Digital Modeling Method and System for Punching Dies Based on 3D Technology

Abstract

The present invention relates to the technical field of die modeling, and discloses a digital modeling method and system for a punching die based on 3D technology, including: determining the type of the basic structure corresponding to the punching die and its corresponding hierarchical structure; performing point cloud scanning on the existing products of the punching die to obtain die point cloud data, calculating the material fatigue coefficient corresponding to the punching die, and calculating the assembly tolerance coefficient corresponding to the punching die; performing collaborative optimization processing on the initial punching die model to obtain an optimized punching die model, calculating the contour disorder degree corresponding to the optimized punching die model, and formulating the optimal assembly order corresponding to each component in the optimized punching die model; calculating the blanking force coefficient corresponding to the optimized punching die model, planning the optimal assembly path corresponding to each component in the optimized punching die model, and performing digital modeling processing on the corresponding punching die to obtain a modeling result. The present invention can improve the accuracy of digital modeling of punching dies.

Description

Technical Field

[0001] The present invention relates to a digital modeling method and system for a punching die based on 3D technology, belonging to the technical field of die modeling. Background Art

[0002] Under the wave of the booming development of modern manufacturing industry, as a key tooling in the manufacturing process of many industrial products, punching dies are widely used in fields such as automobiles, electronics, and household appliances. Their market demand shows a continuous upward trend. With the accelerating product iteration and the increasingly stringent requirements for component accuracy and quality, the traditional punching die design and manufacturing methods are increasingly difficult to meet the high requirements of current industrial production.

[0003] The design of traditional punching dies mostly relies on experienced engineers to manually draw designs based on two-dimensional drawings. During the process, parameters such as dimensions need to be repeatedly calculated, which is not only time-consuming and laborious, but also extremely prone to design mistakes due to human negligence. In the manufacturing link, mainly based on the design drawings, it is gradually formed through conventional machining processes such as turning, milling, planing, and grinding. The machining accuracy is greatly restricted by factors such as the skill level of workers and the accuracy of equipment, and it is difficult to ensure the high-precision requirements of complex punching dies.

[0004] When it comes to the optimization and improvement of dies, usually, it is necessary to wait until problems are exposed in actual production, and then based on on-site feedback, the die is manually disassembled for observation and measurement, and the root cause of the problem is judged based on experience, and then the die is modified. The whole process is long and cumbersome, which greatly affects the production efficiency and the stability of product quality. This punching die design and manufacturing mode based on manual experience and traditional processes will lead to a reduction in the accuracy of digital modeling of punching dies. Summary of the Invention

[0005] The present invention provides a digital modeling method and system for a punching die based on 3D technology, and its main purpose is to improve the accuracy of digital modeling of punching dies.

[0006] To achieve the above object, a digital modeling method for a punching die based on 3D technology provided by the present invention includes:

[0007] Obtain the design requirements of the punching die to be modeled and its corresponding punching process parameters, analyze the production environment characteristics corresponding to the punching die, and combine the design requirements and the production environment characteristics to determine the basic architecture type corresponding to the punching die and its corresponding hierarchical structure;

[0008] Perform point cloud scanning on the existing product of the punching die to obtain die point cloud data. Extract the key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, collect the material property data corresponding to the punching die, calculate the material fatigue coefficient corresponding to the punching die based on the material property data, and evaluate the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient.

[0009] Combine the infrastructure type, the hierarchical structure, the key punching structure features, and the structural dimension information to perform modeling processing on the punching die to obtain an initial punching die model. Calculate the assembly tolerance coefficient corresponding to the punching die in combination with the design requirements and the structural dimension information.

[0010] Combine the fatigue damage evolution and the assembly tolerance coefficient to perform collaborative optimization processing on the initial punching die model to obtain an optimized punching die model. Calculate the contour disorder degree corresponding to the optimized punching die model, and formulate the optimal assembly order for each component in the optimized punching die model based on the contour disorder degree.

[0011] Calculate the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters, plan the optimal assembly path for each component in the optimized punching die model based on the blanking force coefficient, and perform digital modeling processing on the punching die in combination with the optimal assembly order and the optimal assembly path to obtain a modeling result.

[0012] Optionally, the determining the infrastructure type and its corresponding hierarchical structure of the punching die in combination with the design requirements and the production environment characteristics includes:

[0013] Conduct a detailed analysis of the design requirements to obtain a list of detailed requirements.

[0014] Collect data on the production environment characteristics to obtain an environmental characteristics dataset.

[0015] Conduct conditional mining analysis on the list of detailed requirements to obtain key design constraint conditions.

[0016] Analyze the dominant environmental factors of the punching die based on the environmental characteristics dataset.

[0017] Combine the key design constraint conditions and the dominant environmental factors to match the infrastructure type and its corresponding hierarchical structure of the punching die.

[0018] Optionally, the extracting the key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data includes:

[0019] Perform noise reduction processing on the mold point cloud data to obtain noise-reduced mold point cloud data;

[0020] Perform fitting processing on the noise-reduced mold point cloud data to obtain fitted mold point cloud data;

[0021] Calculate the point cloud distance of the fitted mold point cloud data;

[0022] Based on the point cloud distance, perform discrete point removal processing on the fitted mold point cloud data to obtain target fitted mold point cloud data;

[0023] Perform region segmentation processing on the target fitted mold point cloud data to obtain regional mold point cloud data;

[0024] Extract structural features from the regional mold point cloud data to obtain initial mold structural features;

[0025] Perform feature screening on the initial mold structural features to obtain the key punching structural features corresponding to the punching mold;

[0026] Perform dimensional measurement on the regional mold point cloud data to obtain the structural dimension information corresponding to the punching mold.

[0027] Optionally, the calculating the material fatigue coefficient corresponding to the punching mold based on the material property data includes:

[0028] Based on the material property data, determine the performance parameter values corresponding to the punching mold;

[0029] Based on the performance parameter values, construct a material body model corresponding to the punching mold;

[0030] Based on the material body model, create a mold finite element model corresponding to the punching mold;

[0031] Perform stress simulation analysis on the mold finite element model to obtain stress simulation parameters and the number of stress cycles;

[0032] Combine the stress simulation parameters, the number of stress cycles, and the mold finite element model to calculate the material fatigue coefficient corresponding to the punching mold.

[0033] Optionally, the combining the stress simulation parameters, the number of stress cycles, and the mold finite element model to calculate the material fatigue coefficient corresponding to the punching mold includes:

[0034] Analyze the fatigue critical regions and their corresponding region weights in the mold finite element model, and count the number of regions corresponding to the fatigue critical regions;

[0035] Determine the regional stress cycle times corresponding to the fatigue critical region based on the stress cycle times;

[0036] Query the theoretical cycle times corresponding to the fatigue critical region based on the stress simulation parameters;

[0037] Combining the regional weight, the regional stress cycle times, the regional theoretical cycle times, and the number of regions, the material fatigue coefficient corresponding to the punching die can be calculated by the following formula:

[0038] ;

[0039] where A represents the material fatigue coefficient corresponding to the punching die, represents the regional weight corresponding to the a-th region in the fatigue critical region, represents the regional stress cycle times corresponding to the a-th region in the fatigue critical region, represents the regional theoretical cycle times corresponding to the a-th region in the fatigue critical region, a represents the serial number corresponding to the fatigue critical region, and q represents the number of fatigue critical regions.

[0040] Optionally, the calculating the assembly tolerance coefficient corresponding to the punching die by combining the design requirements and the structural dimension information includes:

[0041] Determine the assembly accuracy requirements of the punching die in the assembly process based on the design requirements;

[0042] Determine the key assembly parts corresponding to the punching die based on the assembly accuracy requirements, and extract the assembly part dimensions corresponding to the key assembly parts from the structural dimension information;

[0043] Calculate the dimensional deviation value corresponding to the key assembly part based on the assembly part dimension and the preset standard dimension;

[0044] Analyze the part function attributes corresponding to the key assembly part, and set the part importance corresponding to the key assembly part based on the part function attributes;

[0045] Combining the preset standard dimension, the dimensional deviation value, and the part importance, the assembly tolerance coefficient corresponding to the punching die can be calculated by the following formula:

[0046] ;

[0047] where D represents the assembly tolerance coefficient corresponding to the punching die, represents the dimensional deviation value corresponding to the b-th part in the key assembly parts, It represents the preset standard dimension corresponding to the b-th part in the key assembly parts. It represents the importance degree of the b-th part in the key assembly parts. b represents the serial number corresponding to the key assembly parts, and r represents the number of key assembly parts.

[0048] Optionally, combining the fatigue damage evolution and the assembly tolerance coefficient, performing collaborative optimization processing on the initial punching die model to obtain an optimized punching die model, including:

[0049] Based on the fatigue damage evolution, determining the fatigue-sensitive area in the initial punching die model;

[0050] Performing structural optimization processing on the fatigue-sensitive area to obtain an optimized sensitive area;

[0051] Based on the assembly tolerance coefficient, performing assembly adaptability adjustment on the optimized sensitive area to obtain an adapted assembly area;

[0052] Based on the adapted assembly area, performing optimization integration on the initial punching die to obtain an optimized punching die model.

[0053] Optionally, calculating the contour disorder degree corresponding to the optimized punching die model, including:

[0054] Performing triangular meshing on the optimized punching die model to obtain a die triangular mesh model;

[0055] Extracting the model contour line of the die triangular mesh model, performing point sampling on the model contour line to obtain contour line points;

[0056] Calculating the curvature change rate between the contour line points;

[0057] Performing vectorization processing on the model contour line to obtain a contour line vector, and based on the contour line vector, calculating the included angle deviation amount between the contour line points;

[0058] Combining the curvature change rate and the included angle deviation amount, calculating the contour disorder degree corresponding to the optimized punching die model.

[0059] Optionally, calculating the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters, including:

[0060] Extracting the die punching diameter and die plate thickness corresponding to the optimized punching die model from the punching process parameters;

[0061] Measuring the die shear strength corresponding to the optimized punching die model;

[0062] Calculate the punching perimeter of the optimized punching die model based on the punching diameter of the die.

[0063] Combined with the punching perimeter of the die, the shear strength of the die, and the thickness of the die plate, the blanking force coefficient corresponding to the optimized punching die model can be calculated by the following formula:

[0064] ;

[0065] Where, F represents the blanking force coefficient corresponding to the optimized punching die model, H represents the punching perimeter of the die, M represents the shear strength of the die, and N represents the thickness of the die plate.

[0066] To solve the above problems, the present invention also provides a digital modeling system for punching dies based on 3D technology, and the system includes:

[0067] An architecture analysis module, configured to obtain the design requirements of the punching die to be modeled and its corresponding punching process parameters, analyze the production environment characteristics corresponding to the punching die, and determine the basic architecture type and its corresponding hierarchical structure corresponding to the punching die in combination with the design requirements and the production environment characteristics;

[0068] A fatigue damage evolution evaluation module, configured to perform point cloud scanning on the existing products of the punching die to obtain die point cloud data, extract the key punching structure features and structure dimension information corresponding to the punching die from the die point cloud data, collect the material performance data corresponding to the punching die, calculate the material fatigue coefficient corresponding to the punching die based on the material performance data, and evaluate the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient;

[0069] An assembly tolerance coefficient calculation module, configured to perform modeling processing on the punching die in combination with the basic architecture type, the hierarchical structure, the key punching structure features, and the structure dimension information to obtain an initial punching die model, and calculate the assembly tolerance coefficient corresponding to the punching die in combination with the design requirements and the structure dimension information;

[0070] An optimal assembly order determination module, configured to perform collaborative optimization processing on the initial punching die model in combination with the fatigue damage evolution and the assembly tolerance coefficient to obtain an optimized punching die model, calculate the contour disorder degree corresponding to the optimized punching die model, and determine the optimal assembly order corresponding to each component in the optimized punching die model based on the contour disorder degree;

[0071] A digital modeling module, configured to calculate a blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters, plan an optimal assembly path corresponding to each component in the optimized punching die model based on the blanking force coefficient, and perform digital modeling processing on the punching die in combination with the optimal assembly order and the optimal assembly path to obtain a modeling result.

[0072] Compared with the problems in the background art, the present invention determines the type of the basic architecture corresponding to the punching die and its corresponding hierarchical structure by combining the design requirements and the characteristics of the production environment, which can provide a solid basis for the accurate modeling and efficient manufacturing of the subsequent punching die, ensure the stable operation of the die in actual production, and meet the punching process requirements. Further, the present invention extracts the key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, which can comprehensively and deeply understand the actual situation of the punching die, accurately grasp its structural characteristics, and thus provide an important basis for the subsequent modeling process of the punching die. Further, the present invention performs modeling processing on the punching die by combining the type of the basic architecture, the hierarchical structure, the key punching structure features and the structural dimension information, which can highly restore the actual structure of the punching die. By combining the design requirements and the structural dimension information, the assembly tolerance coefficient corresponding to the punching die is calculated. On the one hand, potential assembly hazards can be insightfully predicted at the initial stage of die design, the die structure can be optimized in advance, and the later modification cost can be reduced; on the other hand, in the actual manufacturing and assembly process of the die, clear and quantitative assembly guidance is provided for the operators. Further, the present invention performs collaborative optimization processing on the initial punching die model by combining the fatigue damage evolution and the assembly tolerance coefficient, which can avoid potential problems during the use of the die and greatly improve the durability and assembly convenience of the die. The present invention calculates the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters, and can scientifically plan the optimal assembly path of each component according to the blanking force coefficient, which not only ensures the assembly accuracy but also fully considers the subsequent working stress during the assembly process, effectively reducing the die assembly difficulty and production cost. Therefore, the digital modeling method and system for a punching die based on 3D technology provided by the embodiments of the present invention can improve the accuracy of digital modeling of the punching die. Description of the Drawings

[0073] Figure 1 It is a schematic flow chart of a digital modeling method for a punching die based on 3D technology provided by an embodiment of the present invention;

[0074] Figure 2 It is a schematic module diagram for implementing the digital modeling method for a punching die based on 3D technology provided by an embodiment of the present invention.

[0075] The realization of the purpose, functional features and advantages of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0076] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0077] An embodiment of the present application provides a digital modeling method for a punching die based on 3D technology. The execution subject of the digital modeling method for a punching die based on 3D technology includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the digital modeling method for a punching die based on 3D technology can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0078] Embodiment 1:

[0079] Refer to Figure 1 As shown, it is a flowchart of a digital modeling method for a punching die based on 3D technology provided by an embodiment of the present invention. In this embodiment, the digital modeling method for a punching die based on 3D technology includes:

[0080] S1. Obtain the design requirements of the punching die to be modeled and its corresponding punching process parameters, analyze the production environment characteristics corresponding to the punching die, and combine the design requirements and the production environment characteristics to determine the basic architecture type corresponding to the punching die and its corresponding hierarchical structure.

[0081] By combining the design requirements and the production environment characteristics of the present invention to determine the basic architecture type corresponding to the punching die and its corresponding hierarchical structure, it can provide a solid basis for the accurate modeling and efficient manufacturing of the subsequent punching die, ensure the stable operation of the die in actual production, and meet the punching process requirements.

[0082] It should be explained that the punching die to be modeled is a die used to punch specific-shaped holes in materials such as plates. Its design requirements cover many aspects such as punching shape, dimensional accuracy, number of punches, material adaptability, etc. The punching process parameters include key data such as punching force magnitude, punching speed, die clearance, etc. The production environment characteristics refer to objective conditions such as temperature, humidity, vibration conditions, dust content in the actual use place of the die, and the accuracy level of supporting equipment. The basic architecture type is the main structure style of the punching die, such as a simple single-station architecture, a composite multi-station architecture, a progressive continuous architecture, etc. The hierarchical structure describes the subdivision hierarchical relationship of the die from the overall framework to each internal functional component.

[0083] In detail, the determining of the infrastructure type corresponding to the punching die and its corresponding hierarchical structure in combination with the design requirements and the production environment characteristics includes:

[0084] Performing detailed analysis on the design requirements to obtain a detailed list of requirements;

[0085] Collecting data on the production environment characteristics to obtain an environment characteristic data set;

[0086] Perform condition mining analysis on the detailed requirement list to obtain key design constraint conditions;

[0087] Analyzing the dominant environmental factors of the punching die according to the environmental characteristic data set;

[0088] By combining the key design constraints and the dominant environmental factors, the infrastructure type corresponding to the punching die and its corresponding hierarchical structure are matched.

[0089] It should be explained that the detailed requirements list is a detailed form that classifies and lists the design requirements according to different dimensions. The environmental characteristic data set is a collection of various indicators of the production environment presented in a quantitative form. The key design constraints are the clauses in the design requirements that have a decisive restrictive effect on the mold architecture. For example, extremely high punching accuracy requirements may limit the mold to use only a precision-guided infrastructure. The dominant environmental factors are the factors that have the greatest impact on the life and precision of the mold in the production environment. For example, the heat dissipation and anti-rust structure design of the mold need to be considered in a high temperature and high humidity environment.

[0090] Furthermore, the combing of the design requirements can be done with the help of professional requirements management software to achieve a systematic presentation of the requirements; the monitoring of the production environment characteristics uses various sensors and data acquisition instruments to ensure the accuracy and reliability of the data; the conditional mining analysis of the requirements list is carried out to obtain key design constraints, such as the punching size accuracy requirement to be within ±0.05mm, which becomes a key constraint to promote the selection of a high-precision matching architecture for the mold; through the analysis of the environmental characteristics data set, if the humidity of the production environment is higher than 80% for a long time, then moisture and rust prevention are the dominant environmental factors, and the mold design needs to add sealing and protection levels; based on the key design constraints and dominant environmental factors, if the punching task is large-scale with moderate accuracy requirements and a relatively stable environment, then a progressive continuous architecture is more appropriate; when determining the hierarchical structure, considering the high frequency of punching, high-wear-resistant materials should be selected at the punch level and the cooling channel design should be optimized, and the guide accuracy should be increased at the guide component level to ensure that the punching mold achieves the best performance under the given design requirements and production environment.

[0091] S2. Perform point cloud scanning on the existing products of the punching die to obtain die point cloud data. Extract the key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, collect the material property data corresponding to the punching die, calculate the material fatigue coefficient corresponding to the punching die based on the material property data, and evaluate the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient.

[0092] By extracting the key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, the present invention can comprehensively and deeply insight into the actual situation of the punching die, accurately grasp its structural characteristics, and thus provide an important basis for the subsequent modeling process of the punching die. It should be explained that the die point cloud data is a set of massive three-dimensional coordinate information reflecting the surface of the existing products of the punching die obtained by high-precision laser scanning technology. The key punching structure features are the shape, edge, guide and other structural features that are accurately identified and extracted from the die point cloud data by professional algorithms and play a key decisive role in the punching function of the punching die. The structural dimension information is the key numerical values such as punching, length, width and thickness of the punching die related to manufacturing, assembly and performance evaluation measured from the die point cloud data by precise measuring tools. Further, the point cloud scanning of the existing products of the punching die can be realized by a three-dimensional scanner equipped with a high-precision laser rangefinder, which can quickly capture a large number of three-dimensional coordinate points on the die surface to construct point cloud data.

[0093] Specifically, the extraction of the key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data includes:

[0094] Perform noise reduction processing on the die point cloud data to obtain noise-reduced die point cloud data;

[0095] Perform fitting processing on the noise-reduced die point cloud data to obtain fitted die point cloud data;

[0096] Calculate the point cloud distance of the fitted die point cloud data;

[0097] Based on the point cloud distance, perform discrete point removal processing on the fitted die point cloud data to obtain target fitted die point cloud data;

[0098] Perform region segmentation processing on the target fitted die point cloud data to obtain regional die point cloud data;

[0099] Perform structure feature extraction on the regional die point cloud data to obtain initial die structure features;

[0100] Perform feature screening on the initial die structure features to obtain the key punching structure features corresponding to the punching die;

[0101] The dimension of the regional mold point cloud data is measured to obtain the structural dimension information corresponding to the punching mold.

[0102] It should be explained that the denoised mold point cloud data is the point cloud data that is purer and can better reflect the true shape of the mold after the mold point cloud data is processed to remove noise interference. The fitted mold point cloud data is the point cloud data whose surface is smooth and continuous through a suitable mathematical fitting algorithm, which is more conducive to subsequent analysis. The point cloud distance is the geometric distance between the point and the surrounding neighborhood points based on the fitted mold point cloud data. The target fitted mold point cloud data is the point cloud data obtained after the discrete points in the fitted mold point cloud data are eliminated, that is, relatively regular data after removing those redundant points that may interfere with the analysis and do not conform to the normal curvature characteristics. The initial mold structure feature is the relevant feature information that can reflect the structural and morphological characteristics of each part of the punching mold, which is initially presented by the regional mold point cloud data through feature extraction and other operations.

[0103] Furthermore, the mold point cloud data can be denoised by a bilateral filtering algorithm that adaptively adjusts the filter window based on the point cloud data density to obtain denoised mold point cloud data; the denoised mold point cloud data can be fitted by a surface fitting algorithm to obtain fitted mold point cloud data; the degree of curvature of each point can be accurately quantified by constructing a local covariance matrix and solving the eigenvalues in combination with principal component analysis, and the local curvature of the fitted mold point cloud data can be calculated; based on the local curvature, a dynamic threshold can be set according to the statistical law of the curvature of similar high-quality molds, and the discrete points can be accurately eliminated by repeated verification with an iterative backtracking algorithm, and the fitted mold point cloud data can be subjected to discrete point elimination processing to obtain the target fitted mold point cloud data; the punching, punch guide, and chip removal areas can be intelligently divided by executing a regional growth segmentation algorithm guided by curvature similarity and taking into account the connectivity of the point cloud. The target fitting mold point cloud data is segmented into key areas to obtain regional mold point cloud data; the improved PointNet++ network that integrates multi-scale feature extraction and attention focusing mechanism can be introduced to deeply mine key structural details, extract structural features from the regional mold point cloud data, and obtain initial mold structural features; the initial mold structural features can be screened by adopting a feature screening algorithm that combines random forest feature importance evaluation and forward search strategy to obtain key punching structural features corresponding to the punching mold; the point cloud dimension measurement tool built into the professional industrial measurement software PolyWorks|Modeler and combined with high-precision laser scanning calibration technology can be used to directly accurately locate and measure on the point cloud model, measure the dimensions of the regional mold point cloud data, and obtain structural dimension information corresponding to the punching mold.

[0104] Based on the material property data, the present invention calculates the material fatigue coefficient corresponding to the punching die, and the degree of the resistance of the punching die to fatigue failure can be understood through the material fatigue coefficient, thereby providing a basis for the subsequent evaluation of the fatigue damage evolution of the punching die under the expected punching frequency. It should be noted that the material fatigue coefficient is a parameter used to measure the ability of the material corresponding to the punching die to resist fatigue failure under cyclic loading, and reflects the difficulty of the material to undergo fatigue failure after experiencing multiple stress cycles.

[0105] Specifically, the calculation of the material fatigue coefficient corresponding to the punching die based on the material property data includes:

[0106] Based on the material property data, determine the performance parameter values corresponding to the punching die;

[0107] Based on the performance parameter values, construct the material body model corresponding to the punching die;

[0108] Based on the material body model, create the finite element model of the die corresponding to the punching die;

[0109] Perform stress simulation analysis on the finite element model of the die to obtain stress simulation parameters and the number of stress cycles;

[0110] Combine the stress simulation parameters, the number of stress cycles and the finite element model of the die to calculate the material fatigue coefficient corresponding to the punching die.

[0111] It should be noted that the performance parameter values are the numerical values of various performance indexes of the material corresponding to the punching die, the material body model is a theoretical model constructed based on the material characteristics corresponding to the punching die, the finite element model of the die is a numerical model obtained by discretizing the die structure into finite elements corresponding to the punching die, and the stress simulation parameters and the number of stress cycles are respectively the parameters used to describe the stress and the specific number of stress cycles obtained from the stress simulation analysis of the finite element model of the die.

[0112] Furthermore, based on the material property data, use professional data statistics and analysis methods to determine the performance parameter values corresponding to the punching die; based on the performance parameter values, construct the material body model corresponding to the punching die according to the stress-strain characteristics of the material and the actual working conditions; based on the material body model, create the finite element model of the die corresponding to the punching die with the help of advanced finite element modeling software; the stress simulation analysis of the finite element model of the die can be carried out by accurately setting boundary conditions, loading parameters and simulation working conditions in the finite element analysis software to obtain stress simulation parameters and the number of stress cycles.

[0113] Further, as an alternative embodiment of the present invention, calculating the material fatigue coefficient corresponding to the punching die by combining the stress simulation parameters, the number of stress cycles, and the finite element model of the die includes:

[0114] Analyze the fatigue critical regions and their corresponding regional weights in the finite element model of the die, and count the number of regions corresponding to the fatigue critical regions;

[0115] Based on the number of stress cycles, determine the regional stress cycle number corresponding to the fatigue critical region;

[0116] Based on the stress simulation parameters, query the regional theoretical cycle number corresponding to the fatigue critical region;

[0117] Combining the regional weight, the regional stress cycle number, the regional theoretical cycle number, and the number of regions, the material fatigue coefficient corresponding to the punching die can be calculated by the following formula:

[0118] ;

[0119] where A represents the material fatigue coefficient corresponding to the punching die, represents the regional weight corresponding to the a-th region in the fatigue critical region, represents the regional stress cycle number corresponding to the a-th region in the fatigue critical region, represents the regional theoretical cycle number corresponding to the a-th region in the fatigue critical region, a represents the serial number corresponding to the fatigue critical region, and q represents the number of fatigue critical regions.

[0120] It should be explained that the fatigue critical region is a specific part in the finite element model of the die that is specially divided for fatigue analysis due to high stress concentration and easy occurrence of fatigue problems. The regional weight is a coefficient for weighted calculation determined based on the influence degree of the fatigue critical region on the overall fatigue failure of the die. The regional stress cycle number is the specific frequency of the actual stress cycles experienced by the fatigue critical region obtained through finite element simulation analysis. The regional theoretical cycle number is the theoretical stress cycle frequency at fatigue failure deduced based on the material fatigue characteristic curve queried for the fatigue critical region based on the stress simulation parameters.

[0121] Furthermore, the fatigue critical regions in the finite element model of the die can be analyzed through visualizing the stress distribution in the finite element model of the die and combining with the failure case experience of similar dies in the past. The regional weights of the fatigue critical regions can be analyzed by comprehensively considering the structural characteristics of the die, the stress conditions of each part, and the empirical rules summarized by experts based on a large number of practices. Based on the stress simulation parameters, referring to the accurate material fatigue characteristic curves provided by a professional material testing institution and using a fitting algorithm for data backtracking, the theoretical number of cycles corresponding to the fatigue critical regions can be queried.

[0122] In the present invention, by evaluating the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient, the fatigue damage degree of the punching die can be accurately predicted in advance, thereby improving the optimization effect of the subsequent collaborative optimization process of the initial punching die model. It should be noted that the fatigue damage evolution is a dynamic change process in which, under the expected punching frequency of the punching die, as the number of punching operations increases, the material properties of the key parts of the die gradually deteriorate, and fatigue cracks initiate and propagate, resulting in a gradual increase in the overall fatigue damage degree of the die. Furthermore, by evaluating the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient. For example, when the material fatigue coefficient is relatively low, such as 0.2, in the first 50% stage of the expected punching frequency, only slight wear may occur at the punching edge of the die, and the punching quality is basically unaffected. As the punching frequency approaches the expected value, the wear intensifies, and occasional small burrs may appear, but the die can still be used normally, indicating that the fatigue damage develops slowly. On the contrary, if the fatigue coefficient is as high as 0.8, burrs and dimensional deviations may frequently occur at the initial stage of punching, and the risk of punching rupture increases significantly in the middle and late stages, seriously affecting production.

[0123] S3. Combining the infrastructure type, the hierarchical structure, the key punching structure features, and the structural dimension information, perform modeling processing on the punching die to obtain an initial punching die model, and calculate the assembly tolerance coefficient corresponding to the punching die in combination with the design requirements and the structural dimension information.

[0124] In the present invention, by combining the infrastructure type, the hierarchical structure, the key punching structure features, and the structural dimension information, performing modeling processing on the punching die can highly restore the actual structure of the punching die. By calculating the assembly tolerance coefficient corresponding to the punching die in combination with the design requirements and the structural dimension information, on the one hand, potential assembly hazards can be insightfully anticipated in advance at the initial stage of die design, the die structure can be optimized in advance, and the later modification cost can be reduced; on the other hand, during the actual manufacturing and assembly process of the die, clear and quantitative assembly guidance can be provided to the operators. Furthermore, the modeling processing of the punching die can be realized through modeling software, such as CATIA, Creo, etc.

[0125] Specifically, by combining the design requirements and the structural dimension information, the assembly tolerance coefficient corresponding to the punching die is calculated, including:

[0126] Based on the design requirements, determine the assembly precision requirements of the punching die in the assembly process;

[0127] Based on the assembly precision requirements, determine the key assembly parts corresponding to the punching die, and extract the assembly part dimensions corresponding to the key assembly parts from the structural dimension information;

[0128] Based on the assembly part dimensions and the preset standard dimensions, calculate the dimensional deviation values corresponding to the key assembly parts;

[0129] Analyze the part function attributes corresponding to the key assembly parts, and based on the part function attributes, set the part importance corresponding to the key assembly parts;

[0130] Combining the preset standard dimensions, the dimensional deviation values and the part importance, the assembly tolerance coefficient corresponding to the punching die can be calculated through the following formula:

[0131] ;

[0132] where D represents the assembly tolerance coefficient corresponding to the punching die, represents the dimensional deviation value corresponding to the b-th part in the key assembly parts, represents the preset standard dimension corresponding to the b-th part in the key assembly parts, represents the part importance corresponding to the b-th part in the key assembly parts, b represents the serial number corresponding to the key assembly parts, and r represents the number of key assembly parts.

[0133] It should be explained that the assembly precision requirements are the quantitative criteria to ensure the precise assembly of each component of the punching die and achieve the stable and efficient operation of the punching process; the key assembly parts are the "joint" parts corresponding to the punching die, and the assembly quality directly affects the core performance such as the punching precision and service life of the die; the assembly part dimensions are the specific dimension descriptions of the key assembly parts in the structural dimension information; the preset standard dimensions are the design dimensions corresponding to the key assembly parts; the dimensional deviation values are the part dimension differences corresponding to the key assembly parts; the part function attributes are the part function characteristics corresponding to the key assembly parts; the part importance represents the importance degree corresponding to the key assembly parts.

[0134] Further, based on the design requirements, determine the assembly precision requirements of the punching die in the assembly process by combining professional knowledge and past experience; based on the assembly precision requirements, determine the key assembly parts corresponding to the punching die according to the mechanical structure principle. The assembly part dimensions corresponding to the key assembly parts can be extracted from the structural dimension information through an extraction function, and the extraction function is compiled by a scripting language, such as the JS scripting language; calculate the difference between the assembly part dimensions and the preset standard dimensions to obtain the dimensional deviation value corresponding to the key assembly part; the mechanical principle corresponding to the key assembly part can be analyzed to determine the part function attribute, and based on the attribute description of the part function attribute and combined with industry guidelines, set the part importance corresponding to the key assembly part.

[0135] S4. Combine the fatigue damage evolution and the assembly fault tolerance coefficient to perform collaborative optimization processing on the initial punching die model to obtain an optimized punching die model. Calculate the contour disorder degree corresponding to the optimized punching die model, and based on the contour disorder degree, formulate the optimal assembly order for each component in the optimized punching die model.

[0136] Through the present invention, by combining the fatigue damage evolution and the assembly fault tolerance coefficient to perform collaborative optimization processing on the initial punching die model, potential problems during the use of the die can be avoided, and the durability and assembly convenience of the die can be greatly improved. It should be noted that the optimized punching die model is the model obtained by optimizing the initial punching die model in combination with the fatigue damage evolution and the assembly fault tolerance coefficient.

[0137] Specifically, the process of combining the fatigue damage evolution and the assembly fault tolerance coefficient to perform collaborative optimization processing on the initial punching die model to obtain an optimized punching die model includes:

[0138] Based on the fatigue damage evolution, determine the fatigue-sensitive areas in the initial punching die model;

[0139] Perform structural optimization processing on the fatigue-sensitive areas to obtain optimized sensitive areas;

[0140] Based on the assembly fault tolerance coefficient, perform assembly adaptability adjustment on the optimized sensitive areas to obtain adapted assembly areas;

[0141] Based on the adapted assembly areas, perform optimization and integration on the initial punching die to obtain an optimized punching die model.

[0142] It should be explained that the fatigue-sensitive area is the key part in the initial punching die model that is prone to fatigue damage due to factors such as force during long-term stamping operations. The optimized sensitive area is the area formed after the fatigue-sensitive area is processed by using structural optimization means such as topology optimization, wall thickness adjustment, and surface roughness improvement to enhance its fatigue resistance. The adapted assembly area is the area that meets the assembly requirements and can ensure the overall performance after the optimized sensitive area is improved in terms of assembly adaptability, such as optimizing the fit tolerance, adjusting the assembly sequence, and adding positioning structures, based on the assembly tolerance coefficient.

[0143] Furthermore, based on the fatigue damage evolution, professional die design engineers accurately determine the fatigue-sensitive area in the initial punching die model with the help of advanced finite element analysis software and long-term accumulated practical experience data. A technical team proficient in structural mechanics and materials science uses advanced topology optimization technology and reasonable material property allocation strategies to perform structural optimization on the fatigue-sensitive area to obtain the optimized sensitive area. Based on the assembly tolerance coefficient, assembly engineers with rich assembly process knowledge perform assembly adaptability adjustment on the optimized sensitive area according to accurate tolerance calculations and scientific assembly process planning to obtain the adapted assembly area. Based on the adapted assembly area, through a collaborative design platform, personnel from multiple departments such as design, process, and assembly closely cooperate to optimize and integrate the initial punching die to obtain the optimized punching die model.

[0144] By calculating the contour disorder degree corresponding to the optimized punching die model, the present invention can intuitively reflect the irregularity degree of the surface of the optimized punching die model, improving the formulation of the optimal assembly order corresponding to each component in the subsequent optimized punching die model. It should be explained that the contour disorder degree represents the shape irregularity degree corresponding to the optimized punching die model.

[0145] Specifically, the calculation of the contour disorder degree corresponding to the optimized punching die model includes:

[0146] Perform triangular meshing on the optimized punching die model to obtain the die triangular mesh model;

[0147] Extract the model contour line of the die triangular mesh model, and perform point sampling on the model contour line to obtain the contour line points;

[0148] Calculate the curvature change rate between the contour line points;

[0149] Perform vectorization on the model contour line to obtain the contour line vector, and based on the contour line vector, calculate the included angle deviation amount between the contour line points;

[0150] Calculate the contour disorder degree corresponding to the optimized punching die model by combining the curvature change rate and the included angle deviation amount.

[0151] It should be explained that the die triangular mesh model is a digital model representation formed by triangular meshing of the optimized punching die model, which is composed of triangular patches. The model contour line is the outer boundary line of the die triangular mesh model, showing the shape characteristics of the model in the two-dimensional plane. The contour line points are the specific coordinate position points obtained after discretization of the model contour line. The curvature change rate is a quantitative index reflecting the change in the curve bending degree between the contour line points, reflecting the local morphological changes of the contour line. The contour line vector is a mathematical description form with direction and magnitude transformed from the model contour line based on the vector representation method for subsequent geometric operations. The included angle deviation amount is the difference between the included angle formed by two adjacent contour lines between the contour line points and the standard angle or expected angle, used to measure the irregularity of the contour line angle change.

[0152] Furthermore, the optimized punching die model can be triangulated by the triangulation algorithm built in professional computer-aided design (CAD) software to obtain the die triangular mesh model. The model contour line can be accurately located and extracted from the die triangular mesh model by a contour extraction tool developed based on the principles of graphics. The contour line points can be obtained by digitizing the model contour line with a reasonable sampling interval. The curvature change rate between the contour line points can be calculated by using numerical calculus methods. The model contour line can be vectorized by a vector transformation algorithm, such as the vector projection algorithm, to obtain the contour line vector. Based on the contour line vector, the included angle deviation amount between the contour line points can be calculated by the vector dot product algorithm. By normalizing the curvature change rate and the included angle deviation amount and adding the normalized results, the contour disorder degree corresponding to the optimized punching die model can be obtained.

[0153] Based on the contour disorder degree, the present invention formulates the optimal assembly order corresponding to each component in the optimized punching die model, which can reduce the assembly difficulty and time consumption and improve the assembly efficiency. It should be explained that the optimal assembly order is the assembly sequence corresponding to each component in the optimized punching die model arranged according to certain rules and logics, which can achieve the best comprehensive effects such as the highest assembly efficiency, the best performance and the lowest cost of the die. Further, based on the contour disorder degree, the optimal assembly order corresponding to each component in the optimized punching die model is formulated. If the value of the contour disorder degree is high, it means that the precision requirements of the corresponding component are strict and the assembly difficulty is great. Experienced assembly workers are preferably arranged, high-precision assembly tooling is adopted, and fine assembly is carried out in the order from inside to outside and from key to secondary; if the value of the contour disorder degree is low, it indicates that the component assembly is relatively simple, the process can be appropriately simplified, the assembly order can be flexibly arranged, and novice workers can be involved to improve the overall assembly efficiency, so as to ensure that each component is assembled in the optimal order.

[0154] S5. Based on the punching process parameters, calculate the blanking force coefficient corresponding to the optimized punching die model. Based on the blanking force coefficient, plan the optimal assembly path corresponding to each component in the optimized punching die model. Combine the optimal assembly order and the optimal assembly path, and perform digital modeling processing on the corresponding punching die to obtain a modeling result.

[0155] Based on the punching process parameters, the present invention calculates the blanking force coefficient corresponding to the optimized punching die model. According to the blanking force coefficient, the optimal assembly path of each component can be scientifically planned, which not only ensures the assembly precision but also fully considers the subsequent working force during the assembly process, effectively reducing the die assembly difficulty and production cost. It should be explained that the blanking force coefficient is corresponding to the optimized punching die model and is a quantitative standard used to measure the relative relationship between the actual blanking force and the standard blanking force under specific punching process parameters, reflecting the load degree of the die during punching operation and providing a basis for die optimization and assembly path planning.

[0156] Specifically, the calculating the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters includes:

[0157] Extract the die punching diameter and die plate thickness corresponding to the optimized punching die model from the punching process parameters;

[0158] Measure the die shear strength corresponding to the optimized punching die model;

[0159] Based on the die punching diameter, calculate the die punching perimeter corresponding to the optimized punching die model;

[0160] Combined with the punching perimeter of the die, the shear strength of the die, and the thickness of the die plate, the blanking force coefficient corresponding to the optimized punching die model can be calculated by the following formula:

[0161] ;

[0162] Wherein, F represents the blanking force coefficient corresponding to the optimized punching die model, H represents the punching perimeter of the die, M represents the shear strength of the die, and N represents the thickness of the die plate.

[0163] It should be explained that the thickness of the die plate and the punching diameter of the die are the key geometric parameters regarding the applicable plate thickness and punching size corresponding to the optimized punching die model in the punching process parameters. The shear strength of the die is a mechanical parameter reflecting the ability of the processing material of the optimized punching die model to resist shear failure. The punching perimeter of the die is a geometric parameter related to the blanking operation calculated based on the punching diameter.

[0164] Furthermore, the shear strength of the die corresponding to the optimized punching die model can be measured by a professional material mechanics testing instrument according to the standard shear strength testing method; multiplying the punching diameter of the die by π gives the punching perimeter of the die corresponding to the optimized punching die model.

[0165] The present invention plans the optimal assembly path corresponding to each component in the optimized punching die model based on the blanking force coefficient, can optimize the assembly process according to the force condition of the optimized punching die model, reduce the risk of component wear, and perform digital modeling processing on the punching die in combination with the optimal assembly order and the optimal assembly path, thereby improving the modeling accuracy of the punching die.

[0166] It should be noted that the optimal assembly path is the best assembly trajectory corresponding to each component in the optimized punching die model. Further, based on the blanking force coefficient, the optimal assembly path corresponding to each component in the optimized punching die model is planned. For example, when the blanking force coefficient is high, it indicates that the optimized punching die model bears a large impact force during the punching process. The assembly path should preferably start from the strong support components at the bottom, such as the die base, and use high-precision positioning tools to ensure its accurate positioning, laying a stable foundation for subsequent assembly. Then, the core components such as the punch and the die cavity are assembled in the order from inside to outside and from the key components to the secondary components. A buffer space is reserved during the assembly process to relieve the impact of the blanking force and ensure the stability of the coordinated operation of each component. If the blanking force coefficient is low, it means that the optimized punching die model is subjected to relatively small forces. The assembly path can be appropriately flexible. The small auxiliary components are assembled first, and the assembly steps of some adjacent components are combined to improve the overall assembly efficiency and reduce the use of unnecessary tools. Combining the optimal assembly order and the optimal assembly path, digital modeling processing of the corresponding punching die can be performed through computer-aided software, and the computer-aided software includes UG NX software.

[0167] Compared with the problems described in the background art, by combining the design requirements and the characteristics of the production environment, the present invention determines the corresponding infrastructure type and its corresponding hierarchical structure of the punching die, which can provide a solid basis for the accurate modeling and efficient manufacturing of the subsequent punching die, ensure the stable operation of the die in actual production, and meet the punching process requirements. Further, by extracting the key punching structure features and structure dimension information corresponding to the punching die from the die point cloud data, the present invention can comprehensively and deeply understand the actual situation of the punching die, accurately grasp its structural characteristics, and thus provide an important basis for the subsequent modeling process of the punching die. Further, by combining the infrastructure type, the hierarchical structure, the key punching structure features and the structure dimension information, the present invention performs a modeling process on the punching die, which can highly restore the actual structure of the punching die. By combining the design requirements and the structure dimension information, the assembly tolerance coefficient corresponding to the punching die is calculated. On the one hand, potential assembly hazards can be prospectively identified in the initial stage of die design, the die structure can be optimized in advance, and the later modification cost can be reduced; on the other hand, in the actual manufacturing and assembly process of the die, clear and quantitative assembly guidance is provided for the operators. Further, by combining the fatigue damage evolution and the assembly tolerance coefficient, the present invention performs a collaborative optimization process on the initial punching die model, which can avoid potential problems during the use of the die and greatly improve the durability and assembly convenience of the die. By calculating the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters, the present invention can scientifically plan the optimal assembly path of each component according to the blanking force coefficient, ensure the assembly accuracy, and fully consider the subsequent working force during the assembly process, effectively reducing the die assembly difficulty and production cost. Therefore, the digital modeling method and system for punching die based on 3D technology provided by the embodiments of the present invention can improve the accuracy of digital modeling of punching die.

[0168] Embodiment 2:

[0169] As Figure 2 shown, it is a functional module diagram of a digital modeling system for punching die based on 3D technology according to the present invention.

[0170] The digital modeling system 200 for punching die based on 3D technology according to the present invention can be installed in an electronic device. According to the realized functions, the digital modeling system for punching die based on 3D technology can include an architecture analysis module 201, a fatigue damage evolution evaluation module 202, an assembly tolerance coefficient calculation module 203, an optimal assembly order determination module 204 and a digital modeling module 205. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0171] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0172] The architecture analysis module 201 is configured to obtain the design requirements of the punching die to be modeled and its corresponding punching process parameters, analyze the production environment characteristics corresponding to the punching die, and determine the basic architecture type corresponding to the punching die and its corresponding hierarchical structure in combination with the design requirements and the production environment characteristics;

[0173] The fatigue damage evolution evaluation module 202 is configured to perform point cloud scanning on the existing products of the punching die to obtain die point cloud data, extract the key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, collect the material property data corresponding to the punching die, calculate the material fatigue coefficient corresponding to the punching die based on the material property data, and evaluate the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient;

[0174] The assembly tolerance coefficient calculation module 203 is configured to perform modeling processing on the punching die in combination with the basic architecture type, the hierarchical structure, the key punching structure features and the structural dimension information to obtain an initial punching die model, and calculate the assembly tolerance coefficient corresponding to the punching die in combination with the design requirements and the structural dimension information;

[0175] The optimal assembly order determination module 204 is configured to perform collaborative optimization processing on the initial punching die model in combination with the fatigue damage evolution and the assembly tolerance coefficient to obtain an optimized punching die model, calculate the contour disorder degree corresponding to the optimized punching die model, and determine the optimal assembly order corresponding to each component in the optimized punching die model based on the contour disorder degree;

[0176] The digital modeling module 205 is configured to calculate the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters, plan the optimal assembly path corresponding to each component in the optimized punching die model based on the blanking force coefficient, and perform digital modeling processing on the punching die in combination with the optimal assembly order and the optimal assembly path to obtain a modeling result.

[0177] Specifically, each module in the digital modeling system 200 for realizing the digital modeling of the punching die based on 3D technology in the embodiments of the present invention adopts the same technical means as those Figure 1 described in the digital modeling method for realizing the digital modeling of the punching die based on 3D technology, and can produce the same technical effects, which will not be elaborated here.

[0178] It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A digital modeling method for a punching die based on 3D technology, characterized in that, The method includes: Obtaining the design requirements of the punching die to be modeled and its corresponding punching process parameters, analyzing the production environment characteristics corresponding to the punching die, and combining the design requirements and the production environment characteristics to determine the corresponding basic architecture type and its corresponding hierarchical structure of the punching die; Performing point cloud scanning on the existing products of the punching die to obtain die point cloud data, extracting the key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, collecting the material property data corresponding to the punching die, calculating the material fatigue coefficient corresponding to the punching die based on the material property data, and evaluating the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient; Combining the basic architecture type, the hierarchical structure, the key punching structure features and the structural dimension information, performing modeling processing on the punching die to obtain an initial punching die model, and calculating the assembly tolerance coefficient corresponding to the punching die by combining the design requirements and the structural dimension information; Combining the fatigue damage evolution and the assembly tolerance coefficient, performing collaborative optimization processing on the initial punching die model to obtain an optimized punching die model, calculating the contour disorder degree corresponding to the optimized punching die model, and formulating the optimal assembly order corresponding to each component in the optimized punching die model based on the contour disorder degree; Calculating the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters, planning the optimal assembly path corresponding to each component in the optimized punching die model based on the blanking force coefficient, and performing digital modeling processing on the punching die by combining the optimal assembly order and the optimal assembly path to obtain a modeling result.

2. The digital modeling method for a punching die implemented based on 3D technology according to claim 1, wherein, The step of combining the design requirements and the production environment characteristics to determine the corresponding basic architecture type and its corresponding hierarchical structure of the punching die includes: Performing requirement detail analysis on the design requirements to obtain a requirement detail list; Collecting data on the production environment characteristics to obtain an environmental characteristic data set; Performing conditional mining analysis on the requirement detail list to obtain key design constraint conditions; Analyzing the dominant environmental factors of the punching die based on the environmental characteristic data set; Matching the corresponding basic architecture type and its corresponding hierarchical structure of the punching die by comprehensively considering the key design constraint conditions and the dominant environmental factors.

3. The digital modeling method for a punching die implemented based on 3D technology according to claim 1, characterized in that, The step of extracting the key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data includes: Performing noise reduction processing on the die point cloud data to obtain noise-reduced die point cloud data; Performing fitting processing on the noise-reduced die point cloud data to obtain fitted die point cloud data; Calculating the point cloud distance of the fitted die point cloud data; Performing discrete point removal processing on the fitted die point cloud data based on the point cloud distance to obtain target fitted die point cloud data; Performing region segmentation processing on the target fitted die point cloud data to obtain regional die point cloud data; Performing structural feature extraction on the regional die point cloud data to obtain initial die structural features; Feature screening is performed on the initial die structure features to obtain the key punching structure features corresponding to the punching die; Dimensional measurement is performed on the regional die point cloud data to obtain the structural dimension information corresponding to the punching die.

4. The digital modeling method for a punching die implemented based on 3D technology according to claim 1, characterized in that Calculating the material fatigue coefficient corresponding to the punching die based on the material property data includes: Based on the material property data, determining the performance parameter values corresponding to the punching die; Based on the performance parameter values, constructing a material body model corresponding to the punching die; Based on the material body model, creating a finite element model of the die corresponding to the punching die; Performing stress simulation analysis on the finite element model of the die to obtain stress simulation parameters and the number of stress cycles; Combining the stress simulation parameters, the number of stress cycles, and the finite element model of the die to calculate the material fatigue coefficient corresponding to the punching die.

5. The digital modeling method for a punching die implemented based on 3D technology according to claim 4, characterized in that, The combining the stress simulation parameters, the number of stress cycles, and the finite element model of the die to calculate the material fatigue coefficient corresponding to the punching die includes: Analyzing the fatigue critical regions and their corresponding regional weights in the finite element model of the die, and counting the number of regions corresponding to the fatigue critical regions; Based on the number of stress cycles, determining the regional stress cycle number corresponding to the fatigue critical region; Based on the stress simulation parameters, querying the regional theoretical cycle number corresponding to the fatigue critical region; Combining the regional weight, the regional stress cycle number, the regional theoretical cycle number, and the number of regions, and calculating the material fatigue coefficient corresponding to the punching die through the following formula: ； Among them, A represents the material fatigue coefficient corresponding to the punching die, represents the area weight corresponding to the a-th area in the fatigue critical area, represents the number of area stress cycles corresponding to the a-th area in the fatigue critical area, represents the number of area theoretical cycles corresponding to the a-th area in the fatigue critical area, a represents the serial number corresponding to the fatigue critical area, and q represents the number of fatigue critical areas.

6. The digital modeling method for a punching die implemented based on 3D technology according to claim 1, wherein, The combining the design requirements and the structural dimension information to calculate the assembly tolerance coefficient corresponding to the punching die includes: Based on the design requirements, determining the assembly accuracy requirements of the punching die in the assembly process; Based on the assembly accuracy requirements, determining the key assembly parts corresponding to the punching die, and extracting the assembly part dimensions corresponding to the key assembly parts from the structural dimension information; Based on the assembly part dimensions and the preset standard dimensions, calculating the dimensional deviation value corresponding to the key assembly part; Analyzing the part function attributes corresponding to the key assembly part, and setting the part importance corresponding to the key assembly part based on the part function attributes; Combining the preset standard dimensions, the dimensional deviation value, and the part importance, and calculating the assembly tolerance coefficient corresponding to the punching die through the following formula: ； Among them, D represents the assembly tolerance coefficient corresponding to the punching die, represents the dimensional deviation value corresponding to the b-th part in the key assembly parts, represents the preset standard dimension corresponding to the b-th part in the key assembly parts, represents the importance of the b-th part in the key assembly parts, b represents the serial number corresponding to the key assembly parts, and r represents the number of key assembly parts.

7. The digital modeling method for a punching die implemented based on 3D technology according to claim 1, characterized in that, The combining the fatigue damage evolution and the assembly tolerance coefficient to perform collaborative optimization processing on the initial punching die model to obtain an optimized punching die model includes: Based on the fatigue damage evolution, determining the fatigue sensitive regions in the initial punching die model; Performing structural optimization processing on the fatigue sensitive regions to obtain optimized sensitive regions; Based on the assembly tolerance coefficient, performing assembly adaptability adjustment on the optimized sensitive regions to obtain adapted assembly regions; Based on the adapted assembly regions, performing optimization integration on the initial punching die to obtain an optimized punching die model.

8. The digital modeling method for a punching die implemented based on 3D technology according to claim 1, wherein, Calculating the contour disorder degree corresponding to the optimized punching die model includes: Perform triangular meshing on the optimized punching die model to obtain a die triangular mesh model; Extract the model contour line of the die triangular mesh model, and perform point sampling on the model contour line to obtain contour line points; Calculate the curvature change rate between the contour line points; Perform vectorization processing on the model contour line to obtain a contour line vector. Based on the contour line vector, calculate the included angle deviation amount between the contour line points. The included angle deviation amount is the difference between the included angle formed by two adjacent contour lines between the contour line points and the standard angle or expected angle, and is used to measure the irregularity of the contour line angle change; Combine the curvature change rate and the included angle deviation amount to calculate the contour disorder degree corresponding to the optimized punching die model.

9. The digital modeling method for punching dies based on 3D technology according to claim 1, characterized in that, The calculating the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters includes: Extract the die punching diameter and die plate thickness corresponding to the optimized punching die model from the punching process parameters; Measure the die shear strength corresponding to the optimized punching die model; Calculate the die punching perimeter corresponding to the optimized punching die model based on the die punching diameter; Combine the die punching perimeter, the die shear strength, and the die plate thickness, and calculate the blanking force coefficient corresponding to the optimized punching die model through the following formula: ； Wherein, F represents the blanking force coefficient corresponding to the optimized punching die model, H represents the die punching perimeter, M represents the die shear strength, and N represents the die plate thickness.

10. A digital modeling system for a punching die implemented based on 3D technology, characterized in that, The system includes: An architecture analysis module, configured to obtain the design requirements of the punching die to be modeled and its corresponding punching process parameters, analyze the production environment characteristics corresponding to the punching die, and combine the design requirements and the production environment characteristics to determine the basic architecture type corresponding to the punching die and its corresponding hierarchical structure; A fatigue damage evolution evaluation module, configured to perform point cloud scanning on the existing products of the punching die to obtain die point cloud data, extract the key punching structure features and structure dimension information corresponding to the punching die from the die point cloud data, collect the material property data corresponding to the punching die, calculate the material fatigue coefficient corresponding to the punching die based on the material property data, and evaluate the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient; An assembly tolerance coefficient calculation module, configured to perform modeling processing on the punching die by combining the basic architecture type, the hierarchical structure, the key punching structure features, and the structure dimension information to obtain an initial punching die model, and calculate the assembly tolerance coefficient corresponding to the punching die by combining the design requirements and the structure dimension information; An optimal assembly order determination module, configured to perform collaborative optimization processing on the initial punching die model by combining the fatigue damage evolution and the assembly tolerance coefficient to obtain an optimized punching die model, calculate the contour disorder degree corresponding to the optimized punching die model, and determine the optimal assembly order corresponding to each component in the optimized punching die model based on the contour disorder degree; A digital modeling module, which is used to calculate the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters, plan the optimal assembly path corresponding to each component in the optimized punching die model based on the blanking force coefficient, and perform digital modeling processing on the punching die in combination with the optimal assembly order and the optimal assembly path to obtain a modeling result.

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