A high-throughput modeling and computing method and system for metal matrix composites

Abstract

The present application relates to the technical field of metal matrix composites, and provides a high-throughput modeling and calculation method for metal matrix composites, comprising the following steps: S1: determining the parameters of particles and matrix in different particle reinforced metal matrix composites; S2: establishing brick masonry configuration, multi-core structure and Belytschko configuration based on the parameters of particles and matrix; S3: establishing gradient distribution configuration based on the parameters of particles and matrix, including surface layer hard structure and core layer hard structure and cobweb structure; S4: establishing honeycomb structure, micro-tube structure and uniform structure based on the parameters of particles and matrix; S5: batch setting of boundary conditions required for each model in uniaxial tension, creating and gradually submitting jobs, and automatically extracting parameters including stress-strain curve, stress nephogram and energy release rate at any moment of uniaxial tension. The present application solves the problem of reducing the cost required for exploring high-performance materials in high-throughput modeling and calculation of particle reinforced metal matrix composites.

Description

Technical Field

[0001] This invention relates to the technical field of metal matrix composites, and more particularly to a high-throughput modeling and calculation method and system for metal matrix composites. Background Technology

[0002] Particle-reinforced metal matrix composites (such as silicon carbide / aluminum matrix composites) have broad application prospects in aerospace, automotive manufacturing, and electronic packaging due to their high strength, high modulus, good wear resistance, and thermal stability. The introduction of reinforcing phases can significantly improve the mechanical properties of the matrix alloy while maintaining low density, making it an ideal choice for lightweight, high-performance materials.

[0003] However, the performance of particle-reinforced metal matrix composites is highly dependent on a variety of factors, including matrix composition, reinforcing phase characteristics (such as size, morphology, and distribution), and interfacial bonding state. This multi-dimensional design space makes traditional trial-and-error methods extremely costly in terms of time and money when optimizing material properties. For example, selecting the optimal reinforcing phase distribution pattern solely through experimental methods often requires preparing a large number of samples and conducting tedious mechanical tests, which is extremely inefficient.

[0004] High-throughput modeling and finite element method (FEM) calculations offer an effective approach to addressing this problem, enabling rapid evaluation of the mechanical response of different structural designs through numerical simulation. However, traditional high-throughput methods typically consider only randomly distributed reinforcing phase configurations, neglecting specific microstructural designs (such as gradient distributions, multi-core structures, and honeycomb configurations) that can achieve synergistic strength-toughness enhancements. This simplification makes it difficult to fully explore the performance potential of composite materials through simulation results, especially in applications requiring a balance between high strength and high toughness. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to provide a high-throughput modeling and calculation method and system for metal matrix composites, thereby reducing the cost required for exploring high-performance materials in the high-throughput modeling and calculation of particle-reinforced metal matrix composites.

[0006] The above-mentioned objective of this invention is achieved through the following technical solutions:

[0007] A high-throughput modeling and computation method for metal matrix composites includes the following steps:

[0008] S1: Determine the particle and matrix parameters in metal matrix composites with different particle reinforcements;

[0009] S2: Based on particle and matrix parameters, brick masonry configurations, multi-core structures, and Brigand structures are established.

[0010] S3: Establish gradient distribution configurations based on particle and matrix parameters, including surface hard and core hard structures as well as spider web structures;

[0011] S4: Establish honeycomb structures, microtubule structures, and uniform structures based on particle and matrix parameters;

[0012] S5: Batch setting of boundary conditions required for each model during uniaxial stretching, creating and progressively submitting jobs, and automatically extracting parameters including stress-strain curves, stress contour maps, and energy release rates at any time during uniaxial stretching.

[0013] Further, in step S1, the particle and matrix parameters in different particle-reinforced metal matrix composites are determined, specifically as follows:

[0014] S11: Matrix parameter quantification: Parameters including matrix chemical composition, elastic modulus, Poisson's ratio, density, and fracture strain are determined through microscopic methods including first-principles calculations and molecular dynamics.

[0015] S12: Enhancement Parameter Extraction: Combining scanning electron microscope images, quantify parameters of particles including volume fraction, average equivalent diameter, diameter distribution variance, configuration type, particle distribution kurtosis, particle distribution skewness, particle specific surface area, average particle center distance, minimum distance between adjacent particles, and particle shape coefficient.

[0016] Furthermore, in step S2, the brickwork configuration, multi-core structure, and Brighamnian structure are established based on particle and matrix parameters, specifically as follows:

[0017] S21: Establishment of configuration design framework: Based on the spatial distribution characteristics of the reinforcer, three types of reinforcer-matrix spatial topological relationships are defined, and the representative volume element RVE is divided into a 128×128×128 voxel mesh;

[0018] S22: Modeling and implementation of brick masonry structure: Based on Abaqus / CAEAPI and SciPy library, the reinforcement size is generated by log-normal distribution and the KD tree spatial search algorithm is used to realize the layered arrangement. The three-dimensional periodic structure is constructed by staggered mesh displacement strategy, and the reinforcement phase and matrix phase are separated by voxelization set segmentation.

[0019] The log-normal distribution is controlled by the log-normal distribution of particle size, and the specific formula is as follows:

[0020]

[0021] Where r is the particle radius; D50 is the target median particle size; C v S is the coefficient of variation; k σ is the skewness coefficient, μ is the mean of the log-normal distribution, and σ is the standard deviation of the log-normal distribution.

[0022] A staggered mesh displacement strategy is used to construct a three-dimensional periodic structure, employing interlayer staggered stacking topology control. The specific formula is as follows:

[0023]

[0024] Where Δx and Δy are the planar spacing; δx and δy are the random perturbation amplitudes; Δz is the interlayer reference spacing; x0 and y0 are the initial coordinates of the enhancement in the x and y directions, respectively; z shift The displacement of the reinforcement in the z direction is denoted by i and j, which are integer parameters used in the plane to index structural units at different positions or in different layers. They are used to traverse and locate the reinforcement in the planar array. layer_idx is the layer index parameter. layer_idxmod 2 means taking the remainder of layer index layer_idx with respect to 2.

[0025] S23: Multi-core structure modeling implementation: Adaptive layer height generation algorithm combined with staggered displacement strategy is adopted. Through horizontal spacing grid arrangement and log-normal distribution radius generation, non-overlapping random distribution of multi-core reinforcement in three-dimensional space is realized, and KD tree collision detection algorithm is used to ensure structural integrity.

[0026] The horizontal grid spacing uses a dynamic, adaptive spacing design, as specified in the formula:

[0027]

[0028] k is the correction factor, grid_spacing is the preset horizontal grid reference spacing during modeling, and min_spacing is the minimum allowable spacing of the multi-core reinforcement in the horizontal direction;

[0029] S24: Burigan structure modeling and implementation: Using the spiral layering algorithm combined with fiber discretization technology, the spiral arrangement of the reinforcement in the continuous rotating γ° layer is realized through the generation of log-normal distribution radius and dynamic boundary detection, thus constructing a biomimetic Burigan structure;

[0030] S25: Based on the above three types of structural modeling methods, the configuration is automatically generated through a unified parametric framework, and brick masonry structure, multi-core structure and Brigham structure are formed into a complementary design space.

[0031] Further, in step S3, a gradient distribution configuration is established based on particle and matrix parameters, including a surface hard layer, a core hard structure, and a spiderweb structure, specifically:

[0032] S31: Gradient surface hardening configuration implementation: A multi-layer concentric expansion algorithm is adopted, combined with log-normal distribution particle size generation and KD tree space optimization technology. Through dynamic interlayer spacing control and staggered grid arrangement strategy, the gradient distribution of the reinforcing phase from the center layer to the periphery is realized, and a multi-layer gradient structure model with controllable volume fraction and particle size statistical characteristics is constructed.

[0033] Dynamic interlayer spacing control and staggered mesh arrangement strategy, employing gradient surface hardening configuration with staggered stacking topology control, the specific formula is as follows:

[0034]

[0035] x ij and y ij For the reinforcement in the gradient surface hardening structure, the horizontal coordinates of the x and y axes at the i-th layer and the j-th position are given, and the control parameters for the interleaving between the k layers are given. B is the horizontal reference length of the gradient structure.

[0036] S32: Gradient core hardening configuration implementation: The inverse layering algorithm is adopted, combined with log-normal distribution particle size generation and two-layer KD tree space optimization technology. Through dynamic mesh scaling strategy and staggered layer perturbation mechanism, the inverse gradient distribution of the enhancement phase from the outermost layer to the center layer is realized. The finite element set is constructed through voxelized collision detection algorithm and adaptive layer filling, and finally a gradient structure report containing 15 feature parameters is generated.

[0037] The staggered layer perturbation mechanism employs a gradient core layer hardening configuration with interlayer staggered stacking topology control. The specific formula is as follows:

[0038]

[0039] The perturbation term is δ=Δ·β·(rand()-0.5), β=0.1, z layer Let z be the position of the layer-1 reinforcement in the z-axis direction. center OΔ represents the reference coordinates of the central layer of the gradient core hardening structure in the z-axis direction. z The distance of the fluctuation in the Z direction;

[0040] S33: Spider Web Structure Modeling: A hexagonal spoke-ring composite algorithm is adopted, combined with log-normal distribution particle size generation and dual-scale KD tree spatial optimization technology. Through adaptive layer adjustment and dynamic radius expansion, the biomimetic gradient distribution of the reinforcing phase in the Z-axis direction is realized. A finite element set is constructed through the spoke-ring collaborative arrangement strategy and angle offset mechanism, and finally a structure report containing 13 feature parameters is generated.

[0041] The formula for generating spokes is:

[0042]

[0043] Where R j Let θ be the radius of the j-th layer network, Δθ be the inter-network rotation offset angle, and θ be the radius of the j-th layer network. i Let be the reference angle of the i-th spoke;

[0044] The formula for interpolation around the edge is:

[0045]

[0046] Where, x t and y t Let x be the two-dimensional coordinates of the interpolation point on the ring edge. i and y i Let x be the coordinate of the endpoint of the i-th spoke, t be the interpolation parameter, and x be the coordinate of the endpoint of the i-th spoke. i+1 and y i+1 Let be the coordinates of the endpoint of the (i+1)th spoke.

[0047] Furthermore, in step S4, honeycomb structures, microtubule structures, and uniform structures are established based on particle and matrix parameters, specifically as follows:

[0048] S41: Cellular structure implementation: The hexagonal vertex-edge collaborative algorithm is adopted, combined with log-normal distribution particle size generation and dual-scale KD tree space optimization technology. Through dynamic interlayer rotation and adaptive edge filling, the regular cellular distribution of the enhancement phase in the Z-axis direction is achieved. The model constructs a finite element set through vertex-edge collaborative arrangement strategy, and finally generates a structure report containing 12 feature parameters.

[0049] The coordinates of the vertices of the hexagonal honeycomb structure (x v ,y v ,z v The calculation formula is:

[0050]

[0051] In the formula (x c ,y c ) represents the center coordinates of the cell; R represents the radius of the inscribed circle of the cell; θ0 represents the interlayer rotation offset angle; k is the index parameter of the hexagon vertex;

[0052] The formula for controlling the particle spacing within the edge in the dynamic spacing constraint formula is:

[0053]

[0054] Where L edge D is the side length of the hexagon; nnd For the nearest neighbor distance of the target, N points The number of particles that can be arranged within the hexagonal sides;

[0055] S42: Microtubule structure implementation: The multi-cylinder spiral layering algorithm is adopted, combined with log-normal distribution particle size generation and three-dimensional KD tree space optimization technology. Through adaptive cylinder distribution and radial gradient expansion, the spiral gradient distribution of the reinforcing phase in the Z-axis direction is realized. The model constructs a finite element set through dynamic cylinder generation and concentric circle layer control, and finally generates a structure report containing 16 characteristic parameters.

[0056] The helical ascent topology control formula for microtubule structures is:

[0057]

[0058] Where η is the number of rotations, H is the cylinder height, α is the rotation angle per unit height, θ is the rotation angle of the reinforcing phase, N is the number of segments of the microtubule structure in the circumferential direction, i is the index parameter of the microtubule unit, and z is the Z-axis coordinate of the current position in the microtubule structure.

[0059] S43: Uniform Structure Implementation: A cubic mesh filling algorithm is adopted, combined with log-normal distribution particle size generation and three-dimensional KD tree space optimization technology. Through adaptive mesh division and repeated filling strategy, the uniform distribution of the reinforcing phase in the RVE space is achieved. The model constructs a finite element set through a voxelized collision detection algorithm, and finally generates a structure report containing 12 feature parameters.

[0060] Furthermore, in step S5, the boundary conditions required for each model during uniaxial tension are set in batches, jobs are created and submitted progressively, and parameters including stress-strain curves, stress contour maps, and energy release rates at any time during uniaxial tension are automatically extracted, specifically:

[0061] S51: Finite element model parameterization settings: Based on the geometric model generated in steps S1-S4, the matrix and reinforcement material parameters are automatically set in Abaqus, and boundary conditions such as fixation, loading, and contact are automatically applied.

[0062] S52: Batch Job Creation and Intelligent Submission: Uses Python scripts to automatically create analysis jobs, generate independent jobs for each configuration, set parallel computing parameters, automatically detect hardware resources and dynamically adjust the submission order, and implement job queue management;

[0063] S53: Intelligent extraction and analysis of results data: Automatically extracts key mechanical response data, including stress-strain curve data, damage evolution parameters, energy release rate, Mises stress cloud map and other key physical property data.

[0064] A high-throughput modeling and computation system for metal matrix composites, used to perform the high-throughput modeling and computation method for metal matrix composites as described above, includes:

[0065] The material parameter definition module is used to determine the particle and matrix parameters in different particle-reinforced metal matrix composites;

[0066] Discrete distribution configuration modeling module, used to establish brick masonry configurations, multi-core structures and Brigand structures based on particle and matrix parameters;

[0067] The gradient distribution configuration modeling module is used to establish gradient distribution configurations based on particle and matrix parameters, including surface hard and core hard structures as well as spider web structures.

[0068] The regular topology modeling module is used to create honeycomb structures, microtubule structures, and uniform structures based on particle and matrix parameters.

[0069] The high-throughput simulation analysis module is used to batch set the boundary conditions required for each model during uniaxial tension, create and submit jobs step by step, and automatically extract parameters including stress-strain curves, stress contour maps, and energy release rates at any time during uniaxial tension.

[0070] A computer device includes a memory and one or more processors, the memory storing computer code that, when executed by the one or more processors, causes the one or more processors to perform the method described above.

[0071] A computer-readable storage medium storing computer code that, when executed, performs the method described above.

[0072] This invention presents a high-throughput modeling and computation method for metal matrix composites that, for the first time, integrates various optimized reinforcing phase configurations (such as brickwork configurations, gradient distributions, and honeycomb structures) into a unified computational framework and achieves batch simulation and data extraction through an automated process. Compared to existing research, this method offers the following advantages:

[0073] (1) Comprehensiveness: It covers a variety of known enhancement phase distribution modes that can enhance the strong-tough synergistic effect, avoiding the limitations of traditional random distribution models;

[0074] (2) High efficiency: The entire process of modeling, calculation and data extraction is processed in a high-throughput manner through automated scripts, which significantly reduces the computational cost;

[0075] (3) Scalability: The framework supports the flexible addition of new configurations or parameters, providing standardized tools for subsequent material design.

[0076] This method can not only accelerate the design of high-performance composite materials, but also provide theoretical guidance for experimental research and reduce the waste of resources caused by blind trial and error. Attached Figure Description

[0077] Figure 1 This is a flowchart of the overall calculation method in one embodiment of the present invention;

[0078] Figure 2 This is a schematic diagram of a particle-reinforced metal matrix composite brick structure, a multi-core structure, and a Brugon structure in one embodiment of the present invention;

[0079] Figure 3 This is a schematic diagram of the gradient surface hardening structure, gradient core hardening structure, and spider web structure of a particle-reinforced metal matrix composite material in one embodiment of the present invention.

[0080] Figure 4 This is a schematic diagram of the honeycomb structure, microtubule structure, and uniform distribution structure of a particle-reinforced metal matrix composite material in one embodiment of the present invention. Detailed Implementation

[0081] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0082] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0083] First Embodiment

[0084] like Figure 1 As shown, this embodiment provides a high-throughput modeling and calculation method for metal matrix composites, including the following steps:

[0085] S1: Determine the particle and matrix parameters in metal matrix composites with different particle reinforcements.

[0086] In this embodiment, step S1 specifically includes:

[0087] S11: Matrix parameter quantification: Parameters including matrix chemical composition, elastic modulus, Poisson's ratio, density, and fracture strain are determined through microscopic methods including first-principles calculations and molecular dynamics.

[0088] S12: Enhancement Parameter Extraction: Combining scanning electron microscope images, quantify parameters of particles including volume fraction, average equivalent diameter, diameter distribution variance, configuration type, particle distribution kurtosis, particle distribution skewness, particle specific surface area, average particle center distance, minimum distance between adjacent particles, and particle shape coefficient.

[0089] S2: As Figure 2 As shown, brick masonry configurations, multi-core structures, and Brigand structures are established based on particle and matrix parameters.

[0090] In this embodiment, step S2 specifically includes:

[0091] S21: Establishment of configuration design framework: Based on the spatial distribution characteristics of the reinforcer, three types of reinforcer-matrix spatial topological relationships are defined, and the representative volume element RVE is divided into a 128×128×128 voxel mesh;

[0092] S22: Modeling and implementation of brick masonry structure: Based on Abaqus / CAEAPI and SciPy library, the reinforcement size is generated by log-normal distribution and the KD tree spatial search algorithm is used to realize the layered arrangement. The three-dimensional periodic structure is constructed by staggered mesh displacement strategy, and the reinforcement phase and matrix phase are separated by voxelization set segmentation.

[0093] The log-normal distribution is controlled by the log-normal distribution of particle size, and the specific formula is as follows:

[0094]

[0095] Where r is the particle radius; D50 is the target median particle size; C v S is the coefficient of variation; k σ is the skewness coefficient, μ is the mean of the log-normal distribution, and σ is the standard deviation of the log-normal distribution.

[0096] A staggered mesh displacement strategy is used to construct a three-dimensional periodic structure, employing interlayer staggered stacking topology control. The specific formula is as follows:

[0097]

[0098] Where Δx and Δy are the planar spacing; δx and δy are the random perturbation amplitudes; Δz is the interlayer reference spacing; x0 and y0 are the initial coordinates of the enhancement in the x and y directions, respectively; z shift The displacement of the reinforcement in the z direction is denoted by i and j, which are integer parameters used in the plane to index structural units at different positions or in different layers. They are used to traverse and locate the reinforcement in the planar array. layer_idx is the layer index parameter. layer_idxmod 2 means taking the remainder of layer index layer_idx with respect to 2.

[0099] S23: Multi-core structure modeling implementation: Adaptive layer height generation algorithm combined with staggered displacement strategy is adopted. Through horizontal spacing grid arrangement and log-normal distribution radius generation, non-overlapping random distribution of multi-core reinforcement in three-dimensional space is realized, and KD tree collision detection algorithm is used to ensure structural integrity.

[0100] The horizontal grid spacing uses a dynamic, adaptive spacing design, as specified in the formula:

[0101]

[0102] k is the correction factor, grid_spacing is the preset horizontal grid reference spacing during modeling, and min_spacing is the minimum allowable spacing of the multi-core reinforcement in the horizontal direction;

[0103] S24: Burigan structure modeling and implementation: Using the spiral layering algorithm combined with fiber discretization technology, the spiral arrangement of the reinforcement in the continuous rotating γ° layer is realized through the generation of log-normal distribution radius and dynamic boundary detection, thus constructing a biomimetic Burigan structure;

[0104] S25: Based on the above three types of structural modeling methods, the configuration is automatically generated through a unified parametric framework, and brick masonry structure, multi-core structure and Brigham structure are formed into a complementary design space.

[0105] S3: As Figure 3 As shown, a gradient distribution configuration is established based on particle and matrix parameters, including a surface hard layer, a core hard structure, and a spider web structure.

[0106] In this embodiment, step S3 specifically includes:

[0107] S31: Gradient surface hardening configuration implementation: A multi-layer concentric expansion algorithm is adopted, combined with log-normal distribution particle size generation and KD tree space optimization technology. Through dynamic interlayer spacing control and staggered grid arrangement strategy, the gradient distribution of the reinforcing phase from the center layer to the periphery is realized, and a multi-layer gradient structure model with controllable volume fraction and particle size statistical characteristics is constructed.

[0108] Dynamic interlayer spacing control and staggered mesh arrangement strategy, employing gradient surface hardening configuration with staggered stacking topology control, the specific formula is as follows:

[0109]

[0110] x ij and y ij For the reinforcement in the gradient surface hardening structure, the horizontal coordinates of the x and y axes at the i-th layer and the j-th position are given, and the control parameters for the interleaving between the k layers are given. B is the horizontal reference length of the gradient structure.

[0111] S32: Gradient core hardening configuration implementation: The inverse layering algorithm is adopted, combined with log-normal distribution particle size generation and two-layer KD tree space optimization technology. Through dynamic mesh scaling strategy and staggered layer perturbation mechanism, the inverse gradient distribution of the enhancement phase from the outermost layer to the center layer is realized. The finite element set is constructed through voxelized collision detection algorithm and adaptive layer filling, and finally a gradient structure report containing 15 feature parameters is generated.

[0112] The staggered layer perturbation mechanism employs a gradient core layer hardening configuration with interlayer staggered stacking topology control. The specific formula is as follows:

[0113]

[0114] The perturbation term is δ=Δ·β·(rand()-0.5), β=0.1, z layer Let z be the position of the layer-1 reinforcement in the z-axis direction. center OΔ represents the reference coordinates of the central layer of the gradient core hardening structure in the z-axis direction. z The distance of the fluctuation in the Z direction;

[0115] S33: Spider Web Structure Modeling: A hexagonal spoke-ring composite algorithm is adopted, combined with log-normal distribution particle size generation and dual-scale KD tree spatial optimization technology. Through adaptive layer adjustment and dynamic radius expansion, the biomimetic gradient distribution of the reinforcing phase in the Z-axis direction is realized. A finite element set is constructed through the spoke-ring collaborative arrangement strategy and angle offset mechanism, and finally a structure report containing 13 feature parameters is generated.

[0116] The formula for generating spokes is:

[0117]

[0118] Where R j Let θ be the radius of the j-th layer network, Δθ be the inter-network rotation offset angle, and θ be the radius of the j-th layer network. i Let be the reference angle of the i-th spoke;

[0119] The formula for interpolation around the edge is:

[0120]

[0121] Where, x t and y t Let x be the two-dimensional coordinates of the interpolation point on the ring edge. i and y i Let x be the coordinate of the endpoint of the i-th spoke, t be the interpolation parameter, and x be the coordinate of the endpoint of the i-th spoke. i+1 and y i+1 Let be the coordinates of the endpoint of the (i+1)th spoke.

[0122] S4: As Figure 4As shown, honeycomb structures, microtubule structures, and uniform structures are established based on particle and matrix parameters.

[0123] In this embodiment, step S4 specifically includes:

[0124] S41: Cellular structure implementation: The hexagonal vertex-edge collaborative algorithm is adopted, combined with log-normal distribution particle size generation and dual-scale KD tree space optimization technology. Through dynamic interlayer rotation and adaptive edge filling, the regular cellular distribution of the enhancement phase in the Z-axis direction is achieved. The model constructs a finite element set through vertex-edge collaborative arrangement strategy, and finally generates a structure report containing 12 feature parameters.

[0125] The coordinates of the vertices of the hexagonal honeycomb structure (x v ,y v ,z v The calculation formula is:

[0126]

[0127] In the formula (x c ,y c ) represents the center coordinates of the cell; R represents the radius of the inscribed circle of the cell; θ0 represents the interlayer rotation offset angle; k is the index parameter of the hexagon vertex;

[0128] The formula for controlling the particle spacing within the edge in the dynamic spacing constraint formula is:

[0129]

[0130] Where L edge D is the side length of the hexagon; nnd For the nearest neighbor distance of the target, N points The number of particles that can be arranged within the hexagonal sides;

[0131] S42: Microtubule structure implementation: The multi-cylinder spiral layering algorithm is adopted, combined with log-normal distribution particle size generation and three-dimensional KD tree space optimization technology. Through adaptive cylinder distribution and radial gradient expansion, the spiral gradient distribution of the reinforcing phase in the Z-axis direction is realized. The model constructs a finite element set through dynamic cylinder generation and concentric circle layer control, and finally generates a structure report containing 16 characteristic parameters.

[0132] The helical ascent topology control formula for microtubule structures is:

[0133]

[0134] Where η is the number of rotations, H is the cylinder height, α is the rotation angle per unit height, θ is the rotation angle of the reinforcing phase, N is the number of segments of the microtubule structure in the circumferential direction, i is the index parameter of the microtubule unit, and z is the Z-axis coordinate of the current position in the microtubule structure.

[0135] S43: Uniform Structure Implementation: A cubic mesh filling algorithm is adopted, combined with log-normal distribution particle size generation and three-dimensional KD tree space optimization technology. Through adaptive mesh division and repeated filling strategy, the uniform distribution of the reinforcing phase in the RVE space is achieved. The model constructs a finite element set through a voxelized collision detection algorithm, and finally generates a structure report containing 12 feature parameters.

[0136] S5: Batch setting of boundary conditions required for each model during uniaxial stretching, creating and progressively submitting jobs, and automatically extracting parameters including stress-strain curves, stress contour maps, and energy release rates at any time during uniaxial stretching.

[0137] In this embodiment, step S5 specifically includes:

[0138] S51: Finite element model parameterization settings: Based on the geometric model generated in steps S1-S4, the matrix and reinforcement material parameters are automatically set in Abaqus, and boundary conditions such as fixation, loading, and contact are automatically applied.

[0139] S52: Batch Job Creation and Intelligent Submission: Uses Python scripts to automatically create analysis jobs, generate independent jobs for each configuration, set parallel computing parameters, automatically detect hardware resources and dynamically adjust the submission order, and implement job queue management;

[0140] S53: Intelligent extraction and analysis of results data: Automatically extracts key mechanical response data, including stress-strain curve data, damage evolution parameters, energy release rate, Mises stress cloud map and other key physical property data.

[0141] Second Embodiment

[0142] This embodiment provides a high-throughput modeling and calculation system for metal matrix composites for performing the high-throughput modeling and calculation method for metal matrix composites as described in the first embodiment, characterized in that it includes:

[0143] The material parameter definition module is used to determine the particle and matrix parameters in different particle-reinforced metal matrix composites;

[0144] Discrete distribution configuration modeling module, used to establish brick masonry configurations, multi-core structures and Brigand structures based on particle and matrix parameters;

[0145] The gradient distribution configuration modeling module is used to establish gradient distribution configurations based on particle and matrix parameters, including surface hard and core hard structures as well as spider web structures.

[0146] The regular topology modeling module is used to create honeycomb structures, microtubule structures, and uniform structures based on particle and matrix parameters.

[0147] The high-throughput simulation analysis module is used to batch set the boundary conditions required for each model during uniaxial tension, create and submit jobs step by step, and automatically extract parameters including stress-strain curves, stress contour maps, and energy release rates at any time during uniaxial tension.

[0148] A computer-readable storage medium stores computer code that, when executed, performs the methods described above. Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0149] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

[0150] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 specification.

[0151] It should be noted that the above embodiments can be freely combined as needed. The above description is only a preferred embodiment of the present invention. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A high-throughput modeling and calculation method for metal matrix composites, characterized in that, Includes the following steps: S1: Determine the particle and matrix parameters in metal matrix composites with different particle reinforcements; S2: Based on particle and matrix parameters, brick masonry configurations, multi-core structures, and Brigand structures are established. S3: Establish gradient distribution configurations based on particle and matrix parameters, including surface hard and core hard structures as well as spider web structures; S4: Establish honeycomb structures, microtubule structures, and uniform structures based on particle and matrix parameters; S5: Batch setting of boundary conditions required for each model during uniaxial stretching, creating and progressively submitting jobs, and automatically extracting parameters including stress-strain curves, stress contour maps, and energy release rates at any time during uniaxial stretching. In step S2, brick masonry configurations, multi-core structures, and Brigan structures are established based on particle and matrix parameters, specifically as follows: S21: Establishment of configuration design framework: Based on the spatial distribution characteristics of the reinforcer, three types of reinforcer-matrix spatial topological relationships are defined, and the representative volume element RVE is divided into a 128×128×128 voxel mesh; S22: Modeling and implementation of brick masonry structure: Based on Abaqus / CAE API and SciPy library, the reinforcement size is generated by log-normal distribution and the KD tree spatial search algorithm is used to realize the layered arrangement. A three-dimensional periodic structure is constructed by staggered mesh displacement strategy, and the reinforcement phase and matrix phase are separated by voxelization set segmentation. The log-normal distribution is controlled by the log-normal distribution of particle size, and the specific formula is as follows: Where r is the particle radius; D50 is the target median particle size; C v S is the coefficient of variation; k This is the skewness coefficient. The mean of a log-normal distribution. The standard deviation of the log-normal distribution; A staggered mesh displacement strategy is used to construct a three-dimensional periodic structure, employing interlayer staggered stacking topology control. The specific formula is as follows: Where Δx and Δy are the planar spacing; δx and δy are the random disturbance amplitudes; and Δz is the inter-layer reference spacing. and These are the initial coordinates of the augmentation body in the x and y directions, respectively. The displacement of the reinforcement in the z direction is denoted by i and j, which are integer parameters used in the plane to index structural units at different positions or in different layers. They are used to traverse and locate the reinforcement in the planar array. layer_idx is the layer index parameter. layer_idx mod 2 means taking the remainder of the layer index layer_idx divided by 2. S23: Multi-core structure modeling implementation: Adaptive layer height generation algorithm combined with staggered displacement strategy is adopted. Through horizontal spacing grid arrangement and log-normal distribution radius generation, non-overlapping random distribution of multi-core reinforcement in three-dimensional space is realized, and KD tree collision detection algorithm is used to ensure structural integrity. The horizontal grid spacing uses a dynamic, adaptive spacing design, as specified in the formula: As a correction factor, grid_spacing is the preset horizontal grid reference spacing during modeling, and min_spacing is the minimum allowable spacing of multi-core reinforcements in the horizontal direction; S24: Burigan structure modeling and implementation: Using the spiral layering algorithm combined with fiber discretization technology, the spiral arrangement of the reinforcement in the continuous rotating γ° layer is realized through the generation of log-normal distribution radius and dynamic boundary detection, thus constructing a biomimetic Burigan structure; S25: Based on the above three types of structural modeling methods, the configuration is automatically generated through a unified parametric framework, and brick masonry structure, multi-core structure and Brigham structure are formed into a complementary design space.

2. The high-throughput modeling and calculation method for metal matrix composites according to claim 1, characterized in that, In step S1, the particle and matrix parameters in different particle-reinforced metal matrix composites are determined, specifically as follows: S11: Matrix parameter quantification: Parameters including matrix chemical composition, elastic modulus, Poisson's ratio, density, and fracture strain are determined through microscopic methods including first-principles calculations and molecular dynamics. S12: Enhancement Parameter Extraction: Combining scanning electron microscope images, quantify parameters of particles including volume fraction, average equivalent diameter, diameter distribution variance, configuration type, particle distribution kurtosis, particle distribution skewness, particle specific surface area, average particle center distance, minimum distance between adjacent particles, and particle shape coefficient.

3. The high-throughput modeling and calculation method for metal matrix composites according to claim 1, characterized in that, In step S3, a gradient distribution configuration is established based on particle and matrix parameters, including a surface hard layer, a core hard structure, and a spiderweb structure, specifically: S31: Gradient surface hardening configuration implementation: A multi-layer concentric expansion algorithm is adopted, combined with log-normal distribution particle size generation and KD tree space optimization technology. Through dynamic interlayer spacing control and staggered grid arrangement strategy, the gradient distribution of the reinforcing phase from the center layer to the periphery is realized, and a multi-layer gradient structure model with controllable volume fraction and particle size statistical characteristics is constructed. Dynamic interlayer spacing control and staggered mesh arrangement strategy, employing gradient surface hardening configuration with staggered stacking topology control, the specific formula is as follows: and For the reinforcement in the gradient surface hardening structure, the horizontal coordinates of the x and y axes at the i-th layer and the j-th position are given, and the control parameters for the interleaving between k layers are given. B is the horizontal reference length of the gradient structure. S32: Gradient core hardening configuration implementation: The inverse layering algorithm is adopted, combined with log-normal distribution particle size generation and two-layer KD tree space optimization technology. Through dynamic mesh scaling strategy and staggered layer perturbation mechanism, the inverse gradient distribution of the enhancement phase from the outermost layer to the center layer is realized. The finite element set is constructed through voxelized collision detection algorithm and adaptive layer filling, and finally a gradient structure report containing 15 feature parameters is generated. The staggered layer perturbation mechanism employs a gradient core layer hardening configuration with interlayer staggered stacking topology control. The specific formula is as follows: The disturbance term is , Let be the position of the layer-1 reinforcement in the z-axis direction. Here are the reference coordinates of the central layer of the gradient core hardening structure in the z-axis direction. The distance of the fluctuation in the Z direction; S33: Spider Web Structure Modeling: A hexagonal spoke-ring composite algorithm is adopted, combined with log-normal distribution particle size generation and dual-scale KD tree spatial optimization technology. Through adaptive layer adjustment and dynamic radius expansion, the biomimetic gradient distribution of the reinforcing phase in the Z-axis direction is realized. A finite element set is constructed through the spoke-ring collaborative arrangement strategy and angle offset mechanism, and finally a structure report containing 13 feature parameters is generated. The formula for generating spokes is: Where R j Let be the radius of the j-th layer network, and Δθ be the inter-network rotation offset angle. Let be the reference angle of the i-th spoke; The formula for interpolation around the edge is: in, and These are the two-dimensional coordinates of the interpolation points on the ring edge. and Let be the coordinates of the endpoint of the i-th spoke, and t be the interpolation parameter. and Let be the coordinates of the endpoint of the (i+1)th spoke.

4. The high-throughput modeling and calculation method for metal matrix composites according to claim 3, characterized in that, In step S4, honeycomb structures, microtubule structures, and uniform structures are established based on particle and matrix parameters, specifically as follows: S41: Cellular structure implementation: The hexagonal vertex-edge collaborative algorithm is adopted, combined with log-normal distribution particle size generation and dual-scale KD tree space optimization technology. Through dynamic interlayer rotation and adaptive edge filling, the regular cellular distribution of the enhancement phase in the Z-axis direction is achieved. The model constructs a finite element set through vertex-edge collaborative arrangement strategy, and finally generates a structure report containing 12 feature parameters. honeycomb hexagon vertex coordinates The calculation formula is: In the formula (x c ,y c ) represents the center coordinates of the cell; R represents the radius of the inscribed circle of the cell; θ0 represents the interlayer rotation offset angle; k is the index parameter of the hexagon vertex; The formula for controlling the particle spacing within the edge in the dynamic spacing constraint formula is: Where L edge D is the side length of the hexagon; nnd For the nearest neighbor distance of the target, N points The number of particles that can be arranged within the hexagonal sides; S42: Microtubule structure implementation: The multi-cylinder spiral layering algorithm is adopted, combined with log-normal distribution particle size generation and three-dimensional KD tree space optimization technology. Through adaptive cylinder distribution and radial gradient expansion, the spiral gradient distribution of the reinforcing phase in the Z-axis direction is realized. The model constructs a finite element set through dynamic cylinder generation and concentric circle layer control, and finally generates a structure report containing 16 characteristic parameters. The helical ascent topology control formula for microtubule structures is: Where η is the number of revolutions and H is the height of the cylinder. The rotation angle is the unit height. To enhance the rotation angle of the phase, N is the number of segments of the microtubule structure in the circumferential direction, i is the index parameter of the microtubule unit, and z is the Z-axis coordinate of the current position in the microtubule structure; S43: Uniform Structure Implementation: A cubic mesh filling algorithm is adopted, combined with log-normal distribution particle size generation and three-dimensional KD tree space optimization technology. Through adaptive mesh division and repeated filling strategy, the uniform distribution of the reinforcing phase in the RVE space is achieved. The model constructs a finite element set through a voxelized collision detection algorithm, and finally generates a structure report containing 12 feature parameters.

5. The high-throughput modeling and calculation method for metal matrix composites according to claim 1, characterized in that, In step S5, the boundary conditions required for each model during uniaxial tension are set in batches, jobs are created and submitted progressively, and parameters including stress-strain curves, stress contour maps, and energy release rates at any time during uniaxial tension are automatically extracted. Specifically: S51: Finite element model parameterization settings: Based on the geometric model generated in steps S1-S4, the matrix and reinforcement material parameters are automatically set in Abaqus, and boundary conditions such as fixation, loading, and contact are automatically applied. S52: Batch Job Creation and Intelligent Submission: Uses Python scripts to automatically create analysis jobs, generate independent jobs for each configuration, set parallel computing parameters, automatically detect hardware resources and dynamically adjust the submission order, and implement job queue management; S53: Intelligent extraction and analysis of results data: Automatically extracts key mechanical response data, including stress-strain curve data, damage evolution parameters, energy release rate, Mises stress cloud map and other key physical property data.

6. A high-throughput modeling and calculation system for metal matrix composites for executing the high-throughput modeling and calculation method for metal matrix composites as described in any one of claims 1-5, characterized in that, include: The material parameter definition module is used to determine the particle and matrix parameters in different particle-reinforced metal matrix composites; Discrete distribution configuration modeling module, used to establish brick masonry configurations, multi-core structures and Brigand structures based on particle and matrix parameters; The gradient distribution configuration modeling module is used to establish gradient distribution configurations based on particle and matrix parameters, including surface hard and core hard structures as well as spider web structures. The regular topology modeling module is used to build honeycomb structures, microtubule structures, and uniform structures based on particle and matrix parameters. The high-throughput simulation analysis module is used to batch set the boundary conditions required for each model during uniaxial tension, create and submit jobs step by step, and automatically extract parameters including stress-strain curves, stress contour maps, and energy release rates at any time during uniaxial tension.

7. A computer device comprising a memory and one or more processors, the memory storing computer code that, when executed by the one or more processors, causes the one or more processors to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing computer code, wherein when the computer code is executed, the method of any one of claims 1 to 5 is performed.

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