Intelligent inversion method for simulating self-organization mechanism of lunar soil brick pores by laser additive manufacturing

Abstract

The present application belongs to the technical field of extraterrestrial building construction, and discloses a pore self-organizing mechanism intelligent inversion method for simulating lunar soil bricks by laser additive manufacturing. The method comprises the following steps: discretizing the material in the molten pool of the simulating lunar soil bricks by laser additive manufacturing into a plurality of units, each unit being mapped as a cell, and all cells forming a cellular automaton; establishing a state vector update formula for each cell with respect to a plurality of to-be-inverted parameters; establishing a target function of the cellular automaton containing a survival function, a mass conservation penalty term and an energy conservation penalty term, and solving the to-be-inverted parameter value corresponding to the minimum of the target function by using the actual pore survival function value of the simulating lunar soil bricks; substituting the to-be-inverted parameter value into the state vector update formula of each cell to update the state vector of each cell; and thus realizing the pore inversion of the simulating lunar soil bricks. Through the present application, the quantitative analysis and high-precision prediction of the pore formation mechanism are realized.

Description

Technical Field

[0001] This invention belongs to the technical field of extraterrestrial building construction, and more specifically, relates to an intelligent inversion method for simulating the pore self-organization mechanism of lunar soil bricks using laser additive manufacturing. Background Technology

[0002] Basic building materials required for lunar surface construction can be achieved through in-situ resource utilization (ISRU) technology, with laser additive construction (LAC) considered one of the most promising processes. This method utilizes a high-energy laser beam to melt and sinter lunar regolith-simulated materials in a vacuum environment, thereby rapidly forming structural components. However, during LAC, due to the coupling effect of gas behavior, material volatilization, and the dynamic instability of the molten pool under vacuum conditions, complex multi-scale porous microstructures often form inside the construct. The microscopic physical mechanism of this formation is still unclear, and the pore size distribution is difficult to control effectively. These pores directly affect the material's density, strength, thermal conductivity, and other properties, and are key factors in achieving high-quality lunar regolith building materials, becoming a technical bottleneck restricting the preparation of high-quality lunar regolith structural components.

[0003] Existing research is largely based on reductionism, focusing on the analysis of local physicochemical mechanisms, but failing to reveal the overall coupled evolution and self-organization mechanisms of porous systems. Furthermore, traditional modeling methods, when dealing with pore structure inversion problems, often rely solely on experimental data, lacking effective integration of key physical constraints such as molten pool dynamics, heat and mass transfer, resulting in insufficient generalization ability of models in predicting pore size distribution and formation mechanisms, making it difficult to accurately describe and control the pore formation process. Therefore, there is an urgent need for an inversion method that integrates experimental observations, physical laws, and intelligent optimization algorithms to systematically reveal the self-organization formation mechanism of simulated lunar regolith brick pore structures during laser additive manufacturing, providing theoretical basis and methodological support for the preparation of lunar regolith building materials with controllable pore size and optimized performance. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides an intelligent inversion method for simulating the pore self-organization mechanism of lunar soil bricks using laser additive manufacturing, solving the problems of unclear microscopic physical mechanisms of pore formation and uncontrollable size distribution in simulated lunar soil bricks during laser additive manufacturing.

[0005] To achieve the above objectives, according to one aspect of the present invention, a smart inversion method for simulating the pore self-organization mechanism of lunar soil bricks using laser additive manufacturing is provided, the method comprising the following steps:

[0006] The material in the simulated lunar soil brick molten pool is discretized into multiple units using laser additive manufacturing. Each unit is mapped to a cell, and all cells form a cellular automaton. The state vector update formula for each cell with respect to multiple inversion parameters is established using the law of conservation of energy, phase transition dynamics, and the law of conservation of mass.

[0007] The objective function of the cellular automaton is established, which includes a survival function, a mass conservation penalty, and an energy conservation penalty. The value of the parameter to be inverted is solved when the objective function is minimized by using the actual pore survival function value of the simulated lunar regolith.

[0008] The inversion parameters are substituted into the state vector update formula of each cell to update the state vector of each cell; thus, the porosity inversion of the simulated lunar soil brick is realized.

[0009] More preferably, the state vector includes temperature, melt fraction, and porosity fraction.

[0010] More preferably, the temperature is updated according to the following formula:

[0011]

[0012] in, Time of the first The temperature of each cell, It is a cell index. It was a moment. It is a unit time interval. Time of the first The temperature of each cell, It is cell density. It is the specific heat capacity of the material. It is a discrete heat conduction operator. It is laser energy deposition. It is the latent heat of phase transition per unit volume. The first unit of time The change in melt fraction of each cell.

[0013] More preferably, the melt fraction is updated according to the following formula:

[0014]

[0015]

[0016] in, Time of the first The melt fraction of each cell, Time of the first The melt fraction of each cell, It is the solidification rate constant. It is the first one using temperature mapping The melt fraction of each cell, Time of the first The temperature of each cell, It is a solidus line. It is the liquidus line.

[0017] More preferably, the update formula for the porosity fraction is as follows:

[0018]

[0019]

[0020]

[0021]

[0022] in, It is a cell index. yes Time of the first Porosity of each cell Time of the first Porosity of each cell This indicates that it originates from gas generation (material decomposition or initial sequestration). This indicates the first [condition caused by gas flow]. Changes in the volume fraction of each cell It indicates solidification capture. It's the trigger temperature. It is the growth rate constant. Time of the first The temperature of each cell, It is the empirically effective transfer coefficient. yes Time of the first Porosity of each cell It is the first The cell and the first cell The distance between cells , For cells The neighborhood, It's about capture efficiency. , Time of the first The melt fraction of each cell, Time of the first Melt fraction of each cell.

[0023] More preferably, the objective function is as follows:

[0024]

[0025]

[0026] in, It is a cell index. It is a multi-objective function. It is the set of parameters to be inverted. This is a survival function data item used to measure the consistency between the simulated pore size distribution and the experimental pore size distribution. It is a mass conservation penalty term, used to constrain mass conservation during the simulation process. It is an energy conservation penalty term used to constrain the energy conservation of the model during its time evolution. It is the survival function data item weight factor. It is a penalty factor for the conservation of quality. It is a penalty factor for energy conservation. It is cell density. The volume of a cell. It is a discrete heat conduction operator. It is the specific heat capacity of the material. It is the latent heat of phase transition per unit volume. The solidification rate constant is It is a solidus line. It is the liquidus line.

[0027] More preferably, the formulas for the survival function data image, the mass conservation penalty, and the energy conservation penalty are as follows:

[0028]

[0029]

[0030]

[0031] in, This is a survival function data item used to measure the consistency between the simulated pore size distribution and the experimental pore size distribution. It is an aperture index. It is the total number of aperture types. It is the first Various pore sizes of samples, It is the first The survival function values ​​of the aperture obtained from cellular automata simulation. It is the first Survival function values ​​obtained from observations of aperture size. It is a mass conservation penalty term, used to constrain mass conservation during the simulation process. It is a time index. It is the total number of moments. It is the first At that moment, It is the first The mass obtained from cellular automata simulation at a given moment. It is the initial mass. It is an energy conservation penalty term used to constrain the energy conservation of the model during its time evolution. It is a cell index. It is the total number of cells. It is the first The cellular energy residual at each moment It is the normalization coefficient.

[0032] More preferably, the objective function is solved using a particle swarm optimization algorithm.

[0033] More preferably, the method for obtaining the actual porosity survival function value of the simulated lunar soil brick is as follows:

[0034] A three-dimensional model of a real simulated lunar soil brick is constructed and the three-dimensional model is sliced ​​at the pixel level to obtain the porosity information in each slice. The porosity is reconstructed using the porosity information in all slices.

[0035] The reconstructed pores are morphologically segmented to obtain individual pores. Each pore is equivalent to an equivalent sphere, and the survival function value of the pore is calculated using the equivalent diameter and total number of all equivalent spheres.

[0036] According to another aspect of the present invention, a smart inversion system for simulating the pore self-organization mechanism of lunar soil bricks using laser additive manufacturing is provided. The system includes an actuator for performing the aforementioned smart inversion method for simulating the pore self-organization mechanism of lunar soil bricks using laser additive manufacturing.

[0037] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0038] 1. This invention establishes a physically enhanced intelligent inversion model, constructs a cellular automaton that integrates energy, mass conservation, and phase transition dynamics, and employs a multi-objective optimization method to synchronously match pore observation data with physical constraints, thereby clearly revealing the microscopic mechanism of pore formation. This solves the problems of unclear pore formation mechanism and uncontrollable size distribution, and provides a theoretical basis and virtual experimental platform for the process optimization of precise control of pores in laser additive manufacturing.

[0039] 2. This invention significantly improves the reliability and physical credibility of the inversion results by constructing a multi-objective objective function and simultaneously matching data with physical rules. While intelligently inverting based on observation data, it ensures that the inversion results are physically real and feasible, producing a model with both high data fitting accuracy and high physical fidelity.

[0040] 3. This invention uses mathematical modeling to embed physical rules as intrinsic and mandatory guiding principles into the core optimization process of the inversion algorithm. This not only suppresses the interference caused by noise in the observation data and improves the stability of the inversion process, but also ensures that the inversion results conform to physical rules. This achieves the organic integration of experimental observation data and physical constraint information, making the final inversion results robust, reliable and interpretable.

[0041] 4. The rule parameters obtained by the inversion of this invention have clear physical meanings and directly reveal the microscopic physical mechanism of pore formation, rather than just black box prediction. In addition, the inverted and calibrated cellular automata model can serve as an efficient virtual experimental platform for low-cost, high-throughput process optimization and performance design of lunar soil bricks. Attached Figure Description

[0042] Figure 1 This is a schematic flowchart of an intelligent inversion method for pore self-organization mechanism of simulated lunar soil bricks constructed by laser additive manufacturing, according to a preferred embodiment of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0044] like Figure 1 As shown, the intelligent inversion method for simulating the pore self-organization mechanism of lunar soil bricks using laser additive manufacturing of the present invention comprises the following detailed steps:

[0045] S1. Observe and analyze the three-dimensional pore microstructure inside simulated lunar soil bricks constructed using laser additive manufacturing.

[0046] First, high-resolution X-ray CT scanning technology was used to obtain a micron-level three-dimensional model containing information on the internal pore microstructure of simulated lunar soil bricks constructed using laser additive manufacturing.

[0047] The 3D model is sliced ​​pixel-by-pixel along the Z-axis. Each 2D slice is then segmented using a thresholding method to mark the pore portions, resulting in a pore information mask. The moving cube method is then used to reconstruct the 3D model of the pore portions from all the pore information masks along the Z-axis.

[0048] Subsequently, morphological segmentation was performed on the 3D model of the pore portion to segment out individual pores.

[0049] Finally, each pore was fitted to an equivalent sphere using the least squares method.

[0050] By statistically analyzing the equivalent diameter and corresponding number of all pore equivalent spheres, the actual survival function value of the pores is obtained, providing data basis for the intelligent inversion of the pore self-organization mechanism in the subsequent laser additive construction of simulated lunar soil bricks.

[0051] S2. Establish a cellular automata evolution model for physically enhanced porous microstructures.

[0052] Based on physical principles such as energy conservation, phase transition dynamics, and mass conservation, a physical enhancement cellular automata evolution model for porous microstructures is established to simulate the forming process of simulated lunar soil bricks using laser additive manufacturing.

[0053] First, the material within the molten pool used in the laser additive fabrication of simulated lunar regolith bricks is discretized into multiple material elements. In this embodiment, it is discretized into 100×100×50 material elements, and each material element is mapped to a cell with a size of 5μm. The state vector of each cell is defined as follows:

[0054]

[0055] in, The cell number, Temperature, reflecting the thermal state at that location; Melt fraction, representing the volume fraction of the molten phase within a unit cell; Porosity fraction represents the volume fraction of the cell occupied by gas; simultaneously, the cell material and geometric parameters are defined. Cell density; The volume of a unit cell; This is the model parameter set.

[0056] Then, based on physical rules, the evolution rules of the cellular automata are defined, and the evolution of the porous microstructure cells is controlled by a unified evolution operator:

[0057]

[0058] in, For cells ; External input (such as laser energy).

[0059] The evolution operator is represented in component form as follows:

[0060] (1) Temperature changes that follow the law of conservation of energy

[0061]

[0062] in, For discrete heat conduction operators; Cell density; Specific heat capacity of the material; Laser energy deposition; The latent heat of phase change per unit volume.

[0063] (2) Melt fraction change following phase transition kinetics

[0064]

[0065]

[0066] in, It is the solidification rate constant; For the first temperature mapping The melt fraction of each cell; It is a solidus line; This is the liquidus line.

[0067] (3) The generation, growth, merging and fixation of pore fraction following the law of mass conservation

[0068]

[0069] This indicates that it originates from gas generation (material decomposition or initial sequestration). It is the trigger temperature, which is taken in this embodiment. ;

[0070] This represents the change in volume fraction caused by gas flow. The empirically effective transfer coefficient is taken in this embodiment. ;

[0071] , indicating solidification capture, To improve capture efficiency, this embodiment uses... .

[0072] S3. Conduct parameter inversion of the physical augmented cellular automata evolution model based on intelligent algorithms.

[0073] First, based on the output of the established physical augmented cellular automata evolution model, the following multi-objective optimization function is constructed:

[0074]

[0075] , is the set of parameters to be inverted;

[0076] , is a survival function data term used to measure the consistency between simulated and experimental pore size distributions. The aperture survival function obtained from the simulation, The survival function is obtained from actual observations. For pore size sample points, The total number of samples;

[0077] This is a penalty term for mass conservation, used to constrain mass conservation during the simulation process. yes Simulation quality at any given moment It is the initial mass;

[0078] , which is the energy conservation penalty term used to constrain the energy conservation of the model during time evolution, where, For the cell energy balance residual, These are the normalization coefficients;

[0079] , is the weighting factor; , which is the penalty factor.

[0080] Then, the parameters of the cellular automata evolution model are searched using the Particle Swarm Optimization (PSO) algorithm. This is achieved by randomly setting the model parameter set under initial conditions. Initialize the cellular automaton evolution model. Set the number of particles in the PSO algorithm to 40 and the maximum number of iterations to 200. In each iteration, In the cellular automata evolution model, the optimized function value is calculated based on the fireworks results, and the optimal value is continuously searched. After 200 iterations, the optimized inversion parameter combination is obtained. Finally, the optimized inversion parameter combination is fed back into the cellular automata evolution model, enabling it to map the self-organizing mechanism of pores in simulated lunar regolith bricks constructed using laser additive manufacturing, thus completing the aforementioned intelligent inversion.

[0081] Those skilled in the art will readily understand that the above description is merely 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 within the scope of protection of the present invention.

Claims

1. A method for intelligent inversion of pore self-organization mechanisms of laser additive built simulated lunar soil bricks, characterized in that, The method comprises the following steps: discretizing the material in the melt pool of the laser additive manufacturing simulated lunar soil brick into a plurality of units, each unit being mapped as a cell, all the cells forming a cellular automaton; using the law of conservation of energy, phase change dynamics and the law of conservation of mass to establish a state vector update formula of each cell with respect to a plurality of to-be-inverted parameters; establishing an objective function of the cellular automaton containing a survival function, a mass conservation penalty term and an energy conservation penalty term, and solving the to-be-inverted parameter values corresponding to the minimum of the objective function by using the actual pore survival function value of the simulated lunar soil brick; substituting the to-be-inverted parameter values into the state vector update formula of each cell to update the state vector of each cell, thereby realizing the pore inversion of the simulated lunar soil brick; the state vector comprises temperature, melt fraction and pore fraction; the update of the temperature is performed according to the following formula: in, Time of the first The temperature of each cell It is a cell index. It was a moment. It is a unit time interval. Time of the first The temperature of each cell It is cell density. It is the specific heat capacity of the material. It is a discrete heat conduction operator. It is laser energy deposition. It is the latent heat of phase transition per unit volume. The first unit of time The change in melt fraction of each cell; the update of the melt fraction is performed according to the following formula: wherein, the melt fraction of the cell at time , the melt fraction of the cell at time , is a solidification rate constant, is the melt fraction of the cell using temperature mapping at time , the temperature of the cell at time , is the solidus, is the liquidus; the update formula of the pore fraction is as follows: wherein, is the cell index, is the porosity of the th cell at time, the porosity of the th cell at time, is the volume fraction change of the th cell due to gas generation, material decomposition or initial sequestration, is the volume fraction change of the th cell due to gas flow, is the solidification capture, is the growth rate constant, the temperature of the th cell at time, is the empirical effective diffusivity, is the porosity of the th cell at time, is the distance between the th cell and the th cell, , is the neighborhood of the cell , is the capture efficiency, , the melt fraction of the th cell at time, the melt fraction of the th cell at time.

2. The intelligent inversion method for simulating the pore self-organization mechanism of lunar soil bricks using laser additive manufacturing as described in claim 1, characterized in that, the objective function is as follows: wherein, is a cell index, is a multi-objective function, is a set of parameters to be inverted, is a survival function data item for measuring the consistency of simulated pore size distribution with experimental pore size distribution, is a mass conservation penalty term for constraining the mass conservation during simulation, is an energy conservation penalty term for constraining the energy conservation of the model in time evolution, is a survival function data item weight factor, is a mass conservation penalty term penalty factor, is an energy conservation penalty term penalty factor, is a cell density, is a cell volume, is a discrete heat conduction operator, is a material specific heat capacity, is a latent heat of phase change per unit volume, is a solidification rate constant, is a solidus, is a liquidus.

3. The intelligent inversion method for simulating the pore self-organization mechanism of lunar soil bricks using laser additive manufacturing as described in claim 1, characterized in that, the formula of the survival function data item, the mass conservation penalty term and the energy conservation penalty term is as follows: wherein, is a survival function data item for measuring the consistency of the simulated aperture distribution and the experimental aperture distribution, is an aperture index, is the total number of aperture types, is an aperture sample of the th aperture type, is a survival function value of the th aperture obtained by the cellular automaton simulation, is a survival function value of the th aperture obtained by observation, is a mass conservation penalty term for constraining the mass conservation in the simulation process, is a time index, is the total number of times, is the mass of the th time, is the mass of the th time obtained by the cellular automaton simulation, is the initial mass, is an energy conservation penalty term for constraining the energy conservation of the model in time evolution, is a cell index, is the total number of cells, is the cell energy residual of the th time, is a normalization coefficient.

4. The method of claim 1, wherein, the solving of the objective function adopts a particle swarm optimization algorithm.

5. The method of claim 1, wherein, The method for obtaining the actual pore survival function value of the simulated lunar soil brick is as follows: constructing a three-dimensional model of the actual simulated lunar soil brick and performing pixel-level slicing on the three-dimensional model to obtain pore information in each slice, reconstructing pores by using the pore information in all the slices; performing morphological segmentation on the reconstructed pores to obtain single pores, equivalent spheres are used to equivalent each pore, and the survival function value of the pores is calculated by using the equivalent diameters and total number of all the equivalent spheres.

6. A system for intelligent inversion of pore self-organization mechanisms of laser additive built simulated lunar soil bricks, characterized in that, The system comprises an executor configured to execute the intelligent inversion method of the self-organizing mechanism of the pores of the laser additive manufacturing simulated lunar soil brick according to any one of claims 1-5.

Images