flow chart of discrete element modeling of compaction behavior of printing materials provided by the present invention;

[0048] Figure 3 It is a discrete element parameter inversion flow chart based on distributed reinforcement learning provided by the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] See also Figures 1 to 3 The present invention provides a discrete element modeling method for compaction behavior of printing materials, the discrete element modeling method comprising:

[0051] S1, establishing a discrete element model of the compaction behavior of printing materials by discrete element method, determining the parameters to be optimized in the discrete element model and their ranges; obtaining the compaction behavior test measurement results of the printing material sample, and establishing an objective function with the goal of minimizing the deviation between the numerical calculation results obtained by simulating the compaction behavior of the printing material by discrete element model and the test measurement results;

[0052] S2, establish a distributed reinforcement learning model: use the objective function value and the parameters to be optimized as the state space elements of the distributed reinforcement learning model, set the reward structure and the action space of the parameters to be optimized, where the reward structure is the inverse of the objective function value;

[0053] S3, solve the distributed reinforcement learning model, obtain the optimized parameters, and complete the discrete element modeling of the printing material compression behavior; wherein, in each iteration of the distributed reinforcement learning model solution process, the action combination of the parameters to be optimized is substituted into the discrete element model to simulate the printing material compression behavior to obtain the objective function value of each action combination.

[0054] In this embodiment, an objective function is established based on the compression behavior test data of the printed material sample, and then the parameters of the discrete element model are optimized based on the objective function, so that the discrete element simulation model can more realistically and accurately reflect the mechanical behavior of the material. The discrete element model constructed after the parameters are determined can simulate the compression behavior of the printed material for mechanical research on the printed material.

[0055] The modeling method is based on the distributed reinforcement learning algorithm to determine the model parameters by performing parameter inversion in the discrete element model, that is, the distributed reinforcement learning algorithm generates multiple action combinations of parameters to be optimized in each iteration, that is, generates multiple specific value combinations of each parameter, and the multiple specific value combinations of each parameter can be respectively brought into the discrete element model to perform compaction behavior simulation consistent with the experiment, and the numerical calculation results corresponding to the multiple specific value combinations of each parameter are obtained. By calculating the deviation with the experimental measurement results, the objective function values ​​corresponding to the multiple specific value combinations of each parameter can be obtained, and then the better specific value combination of each parameter is selected according to the objective function value, and then the next iterative optimization is performed based on the better specific value combination of each parameter, and finally the optimized specific value combination of each parameter is output when the end condition of the distributed reinforcement learning algorithm is met, which is the determined model parameters.

[0056] Specifically, the discrete element model of the compaction behavior of printing materials established in S1 specifically includes:

[0057] According to the printed material sample, a sample model in a discrete element model is established, and the particle type and particle contact properties in the sample model are determined;

[0058] Construct parameter adjustment model in discrete element model based on Hertz contact model;

[0059] The loading model in the discrete element model is established based on the compaction behavior test of the printed material sample;

[0060] The parameters in the parameter adjustment model are the parameters to be optimized in the discrete element model.

[0061] refer to Figure 1 and Figure 2 The printing material can be printed lunar soil bricks, and can also be applied to other printing materials, without specific limitation. In the discrete element modeling method of the printing material compaction behavior provided in this embodiment, a sample model in the discrete element model is established according to the printing material sample, and the particle type and particle contact properties in the sample model are determined, specifically including:

[0062] First, the appearance size and morphological characteristics of the printed material sample and the printed material product are obtained, wherein the appearance size and morphological characteristics of the printed material sample include: sample length, width, height, number of printing layers, melt channel height, melt channel width and printing route; according to the appearance size and morphological characteristics of the printed material sample, a three-dimensional sample model is established; the sample model can be created in a discrete element particle flow program (PFC).

[0063] The sample model is determined to include three different types of particles, namely, melt layer particles, inter-melt particles and melt particles. Specifically, according to the characteristics of laser melting printing, the printed material sample is divided into three regions according to the remelting method. The single-channel printing area that has not been remelted is divided into the melt channel area, and the melt channel particles are used for modeling, that is, the particles in this part of the area are melt channel particles; the two melt channel remelting areas in the same plane are divided into the inter-melt channel area, and the inter-melt channel particles are used for modeling, that is, the particles in this part of the area are inter-melt channel particles; the remelting areas in the upper and lower layers of different planes are divided into the melt channel interlayer area, and the melt channel interlayer particles are used for modeling, that is, the particles in this part of the area are melt channel interlayer particles.

[0064] Secondly, according to the properties of different regions of the molten laser printing material sample, six types of particle contact properties are created for the particles in the sample model, namely: melt channel particle-melt channel particle contact, inter-melt channel particle-melt channel particle contact, inter-melt channel particle-melt channel particle contact, melt channel particle-melt channel particle contact, melt channel particle-melt channel particle contact, and inter-melt channel particle-melt channel particle contact. All initial contacts are set to parallel bonding models, and the calculation method is incremental. When the bonding is broken, the model is allowed to degenerate into a linear contact model.

[0065] Furthermore, according to the printed material sample, establishing the sample model in the discrete element model also includes:

[0066] According to the CT scanning results of the printed material sample, a three-dimensional model of the cracks in the sample can be created as a crack stl file, and the three-dimensional model of the cracks is imported into the sample model by deleting particles.

[0067] Furthermore, the parameter adjustment model in the discrete element model based on the Hertz contact model specifically includes:

[0068] A nonlinear contact parameter adjustment model, namely a Hertz-like model, is constructed based on the Hertz contact model. The nonlinear contact parameter adjustment model is specifically as follows:

[0069]

[0070] The particle stiffness update formula can be derived from the above formula. The particle stiffness calculation formula in the nonlinear contact parameter adjustment model is:

[0071]

[0072] Where F is the contact force between particles, K n is the contact stiffness between particles, E * is the equivalent elastic modulus of the contact particles to be optimized, R *is the equivalent radius of the contact particles, δ is the overlap displacement between the contact particles, and α is the exponential parameter to be optimized. Since the overlap displacement of particles in the particle flow program is difficult to calculate, it is assumed that δ is linearly related to the strain of the material compression process, and δ = βε + c, where ε is the compression strain, β and c are the coefficients to be optimized related to the overlap amount. When the parameters are optimized, c can be a constant. Therefore, the parameters to be optimized in the discrete element model are α, β and E. * c can also be used as a parameter to be optimized and added to the state space of the distributed reinforcement learning model to define the action space and value range without any specific limitation.

[0073] In the particle flow program (PFC), that is, when simulating with a discrete element model, the elastic modulus E used for calculation and updating is converted using the following formula:

[0074] E=K n L / A;

[0075] Where A = πr 2 , L=R (1) +R (2) , r = min(R (1) ,R (2) ), R (1) With R (2) The diameter of the two contacting particles is the average radius of the particles in the group. That is, during the simulation process, the model will be adjusted according to the nonlinear contact parameters and the elastic modulus of the particles will be continuously updated according to the calculation step settings, making the simulation closer to the actual situation and the simulation results more accurate and reliable.

[0076] Further, establishing a loading model in the discrete element model according to the compaction behavior test of the printing material sample specifically includes: establishing a loading plate in the loading model according to the test parameters and determining a loading speed of the loading plate;

[0077] Correspondingly, the compaction behavior of the printed materials in S1 and S3 is simulated by the discrete element model as follows:

[0078] The sample model is loaded and simulated according to the loading model. The real-time stress-strain relationship of the sample model is calculated during the simulation process, and the contact fracture process is recorded. At the same time, the real-time strain is taken at each calculation step of the loading simulation process, and the model is adjusted according to the real-time strain and nonlinear contact parameters to update and record the elastic modulus of the particles in the sample model, thereby realizing nonlinear update of the elastic modulus of the particles.

[0079] In some specific embodiments, the compaction behavior of the printed material sample can be uniaxial compression, and a displacement servo compression simulation process model is established according to the uniaxial compression test process. The simulation main program includes four processes: loading plate creation, servo loading speed determination, stress-strain calculation, and contact fracture recording. The sub-program includes strain retrieval for each calculation step of the loading process, parameter update and parameter recording under the Hertz-like model. The sub-program is embedded in the main program using the Python interface and callback function in the particle flow program to achieve nonlinear update of the particle modulus, thereby achieving nonlinear compaction characteristics in the compression stage.

[0080] refer to Figure 3 This embodiment provides a discrete element parameter inversion method based on distributed reinforcement learning. First, the objective function is determined. The objective function is established in S1 and specifically includes:

[0081] Obtaining a stress-strain curve of a printed material sample measured under a uniaxial compression test, extracting a plurality of strain characteristic points, and obtaining a plurality of strain characteristic points measured in the test and a plurality of corresponding stress values;

[0082] Obtaining a stress-strain curve obtained by numerical calculation when the printed material is subjected to uniaxial compression simulation through a discrete element model, and obtaining a plurality of stress values ​​corresponding to a plurality of strain characteristic points obtained by numerical calculation;

[0083] An objective function is established with the goal of minimizing the deviation between multiple stress values ​​calculated numerically and multiple stress values ​​measured experimentally.

[0084] Specifically, according to the stress-strain curve obtained by the uniaxial compression test, extracting multiple strain characteristic points can be specifically as follows: first determine the strain value corresponding to the peak stress in the stress-strain curve obtained by the test measurement as the reference strain value, and take the strain values ​​of 20%, 40%, 60%, 80% and 100% of the reference strain value as the strain characteristic point, and add a strain characteristic point after the sample is destroyed, that is, select a strain value after the sample is destroyed in the stress-strain curve as the strain characteristic point. The strain value after the sample is destroyed can be the strain value corresponding to the stress after the sample is destroyed being 60% of the peak stress.

[0085] In some specific embodiments, there are 6 strain characteristic points, and the objective function F is specifically:

[0086]

[0087] Among them, α, β, E * , … are the parameters to be optimized in the discrete element model, λ k is the optimization weight of the kth group of stress values, σ k ′ is the stress value of the kth characteristic point calculated numerically, σ k is the stress value of the kth characteristic point measured in the test.

[0088] Furthermore, the state space, action space and reward structure of the distributed reinforcement learning algorithm are defined. The distributed reinforcement learning model in S2 is specifically as follows: select the objective function value and the parameters to be optimized as the state design, that is, the state space S t =[F,α,β,E * , ...], where F is the objective function value; the range of the parameter to be optimized follows 1.5<α<9.5, 1<β<9, E * min <E * <E * max ; The action space of each parameter to be optimized is three different update forms designed for each parameter:

[0089]

[0090] Among them, n is the action update step length; the reward structure R is set to the inverse of the objective function, that is, i is the number of iterations.

[0091] Furthermore, the distributed reinforcement learning (D4PG) model parameters are initialized, including the training batch size, tracking length, number of actor networks, playback size, exploration coefficient, learning rate, and weights of actor networks and critic networks. The above parameters are conventional setting parameters in the distributed reinforcement learning algorithm, which can be selected and determined based on empirical values, and the specific settings are not limited. According to the D4PG algorithm procedure, a set of initial parameter update strategies are generated.

[0092] Furthermore, the discrete element program and the python interface are used to input the parameter update value into the established discrete element simulation model, calculate the characteristic point value of the simulation curve, and calculate the updated objective function value. Repeat the above steps: parameter update - discrete element calculation - reinforcement learning model calculation of new parameters until the program converges or reaches the maximum iteration limit, and obtain the optimal parameter update strategy and the final parameter update value. This value is applied to the discrete element model parameters. The determined discrete element model can be used in the simulation modeling of the stretching, bending and service performance of printed parts, which can simulate material behavior more realistically.

[0093] This embodiment also provides a discrete element modeling system for the compression behavior of printing materials, the system comprising a memory and a processor, the memory storing a computer program, and the processor executing the discrete element modeling method for the compression behavior of printing materials described in any of the above embodiments when executing the computer program.

[0094] In some specific embodiments, the printed material may be printed lunar soil bricks. This embodiment provides a discrete element modeling method for the compaction behavior of lunar soil powder printed materials and a reinforcement learning method for parameter inversion, which mainly includes the following steps: establishing a discrete element model based on the simulated lunar soil printed sample size, morphology and pore characteristics; extracting important features based on the uniaxial compression stress-strain curve of the lunar soil printed sample; adjusting the contact properties between particles according to the Hertzian-like nonlinear contact model; establishing a parameter inversion model based on distributed reinforcement learning (D4PG); establishing a particle flow program (PFC) simulation and parameter inversion model interaction program to obtain the optimal parameters to ensure the unity of the simulation model and the experimental behavior. This embodiment lays the foundation for analyzing the microscopic contact characteristics of printed materials and the requirements of the macroscopic destruction simulation process, and can also be extended to the discrete element parameter calibration technology of other materials with compaction behavior.

[0095] In order to solve the problems of unclear mechanical behavior of printed lunar soil bricks, difficult calibration of simulation material parameters, and poor accuracy, this embodiment provides a discrete element modeling method for the compaction behavior of lunar soil powder printing materials and a reinforcement learning method for parameter inversion. This method introduces nonlinear contact behavior between particles, explores the compaction process of lunar soil bricks during uniaxial compression, and inverts the nonlinear contact behavior parameters based on distributed reinforcement learning (D4PG) method, laying a foundation for analyzing the microscopic contact characteristics of printed materials and the requirements of macroscopic destruction simulation process.

[0096] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A discrete element modeling method for compaction behavior of printing materials, characterized in that: include: S1, establishing a discrete element model of the compaction behavior of the printing material by using a discrete element method, and determining the parameters to be optimized in the discrete element model and their ranges; Obtaining the test measurement results of the compaction behavior of the printed material sample, and establishing an objective function with the goal of minimizing the deviation between the numerical calculation results obtained by discrete element model simulation and the test measurement results of the compaction behavior of the printed material; S2, establish a distributed reinforcement learning model: use the objective function value and the parameters to be optimized as the state space elements of the distributed reinforcement learning model, set the reward structure and the action space of the parameters to be optimized, where the reward structure is the inverse of the objective function value; S3, solve the distributed reinforcement learning model, obtain the optimized parameters, and complete the discrete element modeling of the printing material compression behavior; wherein, in each iteration of the distributed reinforcement learning model solution process, the action combination of the parameters to be optimized is substituted into the discrete element model to simulate the printing material compression behavior to obtain the objective function value of each action combination.

2. The discrete element modeling method for the compaction behavior of printing materials according to claim 1, characterized in that: The discrete element model of the compaction behavior of printing materials established in S1 specifically includes: According to the printed material sample, a sample model in a discrete element model is established, and the particle type and particle contact properties in the sample model are determined; Construct parameter adjustment model in discrete element model based on Hertz contact model; The loading model in the discrete element model is established based on the compaction behavior test of the printed material sample; The parameters in the parameter adjustment model are the parameters to be optimized in the discrete element model.

3. The discrete element modeling method for the compaction behavior of printing materials according to claim 2, characterized in that: According to the printed material sample, a sample model in the discrete element model is established, and the particle type and particle contact properties in the sample model are determined, including: According to the appearance size and morphological characteristics of the printed material sample, a three-dimensional sample model is established; wherein the appearance size and morphological characteristics of the printed material sample include: sample length, width, height, number of printing layers, melt channel height, melt channel width and printing route; The sample model was determined to include three different types of particles, namely, particles between melt layers, particles between melt channels, and melt channel particles; Six types of particle contact properties are created, namely: melt particle-melt particle contact, particle-melt particle contact between melt channels, particle-melt layer particle contact, melt particle-melt layer particle contact, melt particle-melt layer particle contact, and melt layer particle-melt particle contact.

4. The discrete element modeling method for the compaction behavior of printing materials according to claim 2, characterized in that: According to the printed material sample, the sample model in the discrete element model is also established: According to the CT scanning results of the printed material sample, a three-dimensional model of cracks in the sample is created, and the three-dimensional model of cracks is imported into the sample model by deleting particles.

5. The discrete element modeling method for the compaction behavior of printing materials according to claim 2, characterized in that: The parameter adjustment model in the discrete element model based on the Hertz contact model specifically includes: A nonlinear contact parameter adjustment model is constructed based on the Hertz contact model. The particle stiffness calculation formula in the nonlinear contact parameter adjustment model is: Among them, K n is the contact stiffness between particles, E * is the equivalent elastic modulus of the contact particles to be optimized, R * is the equivalent radius of the contact particles, δ is the overlap displacement between the contact particles, and α is the exponential parameter to be optimized; δ=βε+c, where ε is the compressive strain, β and c are the coefficients to be optimized related to the overlap amount; The elastic modulus E used in the calculation update is converted using the following formula: E=K n L / A; Where A = πr 2 , L=R (1) +R (2) , r = min(R (1) ,R (2) ), R (1) With R (2) To find the diameters of two particles in contact, the average radius of the particles in the group was taken in the calculation.

6. The discrete element modeling method for the compaction behavior of printing materials according to claim 5, characterized in that: Establishing a loading model in a discrete element model according to a compression behavior test of a printing material sample specifically includes: establishing a loading plate in the loading model according to test parameters and determining a loading speed of the loading plate; Correspondingly, the compaction behavior of the printed materials in S1 and S3 is simulated by the discrete element model as follows: The sample model is loaded and simulated according to the loading model. The real-time stress-strain relationship of the sample model is calculated during the simulation process, and the contact fracture process is recorded. At the same time, the real-time strain is taken at each calculation step of the loading simulation process, and the model is adjusted according to the real-time strain and nonlinear contact parameters to update and record the elastic modulus of the particles in the sample model, thereby realizing nonlinear update of the elastic modulus of the particles.

7. The discrete element modeling method for the compaction behavior of printing materials according to claim 1, characterized in that: The objective function established in S1 specifically includes: Obtaining a stress-strain curve of a printed material sample measured under a uniaxial compression test, extracting a plurality of strain characteristic points, and obtaining a plurality of strain characteristic points measured in the test and a plurality of corresponding stress values; Obtaining a stress-strain curve obtained by numerical calculation when the printed material is subjected to uniaxial compression simulation through a discrete element model, and obtaining a plurality of stress values ​​corresponding to a plurality of strain characteristic points obtained by numerical calculation; An objective function is established with the goal of minimizing the deviation between multiple stress values ​​calculated numerically and multiple stress values ​​measured experimentally.

8. The discrete element modeling method for the compaction behavior of printing materials according to claim 7, characterized in that: The objective function F is specifically: Among them, α, β, E * , … are the parameters to be optimized in the discrete element model, λ k To optimize the weight, σ k ′ is the stress value of the characteristic point calculated numerically, σ k is the stress value of the characteristic point measured in the test.

9. The discrete element modeling method for compaction behavior of printing materials according to claim 5, characterized in that: The distributed reinforcement learning model in S2 is specifically: the state space S t =[F,α,β,E * , ...], where F is the objective function value; the range of the parameter to be optimized follows 1.5<α<9.5, 1<β<9, E * min <E * <E * max ; The action space of each parameter to be optimized is specifically: Among them, n is the action update step length; the reward structure R is set to the inverse of the objective function, that is, 10. A discrete element modeling system for compaction behavior of printed materials, characterized in that: The system includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the discrete element modeling method for the compaction behavior of the printing material according to any one of claims 1 to 9 is executed.

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