Intelligent control method and system for powder forming machine

Abstract

The invention relates to the technical field of powder machines, in particular to an intelligent control method and system for a powder forming machine. The method comprises the steps that powder material parameters and mold parameters are obtained, the powder material parameters comprise particle size distribution, yield strength and a friction coefficient, and the mold parameters comprise mold geometric parameters; based on a discrete element method, particle dynamic analysis is conducted on the powder material parameters, particle mechanical motion characteristic data are generated, and the particle mechanical motion characteristic data comprise particle position data, speed data, contact force data and force chain distribution data constructed by the contact force data; based on a finite element method, structural mechanics analysis is conducted on the mold parameters, mold macromechanics response data are generated, and the mold macromechanics response data comprise stress distribution data, strain distribution data and displacement distribution data; and according to the particle mechanical motion characteristic data and the mold macromechanical response data, generating a powder pressing path control instruction of the mold.

Description

Technical Field

[0001] The present application relates to the technical field of powder machines, and in particular to an intelligent control method and system for a powder forming machine. Background Art

[0002] A powder machine is a mechanical equipment widely used in industrial production. It is mainly used to process various powdered materials, covering multiple production links such as powder mixing, molding, drying, and packaging.

[0003] In the powder forming process, traditional control methods often fail to fully consider the characteristics of the powder material and the mechanical response of the mold during the forming process, resulting in problems such as unstable forming quality and shortened mold life.

[0004] Factors such as the flow and interaction of powder particles, the stress distribution and deformation of the mold have an important influence on the molding effect, but traditional methods are difficult to accurately simulate and control these factors, so they are in urgent need of improvement. Summary of the invention

[0005] Based on this, it is necessary to provide an intelligent control method and system for a powder forming machine that can improve the pressing effect of the powder machine in order to address the above technical problems.

[0006] In a first aspect, the present application provides an intelligent control method for a powder forming machine, the method comprising: Obtaining powder material parameters and mold parameters, wherein the powder material parameters include particle size distribution, yield strength and friction coefficient, and the mold parameters include mold geometry parameters; Based on the discrete element method, the particle dynamics analysis of the powder material parameters is performed to generate the particle mechanical motion characteristic data, which includes the particle position data, velocity data, contact force data and the force chain distribution data constructed by the contact force data; Perform structural mechanics analysis on mold parameters based on finite element method to generate mold macroscopic mechanical response data, which includes stress distribution data, strain distribution data and displacement distribution data; The powder pressing path control instructions of the mold are generated according to the particle mechanical motion characteristic data and the mold macroscopic mechanical response data.

[0007] In one embodiment, a particle dynamics analysis is performed on powder material parameters based on a discrete element method to generate particle mechanical motion characteristic data, including: The powder particles are modeled as a collection of discrete particles with particle size distribution and friction coefficient; Establish a particle-to-particle contact mechanics model including normal contact force and tangential friction force, and define the contact boundary conditions between particles and mold wall; In the discrete time step of the pressing process, the composite force of each particle is iteratively calculated; the composite force includes gravity, contact force between particles and force on the mold wall; According to the updated position data and speed data of the particles, until the pressing process is terminated; The particle motion trajectory data is extracted and a force chain network is constructed. The force chain network includes the spatial direction, strength and topological structure characteristic parameters of the force chain.

[0008] In one embodiment, structural mechanics analysis of mold parameters is performed based on the finite element method to generate macroscopic mechanical response data of the mold, including: Construct a three-dimensional parametric model based on the mold geometry parameters and perform finite element meshing; Define the elastic-plastic constitutive model of the mold material, and the model parameters include elastic modulus, Poisson's ratio and dynamic yield strength; The powder-die interaction force field data is converted into a distributed load on the die surface and applied to the corresponding boundary of the finite element model; The finite element control equations including material nonlinearity are solved to obtain the stress data, strain data and displacement distribution data of the mold.

[0009] In one embodiment, the method further comprises: Generate stress nephogram, strain nephogram and displacement vector diagram of key sections of the mold according to the stress data, strain data and displacement distribution data of the mold; Calculate the maximum equivalent stress value, maximum plastic strain value and maximum displacement; When the maximum equivalent stress value is less than the dynamic yield strength of the mold material, it is determined that the stress data, strain data and displacement distribution data of the mold have passed the verification.

[0010] In one embodiment, the powder pressing path control instruction of the mold is generated according to the particle mechanical motion characteristic data and the mold macroscopic mechanical response data, including: The contact force data in the particle mechanical motion characteristic data and the stress distribution data in the mold macroscopic mechanical response data are coupled and calculated to determine the powder-mold interaction force field data; Based on the collaborative analysis of particle mechanical motion characteristic data, mold macroscopic mechanical response data and powder-mold interaction force field data, the powder pressing path control instructions of the mold are generated.

[0011] In one embodiment, the contact force data in the particle mechanical motion characteristic data and the stress distribution data in the mold macroscopic mechanical response data are coupled and calculated to determine the powder-mold interaction force field data, including: Establish the mapping relationship between discrete element particle contact force and finite element node force, and use Gaussian interpolation method to convert discrete contact force into continuous distributed load Through bidirectional coupling iterative calculation, the contact force distribution of the discrete element system and the stress distribution of the finite element system meet the force balance condition; Record the spatial distribution data of the interaction force field when the convergence condition is reached.

[0012] In one embodiment, based on the collaborative analysis of particle mechanical motion characteristic data, mold macroscopic mechanical response data and powder-mold interaction force field data, a powder pressing path control instruction of the mold is generated, including: Construct a multi-dimensional feature matrix including force chain network features, mold deformation features and interaction force field features; Based on the genetic algorithm, the pressing path parameters are optimized by multiple objectives to generate an optimized pressing path parameter set including the pressing speed curve, pressure loading curve and holding time; the multiple optimization objectives include minimizing the maximum equivalent stress of the mold, maximizing the uniformity of the powder pressing density and minimizing the pressing energy consumption; According to the optimized pressing path parameter set, the powder pressing path control instructions of the mold are generated.

[0013] In one embodiment, generating a powder pressing path control instruction of a mold according to an optimized pressing path parameter set includes: Acquire real-time sensing data of the mold, including mold surface pressure distribution, punch displacement and mold temperature; Based on real-time sensor data, calculate the deviation dynamics between the current suppression state and the target state; According to the deviation dynamics, the set of pressure path parameters is adjusted to obtain the optimal pressure path; Generate powder pressing path control instructions for the mold based on the optimal pressure path.

[0014] In one embodiment, adjusting the set of pressing path parameters according to the deviation dynamics to obtain the optimal pressure path includes: The model predictive control algorithm is used to adjust the path parameters of the pressing path parameter set according to the deviation dynamics to obtain the optimal pressure path; the path parameters include pressure gradient, pressurization timing and holding time.

[0015] In a second aspect, the present application also provides an intelligent control system for a powder forming machine, comprising: An acquisition module, used for acquiring powder material parameters and mold parameters, wherein the powder material parameters include particle size distribution, yield strength and friction coefficient, and the mold parameters include mold geometry parameters; Powder analysis module, used to perform particle dynamics analysis on powder material parameters based on discrete element method, and generate particle mechanical motion characteristic data, which includes particle position data, velocity data, contact force data and force chain distribution data constructed by contact force data; The mold analysis module is used to perform structural mechanics analysis on mold parameters based on the finite element method and generate mold macroscopic mechanical response data, which includes stress distribution data, strain distribution data and displacement distribution data; The path instruction generation module is used to generate the powder pressing path control instructions of the mold according to the particle mechanical motion characteristic data and the mold macroscopic mechanical response data.

[0016] The intelligent control method and system of the above-mentioned powder forming machine, this application comprehensively considers the particle mechanical motion characteristic data and the mold macroscopic mechanical response data to generate the powder pressing path control instruction, which can realize the precise control of the powder forming process. According to the flow and compaction requirements of the powder, the parameters such as the pressing speed, pressure and holding time are reasonably adjusted to make the powder evenly filled and compacted in the mold, thereby improving the density uniformity, dimensional accuracy and mechanical properties of the molded product, reducing the occurrence of molding defects, and improving the stability of molding quality.

[0017] The intelligent control of the powder forming process is realized by combining the powder material characteristics, the mold mechanical response and the pressing path control. The system can automatically adjust the pressing parameters according to the real-time data to adapt to different powder materials and mold conditions, improve the production efficiency and product quality stability, and reduce the errors caused by manual intervention and experience judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A schematic diagram of a flow chart of an intelligent control method for a powder forming machine in one embodiment; Figure 2 A schematic flow chart of the steps of generating particle mechanical motion characteristic data in one embodiment; Figure 3 A schematic flow chart of the steps for generating macroscopic mechanical response data of a mold in one embodiment; Figure 4 A schematic flow chart of a step of determining powder-mold interaction force field data in one embodiment; Figure 5 A schematic flow chart of the steps of generating powder pressing path control instructions for a mold in one embodiment; Figure 6The figure is a flow chart of the steps of generating powder pressing path control instructions for a mold in one embodiment. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application 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 application and are not used to limit the present application.

[0021] In an exemplary embodiment, Figure 1 As shown, an intelligent control method for a powder forming machine is provided, comprising: S101, obtaining powder material parameters and mold parameters.

[0022] Among them, the powder material parameters include particle size distribution, yield strength and friction coefficient, and the mold parameters include mold geometry parameters.

[0023] Optionally, the particle size distribution is measured by a laser particle size analyzer, and its distribution obeys the Rosin-Rammler distribution. This distribution can better describe the statistical characteristics of the powder particle size and provide a basis for subsequent discrete element analysis.

[0024] Optionally, the yield strength and friction coefficient are calibrated by a material testing machine. The yield strength reflects the stress when the powder material begins to produce plastic deformation, and the friction coefficient affects the relative movement between powder particles and between powder and mold.

[0025] Optionally, the mold parameters are mainly mold geometric parameters, such as cavity size, wall thickness, etc. These parameters are obtained through the 3D CAD model, which can accurately describe the shape and structure of the mold and provide an accurate geometric model for finite element analysis.

[0026] S102, performing particle dynamics analysis on powder material parameters based on discrete element method to generate particle mechanical motion characteristic data.

[0027] The particle mechanical motion characteristic data include particle position data, velocity data, contact force data and force chain distribution data constructed by the contact force data.

[0028] It can be understood that the discrete element method is a numerical method for simulating the dynamic behavior of particle systems. It regards the powder as composed of a large number of discrete particles, each of which follows Newton's laws of motion. By considering factors such as contact force and gravity between particles, the movement and interaction of particles during the molding process are simulated.

[0029] In the particle mechanical motion characteristic data, the position data records the spatial position of each particle at different times, reflecting the motion trajectory of the particle during the molding process. The velocity data represents the motion speed of each particle at different times, including the magnitude and direction of the velocity, which helps to analyze the motion state of the particle. The contact force data describes the magnitude and direction of the contact force between particles and between particles and molds, and is the key data for analyzing particle interaction and force transmission. The force chain distribution data is constructed from the contact force data, reflecting the force transmission path and network structure between particles.

[0030] S103, performing structural mechanics analysis on mold parameters based on the finite element method to generate macroscopic mechanical response data of the mold.

[0031] Among them, the mold macroscopic mechanical response data includes stress distribution data, strain distribution data and displacement distribution data.

[0032] It can be understood that the finite element method is a numerical analysis method that discretizes a continuum into a finite number of units. By analyzing and calculating the mechanical properties of each unit, the mechanical response of the entire structure is obtained.

[0033] In the macroscopic mechanical response data of the mold, the stress distribution data indicates the magnitude and direction of the stress borne by the mold at different parts, which can help determine whether the mold will be damaged in strength. The strain distribution data reflects the degree of deformation of the mold under stress. The analysis of strain distribution helps to optimize the design of the mold and improve the molding accuracy. The displacement distribution data describes the displacement of the mold at various parts and can be used to evaluate the overall deformation and stability of the mold.

[0034] S104, generating a powder pressing path control instruction of the mold according to the particle mechanical motion characteristic data and the mold macroscopic mechanical response data.

[0035] Optionally, preprocess the particle mechanical motion characteristic data and mold macroscopic mechanical response data, including data cleaning, filtering, normalization and other operations, to improve the quality and usability of the data. Then extract key features from these data, such as the bulk density of the powder, fluidity index, the maximum stress point of the mold, the maximum deformation area, etc. These features can more concisely describe the mechanical behavior of the powder and the mold, and provide a basis for subsequent analysis and decision-making. For example: Through theoretical analysis, numerical simulation or experimental research, the mapping relationship or mathematical model between the mechanical motion characteristics of particles, the macroscopic mechanical response of molds and the powder pressing path is established. For example, a prediction model can be built based on machine learning algorithms such as neural networks and decision trees, and the particle mechanical motion characteristics data and the macroscopic mechanical response data of molds are input to output the optimal powder pressing path parameters. This model can learn the complex nonlinear relationship between data, thereby achieving accurate prediction and decision-making.

[0036] Alternatively, when generating powder compaction path control instructions, it is usually necessary to consider multiple optimization objectives, such as maximizing powder compaction density, minimizing mold stress, and improving molding efficiency. These objectives may conflict with each other, and it is necessary to use multi-objective optimization algorithms, such as genetic algorithms and particle swarm algorithms, to find a set of optimal compaction path parameters under the premise of meeting certain constraints, so that each objective can be well taken into account. For example, while ensuring that the powder compaction density meets the requirements, the maximum stress of the mold is reduced as much as possible to extend the service life of the mold.

[0037] According to the optimized pressing path parameters, specific powder pressing path control instructions are generated. These instructions usually include the settings of parameters such as pressing speed, pressure, pressing time, and holding time.

[0038] In an exemplary embodiment, Figure 2 As shown, based on the discrete element method, the particle dynamics analysis of the powder material parameters is performed to generate particle mechanical motion characteristic data, including: S201, the powder particles are modeled as a discrete particle set with a particle size distribution and a friction coefficient.

[0039] The powder particles are modeled as a collection of discrete particles with a specific particle size distribution and friction coefficient. The particle size distribution is determined by previously acquired measurement data and usually obeys the Rosin-Rammler distribution. The friction coefficient is obtained by calibrating the material testing machine. This modeling method can more realistically reflect the actual situation of the powder particles.

[0040] S202, establishing a particle-to-particle contact mechanics model including normal contact force and tangential friction force, and defining contact boundary conditions between the particles and the mold wall.

[0041] Optionally, a particle-to-particle contact mechanics model including normal contact force and tangential friction force is established. The normal contact force describes the force generated when particles squeeze each other, while the tangential friction force considers the resistance when particles slide relative to each other.

[0042] Common contact mechanics models include the Hertz-Mindlin model, which can accurately simulate the contact behavior between particles.

[0043] Furthermore, the contact boundary conditions between the particles and the mold wall are defined to clarify the mechanical behavior of the particles when in contact with the mold wall, such as specifying the friction coefficient and elastic recovery coefficient between the particles and the mold wall, so as to ensure that the simulation results are consistent with the actual situation. S203, iteratively calculating the composite force of each particle within the discrete time step of the pressing process.

[0044] The composite forces include gravity, contact force between particles and force acting on the mold wall.

[0045] Optionally, the composite force of each particle is iteratively calculated within a discrete time step of the pressing process. The composite force of the particle includes gravity, contact force between particles and force acting on the mold wall.

[0046] It is understandable that gravity is an inherent property of the particles themselves, the contact force between particles is calculated based on the established contact mechanics model, and the force on the mold wall is determined based on the contact between the particles and the mold wall and the boundary conditions. Through continuous iterative calculations, the force on the particles can be updated in real time.

[0047] S204, updating the position data and speed data of the particles until the pressing process is terminated.

[0048] Optionally, according to Newton's law of motion (F=ma, where F is the resultant force on the particle, m is the mass of the particle, and a is the acceleration of the particle), the position data and velocity data of the particle are updated.

[0049] Optionally, in each discrete time step, the acceleration is calculated according to the force applied to the particle, and the change in velocity and position is obtained. This process is repeated until the pressing process is terminated. In this way, the motion trajectory of the particle during the entire pressing process can be simulated.

[0050] S205, extracting particle motion trajectory data and constructing a force chain network, the force chain network including the force chain spatial direction, strength and topological structure characteristic parameters.

[0051] Optionally, the movement trajectory data of the particles are extracted from the above calculation process, and these data record the position information of the particles at different times and reflect the movement path of the particles.

[0052] The force chain network is constructed based on the contact force data between particles. The force chain network is a network structure composed of particles that contact and transmit force to each other, including the spatial direction, strength and topological structure characteristic parameters of the force chain.

[0053] It can be understood that the spatial direction of the force chain describes the direction in which the force is transmitted between particles, the strength of the force chain indicates the magnitude of the force, and the topological structural characteristic parameters reflect the overall structure and connection mode of the force chain network. By analyzing the force chain network, we can gain a deeper understanding of the force transmission mechanism and mechanical properties inside the powder particles.

[0054] In this embodiment, through precise modeling and a reasonable contact mechanics model, the dynamic behavior of powder particles during the pressing process can be simulated more accurately, providing a reliable basis for the optimization of the powder forming process.

[0055] In an exemplary embodiment, Figure 3 As shown in the figure, the structural mechanics analysis of the mold parameters is performed based on the finite element method to generate the mold macroscopic mechanical response data, including: S301, constructing a three-dimensional parametric model according to the mold geometric parameters, and performing finite element meshing.

[0056] Optionally, based on the obtained mold geometry parameters, a professional finite element analysis software is used to construct a three-dimensional parametric model of the mold. The three-dimensional parametric model can accurately reflect the actual shape and size of the mold. After that, the model is meshed by finite elements to discretize the continuous mold structure into a finite number of units and nodes for numerical calculation. The quality and density of the meshing will affect the accuracy and efficiency of the calculation results, and need to be reasonably set according to the complexity of the mold and the analysis requirements.

[0057] S302, defining an elastic-plastic constitutive model of the mold material, wherein the model parameters include elastic modulus, Poisson's ratio, and dynamic yield strength.

[0058] Optionally, an elastic-plastic constitutive model of the mold material is defined, which can describe the elastic and plastic deformation behaviors of the mold material during stress.

[0059] The model parameters include elastic modulus, Poisson's ratio and dynamic yield strength. The elastic modulus reflects the material's ability to resist deformation within the elastic range; the Poisson's ratio describes the relationship between the lateral strain and the longitudinal strain of the material when it is subjected to force; and the dynamic yield strength indicates the stress when the material begins to produce plastic deformation. These parameters can be obtained through material testing or by referring to relevant materials.

[0060] S303, converting the powder-mold interaction force field data into a mold surface distribution load, and applying it to the corresponding boundary of the finite element model.

[0061] Optionally, the die interaction force field data is converted into die surface distribution loads. The powder-die interaction force field data reflects the distribution of forces applied by the powder to the die surface during the pressing process. Through a specific conversion method, these forces are converted into surface distribution loads suitable for finite element analysis and applied to the corresponding boundaries of the finite element model. This can simulate the force conditions of the die during the actual powder pressing process.

[0062] S304, solving the finite element control equation including material nonlinearity to obtain the stress data, strain data and displacement distribution data of the mold.

[0063] Optionally, solve the finite element control equations that include material nonlinearity. Since the mold material may undergo plastic deformation during the stress process, resulting in the material's stress-strain relationship showing nonlinear characteristics, it is necessary to use the finite element control equations that consider material nonlinearity for solution.

[0064] By solving these equations, the stress data, strain data and displacement distribution data of the mold can be obtained.

[0065] Furthermore, the method also includes: generating stress cloud maps, strain cloud maps and displacement vector maps of key sections of the mold based on the stress data, strain data and displacement distribution data of the mold; calculating the maximum equivalent stress value, the maximum plastic strain value and the maximum displacement; when the maximum equivalent stress value is less than the dynamic yield strength of the mold material, determining that the stress data, strain data and displacement distribution data of the mold have passed the verification.

[0066] Among them, according to the stress data, strain data and displacement distribution data of the mold, the stress cloud map, strain cloud map and displacement vector map of the key cross-section of the mold are generated.

[0067] The stress cloud diagram can intuitively display the stress magnitude and distribution of different parts of the mold. The darker the color, the greater the stress. The strain cloud diagram reflects the degree of deformation of the mold. The displacement vector diagram can show the displacement direction and magnitude of each point of the mold. These graphics help engineers quickly understand the mechanical response of the mold and find potential problem areas.

[0068] Among them, the maximum equivalent stress value reflects the maximum stress level borne by the mold; the maximum plastic strain value indicates the maximum degree of plastic deformation of the mold; and the maximum displacement reflects the maximum deformation of the mold.

[0069] When the maximum equivalent stress value is less than the dynamic yield strength of the mold material, the stress data, strain data, and displacement distribution data of the mold are determined to have passed the verification. This means that under the current stress conditions, the mold will not undergo plastic deformation and can meet the design requirements. If the maximum equivalent stress value exceeds the dynamic yield strength of the mold material, the mold design needs to be optimized, such as adjusting the mold geometry, changing the material, etc., and then re-analyzing and verifying.

[0070] In this embodiment, through accurate finite element analysis and data verification, potential problems of the mold can be discovered in the design stage, and optimization can be performed in a timely manner to reduce the failure rate of the mold in actual use.

[0071] In an exemplary embodiment, generating a powder pressing path control instruction of a mold according to the particle mechanical motion characteristic data and the mold macroscopic mechanical response data includes: The contact force data in the particle mechanical motion characteristic data and the stress distribution data in the mold macroscopic mechanical response data are coupled and calculated to determine the powder-mold interaction force field data; based on the collaborative analysis of the particle mechanical motion characteristic data, the mold macroscopic mechanical response data and the powder-mold interaction force field data, the powder pressing path control instructions of the mold are generated.

[0072] Optionally, contact force data is selected from the particle mechanical motion characteristic data, which reflects the magnitude and direction of the interaction force between powder particles and between particles and the mold. At the same time, stress distribution data is selected from the mold macroscopic mechanical response data, which reflects the stress conditions of various parts of the mold during the powder pressing process.

[0073] The contact force data is coupled with the stress distribution data. This is because the contact force of the powder particles will directly act on the mold, affecting the stress distribution of the mold; and the stress state of the mold will in turn affect the movement and contact of the powder particles. Through coupled calculation, the mutual influence between the two can be comprehensively considered to more accurately determine the powder-mold interaction force field data. This force field data fully describes the interaction between the powder and the mold during the entire pressing process, including information such as the magnitude, direction and distribution of the force.

[0074] Furthermore, a collaborative analysis is performed based on the particle mechanical motion characteristics data, the mold macro-mechanical response data and the powder-mold interaction force field data. The particle mechanical motion characteristics data can help us understand the movement trajectory and speed changes of the powder particles during the pressing process; the mold macro-mechanical response data can help us understand the stress, strain and displacement distribution of the mold; and the powder-mold interaction force field data reveals the interaction mechanism between the two.

[0075] Based on the results of the collaborative analysis, the powder pressing path control instructions for the mold are generated. These instructions will accurately specify the various parameters of the powder molding machine during the pressing process, such as pressing speed, pressure, pressing direction and pressing time. For example, if the analysis finds that the stress in a certain part of the mold is too large, the instructions may adjust the pressing speed or pressure to avoid excessive force on that part; if the powder particles are unevenly distributed in certain areas, the instructions may change the pressing direction to promote uniform filling of the powder.

[0076] In this embodiment, by accurately considering the movement of powder particles and the mechanical response of the mold, the generated pressing path control instructions can make the powder fill the mold cavity more evenly, reduce the density differences and defects of the molded products, and thus improve the quality and performance of the products.

[0077] In an exemplary embodiment, Figure 4 As shown, the contact force data in the particle mechanical motion characteristic data and the stress distribution data in the mold macroscopic mechanical response data are coupled and calculated to determine the powder-mold interaction force field data, including: S401, establish a mapping relationship between discrete element particle contact force and finite element node force, and use Gaussian interpolation method to convert discrete contact force into continuous distributed load.

[0078] Alternatively, in the discrete element method, the contact force of powder particles is discretely distributed, while the finite element method requires a continuously distributed load when analyzing the mechanical response of the mold. Therefore, the mapping relationship between the discrete element particle contact force and the finite element node force must be established first. This step is like building a bridge to transfer the discrete particle contact force information to the nodes of the finite element model.

[0079] The Gaussian interpolation method is used to convert discrete contact forces into continuous distributed loads. The Gaussian interpolation method is a commonly used numerical interpolation method that can calculate the function value of any point in the continuous region by weighted average based on the function value at the discrete point. Here, the Gaussian interpolation method is used to convert the discrete particle contact force into a continuously distributed load and apply it to the finite element model, so that the finite element model can accurately simulate the mechanical response of the mold under the action of the powder contact force.

[0080] S402, through bidirectional coupling iterative calculation, the contact force distribution of the discrete element system and the stress distribution of the finite element system meet the force balance condition.

[0081] Alternatively, the interaction between powder and mold is a dynamic process. The movement and contact force of powder particles will affect the stress distribution of the mold, and the stress distribution of the mold will in turn affect the movement and contact state of powder particles. Therefore, a two-way coupled iterative calculation is required.

[0082] During the iterative calculation process, the contact force distribution of the discrete element system and the stress distribution of the finite element system are made to meet the force balance condition. The force balance condition is a basic principle in physics, that is, when an object is in a state of equilibrium under the action of force, the resultant force is zero. In the scenario of powder-mold interaction, it is necessary to ensure that the sum of the contact forces of the powder particles in the discrete element system and the sum of the stresses borne by the mold in the finite element system are balanced in all directions. Through continuous iterative adjustments, the two gradually approach the force balance state.

[0083] S403, recording the spatial distribution data of the interaction force field when the convergence condition is reached.

[0084] Optionally, during the bidirectional coupling iterative calculation process, a convergence condition needs to be set. The convergence condition usually means that when the difference between the contact force distribution of the discrete element system and the stress distribution of the finite element system is less than a preset threshold, the iterative calculation is considered to have reached a convergence state. The setting of this threshold needs to be determined according to the specific application scenario and the calculation accuracy requirements.

[0085] When the convergence condition is reached, the spatial distribution data of the interaction force field at this time is recorded. These data include information such as the magnitude, direction and spatial distribution of the interaction force between the powder and the mold. They are key data for describing the powder-mold interaction force field and provide an important basis for the subsequent generation of powder pressing path control instructions.

[0086] In this embodiment, by establishing a mapping relationship and using bidirectional coupling iterative calculation, the discrete characteristics of the powder particles and the continuous structural characteristics of the mold are fully considered, so that the calculation results more accurately reflect the actual situation of the powder-mold interaction force field.

[0087] In an exemplary embodiment, Figure 5 As shown, based on the collaborative analysis of particle mechanical motion characteristic data, mold macroscopic mechanical response data and powder-mold interaction force field data, the powder pressing path control instructions of the mold are generated, including: S501, constructing a multi-dimensional feature matrix including force chain network features, mold deformation features and interaction force field features.

[0088] Optionally, extract relevant features of the force chain network from the discrete element analysis results, such as the length distribution, strength distribution and topological structure of the force chain. Obtain the deformation data of the mold from the finite element analysis results, and calculate the mold deformation features such as the maximum deformation and deformation distribution of the mold. Extract the interaction force field features such as the magnitude, direction and distribution of the force from the powder-mold interaction force field data. Arrange these features into a multi-dimensional feature matrix according to certain rules, and each row or column of the matrix represents a feature vector.

[0089] S502, performing multi-objective optimization on the pressing path parameters based on a genetic algorithm to generate an optimized pressing path parameter set including a pressing speed curve, a pressure loading curve and a holding time.

[0090] Among them, the multiple optimization objectives include minimizing the maximum equivalent stress of the mold, maximizing the uniformity of powder pressing density and minimizing the pressing energy consumption.

[0091] Optionally, define the value range of the pressing path parameters, including the value range of the pressing speed curve, the value range of the pressure loading curve, and the value range of the holding time. Initialize a set of random pressing path parameters as the initial population of the genetic algorithm. For each individual (i.e., a set of pressing path parameters), perform simulation calculations based on the multi-dimensional feature matrix, evaluate its performance in the three goals of minimizing the maximum equivalent stress of the mold, maximizing the uniformity of the powder pressing density, and minimizing the pressing energy consumption, and calculate the fitness value. Use the selection, crossover, and mutation operations of the genetic algorithm to iteratively update the population and continuously search for a better combination of pressing path parameters. When the preset number of iterations or convergence conditions are met, stop the iteration and select the individual with the best fitness value as the optimized pressing path parameter set.

[0092] S503, generating a powder pressing path control instruction of the mold according to the optimized pressing path parameter set.

[0093] Optionally, according to the optimized pressing path parameter set, parameters such as the pressing speed curve, the pressure loading curve and the holding time are converted into specific control instruction codes. These instruction codes can be transmitted to the control system of the powder forming machine through the interface to control the powder forming machine to perform powder pressing operations according to the optimized path.

[0094] In an exemplary embodiment, Figure 6 As shown, according to the optimized pressing path parameter set, the powder pressing path control instructions of the mold are generated, including: S601, obtaining real-time sensing data of the mold.

[0095] Among them, the real-time sensing data includes the mold surface pressure distribution, punch displacement and mold temperature.

[0096] Optionally, a pressure sensor is installed on the mold surface to measure the pressure distribution on the mold surface; a displacement sensor is installed at the punch position to monitor the displacement of the punch; a temperature sensor is installed at a key position of the mold to obtain the mold temperature in real time.

[0097] S602, based on the real-time sensor data, calculate the deviation dynamics between the current suppression state and the target state.

[0098] Optionally, based on the optimized pressing path parameter set, the target state of the powder pressing process is determined, such as the target pressure distribution, the target punch displacement, and the target mold temperature.

[0099] The real-time sensing data of the mold is compared with the target state, and the deviation dynamics between the current pressing state and the target state are calculated. The deviation dynamics reflects the degree of deviation and change trend between the actual pressing process and the expected target.

[0100] S603, adjusting the pressure path parameter set according to the deviation dynamics to obtain the optimal pressure path.

[0101] Optionally, a model predictive control algorithm is used, which can optimize and adjust the control parameters according to the current state of the system and future prediction information. According to the calculated deviation dynamics, the model predictive control algorithm is used to adjust the path parameters of the suppression path parameter set.

[0102] Path parameters include pressure gradient, pressurization sequence, and holding time. By adjusting these parameters, the pressing process can approach the target state as quickly as possible and obtain the optimal pressure path. For example, if the pressure distribution on the mold surface is uneven, the pressure gradient can be adjusted to make the pressure distribution more uniform; if the punch displacement deviates greatly from the target value, the pressurization sequence can be adjusted to ensure that the punch moves according to the expected displacement.

[0103] S604, generating a powder pressing path control instruction for the mold according to the optimal pressure path.

[0104] Optionally, the optimal pressure path is converted into a specific control instruction code, and the instruction is sent to the control system of the powder forming machine through the communication interface to control the powder forming machine to perform powder pressing operations according to the optimal path.

[0105] Specifically, according to the deviation dynamics, the pressing path parameter set is adjusted to obtain the optimal pressure path, including: using a model predictive control algorithm to adjust the path parameters of the pressing path parameter set according to the deviation dynamics to obtain the optimal pressure path; the path parameters include pressure gradient, pressurization timing and holding time.

[0106] It is understandable that Model Predictive Control (MPC) is an advanced model-based control strategy. It predicts the future behavior of the system and calculates the optimal control input based on the predicted results and the set goals. In the powder pressing scenario, the algorithm uses the dynamic model of the system and combines the current deviation dynamic information to predict the state changes of the pressing process in the future, and then obtains the control parameters that can make the system approach the target state through optimization calculation.

[0107] Before using the model predictive control algorithm, it is necessary to establish a dynamic model of the powder pressing process. This model describes the relationship between the pressing path parameters (pressure gradient, pressurization timing, holding time) and the mold state (mold surface pressure distribution, punch displacement, mold temperature). The model can be established through theoretical analysis, experimental data fitting or machine learning. For example, through a large amount of experimental data, the mathematical relationship between the pressure gradient and the mold surface pressure distribution is established using regression analysis.

[0108] The calculated deviation dynamics between the current pressing state and the target state are used as input to the model predictive control algorithm. The deviation dynamics include information such as the size of the current deviation and the trend of the deviation over time. For example, if the current deviation of the mold surface pressure distribution is large and tends to increase, this indicates that the degree to which the pressing process deviates from the target state is increasing, and the algorithm needs to make more active adjustments.

[0109] Based on the established system model and the current deviation dynamics, the algorithm predicts the state of the pressing process in the future. The prediction time domain is an important parameter that determines the time range of the algorithm's prediction of the future state. A longer prediction time domain can consider longer-term effects, but will increase computational complexity; a shorter prediction time domain focuses more on recent state changes. For example, the changes in the mold surface pressure distribution, punch displacement, and mold temperature in the next few pressing cycles are predicted.

[0110] According to the predicted future state and the set optimization goal, the algorithm adjusts the path parameters of the suppression path parameter set through optimization calculation. The specific adjusted path parameters include: The pressure gradient determines the rate at which pressure increases or decreases during the pressing process. If the current pressure distribution on the mold surface is uneven and the pressure in some areas is too low, the algorithm may increase the pressure gradient to make the pressure rise faster to improve the compaction of these areas; conversely, if the pressure in some areas is too high, the pressure gradient may be reduced.

[0111] The pressurization sequence specifies the time and order of applying pressure at different stages. If the punch displacement deviates greatly from the target value, the algorithm may adjust the pressurization sequence, advancing or delaying the pressurization operation to ensure that the punch moves according to the expected displacement.

[0112] The holding time refers to the duration of holding pressure after reaching the set pressure. If the mold temperature is too high or the powder compaction effect is not ideal, the algorithm may extend the holding time to increase the compaction density of the powder; if the compaction effect is already good, the holding time may be appropriately shortened to improve production efficiency.

[0113] The model predictive control algorithm is an iterative process. In each control cycle, predictions and optimization calculations are re-performed based on the latest deviation dynamics, and path parameters are continuously adjusted to keep the pressing process close to the target state. Through this real-time dynamic adjustment, various uncertainties in the production process can be effectively addressed to ensure the stability of the powder pressing process and the molding quality.

[0114] After continuous adjustment of the model predictive control algorithm, the optimal pressure path that can make the pressing process approach the target state is finally obtained. This optimal pressure path comprehensively considers the current deviation dynamics and future predictions, and can provide accurate operation guidance for the powder forming machine.

[0115] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0116] Based on the same inventive concept, the embodiment of the present application also provides an intelligent control system for a powder forming machine for implementing the intelligent control method for the powder forming machine involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the intelligent control system for one or more powder forming machines provided below can refer to the limitations of the intelligent control method for the powder forming machine above, and will not be repeated here.

[0117] In an exemplary embodiment, an intelligent control system for a powder forming machine includes: An acquisition module, used for acquiring powder material parameters and mold parameters, wherein the powder material parameters include particle size distribution, yield strength and friction coefficient, and the mold parameters include mold geometry parameters; Powder analysis module, used to perform particle dynamics analysis on powder material parameters based on discrete element method, and generate particle mechanical motion characteristic data, which includes particle position data, velocity data, contact force data and force chain distribution data constructed by contact force data; The mold analysis module is used to perform structural mechanics analysis on mold parameters based on the finite element method and generate mold macroscopic mechanical response data, which includes stress distribution data, strain distribution data and displacement distribution data; The path instruction generation module is used to generate the powder pressing path control instructions of the mold according to the particle mechanical motion characteristic data and the mold macroscopic mechanical response data.

[0118] Each module in the intelligent control system of the above powder forming machine can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0119] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0120] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0121] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An intelligent control method for a powder forming machine, characterized in that: The method comprises: Acquiring powder material parameters and mold parameters, wherein the powder material parameters include particle size distribution, yield strength and friction coefficient, and the mold parameters include mold geometry parameters; Performing particle dynamics analysis on the powder material parameters based on discrete element method to generate particle mechanical motion characteristic data, wherein the particle mechanical motion characteristic data includes particle position data, velocity data, contact force data, and force chain distribution data constructed from the contact force data; Perform structural mechanics analysis on the mold parameters based on the finite element method to generate mold macroscopic mechanical response data, wherein the mold macroscopic mechanical response data includes stress distribution data, strain distribution data and displacement distribution data; A powder pressing path control instruction of the mold is generated according to the particle mechanical motion characteristic data and the mold macroscopic mechanical response data.

2. The method according to claim 1, characterized in that The particle dynamics analysis of the powder material parameters based on the discrete element method to generate particle mechanical motion characteristic data includes: Modeling the powder particles as a collection of discrete particles having the particle size distribution and friction coefficient; Establish a particle-to-particle contact mechanics model including normal contact force and tangential friction force, and define the contact boundary conditions between particles and mold wall; In the discrete time step of the pressing process, iteratively calculate the composite force of each particle; the composite force includes gravity, contact force between particles and force acting on the mold wall; According to the updated position data and speed data of the particles, until the pressing process is terminated; The particle motion trajectory data is extracted and a force chain network is constructed, wherein the force chain network includes characteristic parameters of the force chain spatial direction, strength and topological structure.

3. The method according to claim 1, characterized in that: The step of performing structural mechanics analysis on the mold parameters based on the finite element method to generate macroscopic mechanical response data of the mold includes: Constructing a three-dimensional parametric model according to the mold geometric parameters and performing finite element meshing; Define the elastic-plastic constitutive model of the mold material, and the model parameters include elastic modulus, Poisson's ratio and dynamic yield strength; Converting the powder-mold interaction force field data into a mold surface distribution load and applying it to the corresponding boundary of the finite element model; The finite element control equations including material nonlinearity are solved to obtain the stress data, strain data and displacement distribution data of the mold.

4. The method according to claim 3, characterized in that The method further comprises: Generate a stress nephogram, a strain nephogram and a displacement vector diagram of a key section of the mold according to the stress data, strain data and displacement distribution data of the mold; Calculate the maximum equivalent stress value, maximum plastic strain value and maximum displacement; When the maximum equivalent stress value is less than the dynamic yield strength of the mold material, it is determined that the stress data, strain data, and displacement distribution data of the mold pass the verification.

5. The method according to claim 1, characterized in that The step of generating a powder pressing path control instruction of the mold according to the particle mechanical motion characteristic data and the mold macroscopic mechanical response data comprises: The contact force data in the particle mechanical motion characteristic data and the stress distribution data in the mold macroscopic mechanical response data are coupled and calculated to determine the powder-mold interaction force field data; Based on the collaborative analysis of the particle mechanical motion characteristic data, the mold macroscopic mechanical response data and the powder-mold interaction force field data, a powder pressing path control instruction of the mold is generated.

6. The method according to claim 5, characterized in that The coupling calculation of the contact force data in the particle mechanical motion characteristic data and the stress distribution data in the mold macroscopic mechanical response data to determine the powder-mold interaction force field data includes: Establish the mapping relationship between discrete element particle contact force and finite element node force, and use Gaussian interpolation method to convert discrete contact force into continuous distributed load Through bidirectional coupling iterative calculation, the contact force distribution of the discrete element system and the stress distribution of the finite element system meet the force balance condition; Record the spatial distribution data of the interaction force field when the convergence condition is reached.

7. The method according to claim 5, characterized in that The generating of the powder pressing path control instruction of the mold based on the collaborative analysis of the particle mechanical motion characteristic data, the mold macroscopic mechanical response data and the powder-mold interaction force field data comprises: Construct a multi-dimensional feature matrix including force chain network features, mold deformation features and interaction force field features; Based on the genetic algorithm, the pressing path parameters are optimized by multiple objectives to generate an optimized pressing path parameter set including the pressing speed curve, pressure loading curve and holding time; the multiple optimization objectives include minimizing the maximum equivalent stress of the mold, maximizing the uniformity of the powder pressing density and minimizing the pressing energy consumption; According to the optimized pressing path parameter set, a powder pressing path control instruction of the mold is generated.

8. The method according to claim 7, characterized in that The step of generating a powder pressing path control instruction of a mold according to the optimized pressing path parameter set comprises: Acquiring real-time sensing data of the mold, wherein the real-time sensing data includes mold surface pressure distribution, punch displacement and mold temperature; Based on the real-time sensor data, calculating the deviation dynamics between the current suppression state and the target state; According to the deviation dynamics, adjusting the pressing path parameter set to obtain an optimal pressure path; According to the optimal pressure path, a powder pressing path control instruction of the mold is generated.

9. The method according to claim 8, characterized in that The step of adjusting the pressing path parameter set according to the deviation dynamics to obtain an optimal pressure path includes: A model predictive control algorithm is used to adjust the path parameters of the pressing path parameter set according to the deviation dynamics to obtain an optimal pressure path; the path parameters include pressure gradient, pressurization timing and pressure holding time.

10. An intelligent control system for a powder forming machine, characterized in that: The system comprises: An acquisition module, used for acquiring powder material parameters and mold parameters, wherein the powder material parameters include particle size distribution, yield strength and friction coefficient, and the mold parameters include mold geometry parameters; A powder analysis module, for performing particle dynamics analysis on the powder material parameters based on a discrete element method to generate particle mechanical motion characteristic data, wherein the particle mechanical motion characteristic data includes particle position data, velocity data, contact force data, and force chain distribution data constructed from the contact force data; A mold analysis module, used for performing structural mechanics analysis on the mold parameters based on the finite element method to generate mold macroscopic mechanical response data, wherein the mold macroscopic mechanical response data includes stress distribution data, strain distribution data and displacement distribution data; A path instruction generation module is used to generate a powder pressing path control instruction of the mold according to the particle mechanical motion characteristic data and the mold macroscopic mechanical response data.

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