Modeling method and simulation method of multi-scale particle reinforced composite material

Abstract

The invention belongs to the technical field of cutting simulation, and particularly relates to a modeling and simulation method for a multi-scale particle reinforced composite material. Comprising the steps that a workpiece, a cutter and enhanced particle parameters are input, and the enhanced particle parameters comprise a plurality of scale intervals and particle proportions of all the intervals; the workpiece is divided into a particle area and a non-particle area, and the size and position of the particle area are controlled through cutting area factors; and sequentially generating reinforced particles meeting a mutual noninterference condition and an internal condition in the particle area according to a descending order of the scale intervals, building a cutting simulation model after the particles are generated, and implementing differentiated grid division on the particle area and the non-particle area. Through multi-scale interval division and space partitioning strategies, the problem of insufficient accuracy caused by single particle scale in an existing modeling method is solved, and the simulation efficiency is remarkably improved while the model accuracy is ensured.

Description

Technical Field

[0001] This invention belongs to the field of cutting simulation technology, specifically relating to a modeling and simulation method for multi-scale particle-reinforced composite materials. Background Technology

[0002] Particle-reinforced composite materials, such as aluminum-based silicon carbide (SiCp / Al), are widely used in high-end fields such as aerospace, electronic packaging, and precision optics due to their high specific strength, high specific stiffness, and good thermophysical properties. However, the significant difference in physical and mechanical properties between the reinforcing particles and the matrix within these materials leads to their difficult-to-machine characteristics, easily causing problems such as severe tool wear and poor surface finish during cutting.

[0003] Cutting simulation technology is an effective means of studying machining mechanisms, optimizing process parameters, and predicting machining damage. The accuracy of the simulation is highly dependent on the microscopic geometric model of the material used. Currently, in cutting simulation modeling of particle-reinforced composites, the commonly used method is to use spherical particles of a single scale or a single scale range to simulate the reinforcing phase. This simplified modeling approach deviates significantly from the actual situation where reinforcing particles are distributed across multiple scales in real materials. This results in simulation results that cannot accurately reflect the real material removal mechanism, cutting force variations, and crack propagation behavior, thus limiting the guiding value of simulation technology for actual production.

[0004] Furthermore, uniformly distributing high-volume-fraction reinforcing particles throughout the entire workpiece model generates a massive number of geometric entities, leading to a surge in the number of elements and nodes in the subsequent finite element mesh generation. This results in enormous computational resource consumption and extremely low simulation efficiency. This contradiction makes it difficult to effectively conduct high-precision cutting simulations in engineering practice. Summary of the Invention

[0005] The purpose of this invention is to provide a modeling and simulation method for multi-scale particle-reinforced composite materials to solve the problems mentioned in the background art.

[0006] The present invention achieves the above objectives through the following technical solutions: Firstly, this invention proposes a modeling method for multi-scale particle-reinforced composite materials, comprising the following steps: S1: Input the geometric parameters of the workpiece and the tool. The workpiece geometric parameters include the workpiece width and height, as well as the size range and proportion of the reinforcing particles. The tool parameters include the height, width, rake angle, clearance angle, and cutting edge radius. S2: Divide a single scale interval into multiple sub-intervals according to the preset scale interval, and assign a corresponding particle proportion to each sub-interval. S3: Divide the workpiece into a granular area and a non-granular area, and determine the size and position of the granular area based on the set cutting area height factor and width factor; S4: Based on the scale range and the corresponding particle ratio, generate non-interfering reinforcing particles in the particle region, wherein reinforcing particles in the larger scale range are preferentially generated. S5: After generating reinforcing particles for all scale ranges, establish a cutting simulation model based on the generated reinforcing particles, workpiece parameters, and tool parameters.

[0007] Furthermore, the method for determining the preset scale interval is as follows: based on the statistical data of the actual size distribution of reinforcing particles in the target particle reinforced composite material, the intervals are divided equally.

[0008] Furthermore, in step S2, allocating a corresponding particle ratio to each sub-interval includes: Constructing the overall volume of enhanced particles Total volume of workpiece Increase particle volume fraction and the proportion of reinforcing particles in each scale range. A conditional relationship model is used to verify the accuracy of the total volume of reinforcing particles in the generated cutting simulation model after the generation of reinforcing particles in all scale ranges is completed. The conditional relationship model is as follows: ; In the formula, The volume fraction of enhanced particles in scale interval i. The percentage of reinforced particles in scale range i.

[0009] Furthermore, in step S3, determining the size and position of the particle region based on the set cutting zone height factor and width factor includes: Establish a coordinate system with a preset vertex of the workpiece as the origin; Based on workpiece width Workpiece height Cutting zone width factor and cutting zone height factor The coordinates (X, Y) of the corresponding vertex in the particle region are determined by the following formula: ; in, Y is the x-coordinate of the corresponding vertex in the particle region, and Y is the y-coordinate of the corresponding vertex in the particle region.

[0010] Furthermore, step S4 includes: S41: Select an unprocessed scale interval as the current scale interval in descending order of scale interval index values; S42: For the current scale range, based on preset inclusion conditions, a candidate value for the center coordinates of the reinforcing particle is randomly generated within the particle region. , And the candidate value of radius Ri; S43: Determine whether the currently generated reinforcing particles meet the preset non-interference condition; S44: If the judgment result of step S43 is yes, then retain the currently generated reinforcing particles and accumulate the particle volume of the current scale range; if no, then discard the currently generated reinforcing particles and return to step S42. S45: Determine if the total volume of particles in the current scale range satisfies the conditional relationship model; if it does, proceed to step S46; otherwise, return to step S42. S46: Determine whether all scale intervals have been processed; if not, return to step S41; if yes, the enhanced particle model has been generated.

[0011] Furthermore, in step S42, based on preset inherent conditions, a candidate value for the center coordinates of the reinforcing particle is randomly generated within the particle region. , ),include: Based on the candidate radius value Ri of the currently generated reinforcing particle and the coordinates of the corresponding vertex in the particle region determined in step S3, the range of deployable coordinates of its center in the particle region is dynamically determined, so that the boundary of the reinforcing particle is completely contained within the boundary of the particle region. Within the deployable coordinate range ( )and( Within ) randomly generate candidate values ​​for the center coordinates of the circle ( , ); The boundary of the deployable coordinate range is determined by the following formula: ; ; in, To enhance the x-coordinate of the particle's center, To increase the minimum value of the x-coordinate of the particle center, To increase the maximum value of the x-coordinate of the particle center, Let be the ordinate of the particle's center. The minimum value of the ordinate of the particle's center. The maximum value of the ordinate of the particle's center. For the workpiece width, This is the cutting zone width factor. For the workpiece height, For the cutting zone height factor, The radius of the currently generated circular particles. The offset factor is relative to the boundary of the particle region. The value is greater than 1.

[0012] Furthermore, in step S42, the candidate radius value Ri is generated in the following way: The numerical range defined from the currently selected scale interval [R] min R max Within the range, a random value is generated as the candidate radius value Ri.

[0013] Furthermore, in step S43, the determination method for satisfying the non-interference condition includes: Based on the shapely geometry library, a particle with a radius of is created according to the candidate center coordinates (x, y) and the candidate radius Ri of the currently generated particle. A temporary circular buffer geometry, where l is the minimum spacing of the particle boundaries; For each generated particle recorded in the particle library, obtain its center coordinates (x, y). i , y j ) and radius value ; Determine the geometry of the temporary circular buffer zone and its relationship with (x) i , y j (with the center as the center) Does a circular geometry with radius 1 experience spatial interference? If no spatial interference occurs for any of the generated particles in the particle library, then the currently generated reinforcing particle is determined to satisfy the non-interference condition. Where no spatial interference occurs, the boundary of the currently generated particle does not contact the boundary of the existing particle and is not contained within the existing particle. The generated particle satisfies the following distance condition formula: ; in, This represents the distance between the center of the currently generated particle and any previously generated particle.

[0014] Furthermore, in the generated cutting simulation model, the granular region is configured to be meshed using a first density, and the non-granular region is configured to be meshed using a second density, wherein the first density is greater than the second density.

[0015] Secondly, this invention proposes a simulation method for the cutting process of particle-reinforced aluminum matrix composites, comprising the following steps: S101: Obtain the cutting simulation model of the target particle-reinforced aluminum matrix composite material; the cutting simulation model is generated by the multi-scale particle-reinforced composite material modeling method described above; The reinforcing particles in the cutting simulation model are established based on at least two scale intervals and the particle proportions corresponding to each scale interval, and the reinforcing particles do not interfere with each other in the particle area of ​​the workpiece model. S102: In the simulation environment, configure the initial position of the tool and its motion path, the two-phase cohesive interface, the tool-workpiece contact, the workpiece position constraints, and the material parameters of the tool and workpiece for the cutting simulation model; S103: Run the cutting simulation calculation and obtain the simulation results related to the cutting process effect.

[0016] The beneficial effects of this invention are as follows: (1) High accuracy in modeling particle-reinforced composite materials: Compared with material models that use constant scale and single scale intervals, this invention divides the single scale interval into multiple scale intervals and introduces the particle proportion of each scale interval, which has higher accuracy.

[0017] (2) The material model has strong adaptability: Compared with the material model that uses a constant scale and a single scale range, the present invention can match the particle volume fraction and the particle proportion in each scale range with the actual data of the material manufacturer, and has stronger adaptability.

[0018] (3) High efficiency of mesh generation and cutting simulation: The workpiece is divided into granular and non-granular regions. The granular region is seeded with a denser mesh, while the non-granular region is seeded in the opposite way. This can effectively reduce the number of meshes and nodes and improve the efficiency of cutting simulation. Attached Figure Description

[0019] Figure 1 This is a flowchart of a modeling method for multi-scale particle-reinforced composite materials in one embodiment of the present invention; Figure 2 This is another flowchart of a modeling method for multi-scale particle-reinforced composite materials in one embodiment of the present invention; Figure 3 This is a cutting simulation model with a particle volume fraction of 30% in the modeling results of this invention. Figure 4 This is a cutting simulation model with a particle volume fraction of 40% in the modeling results of this invention. Figure 5 This is a cutting simulation model with a particle volume fraction of 50% in the modeling results of this invention. Figure 6 This is the mesh generation result of the simulation model for the workpiece region with a volume fraction of 30% in the modeling result case of this invention; Figure 7 This is the mesh generation result of the simulation model for the undivided workpiece region, which has a volume fraction of 30% in the modeling results of this invention. Detailed Implementation

[0020] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0021] In the machining of particle-reinforced composite materials (such as aluminum-based silicon carbide SiCp / Al), the accuracy of their microstructure is crucial for studying cutting mechanisms and predicting tool wear and workpiece surface integrity. Currently, traditional simulation modeling methods for composite material cutting mainly rely on a single particle scale or a single scale range to simulate the reinforcing phase. This method is based on localized and simplified material morphology assumptions. However, reinforcing particles in real materials exhibit multi-scale distribution characteristics in size, and their spatial distribution has significant randomness. Therefore, relying solely on a simplified model at a single scale makes it difficult to accurately reflect the complex interaction mechanisms between particles and the matrix, and between particles and the tool, during the cutting process. This leads to significant deviations between simulation results and physical experiments, failing to effectively guide actual process optimization.

[0022] To address the aforementioned problems, this invention provides a multi-scale particle-reinforced composite material modeling method for cutting simulation. This method can be applied to corresponding computer-aided engineering systems, computer program products, and computer-readable storage media. The specific process of the method will be described in detail below.

[0023] Please see Figure 1 and Figure 2 One embodiment of the present invention proposes a modeling method for multi-scale particle-reinforced composite materials, comprising the following steps: S1: Input the geometric parameters of the workpiece and the tool. The workpiece geometric parameters include the workpiece width and height, as well as the size range and proportion of the reinforcing particles. The tool parameters include the height, width, rake angle, clearance angle, and cutting edge radius.

[0024] Specifically, workpiece geometric parameters include the workpiece's macroscopic dimensions, such as width w and height h, typically in millimeters (mm). Parameters for the reinforcing particles that need to be entered include: Scale range: This refers to the distribution range of reinforcing particle size, which is usually divided into two main ranges: nanoscale and microscale. For example, for a certain type of aluminum-based silicon carbide composite material, the reinforcing particle size is mainly distributed between 10 μm and 50 μm.

[0025] Particle percentage: refers to the percentage of the volume of each particle in each scale range relative to the total volume of all reinforcing particles.

[0026] Tool geometry parameters include tool height, width, rake angle, clearance angle, and cutting edge radius. These parameters are used to accurately define the interaction between the tool and the workpiece in subsequent simulations. These parameters can be input by the user according to the actual tool model used.

[0027] S2: Based on the preset scale interval, a single scale interval is divided into multiple sub-intervals, and a corresponding particle ratio is assigned to each sub-interval.

[0028] As a preferred method, the preset scale interval is determined by dividing the material into equal intervals based on the statistical data of the actual size distribution of reinforcing particles in the target particle-reinforced composite material.

[0029] For example, as shown in Table 1, taking the 10-50μm particles mentioned above as an example, 10μm can be set as a fixed interval, thereby dividing a single interval into 4 sub-intervals: 10-20μm, 20-30μm, 30-40μm, and 40-50μm.

[0030] Preferably, in step S2, a corresponding particle ratio is assigned to each sub-interval, including: Constructing the overall volume of enhanced particles Total volume of workpiece Increase particle volume fraction and the proportion of reinforcing particles in each scale range. The conditional relationship model is used to verify the accuracy of the total volume of reinforcing particles in the generated cutting simulation model after the generation of reinforcing particles in all scale ranges is completed; the proportion of reinforcing particles in each scale range is also considered. Also known as the scale interval ratio, this ratio defines the target percentage of particle volume in each scale interval relative to the total volume of all reinforcing particles.

[0031] The conditional relationship model is as follows: ; In the formula, The volume fraction of enhanced particles in scale interval i. The percentage of reinforced particles in scale range i.

[0032] Understandably, the core function of this conditional relationship model is verification. After generating reinforcing particles across all scale ranges, the total volume of all generated particles can be calculated to verify whether it satisfies this formula, thereby ensuring that the final cutting simulation model is consistent with the design objectives in terms of the total volume and scale distribution of the reinforcing phase.

[0033] Table 1. Multi-scale ranges and distribution of reinforcing particles ; S3: Divide the workpiece into a granular zone and a non-granular zone, and determine the size and position of the granular zone based on the set cutting zone height factor and width factor.

[0034] It should be noted that, considering that the tool only interacts with a specific area of ​​the workpiece in cutting simulation, generating particles throughout the entire workpiece would result in a huge number of meshes and high computational costs. To solve this problem, this invention divides the workpiece into a granular region (the area where cutting mainly occurs) and a non-granular region (the area far from the toolpath).

[0035] Preferably, in step S3, determining the size and location of the particle region based on the set cutting zone height factor and width factor includes: (1) Establish a coordinate system with a preset vertex of the workpiece as the origin; establish a two-dimensional Cartesian coordinate system with (for example, the lower left vertex) as the origin (0,0). The corresponding vertex coordinates of the workpiece are (w, h).

[0036] (2) Calculate the location of the particle area: based on the workpiece width Workpiece height Cutting zone width factor and cutting zone height factor The coordinates (X, Y) of the corresponding vertex in the particle region are determined by the following formula. These coordinates are the coordinates of the same corner point in the particle region when the preset vertex of the workpiece is taken as the origin. (For example, if the preset origin is the lower left corner vertex of the workpiece, then the corresponding vertex is the lower left corner vertex of the particle region.) As shown in the following formula: ; in, Y is the x-coordinate of the corresponding vertex in the particle region, and Y is the y-coordinate of the corresponding vertex in the particle region.

[0037] and It is a factor between 0 and 1, used to control the size and location of the particle region.

[0038] For example, the top-right vertex of the particle region is assumed to be the top-right corner (w, h) of the workpiece. Therefore, the actual width of the particle region is... This is the difference between the x-coordinate of the predetermined point on the workpiece (the corresponding vertex of the workpiece) and the x-coordinate of that vertex. The actual height is... This refers to the difference between the ordinate of the predetermined point on the workpiece and the ordinate of the vertex. By adjusting... and It can flexibly concentrate particles in key areas near the toolpath.

[0039] S4: Based on the scale range and the corresponding particle ratio, generate non-interfering reinforcing particles within the particle area. Prioritize generating reinforcing particles within the larger scale range. This principle requires that particle generation and placement follow the order from largest to smallest scale range index value. That is, within the particle area, prioritize generating and placing all particles within the largest scale range. After the particles in that range are generated, generate particles in the second largest range, and so on, until the smallest scale range.

[0040] Preferably, step S4 includes: S41: Select an unprocessed scale interval as the current scale interval in descending order of scale interval index value. For example, [40-50μm, 30-40μm, 20-30μm, 10-20μm]) starts processing from the first in the list (largest index value, largest particle size).

[0041] S42: For the current scale interval, based on preset inclusion conditions, randomly generate a candidate value for the center coordinates of the reinforcing particle within the particle region. , ) and the candidate value of radius Ri.

[0042] Preferably, in step S42, based on preset inclusion conditions, a candidate value for the center coordinates of the reinforcing particle is randomly generated within the particle region. , ),include: Based on the candidate radius value Ri of the currently generated reinforcing particle and the coordinates of the corresponding vertex in the particle region determined in step S3, the range of deployable coordinates of its center in the particle region is dynamically determined, so that the boundary of the reinforcing particle is completely contained within the boundary of the particle region. Within the deployable coordinate range ( )and( Within ) randomly generate candidate values ​​for the center coordinates ( , ).

[0043] The boundary of the deployable coordinate range is determined by the following formula: ; ; in, To enhance the x-coordinate of the particle's center, To increase the minimum value of the x-coordinate of the particle center, To increase the maximum value of the x-coordinate of the particle center, Let be the ordinate of the particle's center. The minimum value of the ordinate of the particle's center. The maximum value of the ordinate of the particle's center. For the workpiece width, This is the cutting zone width factor. For the workpiece height, For the cutting zone height factor, The radius of the currently generated circular particles. The offset factor is relative to the boundary of the particle region. The value is greater than 1.

[0044] Preferably, the candidate radius value Ri is generated in step S42 by the following method: The numerical range defined from the currently selected scale interval [R] min R max Within ], a random value is generated as a candidate value Ri for the radius.

[0045] In step S43, the methods for determining whether the non-interference condition is met include: Based on the shapely geometry library, a particle with a radius of is created according to the candidate center coordinates (x, y) and the candidate radius Ri of the currently generated particle. A temporary circular buffer geometry, where l is the minimum spacing of the particle boundaries; For each generated particle recorded in the particle library, obtain its center coordinates (x, y). i , y j ) and radius value ; Determine the geometry of the temporary circular buffer zone and its relationship with (x) i , y j (with the center as the center) Does a circular geometry with radius 1 experience spatial interference? If no spatial interference occurs for any of the generated particles in the particle library, then the currently generated reinforcing particle is determined to satisfy the non-interference condition. Where no spatial interference occurs, the boundary of the currently generated particle does not contact the boundary of the existing particle and is not contained within the existing particle. The generated particle satisfies the following distance condition formula: ; in, This represents the distance between the center of the currently generated particle and any previously generated particle.

[0046] In the specific implementation of step S43, a temporary circular buffer (with a radius value of ) is created based on the Point class object from the shapely library. ), and interacts with the generated particles (also circular buffers, but with a radius value) via the `interacts` method. Interference judgment is performed without including the minimum spacing l), and the distance condition formula is ultimately satisfied in form.

[0047] When newly generated particles are added to the particle library, for Point class objects, there are buffered circle libraries and radius libraries; the former stores the center coordinates of the circle, and the buffered circle radius values ​​are both... The latter does not include the minimum spacing l; the latter stores the radius, and the radius value is... It does not include the minimum spacing l.

[0048] It should be noted that, in the actual implementation, the particle library is a dynamically generated dataset used to record the geometric information of all augmented particles that have successfully passed spatial validation and been formally incorporated into the model. This library is empty at the start of the process and is continuously updated as particles are successfully generated. Each record contains the center coordinates of a particle (…). , The particle library provides the data basis for the "non-interference condition" judgment in step S43. Each newly generated candidate particle must be checked for interference with all existing particles in the particle library.

[0049] S44: If the judgment result of step S43 is yes, then retain the currently generated reinforcing particles and accumulate the particle volume of the current scale range; if no, then discard the currently generated reinforcing particles and return to step S42.

[0050] S45: Determine if the total volume of particles in the current scale interval satisfies the conditional relationship model (as described in step S2 above); if it does, proceed to step S46; otherwise, return to step S42. S46: Determine whether all scale intervals have been processed; if not, return to step S41; if yes, the enhanced particle model has been generated.

[0051] As in the above embodiment, step S3 introduces a cutting factor-based... and The proposed partitioning method divides the workpiece into granular and non-granular regions. Based on the locality of motion in cutting simulation, this method limits particle generation to a critical area near the toolpath through coordinate transformation and factor control. This significantly reduces the number of elements required for subsequent mesh generation and improves computational efficiency while maintaining simulation accuracy.

[0052] Step S4 is responsible for automatically generating non-interfering reinforcing particles within the designated particle region, based on a preset multi-scale distribution (scale intervals and particle proportions). The core of this process is the adoption of a "large-to-small" generation order, prioritizing the processing of large-scale intervals before proceeding to smaller-scale intervals. This strategy ensures that the largest particles, with the most demanding space requirements, receive priority placement space, avoiding the problem of space fragmentation caused by generating small particles first, which would prevent the large particles from being accommodated. For each candidate particle, an "inclusion condition" ensures it is completely located within the particle region, and a "non-interference condition" ensures it maintains a safe distance from all already generated particles. This iterative process continues until the total particle volume across all scale intervals reaches the target value determined by the conditional relationship model, ultimately forming a high-precision cutting simulation model that conforms to both material statistical properties and geometric compatibility.

[0053] S5: After generating reinforcing particles for all scale ranges, establish a cutting simulation model based on the generated reinforcing particles, workpiece parameters, and tool parameters.

[0054] In practice, the workpiece matrix model (excluding the non-particle region and the particle region matrix portion) established in step S3 is merged with the geometric models of all reinforcing particles generated in step S4 using a Boolean merge operation. This operation embeds the particles as independent geometric entities into the workpiece matrix, forming a complete, heterogeneous composite material micro-geometric model.

[0055] The integrated workpiece model and the input tool geometry model are assembled and positioned in the simulation environment. According to the simulation settings, the initial position and motion path of the tool, the two-phase cohesive interface (two phases refer to the Al matrix and SiC particles), the tool-workpiece contact, the workpiece position constraints, and the material parameters of the tool and workpiece are configured.

[0056] The final output is a cutting simulation model file containing precise microscopic geometric features, which can be used for direct finite element cutting simulation calculations.

[0057] Preferably, in the generated cutting simulation model, the granular region is configured with a first-density mesh. Since this region contains a large number of reinforcing particles and is the concentrated area of ​​stress, strain, and damage evolution during cutting, a sufficiently dense mesh is needed to capture the complex interactions between particles and the matrix, stress concentration phenomena, and material removal mechanisms. The non-granular region is configured with a second-density mesh. The non-granular region consists only of the matrix material and is far from the direct cutting area, resulting in relatively gradual changes in its stress and strain field. Therefore, a sparser mesh is sufficient to meet the computational requirements while reducing the total number of elements and nodes. The first density is greater than the second density.

[0058] For example, in the preprocessing module of commercial finite element software (such as Abaqus and ANSYS), different mesh generation control rules can be created for the granular region and the non-granular region respectively, specifying a smaller element size (such as 1 μm) for the granular region and a larger element size (such as 5 μm) for the non-granular region.

[0059] Example 2 Another embodiment of the present invention proposes a simulation method for the cutting process of particle-reinforced aluminum matrix composites. Using the model generated in Example 1, the cutting process is simulated and analyzed. The method specifically includes the following steps: S101: Obtain the cutting simulation model of the target particle-reinforced aluminum matrix composite material; the cutting simulation model is generated by the multi-scale particle-reinforced composite material modeling method in Example 1; Among them, the reinforcing particles in the cutting simulation model are established based on at least two scale intervals and the particle proportions corresponding to each scale interval, and the reinforcing particles do not interfere with each other in the particle area of ​​the workpiece model. S102: In the simulation environment, configure the initial position of the tool and its motion path, the two-phase cohesive interface, the tool-workpiece contact, the workpiece position constraints, and the material parameters of the tool and workpiece for the cutting simulation model.

[0060] S103: Run the cutting simulation calculation and obtain the simulation results related to the cutting process effect.

[0061] It is understood that the specific material used in this invention is a particle-reinforced aluminum matrix composite material, especially a composite material with aluminum or aluminum alloy as the matrix and silicon carbide (SiC) particles as the reinforcing phase.

[0062] To more clearly illustrate the present invention and its advantages, the method provided by the present invention will be further explained below in conjunction with specific embodiments and related partial figures.

[0063] Test parameter settings: Taking a certain type of aluminum-based silicon carbide composite material as an example, the size of its reinforcing particles is mainly concentrated in the range of 10~50μm. The size is further subdivided into four scale intervals, with 10μm as the scale interval. Cutting simulation models are established for aluminum-based silicon carbide composite materials with particle volume fractions of 30%, 40%, and 50%, respectively. The relevant parameters are shown in Table 2.

[0064] Table 2 Workpiece parameters and particle region control parameters ; Modeling results: (1) Cutting simulation model with different particle volume fractions like Figures 3-5As shown, with the parameter settings described above, the reinforcing particles generated by this method have good randomness, match the actual material conditions, and have high accuracy.

[0065] (2) Comparison of the number of elements and nodes in the material model The simulation models with divided workpiece regions and those without divided workpiece regions are compared, as follows: Figure 6 , 7 As shown, after meshing, the former model with meshed workpiece regions has 8717 elements and 8673 nodes, while the latter model without meshed workpiece regions has 26537 elements and 25982 nodes. Furthermore, this method arranges the seeds in the granular region more densely and the seeds in the non-granular region more sparsely, which significantly improves simulation efficiency.

[0066] In summary, this method has high accuracy and adaptability in establishing cutting simulation models and can improve simulation efficiency.

[0067] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated.

[0068] The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0069] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0070] In addition, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0071] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A modeling method for multi-scale particle-reinforced composite materials, characterized in that, Includes the following steps: S1: Input the geometric parameters of the workpiece and the tool. The workpiece geometric parameters include the workpiece width and height, as well as the size range and proportion of the reinforcing particles. The tool parameters include the height, width, rake angle, clearance angle, and cutting edge radius. S2: Divide a single scale interval into multiple sub-intervals according to the preset scale interval, and assign a corresponding particle proportion to each sub-interval. S3: Divide the workpiece into a granular area and a non-granular area, and determine the size and position of the granular area based on the set cutting area height factor and width factor; S4: Based on the scale range and the corresponding particle ratio, generate non-interfering reinforcing particles in the particle region, wherein reinforcing particles in the larger scale range are preferentially generated. S5: After generating reinforcing particles for all scale ranges, establish a cutting simulation model based on the generated reinforcing particles, workpiece parameters, and tool parameters.

2. The modeling method for multi-scale particle-reinforced composite materials according to claim 1, characterized in that, The method for determining the preset scale interval is as follows: based on the statistical data of the actual size distribution of reinforcing particles in the target particle reinforced composite material, the intervals are divided equally.

3. The modeling method for multi-scale particle-reinforced composite materials according to claim 1, characterized in that, In step S2, allocating a corresponding particle percentage to each sub-interval includes: Constructing the overall volume of enhanced particles Total volume of workpiece Increase particle volume fraction and the proportion of reinforcing particles in each scale range. A conditional relationship model is used to verify the accuracy of the total volume of reinforcing particles in the generated cutting simulation model after the generation of reinforcing particles in all scale ranges is completed. The conditional relationship model is as follows: ； In the formula, The volume fraction of enhanced particles in scale interval i. The percentage of reinforced particles in scale range i.

4. The modeling method for multi-scale particle-reinforced composite materials according to claim 3, characterized in that, In step S3, determining the size and location of the particle region based on the set cutting zone height factor and width factor includes: Establish a coordinate system with a preset vertex of the workpiece as the origin; Based on workpiece width Workpiece height Cutting zone width factor and cutting zone height factor The coordinates (X, Y) of the corresponding vertex in the particle region are determined by the following formula: ； in, Y is the x-coordinate of the corresponding vertex in the particle region, and Y is the y-coordinate of the corresponding vertex in the particle region.

5. The modeling method for multi-scale particle-reinforced composite materials according to claim 4, characterized in that, Step S4 includes: S41: Select an unprocessed scale interval as the current scale interval in descending order of scale interval index values; S42: For the current scale range, based on preset inclusion conditions, a candidate value for the center coordinates of the reinforcing particle is randomly generated within the particle region. , And the candidate value of radius Ri; S43: Determine whether the currently generated reinforcing particles meet the preset non-interference condition; S44: If the judgment result of step S43 is yes, then retain the currently generated reinforcing particles and accumulate the particle volume of the current scale range; if no, then discard the currently generated reinforcing particles and return to step S42. S45: Determine if the total volume of particles in the current scale range satisfies the conditional relationship model; if it does, proceed to step S46; otherwise, return to step S42. S46: Determine whether all scale intervals have been processed; if not, return to step S41; if yes, the enhanced particle model has been generated.

6. The modeling method for multi-scale particle-reinforced composite materials according to claim 5, characterized in that, In step S42, based on preset inherent conditions, a candidate value for the center coordinates of an enhanced particle is randomly generated within the particle region. , ),include: Based on the candidate radius value Ri of the currently generated reinforcing particle and the coordinates of the corresponding vertex in the particle region determined in step S3, the range of deployable coordinates of its center in the particle region is dynamically determined, so that the boundary of the reinforcing particle is completely contained within the boundary of the particle region. Within the deployable coordinate range ( )and( Within ) randomly generate candidate values ​​for the center coordinates of the circle ( , ); The boundary of the deployable coordinate range is determined by the following formula: ； ； in, To enhance the x-coordinate of the particle's center, To increase the minimum value of the x-coordinate of the particle center, To increase the maximum value of the x-coordinate of the particle center, Let be the ordinate of the particle's center. The minimum value of the ordinate of the particle's center. The maximum value of the ordinate of the particle's center. For the workpiece width, This is the cutting zone width factor. For the workpiece height, For the cutting zone height factor, The radius of the currently generated circular particles. The offset factor is relative to the boundary of the particle region. The value is greater than 1.

7. The modeling method for multi-scale particle-reinforced composite materials according to claim 6, characterized in that, In step S42, the candidate radius value Ri is generated in the following way: The numerical range defined from the currently selected scale interval [R] min R max Within ], a random value is generated as the candidate value Ri for the radius.

8. The modeling method for multi-scale particle-reinforced composite materials according to claim 5, characterized in that, In step S43, the methods for determining whether the non-interference condition is met include: Based on the shapely geometry library, a particle with a radius of is created according to the candidate center coordinates (x, y) and the candidate radius Ri of the currently generated particle. A temporary circular buffer geometry, where l is the minimum spacing of the particle boundaries; For each generated particle recorded in the particle library, obtain its center coordinates (x, y). i , y j ) and radius value ; Determine the geometry of the temporary circular buffer zone and its relationship with (x) i , y j (with the center as the center) Does a circular geometry with radius 1 experience spatial interference? If no spatial interference occurs for any of the generated particles in the particle library, then the currently generated reinforcing particle is determined to satisfy the non-interference condition. Where no spatial interference occurs, the boundary of the currently generated particle does not contact the boundary of the existing particle and is not contained within the existing particle. The generated particle satisfies the following distance condition formula: ； in, This represents the distance between the center of the currently generated particle and any previously generated particle.

9. The modeling method for multi-scale particle-reinforced composite materials according to claim 5, characterized in that, In the generated cutting simulation model, the granular region is configured to be meshed using a first density, and the non-granular region is configured to be meshed using a second density, wherein the first density is greater than the second density.

10. A simulation method for machining processes of particle-reinforced aluminum matrix composites, characterized in that, Includes the following steps: S101: Obtain a cutting simulation model of the target particle-reinforced aluminum matrix composite material; the cutting simulation model is generated by the multi-scale particle-reinforced composite material modeling method according to any one of claims 1-9; The reinforcing particles in the cutting simulation model are established based on at least two scale intervals and the particle proportions corresponding to each scale interval, and the reinforcing particles do not interfere with each other in the particle area of ​​the workpiece model. S102: In the simulation environment, configure the initial position of the tool and its motion path, the two-phase cohesive interface, the tool-workpiece contact, the workpiece position constraints, and the material parameters of the tool and workpiece for the cutting simulation model; S103: Run the cutting simulation calculation and obtain the simulation results related to the cutting process effect.

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