A Method for Generating a Controllable Parametric Model of Three-Dimensional Braided Composites

Abstract

The present invention relates to a method for generating a controllable parametric model of a three-dimensional braided composite material. By using the kinematic analysis of the four-step three-dimensional braiding process, a trajectory model of the three-dimensional braided preform is generated. Based on the parametric modeling of the trajectory, the geometric reconstruction of the yarn cross-section is implemented. Then, multi-scale pore primitives are generated by randomizing the geometric parameters of the ellipsoid, and randomly placed after random rotation transformation using Euler angles to enhance the spatial heterogeneity characteristics of the pore morphology. Subsequently, using the model dynamic compaction algorithm, a physical accurate simulation of the densification behavior of the yarn and pores during the compaction process is achieved. Finally, using the three-dimensional crack random generation algorithm, the crack characteristics inside the yarn during the compaction process of the three-dimensional braided composite material are accurately simulated, and a three-dimensional braided parametric random model can be obtained. The present invention accurately reproduces the topological structure of the yarns, the initial pore distribution, and the initial damage characteristics inside the three-dimensional braided composite material specimen.

Description

Technical Field

[0001] The present invention relates to a method for generating a model, specifically to a method for generating a controllable parametric model of a three-dimensional braided composite material, belonging to the technical field of image processing. Background Art

[0002] Three-dimensional braided composite materials are advanced structural materials with excellent properties such as high strength, high modulus, and light weight, and are widely used in the fields of aerospace, automotive, shipbuilding, sports equipment, etc. Accurately characterizing three-dimensional braided composite materials is the key to understanding their properties, optimizing designs, improving reliability, and promoting applications. Through multi-scale experimental analysis and modeling, the relationship between the structure and properties of the materials can be comprehensively grasped, providing important support for materials science and engineering applications.

[0003] In current research, the construction of parametric random geometric models of three-dimensional braided composite materials still faces many challenges in accurately describing real structures, multi-scale modeling, uncertainty quantification, and multi-physics field coupling. In particular, it is difficult to accurately simulate complex structures such as yarn fluctuations, defects, and cracks in real materials. At the same time, how to efficiently achieve multi-scale modeling from the mesoscopic to the macroscopic scale and quantify the influence of geometric and material parameter uncertainties on mechanical properties is also a difficult point in current research. In addition, further exploration is still needed to establish a multi-physics field coupling model to comprehensively reflect the behavior of materials under actual working conditions.

[0004] Therefore, to solve the above problems, it is indeed necessary to provide an innovative method for generating a controllable parametric model of three-dimensional braided composite materials to overcome the defects in the prior art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for generating a controllable parametric model of a three-dimensional braided composite material, which can accurately reproduce the internal yarn topology, initial pore distribution, and initial damage characteristics in the three-dimensional braided composite material.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is: a method for generating a controllable parametric model of a three-dimensional braided composite material, which includes the following steps:

[0007] 1), Based on the kinematic analysis of the four-step three-dimensional braiding process, construct a geometric model of the three-dimensional braided preform. On the basis of trajectory parametric modeling, implement geometric reconstruction of the yarn cross-section, and perform spatial mapping of the optimized cross-section along the trajectory path to ensure that the cross-section center coincides strictly with the trajectory nodes and there is no geometric interference between the yarns, and finally obtain a random model of loose yarns;

[0008] 2), Based on the random model of loose yarn, multi-scale pore primitives are generated by randomizing the geometric parameters of the ellipsoid. Then, through random rotation transformation of Euler angles and random placement, the spatial heterogeneity characteristics of pore morphology are enhanced. The optimized pore model is embedded between the topological structures of the loose yarn random model to obtain a random model of loose yarn with pores.

[0009] 3), Using a physics engine based on the Taichi framework, a yarn dynamic compaction algorithm is constructed. After inputting the random model of loose yarn with pores into the algorithm, a physical accurate simulation of the densification behavior of the yarn and pores during the compaction process is realized, and a random model of compact yarn with pores is obtained.

[0010] 4), Using a three-dimensional crack random generation algorithm, three-dimensional random cracks are generated inside the yarn topological structure of the random model of compact yarn with pores to simulate the crack characteristics inside the yarn during the compaction process of three-dimensional woven composites, and finally a three-dimensional woven parametric random model is obtained.

[0011] The method for generating a controllable parametric model of the three-dimensional woven composite material of the present invention is further as follows: The specific content of step 1) is as follows:

[0012] 1-1), Based on the kinematic analysis of the four-step three-dimensional weaving process, a discrete space node coordinate model is established. Then, adjacent weaving step nodes are connected by a piecewise linear interpolation algorithm to generate an initial yarn space trajectory. Further, a high-order polynomial interpolation algorithm is used to optimize the curvature continuity of the trajectory to obtain a smooth space path, and a three-dimensional woven preform trajectory model with a complex space interweaving structure is constructed.

[0013] 1-2), An initial hexagonal contour is randomly generated according to geometric parameters. Subsequently, random displacement perturbations are applied to the hexagonal edge nodes by introducing a Perlin noise field. Finally, the perturbed contour is fitted by a piecewise Bezier curve to generate a high-fidelity yarn cross-section geometry.

[0014] 1-3), The cross-section performs a spatial mapping along the trajectory path, and the geometric interference state is monitored in real time during this process. When interference is detected, the system automatically triggers a non-interference optimization algorithm, and through iterative optimization, a non-interference adjustment of the yarn spatial arrangement is realized.

[0015] The method for generating a controllable parametric model of the three-dimensional woven composite material of the present invention is further as follows: In step 1-3), the non-interference optimization algorithm is specifically as follows:

[0016] 1-3-1), Calculate the planar Euclidean distance according to the center point coordinates of two yarns on the same cross-section, and compare it with the sum of the radii of their circumscribed circles to determine whether the yarn cross-sections interfere.

[0017] 1-3-2), when interference occurs, perform simulated force calculation for trajectory optimization;

[0018] 1-3-3), based on the geometric interference detection results, trigger the global curvature continuity optimization algorithm for the yarn trajectory, realize the re-fitting of the spatial path through parametric spline reconstruction, and finally form a closed-loop feedback mechanism of "interference detection - trajectory optimization - re-fitting".

[0019] The method for generating a controllable parametric model of a three-dimensional braided composite material of the present invention is further as follows: In step 2), the three-dimensional reconstruction algorithm is specifically:

[0020] 2-1), generate multi-scale pore primitives by randomizing the long semi-axis, middle semi-axis, and short semi-axis parameters of the ellipsoid; subsequently, use Euler angle random rotation transformation to realize the random orientation distribution of these primitives in three-dimensional space, thereby constructing a multi-scale pore structure model with anisotropic characteristics;

[0021] 2-2), use the pore-yarn multi-level spatial interference detection algorithm to ensure that the pores can be accurately placed outside the yarn body array and the pores do not interfere with each other, and finally place the pore structure model into the loose yarn random model;

[0022] 2-3), by iterating the above steps in a loop, continuously place simulated pores in the loose yarn random model.

[0023] The method for generating a controllable parametric model of a three-dimensional braided composite material of the present invention is further as follows: In step 2-2), the pore-yarn multi-level spatial interference detection algorithm is specifically:

[0024] 2-2-1), during the placement process of the pore model, first detect whether the center point of the pore interferes with the yarn body array or the pore space that has been placed. If interference is detected, randomly determine the center point position again; if there is no interference, proceed to the next detection;

[0025] 2-2-2), detect whether the 6 main axis endpoints of the rotated pore interfere with the yarn body array or the pore space that has been placed. If interference is detected at any endpoint, randomly determine the center point position again; if there is no interference at all endpoints, continue with the next detection;

[0026] 2-2-3), detect whether the 8 45° direction endpoints of the rotated pore interfere with the yarn body array or the pore space that has been placed; if interference is detected at any endpoint, randomly determine the center point position again; if there is no interference at all endpoints, place the pore space into the loose yarn random model.

[0027] The method for generating a controllable parametric model of the three-dimensional braided composite material of the present invention is further as follows: In step 2-3), after each successful placement, the pore volume is accumulated into the total pore volume; when the cumulative pore volume reaches a preset threshold, the pore placement process is terminated, and finally a random model of loose yarns with pores is obtained; the volume calculation formula of the ellipsoid and the calculation formula of the volume porosity are as follows:

[0028]

[0029]

[0030] Among them, v i Pores represents the volume of the input pore i, a i , b i , c i represent the major axis, medium axis, and minor axis of the current pore; v model represents the volume of the entire random model, and F 3D Pores represents the volume porosity of the current random model.

[0031] The method for generating a controllable parametric model of the three-dimensional braided composite material of the present invention is further as follows: In step 3), the yarn dynamic compaction algorithm is specifically as follows:

[0032] 3-1), on the basis of extracting the geometric profile of the yarn cross-section, discretize its edge into a particle-spring system; in this system, each particle is given the attribute of solid collision to accurately simulate the non-penetrating contact behavior between yarns; at the same time, adjacent particles are connected to each other by linear springs to effectively characterize the elastic recovery characteristics of the yarn material;

[0033] 3-2), when the discretized profiles of adjacent yarns approach each other due to the contraction movement, the system will trigger the particle-level collision detection and response mechanism;

[0034] 3-3), the system presets a global shrinkage ratio threshold as the termination condition of the compaction process; when the minimum distance between yarns reaches this threshold, the system automatically terminates the shrinkage iteration process; subsequently, remap the compacted particle-spring system into a continuous geometric profile, thereby generating a random model of compact yarns with pores.

[0035] The method for generating a controllable parametric model of the three-dimensional braided composite material of the present invention is also as follows: In step 4), the three-dimensional crack random generation algorithm is specifically as follows:

[0036] 4-1), within the internal space of the yarn, first select the starting position of the microcrack, and then randomly determine the spreading range of the microcrack within the yarn according to the preset maximum penetration length value of the microcrack; the microcrack starts from the starting point and expands along a random path until it reaches the termination point;

[0037] 4-2), based on the three-dimensional coordinate starting point and termination point of the microcrack, combined with the three-dimensional coordinates of the yarn centerline at the same z height as the microcrack, calculate the deviation beam, and generate a microcrack trajectory that is consistent with the curvature of the yarn centerline with the yarn centerline as the reference;

[0038] 4-3), on the corrected microcrack trajectory, select the z plane where the midpoint is located, and based on the microcrack generation algorithm of Perlin noise, generate a corresponding microcrack cross-section on this z plane with this midpoint as the starting point;

[0039] 4-4), copy the generated microcrack cross-section to all z heights based on the microcrack path trajectory to form a microcrack surface;

[0040] 4-5), introduce an erosion factor, the erosion factor is 100% at the center point of the microcrack path, then map the distance from one endpoint to the midpoint or the other endpoint to the [-π / 2, π / 2] period based on the cosine function, and combine with Perlin noise perturbation to generate an irregular erosion factor, and finally construct an irregular microcrack voxel in the random model.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The method for generating a controllable parametric model of a three-dimensional braided composite material of the present invention uses a pore-yarn multi-level space interference detection algorithm to achieve efficient positioning and non-interference embedding of the pore space position after rotation, ensuring the statistical consistency between the pore distribution characteristics and real process defects.

[0043] 2. The method for generating a controllable parametric model of a three-dimensional braided composite material of the present invention uses a yarn dynamic compaction algorithm based on a physical engine of the Taichi framework to achieve physical accurate simulation of the yarn densification process.

[0044] 3. The method for generating a controllable parametric model of a three-dimensional braided composite material of the present invention accurately simulates the three-dimensional morphological characteristics of microcracks generated after yarn compaction through a three-dimensional crack random generation algorithm. Description of the Drawings

[0045] Figure 1 is a flowchart of the method for generating a controllable parametric model of a three-dimensional braided composite material of the present invention.

[0046] Figure 2 It is the process diagram of the four-step three-dimensional braiding process in step 1) of the present invention.

[0047] Figure 3 It is the flow chart of the three-dimensional braided preform model construction in step 1) of the present invention.

[0048] Figure 4 It is the generation principle diagram of the random model of the porous loose yarn in step 2) of the present invention.

[0049] Figure 5 It is the principle diagram of the yarn dynamic compaction algorithm in step 3) of the present invention.

[0050] Figure 6 It is the flow chart of the three-dimensional crack random generation algorithm in step 4) of the present invention.

[0051] Figure 7 It is the process diagram of the generation method of the controllable parametric model of the three-dimensional braided composite material of the present invention. Detailed implementation manners

[0052] Please refer to the attached Figure 1 As shown in the figure, the present invention is a method for generating a controllable parametric model of a three-dimensional braided composite material, which includes the following steps:

[0053] 1), as shown in the attached Figure 2 As shown in the figure, based on the kinematic analysis of the four-step three-dimensional braiding process, a three-dimensional braided preform geometric model is constructed. On the basis of trajectory parametric modeling, geometric reconstruction of the yarn cross-section is implemented, and the optimized cross-section is spatially mapped along the trajectory path to ensure that the cross-section center coincides strictly with the trajectory nodes and there is no geometric interference between the yarns, and finally a random model of loose yarns is obtained.

[0054] Specifically, the said step 1) is:

[0055] 1-1), as shown in (a) in the attached Figure 3 As shown in the figure, based on the kinematic analysis of the four-step three-dimensional braiding process, a discrete space node coordinate model is established. Then, the adjacent braiding step nodes are connected by a piecewise linear interpolation algorithm to generate an initial yarn space trajectory. Further, a high-order polynomial interpolation algorithm is used to optimize the curvature continuity of the trajectory to obtain a smooth space path, and a three-dimensional braided preform trajectory model with a complex space interweaving structure is constructed.

[0056] 1-2), as shown in the attached Figure 3As shown in (b), an initial hexagonal contour is randomly generated according to geometric parameters (including the major axis, minor axis, rotation angle, and center point offset, etc.). Subsequently, by introducing a Perlin noise field, random displacement perturbations are applied to the hexagonal edge nodes, thereby effectively simulating the microscopic irregular characteristics of the real yarn edge. On this basis, a piecewise cubic Bézier curve fitting algorithm is used to smooth the perturbed contour, and the curve continuity is ensured by optimizing the control point positions, and finally a high-fidelity yarn cross-section is generated.

[0057] 1-3), the cross-section performs a spatial mapping along the trajectory path, and the geometric interference state is monitored in real time during this process. When interference is detected, the system automatically triggers a non-interference optimization algorithm, and the non-interference adjustment of the yarn spatial arrangement is realized through iterative optimization. Specifically:

[0058] 1-3-1), calculate the planar Euclidean distance D according to the center point coordinates of two yarns on the same cross-section i,j , and compare it with the sum of their circumscribed circle radii to determine whether the yarn cross-sections interfere. That is, when the center distance between two cross-sections is less than the sum of their circumscribed circle radii, it is determined to be in an interference state, and its specific calculation formula is as follows:

[0059]

[0060]

[0061] Among them, (x i , y i ), (x j , y j ) are the cross-section center coordinate points p i and point p j of yarn i and yarn j at the same height, D i,j is the calculated planar Euclidean distance, and the distance threshold Δ given in the formula is the sum of the distances from the cross-section centers of two yarns i, j to their respective farthest vertex coordinates.

[0062] 1-3-2), when interference occurs, calculate the simulated force for optimizing the yarn trajectory. When two yarn cross-sections are determined to interfere with each other, calculate the simulated force F i, j for optimizing the trajectory, and the total simulated repulsive force vector F i of yarn i is calculated as follows:

[0063]

[0064]

[0065] Among them, u i, j is the vector pointing from point p j to point pi Unit direction vector; m i, j is the simulated repulsion force magnitude, and ξ is the repulsion intensity parameter; F i, j is the point p i and the point p j is the repulsion vector between them. F i is the sum of all repulsion vectors received by yarn i (when multiple yarns interfere with yarn i simultaneously).

[0066]

[0067] where p i ' is used to replace the center p of the yarn trajectory at the current height i， Update the trajectory coordinates of the yarn.

[0068] 1 - 3 - 3), based on the geometric interference detection results, trigger the global curvature continuity optimization algorithm for the yarn trajectory, realize the spatial path re - fitting through parametric spline reconstruction, and finally form a "interference detection - trajectory optimization - re - fitting" closed - loop feedback mechanism.

[0069] 2), please refer to the attached instructions Figure 4 as shown in (a). Based on the loose yarn random model, generate multi - scale pore primitives by randomizing the geometric parameters of the ellipsoid, and then use Euler angle random rotation transformation and random placement to enhance the spatial heterogeneity characteristics of the pore morphology. Embed the optimized pore model between the topological structures of the loose yarn random model to obtain a loose yarn random model with pores.

[0070] Specifically, the step 2) is as follows:[[]]

[0071] 2 - 1), generate multi - scale pore primitives by randomizing the parameters of the long semi - axis, middle semi - axis, and short semi - axis of the ellipsoid, as shown in (b) of the attachment. Subsequently, use Euler angle random rotation transformation to realize the random directional distribution of these primitives in three - dimensional space, thereby constructing a multi - scale pore structure model with anisotropic characteristics. Figure 4

[0072] Combined with (c) in the attachment, the Euler angle rotation formula is as follows:[[]] Figure 4

[0073]

[0074]

[0075]

[0076]

[0077] ​​Among them, θ(α), φ(β) and ψ(γ) represent the rotation matrices around the x, y, and z axes respectively; R is the sum of the rotation matrices.

[0078] Next, when the three-dimensional rotation matrix is ​​constructed, the endpoint coordinates of the initial ellipsoid are multiplied by R. In addition to the center point, there are 14 endpoints in total. The coordinate calculation formula of the endpoints after rotation is as follows:

[0079]

[0080]

[0081] Among them, p represents the coordinate value of each endpoint before rotation; x0, y0, z0 represent the three-axis coordinates of the pore origin, x, y, z represent the offset values ​​of the 14 endpoints compared to the origin; p' represents the new coordinate value of each endpoint after three-dimensional rotation.

[0082] 2-2), please refer to the attached Figure 4 As shown in (d), a pore-yarn multi-level spatial interference detection algorithm is used to ensure that the pores can be accurately placed outside the yarn array and that the pores do not interfere with each other. Finally, the pore structure model is placed in a loose yarn random model.

[0083] The pore-yarn multi-level spatial interference detection algorithm is specifically as follows:

[0084] 2-2-1), during the placement of the pore model, first check whether the center point of the pore interferes with the yarn array or the placed pore space. If interference is detected, the center point position is re-randomly determined; if there is no interference, proceed to the next step of detection.

[0085] 2-2-2), detect whether the six main axis endpoints of the rotated pores interfere with the yarn array or the placed pore space. If interference is detected at any endpoint, the center point position is randomly re-determined; if there is no interference at all endpoints, proceed to the next step of detection.

[0086] 2-2-3), detect whether the eight 45° endpoints of the rotated pore interfere with the yarn array or the placed pore space; if interference is detected at any endpoint, the center point position is re-randomly determined; if there is no interference at all endpoints, the pore space is placed in the loose yarn random model.

[0087] 2-3), by iterating the above steps, continue to place simulated pores in the loose yarn random model. After each successful placement, the pore volume is accumulated and added to the total pore volume. When the cumulative pore volume reaches the preset threshold, the pore placement process is terminated, and finally a loose yarn random model with pores is obtained. Among them, the volume calculation formula of the ellipsoid and the volume porosity calculation formula are as follows:

[0088]

[0089]

[0090] Among them, v i Pores represents the input pore i volume (accumulated from the number of spatial voxels), a i , b i , c i represent the major axis, intermediate axis, and minor axis of the current pore; v model represents the volume of the entire random model (including yarn voxels, empty voxels between yarns, and pore voxels), F 3D Pores represents the bulk porosity (%) of the current random model.

[0091] 3), Using a physics engine based on the Taichi framework, a yarn dynamic compaction algorithm is constructed. After inputting the random model of loose yarn with pores into the algorithm, a physical accurate simulation of the densification behavior of yarns and pores during the compaction process is realized, and a random model of compact yarn with pores is obtained.

[0092] Among them, the yarn dynamic compaction algorithm is specifically as follows:

[0093] 3-1), As shown in (a) of the appendix Figure 5 , Based on the extraction of the geometric profile of the yarn cross-section, its edge is discretized into a particle-spring profile. In this system, each particle is given an entity collision attribute to accurately simulate the non-penetrating contact behavior between yarns; at the same time, adjacent particles are connected to each other by linear springs to effectively characterize the elastic recovery characteristics of the yarn material.

[0094] 3-2), When the discretized profiles of adjacent yarns approach each other due to the contraction movement, the system will trigger a particle-level collision detection and response mechanism; this mechanism is based on the law of conservation of momentum and the spring potential energy function, and dynamically calculates the contact force and deformation displacement between the profiles, so as to realize the physical-driven simulation of the extrusion deformation behavior between yarns. As shown in the comparison of the cross-section changes of the random model of loose yarn with pores and the random model of compact yarn with pores in (b) of the appendix Figure 5 , It can be seen that yarns 1’, 2’, 3’ show obvious deformation compared with yarns 1, 2, 3, and pore 1’ also shows obvious deformation due to the extrusion of adjacent yarns compared with pore 1.

[0095] 3-3), The system presets a global shrinkage ratio threshold as the termination condition for the compaction process; when the minimum distance between yarns reaches this threshold, the system automatically terminates the shrinkage iteration process; subsequently, the compacted particle-spring system is remapped into a continuous geometric profile, thereby generating a random model of compact yarn with pores.

[0096] 4), Using a three-dimensional crack random generation algorithm, three-dimensional random cracks are generated inside the yarn topological structure of the random model of the porous compact yarn to simulate the crack characteristics inside the yarn during the compaction process of the three-dimensional braided composite material, and finally a three-dimensional braided parametric random model is obtained.

[0097] Among them, the specific three-dimensional crack random generation algorithm is as follows:

[0098] 4-1), Please refer to the appendix Figure 6 As shown, in the internal space of the yarn topological structure of the random model of the porous compact yarn, first select the starting position of the micro-crack, and then randomly determine the spread range of the micro-crack inside the yarn according to the preset maximum penetration length value of the micro-crack. The micro-crack starts from the starting point and expands along a random path until it reaches the termination point;

[0099] 4-2), Based on the three-dimensional coordinate starting point and termination point of the micro-crack, combined with the three-dimensional coordinates of the yarn center line at the same z height as the micro-crack, calculate the deviation beam, and generate a micro-crack trajectory that is consistent with the curvature of the yarn center line with the yarn center line as the reference; the specific implementation formula is:

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106] Among them, P-Start and P-End represent the three-dimensional coordinate starting point and termination point of the micro-crack randomly generated by the algorithm; P C -Start and P C -End represent the three-dimensional coordinates of the yarn center line at the same z height; Δx, Δy represent the deviations on the x and y axes between the starting and ending points of the micro-crack and the starting and ending points of the yarn center line. Steps represents the total number of coordinate steps to be generated, which is determined by the difference in the z-axis height of the random starting and ending points; Step represents the current coordinate generation step, with a range of [0, Steps].

[0107] 4-3), On the corrected micro-crack trajectory, select the z plane where the midpoint is located. Based on the micro-crack generation algorithm of Perlin noise, a corresponding micro-crack cross-section will be generated on this z plane starting from this center point. The specific implementation formula is as follows:

[0108]

[0109]

[0110]

[0111] In the above formula, P-Mid is used to represent the three-dimensional coordinates of the midpoint of the microcrack trajectory; x and y represent the plane coordinates of the microcrack trajectory at the z-axis height, and they are also the starting point coordinates of the microcrack generation; x i and i Represent the x-axis and y-axis coordinates of the randomly generated end point; dx and dy represent the difference from the starting point to the end point in the x-axis and y-axis directions respectively; t is the normalized step length, and its value range is limited to the interval [0, 1]; D x (t) and D y (t) represents the value of the Perlin noise function in the x-axis and y-axis directions, respectively. Its main function is to control the degree of disturbance on the x-axis and y-axis. A represents the amplitude. This parameter can be appropriately adjusted according to actual needs. The larger its value, the more severe the fluctuation of the microcracks will be.

[0112] 4-4), the generated microcrack section is copied to all z heights based on the microcrack path trajectory to form a microcrack surface.

[0113] 4-5), introduce the erosion factor, the erosion factor is 100% at the center point of the microcrack path, and then based on the cosine function, map the distance from one end point to the midpoint or the other end point to the [-π / 2, π / 2] period, and combine with Perlin noise perturbation to generate irregular erosion factors, and finally construct irregular microcrack voxels in the random model. The specific implementation formula is as follows:

[0114]

[0115]

[0116] Among them, L represents the distance from the starting point P-Mid to the end point P-Start or P-End; l represents the distance from the current point to the point P-Mid; D represents the Perlin noise function; seed represents the random seed, the purpose is to improve the randomness of the erosion factor and make the microcracks more diversified; A represents the amplitude, which regulates the fluctuation degree of the erosion factor; E represents the randomly generated corrosion factor curve, which will determine the corrosion degree of the crack section at different z-axis heights based on this parameter.

[0117] In summary, combined with the attached Figure 7For the visual parametric modeling process, the controllable parametric model generation method of the three-dimensional braided composite material of the present invention utilizes the kinematic analysis of the four-step three-dimensional braiding process to generate a three-dimensional braided preform trajectory model, and on the basis of trajectory parametric modeling, implements geometric reconstruction of the yarn cross-section. Then, multi-scale pore primitives are generated by randomizing the geometric parameters of the ellipsoid, and randomly placed after random rotation transformation of the Euler angles to enhance the spatial heterogeneity characteristics of the pore morphology. Subsequently, using the model dynamic compaction algorithm, physical accurate simulation of the densification behavior of the yarn and pores during the compaction process is realized; finally, using the three-dimensional crack random generation algorithm, the crack characteristics inside the yarn during the compaction process of the three-dimensional braided composite material are accurately simulated, and a three-dimensional braided parametric random model can be obtained, including four-phase characteristics such as yarn, matrix, initial bubbles, and yarn micro-cracks.

[0118] The pore-yarn multi-level space interference detection algorithm utilized by the controllable parametric model generation method of the three-dimensional braided composite material of the present invention realizes the efficient positioning and interference-free embedding of the pore space position after rotation; secondly, a high-performance physics engine based on the Taichi architecture is adopted to develop a yarn dynamic compaction algorithm, which can accurately simulate the physical behavior of the yarn densification process and the change of pore morphology; finally, through the three-dimensional crack random generation algorithm, accurate reconstruction of the three-dimensional morphology characteristics of the micro-cracks after compaction is realized. This method effectively solves the technical problems such as low efficiency of geometric interference detection, too ideal defect morphology, and insufficient yarn compactness in traditional modeling.

[0119] The above specific implementation manners are only preferred embodiments of this creation, and are not used to limit this creation. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this creation shall be included within the protection scope of this creation.

Claims

1. A method for generating a controllable parametric model of a three-dimensional braided composite material, characterized in that: It includes the following steps: 1), Based on the kinematic analysis of the four-step three-dimensional braiding process, construct a three-dimensional braided preform geometric model. On the basis of trajectory parametric modeling, implement geometric reconstruction of the yarn cross-section. Map the optimized cross-section spatially along the trajectory path to ensure that the cross-section center coincides exactly with the trajectory nodes and there is no geometric interference between the yarns, and finally obtain a loose yarn random model; 2), On the basis of the loose yarn random model, generate multi-scale pore primitives by randomizing the geometric parameters of the ellipsoid. Then, use Euler angle random rotation transformation and random placement to enhance the spatial heterogeneity characteristics of the pore morphology. Embed the optimized pore model between the topological structures of the loose yarn random model to obtain a loose yarn random model with pores; 3), Use a physical engine based on the Taichi framework to construct a yarn dynamic compaction algorithm. After inputting the loose yarn random model with pores into the algorithm, physically accurately simulate the densification behavior of the yarns and pores during the compaction process to obtain a compact yarn random model with pores; The specific yarn dynamic compaction algorithm is as follows: 3-1), On the basis of extracting the geometric contour of the yarn cross-section, discretize its edge into a particle-spring system; in this system, each particle is given an entity collision attribute to accurately simulate the non-penetrating contact behavior between the yarns; at the same time, adjacent particles are connected to each other by linear springs to characterize the elastic recovery characteristics of the yarn material; 3-2), When the discretized contours of adjacent yarns approach each other due to the contraction movement, the system will trigger a particle-level collision detection and response mechanism; 3-3), The system presets a global shrinkage ratio threshold as the termination condition of the compaction process; When the minimum distance between the yarns reaches this threshold, the system automatically terminates the shrinkage iteration process; Subsequently, remap the compacted particle-spring system into a continuous geometric contour, thereby generating a compact yarn random model with pores; 4), Use a three-dimensional crack random generation algorithm to generate three-dimensional random cracks inside the yarn topological structure of the compact yarn random model with pores, simulate the crack characteristics inside the yarns during the compaction process of the three-dimensional braided composite material, and finally obtain a three-dimensional braided parametric random model.

2. The method for generating a controllable parametric model of a three-dimensional braided composite material according to claim 1, wherein: The specific step 1) is as follows: 1-1), Based on the kinematic analysis of the four-step three-dimensional braiding process, establish a discrete space node coordinate model, and then connect adjacent braiding step nodes through a piecewise linear interpolation algorithm to generate an initial yarn space trajectory; further use a high-order polynomial interpolation algorithm to optimize the curvature continuity of the trajectory to obtain a smooth space path and construct a three-dimensional braided preform trajectory model with a complex space interweaving structure; 1-2), Randomly generate an initial hexagonal contour according to geometric parameters; Subsequently, apply random displacement perturbations to the hexagonal edge nodes by introducing a Perlin noise field; finally, use a piecewise Bezier curve to fit the perturbed contour to generate a high-fidelity yarn cross-section geometry; 1-3), The cross-section performs a spatial mapping along the trajectory path, and the geometric interference state is monitored in real time during this process; when interference is detected, the system automatically triggers a non-interference optimization algorithm to achieve non-interference adjustment of the yarn spatial arrangement through iterative optimization.

3. The method for generating a controllable parametric model of the three-dimensional braided composite material according to claim 2, characterized in that: In the above steps 1-3), the non-interference optimization algorithm is specifically as follows: 1-3-1), Calculate the planar Euclidean distance based on the center point coordinates of two yarns on the same cross-section, and compare it with the sum of the circumradius of the circumscribed circle to determine whether the yarn cross-sections interfere with each other; 1-3-2), When interference occurs, calculate the simulation force for optimizing the trajectory; 1-3-3), Based on the geometric interference detection results, trigger the global curvature continuity optimization algorithm for the yarn trajectory, and realize the re-fitting of the spatial path through parametric spline reconstruction, and finally form a "interference detection - trajectory optimization - re-fitting" closed-loop feedback mechanism.

4. The method for generating a controllable parametric model of a three-dimensional braided composite material according to claim 1, characterized in that: The above step 2) is specifically as follows: 2-1), Generate multi-scale pore primitives by randomizing the long semi-axis, medium semi-axis, and short semi-axis parameters of the ellipsoid; Subsequently, use the Euler angle random rotation transformation to realize the random directional distribution of these primitives in three-dimensional space, so as to construct a multi-scale pore structure model with anisotropic characteristics; 2-2), Use the pore-yarn multi-level spatial interference detection algorithm to ensure that the pores can be accurately placed outside the yarn body array and the pores do not interfere with each other, and finally place the pore structure model into the loose yarn random model; 2-3), By iterating the above steps in a loop, continuously place simulated pores in the loose yarn random model.

5. The method for generating a controllable parametric model of a three-dimensional braided composite material according to claim 4, characterized in that: In the above step 2-2), the pore-yarn multi-level spatial interference detection algorithm is specifically as follows: 2-2-1), During the placement process of the pore model, first detect whether the center point of the pore interferes with the yarn body array or the pore space that has been placed. If interference is detected, randomly determine the center point position again; If there is no interference, proceed to the next detection; 2-2-2), Detect whether the 6 main axis endpoints of the rotated pore interfere with the yarn body array or the pore space that has been placed. If interference is detected at any endpoint, randomly determine the center point position again; If no interference is detected at all endpoints, continue with the next detection; 2-2-3), Detect whether the 8 45° direction endpoints of the rotated pore interfere with the yarn body array or the pore space that has been placed; If interference is detected at any endpoint, randomly determine the center point position again; If no interference is detected at all endpoints, place the pore space into the loose yarn random model.

6. The method for generating a controllable parametric model of a three-dimensional braided composite material according to claim 4, characterized in that: In the above step 2-3), after each successful placement, accumulate the pore volume into the total pore volume; When the cumulative pore volume reaches the preset threshold, terminate the pore placement process, and finally obtain a loose yarn random model containing pores; Among them, the volume calculation formula of the ellipsoid and the calculation formula of the bulk porosity are as follows: Among them, v i Pores represents the input pore volume of i, a i , b i , c i represent the major axis, intermediate axis, and minor axis of the current pore; v model represents the volume of the entire random model, and F 3D Pores represents the bulk porosity of the current random model.

7. The method for generating a controllable parametric model of a three-dimensional braided composite material according to claim 1, wherein: In the above step 4), the three-dimensional crack random generation algorithm is specifically as follows: 4-1), In the internal space of the yarn, first select the starting position of the micro-crack, and then randomly determine the spreading range of the micro-crack inside the yarn according to the preset maximum penetration length value of the micro-crack; The micro-crack starts from the starting point and extends along a random path until it reaches the end point; 4-2), based on the starting and ending points of the three-dimensional coordinates of the microcrack, combined with the three-dimensional coordinates of the yarn centerline at the same z-height as the microcrack, calculate the deviation beam, and generate a microcrack trajectory that is consistent with the curvature of the yarn centerline with the yarn centerline as the reference. 4-3), on the corrected microcrack trajectory, select the z-plane where the midpoint is located. Based on the microcrack generation algorithm of Perlin noise, starting from the midpoint of the microcrack trajectory, generate the corresponding microcrack cross-section on this z-plane. 4-4), taking the generated microcrack cross-section as the reference with the microcrack trajectory, copy it to all z-heights to form a microcrack surface. 4-5), introduce the erosion factor. The erosion factor is 100% at the midpoint of the microcrack trajectory. Then, based on the cosine function, map the distance from one endpoint to the midpoint or the other endpoint to the [-π / 2, π / 2] period, and combine it with the Perlin noise perturbation to generate an irregular erosion factor, and finally construct an irregular microcrack voxel in the random model.

Images