Digital modeling method and system for realizing punching die based on 3D technology

Abstract

The invention relates to the technical field of die modeling, and discloses a digital modeling method and system for realizing a punching die based on a 3D technology, and the method comprises the steps: determining a basic framework type corresponding to the punching die and a hierarchical structure corresponding to the basic framework type; performing point cloud scanning on an existing product of the punching die to obtain die point cloud data, calculating a material fatigue coefficient corresponding to the punching die, and calculating an assembly fault-tolerant coefficient corresponding to the punching die; carrying out collaborative optimization processing on the initial punching die model to obtain an optimized punching die model, calculating a contour disorder degree corresponding to the optimized punching die model, and formulating an optimal assembly order corresponding to each part in the optimized punching die model; and a blanking force coefficient corresponding to the optimized punching die model is calculated, an optimal assembly path corresponding to each part in the optimized punching die model is planned, digital modeling processing corresponding to the punching die is executed, and a modeling result is obtained. According to the invention, the digital modeling accuracy of the punching die can be improved.

Description

Technical Field

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

[0002] With the booming development of modern manufacturing industry, punching dies, as key tooling in the manufacturing links of many industrial products, are widely used in the fields of automobiles, electronics, home appliances, etc., and their market demand continues to rise. With the acceleration of product iteration and increasingly stringent requirements on component precision and quality, traditional punching die design and manufacturing methods are increasingly unable to meet the high requirements of current industrial production.

[0003] The design of traditional punching dies mostly relies on experienced engineers who manually draw and design based on two-dimensional drawings. During the process, parameters such as dimensions need to be repeatedly calculated, which is not only time-consuming and labor-intensive, but also very easy to cause design errors due to human negligence. In the manufacturing process, it is mainly based on the design drawings and gradually formed through conventional processing techniques such as turning, milling, planing, and grinding. The processing accuracy is greatly restricted by factors such as workers' skill level and equipment accuracy, making it difficult to ensure the high-precision requirements of complex punching dies.

[0004] When it comes to the optimization and improvement of the mold, it is usually necessary to wait until the mold exposes problems in actual production, and then manually disassemble the mold for observation and measurement based on on-site feedback, determine the root cause of the problem based on experience, and then modify the mold. The whole process is lengthy and cumbersome, which greatly affects the production efficiency and product quality stability. This punching mold design and manufacturing model based on manual experience and traditional technology will lead to a decrease in the accuracy of digital modeling of the punching mold. Summary of the invention

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

[0006] To achieve the above purpose, the present invention provides a method for digital modeling of a punching die based on 3D technology, comprising: 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 determining the infrastructure type corresponding to the punching die and its corresponding hierarchical structure in combination with the design requirements and the production environment characteristics; Performing point cloud scanning on the existing products of the punching die to obtain die point cloud data, extracting key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, collecting material performance data corresponding to the punching die, calculating the material fatigue coefficient corresponding to the punching die based on the material performance data, and evaluating the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient; In combination with the infrastructure type, the hierarchical structure, the key punching structure features and the structural dimension information, the punching die is modeled to obtain an initial punching die model, and in combination with the design requirements and the structural dimension information, an assembly fault tolerance coefficient corresponding to the punching die is calculated; In combination with the fatigue damage evolution and the assembly fault tolerance coefficient, the initial punching die model is collaboratively optimized to obtain an optimized punching die model, the contour disorder corresponding to the optimized punching die model is calculated, and based on the contour disorder, the optimal assembly order corresponding to each component in the optimized punching die model is formulated; Based on the punching process parameters, the blanking force coefficient corresponding to the optimized punching die model is calculated. Based on the blanking force coefficient, the optimal assembly path corresponding to each component in the optimized punching die model is planned. Combined with the optimal assembly order and the optimal assembly path, the digital modeling processing corresponding to the punching die is performed to obtain the modeling result.

[0007] Optionally, the determining 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: Performing detailed analysis on the design requirements to obtain a detailed list of requirements; Collecting data on the production environment characteristics to obtain an environment characteristic data set; Perform condition mining analysis on the detailed requirement list to obtain key design constraint conditions; Analyzing the dominant environmental factors of the punching die according to the environmental characteristic data set; 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.

[0008] Optionally, extracting 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 mold point cloud data to obtain noise-reduced mold point cloud data; Performing fitting processing on the noise reduction mold point cloud data to obtain fitting mold point cloud data; Calculating the point cloud distance of the fitting mold point cloud data; Based on the point cloud distance, performing discrete point elimination processing on the fitting mold point cloud data to obtain target fitting mold point cloud data; Performing regional segmentation processing on the target fitting mold point cloud data to obtain regional mold point cloud data; Extracting structural features from the mold point cloud data in the region to obtain initial mold structural features; Performing feature screening on the initial die structure features to obtain key punching structure features corresponding to the punching die; The dimension of the regional mold point cloud data is measured to obtain the structural dimension information corresponding to the punching mold.

[0009] Optionally, the calculating the material fatigue coefficient corresponding to the punching die based on the material performance data includes: Based on the material performance data, determining a performance parameter value corresponding to the punching die; Based on the performance parameter values, construct a material ontology model corresponding to the punching die; Based on the material body model, creating a mold finite element model corresponding to the punching mold; Performing stress simulation analysis on the mold finite element model to obtain stress simulation parameters and stress cycle times; The material fatigue coefficient corresponding to the punching die is calculated by combining the stress simulation parameters, the number of stress cycles and the die finite element model.

[0010] Optionally, the calculating of the material fatigue coefficient corresponding to the punching die by combining the stress simulation parameters, the stress cycle number and the die finite element model includes: Analyzing the fatigue critical areas and their corresponding area weights in the mold finite element model, and counting the number of areas corresponding to the fatigue critical areas; Based on the stress cycle number, determining the regional stress cycle number corresponding to the fatigue critical area; Based on the stress simulation parameters, querying the theoretical number of regional cycles corresponding to the fatigue critical area; 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: ; Among them, A represents the material fatigue coefficient corresponding to the punching die, represents the area weight corresponding to the ath area in the fatigue critical area, represents the number of regional stress cycles corresponding to the ath region in the fatigue critical region, It represents the theoretical cycle number of the ath 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.

[0011] Optionally, the step of calculating the assembly fault tolerance coefficient corresponding to the punching die by combining the design requirements and the structural dimension information includes: Based on the design requirements, determining the assembly accuracy requirements of the punching die in the assembly process; Based on the assembly accuracy requirement, determining the key assembly part corresponding to the punching die, and extracting the assembly part size corresponding to the key assembly part from the structural size information; Based on the assembly part size and the preset standard size, calculating the size 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; In combination with the preset standard size, the size deviation value and the part importance, the assembly tolerance coefficient corresponding to the punching die can be calculated by the following formula: ; Among them, D represents the assembly tolerance coefficient corresponding to the punching die, Indicates the dimensional deviation value corresponding to the bth part in the critical assembly part, Indicates the preset standard size corresponding to the bth part in the critical assembly part. It represents the importance of the bth part in the key assembly parts, b represents the serial number corresponding to the key assembly part, and r represents the number of key assembly parts.

[0012] Optionally, the combining the fatigue damage evolution and the assembly fault tolerance coefficient to collaboratively optimize the initial punching die model to obtain an optimized punching die model includes: Based on the fatigue damage evolution, determining a fatigue sensitive area in the initial punching die model; Performing structural optimization processing on the fatigue sensitive area to obtain an optimized sensitive area; Based on the assembly fault tolerance coefficient, adjusting the assembly adaptability of the optimized sensitive area to obtain an adapted assembly area; Based on the adaptation assembly area, the initial punching die is optimized and integrated to obtain an optimized punching die model.

[0013] Optionally, the calculating the contour disorder corresponding to the optimized punching die model includes: Performing triangular mesh processing on the optimized punching die model to obtain a die triangular mesh model; Extracting a model contour line of the mold triangular mesh model, and performing point sampling on the model contour line to obtain contour line points; Calculating the curvature change rate between the contour line points; Performing vector processing on the model contour line to obtain a contour line vector, and calculating the angle deviation between the contour line points based on the contour line vector; The contour disorder corresponding to the optimized punching die model is calculated by combining the curvature change rate and the angle deviation.

[0014] Optionally, the calculating, based on the punching process parameters, a blanking force coefficient corresponding to the optimized punching die model includes: Extracting the die punching diameter and die plate thickness corresponding to the optimized punching die model from the punching process parameters; Measuring the die shear strength corresponding to the optimized punching die model; Based on the die punching diameter, calculating the die punching circumference corresponding to the optimized punching die model; Combined with the die punching perimeter, the die shear strength and the die plate thickness, the blanking force coefficient corresponding to the optimized punching die model can be calculated by the following formula: ; Among them, F represents the blanking force coefficient corresponding to the optimized punching die model, H represents the die punching circumference, M represents the die shear strength, and N represents the die sheet thickness.

[0015] In order to solve the above problems, the present invention also provides a digital modeling system for punching dies based on 3D technology, the system comprising: An architecture analysis module is used 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 infrastructure type corresponding to the punching die and its corresponding hierarchical structure in combination with the design requirements and the production environment characteristics; A fatigue damage evolution evaluation module is used to perform point cloud scanning on the existing products of the punching die to obtain die point cloud data, extract key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, collect 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; An assembly fault tolerance coefficient calculation module is used to perform modeling processing on the punching die in combination with the infrastructure 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 fault tolerance coefficient corresponding to the punching die in combination with the design requirements and the structural dimension information; An optimal assembly order formulation module is used to perform collaborative optimization processing on the initial punching die model in combination with the fatigue damage evolution and the assembly fault tolerance coefficient to obtain an optimized punching die model, calculate the contour disorder corresponding to the optimized punching die model, and formulate the optimal assembly order corresponding to each component in the optimized punching die model based on the contour disorder; The digital modeling module 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 corresponding to the punching die in combination with the optimal assembly order and the optimal assembly path to obtain a modeling result.

[0016] Compared with the problems described in the background technology, the present invention determines the basic architecture type and the corresponding hierarchical structure corresponding to the punching die by combining the design requirements and the production environment characteristics, which can provide a solid basis for the subsequent precise modeling and efficient manufacturing of the punching die, ensure the stable operation of the die in actual production, and meet the punching process requirements. Furthermore, 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 provide a comprehensive and in-depth insight into the actual condition of the punching die, accurately grasp its structural characteristics, and thus provide an important basis for the subsequent modeling of the punching die. Furthermore, the present invention combines the basic architecture type, the hierarchical structure, the key punching structure features and the structural dimension information to model the punching die, which can highly restore the actual structure of the punching die. According to the design requirements and the structural dimension information, the assembly fault tolerance coefficient corresponding to the punching die is calculated. On the one hand, it can prospectively understand the hidden dangers of assembly at the early stage of die design, optimize the die structure in advance, and reduce the cost of later modifications; on the other hand, in the actual manufacturing and assembly process of the die, it provides clear and quantitative assembly instructions for operators. Furthermore, the present invention combines the fatigue damage evolution and the assembly fault tolerance coefficient to coordinately optimize the initial punching die model, which can avoid potential problems of the die during use 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. According to the blanking force coefficient, the optimal assembly path of each component can be scientifically planned, which not only ensures the assembly accuracy, but also fully considers the subsequent work force during the assembly process, effectively reducing the assembly difficulty and production cost of the die. Therefore, the digital modeling method and system for punching dies based on 3D technology provided in the embodiment of the present invention can improve the accuracy of digital modeling of punching dies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a process for implementing a digital modeling method of a punching die based on 3D technology provided in one embodiment of the present invention; Figure 2 A schematic diagram of a module for implementing the digital modeling method of a punching die based on 3D technology provided in one embodiment of the present invention.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] 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.

[0020] The embodiment of the present application provides a method for digital modeling of a punching die based on 3D technology. The execution subject of the method for digital modeling of a punching die based on 3D technology includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for digital modeling of 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.

[0021] Embodiment 1: Reference Figure 1 FIG. 1 is a flow chart of a method for implementing a digital modeling method of a punching die based on 3D technology according to an embodiment of the present invention. In this embodiment, the method for implementing a digital modeling method of a punching die based on 3D technology includes: 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 determine the infrastructure type corresponding to the punching die and its corresponding hierarchical structure based on the design requirements and the production environment characteristics.

[0022] The present invention determines the infrastructure type and its corresponding hierarchical structure corresponding to the punching die by combining the design requirements and the production environment characteristics, which can provide a solid basis for the subsequent precise modeling and efficient manufacturing of the punching die, ensuring the stable operation of the die in actual production and meeting the punching process requirements.

[0023] It should be explained that the punching die to be modeled is a die used to punch out holes of specific shapes on materials such as plates. Its design requirements cover many aspects such as punching shape, dimensional accuracy, number of punchings, material adaptability, etc. The punching process parameters include key data such as punching force, punching speed, and die gap. The production environment characteristics refer to objective conditions such as temperature, humidity, vibration, dust content, and accuracy level of supporting equipment in the actual use site of the die. The infrastructure type is the main structural 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 subdivided hierarchical relationship of the die from the overall framework to the internal functional components.

[0024] 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: Performing detailed analysis on the design requirements to obtain a detailed list of requirements; Collecting data on the production environment characteristics to obtain an environment characteristic data set; Perform condition mining analysis on the detailed requirement list to obtain key design constraint conditions; Analyzing the dominant environmental factors of the punching die according to the environmental characteristic data set; 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.

[0025] 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.

[0026] 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.

[0027] S2. Perform point cloud scanning on the existing products of the punching die to obtain die point cloud data, extract key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, collect 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.

[0028] The present invention can fully and deeply understand the actual situation of the punching mold and accurately grasp its structural characteristics by extracting the key punching structural features and structural dimension information corresponding to the punching mold from the mold point cloud data, thereby providing an important basis for the subsequent modeling and processing of the punching mold. It should be explained that the mold point cloud data is a collection of massive three-dimensional coordinate information on the surface of existing punching mold products obtained by high-precision laser scanning technology. The key punching structural features are structural features such as shape, cutting edge, and guide that are accurately identified and extracted from the mold point cloud data using professional algorithms and play a key role in the punching function of the punching mold. The structural dimension information is key values ​​such as punching, length, width, and thickness of components that are measured from the mold point cloud data with the help of precise measuring tools and are related to the manufacturing, assembly, and performance evaluation of the punching mold. Furthermore, the point cloud scanning of the existing products of the punching mold can be achieved by a three-dimensional scanner equipped with a high-precision laser rangefinder. The former can quickly capture massive three-dimensional coordinate points on the mold surface to construct point cloud data.

[0029] In detail, extracting 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 mold point cloud data to obtain noise-reduced mold point cloud data; Performing fitting processing on the noise reduction mold point cloud data to obtain fitting mold point cloud data; Calculating the point cloud distance of the fitting mold point cloud data; Based on the point cloud distance, performing discrete point elimination processing on the fitting mold point cloud data to obtain target fitting mold point cloud data; Performing regional segmentation processing on the target fitting mold point cloud data to obtain regional mold point cloud data; Extracting structural features from the mold point cloud data in the region to obtain initial mold structural features; Performing feature screening on the initial die structure features to obtain key punching structure features corresponding to the punching die; The dimension of the regional mold point cloud data is measured to obtain the structural dimension information corresponding to the punching mold.

[0030] 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.

[0031] 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.

[0032] The present invention calculates the material fatigue coefficient corresponding to the punching die based on the material performance data, and the degree of fatigue damage resistance of the punching die 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 explained that the material fatigue coefficient is a parameter used to measure the ability of the material corresponding to the punching die to resist fatigue damage under cyclic loads, and reflects the difficulty of fatigue failure of the material after experiencing multiple stress cycles.

[0033] In detail, the calculating of the material fatigue coefficient corresponding to the punching die based on the material performance data includes: Based on the material performance data, determining a performance parameter value corresponding to the punching die; Based on the performance parameter values, construct a material ontology model corresponding to the punching die; Based on the material body model, creating a mold finite element model corresponding to the punching mold; Performing stress simulation analysis on the mold finite element model to obtain stress simulation parameters and stress cycle times; The material fatigue coefficient corresponding to the punching die is calculated by combining the stress simulation parameters, the number of stress cycles and the die finite element model.

[0034] It should be explained that the performance parameter values ​​are the numerical values ​​of various performance indicators of the material corresponding to the punching die, the material body model is the theoretical model corresponding to the punching die constructed based on the material properties, the die finite element model is the numerical model corresponding to the punching die after the die structure is discretized by finite elements, and the stress simulation parameters and the number of stress cycles are respectively the parameters used to describe stress and the specific number of stress cycles obtained by performing stress simulation analysis on the die finite element model.

[0035] Furthermore, based on the material performance data, professional data statistics and analysis methods are used to determine the performance parameter values ​​corresponding to the punching die; based on the performance parameter values, a material body model corresponding to the punching die is constructed according to the stress-strain characteristics of the material and the actual working conditions; based on the material body model, a mold finite element model corresponding to the punching die is created with the aid of advanced finite element modeling software; the mold finite element model can be subjected to stress simulation analysis by accurately setting boundary conditions, loading parameters and simulation conditions in the finite element analysis software to obtain stress simulation parameters and stress cycle times.

[0036] Further, as an optional embodiment of the present invention, the calculation of the material fatigue coefficient corresponding to the punching die by combining the stress simulation parameters, the stress cycle number and the die finite element model includes: Analyzing the fatigue critical areas and their corresponding area weights in the mold finite element model, and counting the number of areas corresponding to the fatigue critical areas; Based on the stress cycle number, determining the regional stress cycle number corresponding to the fatigue critical area; Based on the stress simulation parameters, querying the theoretical number of regional cycles corresponding to the fatigue critical area; 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: ; Among them, A represents the material fatigue coefficient corresponding to the punching die, represents the area weight corresponding to the ath area in the fatigue critical area, represents the number of regional stress cycles corresponding to the ath region in the fatigue critical region, It represents the theoretical cycle number of the ath 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.

[0037] It should be explained that the fatigue critical area is a specific part in the finite element model of the mold that is specially divided out for fatigue analysis due to its high stress concentration and prone to fatigue problems. The regional weight is the coefficient used for weighted calculation corresponding to the fatigue critical area and determined based on its influence on the overall fatigue failure of the mold. The regional stress cycle number is the specific frequency of stress cycles actually experienced by the fatigue critical area obtained through finite element simulation analysis. The regional theoretical cycle number is the theoretical stress cycle frequency at fatigue failure calculated based on the material fatigue characteristic curve corresponding to the fatigue critical area based on the stress simulation parameters.

[0038] Furthermore, the critical fatigue area in the mold finite element model can be analyzed by visually analyzing the stress distribution of the mold finite element model and combining the past failure case experience of similar molds; the regional weight of the critical fatigue area can be analyzed by comprehensively considering the structural characteristics of the mold, the stress conditions of each part, and the empirical rules summarized by experts based on a large amount of practice; based on the stress simulation parameters, compared with the accurate material fatigue characteristic curve provided by a professional material testing organization, and using the fitting algorithm to reverse the data, the theoretical regional cycle number corresponding to the critical fatigue area can be queried.

[0039] The present invention can accurately predict the fatigue damage degree of the punching die in advance by evaluating the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient, thereby improving the optimization effect of the subsequent collaborative optimization processing of the initial punching die model. It should be explained that the fatigue damage evolution is a dynamic change process in which the material properties of the key parts of the punching die gradually deteriorate and fatigue cracks develop from initiation to expansion under the expected punching frequency as the number of punchings continues to increase, thereby causing the overall fatigue damage degree of the die to gradually deepen. Furthermore, based on the material fatigue coefficient, the fatigue damage evolution of the punching die under the expected punching frequency is evaluated. For example, when the material fatigue coefficient is low, such as 0.2, in the first 50% of the expected punching frequency, the die may only have slight wear on the punching edge, and the punching quality is basically unaffected; as the punching frequency approaches the expected value, the wear intensifies, and tiny burrs occasionally appear, but it 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 appear in the early stage of punching, and the risk of punching rupture will increase greatly in the middle and late stages, seriously affecting production.

[0040] S3. Based on the infrastructure type, the hierarchical structure, the key punching structure features and the structural dimension information, the punching die is modeled to obtain an initial punching die model, and based on the design requirements and the structural dimension information, the assembly fault tolerance coefficient corresponding to the punching die is calculated.

[0041] The present invention models the punching die by combining the infrastructure type, the hierarchical structure, the key punching structure features and the structural dimension information, so as to highly restore the actual structure of the punching die. In combination with the design requirements and the structural dimension information, the assembly fault tolerance coefficient corresponding to the punching die is calculated. On the one hand, it can proactively perceive assembly risks in the early stage of die design, optimize the die structure in advance, and reduce the cost of later modifications; on the other hand, in the actual manufacturing and assembly process of the die, it provides clear and quantitative assembly guidance for operators. Furthermore, the modeling of the punching die can be achieved through modeling software, such as CATIA, Creo, etc.

[0042] In detail, the calculation of the assembly tolerance coefficient corresponding to the punching die by combining the design requirements and the structural dimension information includes: Based on the design requirements, determining the assembly accuracy requirements of the punching die in the assembly process; Based on the assembly accuracy requirement, determining the key assembly part corresponding to the punching die, and extracting the assembly part size corresponding to the key assembly part from the structural size information; Based on the assembly part size and the preset standard size, calculating the size 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; In combination with the preset standard size, the size deviation value and the part importance, the assembly tolerance coefficient corresponding to the punching die can be calculated by the following formula: ; Among them, D represents the assembly tolerance coefficient corresponding to the punching die, Indicates the dimensional deviation value corresponding to the bth part in the critical assembly part, Indicates the preset standard size corresponding to the bth part in the critical assembly part. It represents the importance of the bth part in the key assembly parts, b represents the serial number corresponding to the key assembly part, and r represents the number of key assembly parts.

[0043] It should be explained that the assembly accuracy requirement is a quantitative criterion to ensure the precise assembly of the components of the punching die and to achieve stable and efficient operation of the punching process; the key assembly part is the "joint" part corresponding to the punching die, and the assembly quality directly affects the entire body, affecting the core performance of the die such as punching accuracy and service life; the assembly part size is the specific size description corresponding to the key assembly part in the structural size information; the preset standard size is the design size corresponding to the key assembly part; the size deviation value is the size difference of the part corresponding to the key assembly part; the part functional attribute is the part functional characteristic corresponding to the key assembly part; the part importance indicates the importance of the key assembly part.

[0044] Furthermore, based on the design requirements, combined with professional knowledge and past experience, the assembly accuracy requirements of the punching die in the assembly link are determined; based on the assembly accuracy requirements, the key assembly parts corresponding to the punching die are determined according to the mechanical structure principles, and 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 JS scripting language; the difference between the assembly part dimensions and the preset standard dimensions is calculated to obtain the dimensional deviation value corresponding to the key assembly part; the mechanical principles corresponding to the key assembly parts can be analyzed to determine the functional attributes of the parts, and the part importance corresponding to the key assembly parts can be set based on the attribute description of the functional attributes of the parts and in combination with industry guidelines.

[0045] S4. Combining the fatigue damage evolution and the assembly fault tolerance coefficient, the initial punching die model is collaboratively optimized to obtain an optimized punching die model, the contour disorder corresponding to the optimized punching die model is calculated, and based on the contour disorder, the optimal assembly order corresponding to each component in the optimized punching die model is formulated.

[0046] The present invention combines the fatigue damage evolution and the assembly fault tolerance coefficient to collaboratively optimize the initial punching die model, which can avoid potential problems of the die during use and greatly improve the durability and assembly convenience of the die. It should be explained that the optimized punching die model is a model obtained after the initial punching die model is optimized in combination with the fatigue damage evolution and the assembly fault tolerance coefficient.

[0047] In detail, the initial punching die model is collaboratively optimized by combining the fatigue damage evolution and the assembly fault tolerance coefficient to obtain an optimized punching die model, including: Based on the fatigue damage evolution, determining a fatigue sensitive area in the initial punching die model; Performing structural optimization processing on the fatigue sensitive area to obtain an optimized sensitive area; Based on the assembly fault tolerance coefficient, adjusting the assembly adaptability of the optimized sensitive area to obtain an adapted assembly area; Based on the adaptation assembly area, the initial punching die is optimized and integrated to obtain an optimized punching die model.

[0048] It should be explained that the fatigue sensitive area is a 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 an area formed after the fatigue sensitive area is treated by using structural optimization methods such as topology optimization, wall thickness adjustment, surface roughness improvement, etc. to improve its fatigue resistance. The adaptive assembly area is an area that meets the assembly requirements and can guarantee the overall performance after the optimized sensitive area is improved through assembly adaptability improvements such as matching tolerance optimization according to the assembly fault tolerance coefficient, assembly sequence adjustment, and additional positioning structure.

[0049] Furthermore, based on the fatigue damage evolution, professional mold design engineers use advanced finite element analysis software and long-term accumulated practical experience data to accurately determine the fatigue sensitive area in the initial punching mold model; a technical team proficient in structural mechanics and materials science uses cutting-edge topological optimization technology and reasonable material performance allocation strategies to perform structural optimization on the fatigue sensitive area to obtain an optimized sensitive area; based on the assembly fault tolerance coefficient, assembly engineers with rich assembly process knowledge perform assembly adaptability adjustments on the optimized sensitive area based on precise tolerance calculations and scientific assembly process planning to obtain an adapted assembly area; based on the adapted assembly area, a collaborative design platform is used to allow personnel from multiple departments such as design, process, and assembly to work closely together to optimize and integrate the initial punching mold to obtain an optimized punching mold model.

[0050] The present invention can intuitively reflect the irregularity of the surface of the optimized punching die model by calculating the contour disorder corresponding to the optimized punching die model, thereby improving the subsequent formulation of the optimal assembly order corresponding to each component in the optimized punching die model. It should be explained that the contour disorder indicates the irregularity of the shape corresponding to the optimized punching die model.

[0051] In detail, the calculation of the contour disorder corresponding to the optimized punching die model includes: Performing triangular mesh processing on the optimized punching die model to obtain a die triangular mesh model; Extracting a model contour line of the mold triangular mesh model, and performing point sampling on the model contour line to obtain contour line points; Calculating the curvature change rate between the contour line points; Performing vector processing on the model contour line to obtain a contour line vector, and calculating the angle deviation between the contour line points based on the contour line vector; The contour disorder corresponding to the optimized punching die model is calculated by combining the curvature change rate and the angle deviation.

[0052] It should be explained that the mold triangular mesh model is a digital model representation composed of triangular facets formed after the optimized punching mold model is triangular meshed, and the model contour line is the outer boundary line of the mold triangular mesh model, which shows the shape characteristics of the model on the two-dimensional plane. The contour line points are specific coordinate position points obtained after the model contour line is discretized. The curvature change rate is a quantitative indicator of the change in the degree of curvature of the curve 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 size converted by the vector representation method of the model contour line, which is used for subsequent geometric operations. The angle deviation is the difference between the angle formed by two adjacent contour lines between the contour line points and the standard angle or the expected angle, which is used to measure the irregularity of the contour line angle change.

[0053] Furthermore, the optimized punching die model can be triangulated by the triangulation algorithm built into professional computer-aided design (CAD) software to obtain a triangular mesh model of the die; the contour line of the model can be accurately located and extracted from the triangular mesh model of the die by a contour extraction tool developed based on graphics principles, and the model contour line can be sampled by a digital sampling program with a reasonable sampling interval set to obtain contour line points; 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 conversion algorithm to obtain a contour line vector, such as a vector projection algorithm; based on the contour line vector, the angle deviation between the contour line points can be calculated by a vector dot product algorithm; the contour disorder corresponding to the optimized punching die model can be obtained by normalizing the curvature change rate and the angle deviation, and adding the normalized results.

[0054] The present invention can reduce the difficulty and time consumption of assembly and improve the assembly efficiency by formulating the optimal assembly order corresponding to each component in the optimized punching die model based on the contour disorder. It should be explained that the optimal assembly order is the assembly sequence corresponding to each component in the optimized punching die model, which is arranged according to certain rules and logic and can achieve the best comprehensive effects such as the highest mold assembly efficiency, the best performance and the lowest cost. Furthermore, based on the contour disorder, the optimal assembly order corresponding to each component in the optimized punching die model is formulated. If the contour disorder value is high, it means that the corresponding component has strict precision requirements and great assembly difficulty. Experienced assembly workers are given priority, and high-precision assembly tooling is used to assemble finely from the inside to the outside and from the critical to the secondary. If the contour disorder value is low, it indicates that the assembly of components is relatively simple. The process can be appropriately simplified, the assembly order can be flexibly arranged, and novice workers can be allowed to participate to improve the overall assembly efficiency, thereby ensuring that each component is assembled in the optimal order.

[0055] 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, perform digital modeling processing corresponding to the punching die, and obtain a modeling result.

[0056] The present invention calculates the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters. According to the blanking force coefficient, the optimal assembly path of each component can be scientifically planned, which not only ensures the assembly accuracy, but also fully considers the subsequent work force during the assembly process, and effectively reduces the mold assembly difficulty and production cost. It should be explained that the blanking force coefficient corresponds to the optimized punching die model, and is 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 mold during the punching operation and providing a quantitative standard for mold optimization and assembly path planning.

[0057] In detail, the calculating of the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters includes: Extracting the die punching diameter and die plate thickness corresponding to the optimized punching die model from the punching process parameters; Measuring the die shear strength corresponding to the optimized punching die model; Based on the die punching diameter, calculating the die punching circumference corresponding to the optimized punching die model; Combined with the die punching perimeter, the die shear strength and the die plate thickness, the blanking force coefficient corresponding to the optimized punching die model can be calculated by the following formula: ; Among them, F represents the blanking force coefficient corresponding to the optimized punching die model, H represents the die punching circumference, M represents the die shear strength, and N represents the die sheet thickness.

[0058] It should be explained that the mold plate thickness and the mold punching diameter are respectively the key geometric parameters in the punching process parameters regarding the applicable plate thickness and punching size corresponding to the optimized punching mold model; the mold shear strength is a mechanical parameter that reflects the ability of the processed material of the optimized punching mold model to resist shear failure; the mold punching circumference is its corresponding geometric parameter related to the punching operation calculated based on the punching diameter.

[0059] Furthermore, the mold shear strength corresponding to the optimized punching mold model can be measured by professional material mechanics testing instruments according to standard shear strength testing methods; the mold punching diameter and π are multiplied to obtain the mold punching circumference corresponding to the optimized punching mold model.

[0060] 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 and reduce the risk of component wear according to the stress conditions of the optimized punching die model, and combines the optimal assembly order and the optimal assembly path to perform digital modeling processing corresponding to the punching die, thereby improving the modeling accuracy of the punching die.

[0061] It should be explained that the optimal assembly path is the best assembly trajectory corresponding to each component in the optimized punching die model. Furthermore, 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 is subjected to a large impact force during the punching process. The assembly path gives priority to starting from the bottom strong support components, such as the die base, and uses high-precision positioning tooling to ensure its precise positioning, laying a stable foundation for subsequent assembly; then, the punch, die and other core components are assembled in order from the inside to the outside, first the key and then the secondary. Buffer space is reserved during the assembly process to alleviate the impact of the blanking force and ensure the stability of the coordinated work of each component. If the blanking force coefficient is low, it means that the optimized punching die model is relatively small in force, and the assembly path can be appropriately flexible, small auxiliary components are assembled first, and the assembly steps of some adjacent components are merged to improve the overall assembly efficiency and reduce unnecessary tooling use. In combination with the optimal assembly order and the optimal assembly path, the digital modeling processing corresponding to the punching die can be performed by computer-aided software, and the computer-aided software includes UG NX software.

[0062] Compared with the problems described in the background technology, the present invention determines the basic architecture type and the corresponding hierarchical structure corresponding to the punching die by combining the design requirements and the production environment characteristics, which can provide a solid basis for the subsequent precise modeling and efficient manufacturing of the punching die, ensure the stable operation of the die in actual production, and meet the punching process requirements. Furthermore, 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 provide a comprehensive and in-depth insight into the actual condition of the punching die, accurately grasp its structural characteristics, and thus provide an important basis for the subsequent modeling of the punching die. Furthermore, the present invention combines the basic architecture type, the hierarchical structure, the key punching structure features and the structural dimension information to model the punching die, which can highly restore the actual structure of the punching die. According to the design requirements and the structural dimension information, the assembly fault tolerance coefficient corresponding to the punching die is calculated. On the one hand, it can prospectively understand the hidden dangers of assembly at the early stage of die design, optimize the die structure in advance, and reduce the cost of later modifications; on the other hand, in the actual manufacturing and assembly process of the die, it provides clear and quantitative assembly instructions for operators. Furthermore, the present invention combines the fatigue damage evolution and the assembly fault tolerance coefficient to coordinately optimize the initial punching die model, which can avoid potential problems of the die during use 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. According to the blanking force coefficient, the optimal assembly path of each component can be scientifically planned, which not only ensures the assembly accuracy, but also fully considers the subsequent work force during the assembly process, effectively reducing the assembly difficulty and production cost of the die. Therefore, the digital modeling method and system for punching dies based on 3D technology provided in the embodiment of the present invention can improve the accuracy of digital modeling of punching dies.

[0063] Embodiment 2: like Figure 2 The figure shows a functional module diagram of a digital modeling system for punching dies based on 3D technology according to the present invention.

[0064] The digital modeling system 200 for punching dies based on 3D technology described in the present invention can be installed in an electronic device. According to the functions implemented, the digital modeling system for punching dies based on 3D technology can include an architecture analysis module 201, a fatigue damage evolution evaluation module 202, an assembly fault tolerance coefficient calculation module 203, an optimal assembly order formulation module 204 and a digital modeling module 205. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0065] In the embodiment of the present invention, the functions of each module / unit are as follows: The architecture analysis module 201 is used 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; The fatigue damage evolution evaluation module 202 is used to perform point cloud scanning on the existing product of the punching die to obtain die point cloud data, extract key punching structure features and structural size information corresponding to the punching die from the die point cloud data, collect 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; The assembly fault tolerance coefficient calculation module 203 is used to perform modeling processing on the punching die in combination with the infrastructure 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 fault tolerance coefficient corresponding to the punching die in combination with the design requirements and the structural dimension information; The optimal assembly order formulation module 204 is used to perform collaborative optimization processing on the initial punching die model in combination with the fatigue damage evolution and the assembly fault tolerance coefficient to obtain an optimized punching die model, calculate the contour disorder corresponding to the optimized punching die model, and formulate the optimal assembly order corresponding to each component in the optimized punching die model based on the contour disorder; The digital modeling module 205 is used to calculate the blanking force coefficient corresponding to the optimized punching die model based on the punching process parameters, and plan the optimal assembly path corresponding to each component in the optimized punching die model based on the blanking force coefficient. In combination with the optimal assembly order and the optimal assembly path, the digital modeling processing corresponding to the punching die is performed to obtain a modeling result.

[0066] In detail, the modules in the digital modeling system 200 for implementing a punching die based on 3D technology in the embodiment of the present invention are used in the same manner as described above. Figure 1 The digital modeling method of punching die based on 3D technology described in the text is the same technical means and can produce the same technical effect, so it will not be repeated here.

[0067] It is obvious to those skilled in the art that the present invention is not limited to the details of the above 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.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A digital modeling method for punching die based on 3D technology, characterized in that: The method comprises: 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 determining the infrastructure type corresponding to the punching die and its corresponding hierarchical structure in combination with the design requirements and the production environment characteristics; Performing point cloud scanning on the existing products of the punching die to obtain die point cloud data, extracting key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, collecting material performance data corresponding to the punching die, calculating the material fatigue coefficient corresponding to the punching die based on the material performance data, and evaluating the fatigue damage evolution of the punching die under the expected punching frequency based on the material fatigue coefficient; In combination with the infrastructure type, the hierarchical structure, the key punching structure features and the structural dimension information, the punching die is modeled to obtain an initial punching die model, and in combination with the design requirements and the structural dimension information, an assembly fault tolerance coefficient corresponding to the punching die is calculated; In combination with the fatigue damage evolution and the assembly fault tolerance coefficient, the initial punching die model is collaboratively optimized to obtain an optimized punching die model, the contour disorder corresponding to the optimized punching die model is calculated, and based on the contour disorder, the optimal assembly order corresponding to each component in the optimized punching die model is formulated; Based on the punching process parameters, the blanking force coefficient corresponding to the optimized punching die model is calculated. Based on the blanking force coefficient, the optimal assembly path corresponding to each component in the optimized punching die model is planned. Combined with the optimal assembly order and the optimal assembly path, the digital modeling processing corresponding to the punching die is performed to obtain the modeling result.

2. The digital modeling method for punching die based on 3D technology according to claim 1, characterized in that: The step of determining 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: Performing detailed analysis on the design requirements to obtain a detailed list of requirements; Collecting data on the production environment characteristics to obtain an environment characteristic data set; Perform condition mining analysis on the detailed requirement list to obtain key design constraint conditions; Analyzing the dominant environmental factors of the punching die according to the environmental characteristic data set; 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.

3. The digital modeling method for punching die based on 3D technology according to claim 1, characterized in that: The step of extracting 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 mold point cloud data to obtain noise-reduced mold point cloud data; Performing fitting processing on the noise reduction mold point cloud data to obtain fitting mold point cloud data; Calculating the point cloud distance of the fitting mold point cloud data; Based on the point cloud distance, performing discrete point elimination processing on the fitting mold point cloud data to obtain target fitting mold point cloud data; Performing regional segmentation processing on the target fitting mold point cloud data to obtain regional mold point cloud data; Extracting structural features from the mold point cloud data in the region to obtain initial mold structural features; Performing feature screening on the initial die structure features to obtain key punching structure features corresponding to the punching die; The dimension of the regional mold point cloud data is measured to obtain the structural dimension information corresponding to the punching mold.

4. The digital modeling method for punching die based on 3D technology according to claim 1, characterized in that: The calculating the material fatigue coefficient corresponding to the punching die based on the material performance data includes: Based on the material performance data, determining a performance parameter value corresponding to the punching die; Based on the performance parameter values, construct a material ontology model corresponding to the punching die; Based on the material body model, creating a mold finite element model corresponding to the punching mold; Performing stress simulation analysis on the mold finite element model to obtain stress simulation parameters and stress cycle times; The material fatigue coefficient corresponding to the punching die is calculated by combining the stress simulation parameters, the number of stress cycles and the die finite element model.

5. The method for realizing digital modeling of a punching die based on 3D technology according to claim 4, characterized in that: The step of calculating the material fatigue coefficient corresponding to the punching die by combining the stress simulation parameters, the stress cycle times and the die finite element model includes: Analyzing the fatigue critical areas and their corresponding area weights in the mold finite element model, and counting the number of areas corresponding to the fatigue critical areas; Based on the stress cycle number, determining the regional stress cycle number corresponding to the fatigue critical area; Based on the stress simulation parameters, querying the theoretical number of regional cycles corresponding to the fatigue critical area; Combining the regional weight, the regional stress cycle number, the regional theoretical cycle number and the regional quantity, The material fatigue coefficient corresponding to the punching die is calculated by the following formula: ； Among them, A represents the material fatigue coefficient corresponding to the punching die, represents the area weight corresponding to the ath area in the fatigue critical area, represents the number of regional stress cycles corresponding to the ath region in the fatigue critical region, It represents the theoretical cycle number of the ath 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 method for realizing digital modeling of a punching die based on 3D technology according to claim 1, characterized in that: The step of calculating the assembly fault tolerance coefficient corresponding to the punching die in combination with the design requirements and the structural dimension information includes: Based on the design requirements, determining the assembly accuracy requirements of the punching die in the assembly process; Based on the assembly accuracy requirement, determining the key assembly part corresponding to the punching die, and extracting the assembly part size corresponding to the key assembly part from the structural size information; Based on the assembly part size and the preset standard size, calculating the size 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; In combination with the preset standard size, the size deviation value and the part importance, the assembly tolerance coefficient corresponding to the punching die is calculated by the following formula: ； Among them, D represents the assembly tolerance coefficient corresponding to the punching die, Indicates the dimensional deviation value corresponding to the bth part in the critical assembly part, Indicates the preset standard size corresponding to the bth part in the critical assembly part. It represents the importance of the bth part in the key assembly parts, b represents the serial number corresponding to the key assembly part, and r represents the number of key assembly parts.

7. The method for realizing digital modeling of a punching die based on 3D technology according to claim 1, characterized in that: The step of combining the fatigue damage evolution and the assembly fault tolerance coefficient to collaboratively optimize the initial punching die model to obtain an optimized punching die model includes: Based on the fatigue damage evolution, determining a fatigue sensitive area in the initial punching die model; Performing structural optimization processing on the fatigue sensitive area to obtain an optimized sensitive area; Based on the assembly fault tolerance coefficient, adjusting the assembly adaptability of the optimized sensitive area to obtain an adapted assembly area; Based on the adaptation assembly area, the initial punching die is optimized and integrated to obtain an optimized punching die model.

8. The method for realizing digital modeling of a punching die based on 3D technology according to claim 1, characterized in that: The calculating of the contour disorder corresponding to the optimized punching die model includes: Performing triangular mesh processing on the optimized punching die model to obtain a die triangular mesh model; Extracting a model contour line of the mold triangular mesh model, and performing point sampling on the model contour line to obtain contour line points; Calculating the curvature change rate between the contour line points; Performing vector processing on the model contour line to obtain a contour line vector, and calculating the angle deviation between the contour line points based on the contour line vector; The contour disorder corresponding to the optimized punching die model is calculated by combining the curvature change rate and the angle deviation.

9. The method for realizing digital modeling of a punching die based on 3D technology according to claim 1, characterized in that: The calculating, based on the punching process parameters, the blanking force coefficient corresponding to the optimized punching die model comprises: Extracting the die punching diameter and die plate thickness corresponding to the optimized punching die model from the punching process parameters; Measuring the die shear strength corresponding to the optimized punching die model; Based on the die punching diameter, calculating the die punching circumference corresponding to the optimized punching die model; Combined with the die punching perimeter, the die shear strength and the die plate thickness, the blanking force coefficient corresponding to the optimized punching die model is calculated by the following formula: ； Among them, F represents the blanking force coefficient corresponding to the optimized punching die model, H represents the die punching circumference, M represents the die shear strength, and N represents the die sheet thickness.

10. A digital modeling system for punching dies based on 3D technology, characterized in that: The system comprises: An architecture analysis module is used 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 infrastructure type corresponding to the punching die and its corresponding hierarchical structure in combination with the design requirements and the production environment characteristics; A fatigue damage evolution evaluation module is used to perform point cloud scanning on the existing products of the punching die to obtain die point cloud data, extract key punching structure features and structural dimension information corresponding to the punching die from the die point cloud data, collect 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; An assembly fault tolerance coefficient calculation module is used to perform modeling processing on the punching die in combination with the infrastructure 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 fault tolerance coefficient corresponding to the punching die in combination with the design requirements and the structural dimension information; An optimal assembly order formulation module is used to perform collaborative optimization processing on the initial punching die model in combination with the fatigue damage evolution and the assembly fault tolerance coefficient to obtain an optimized punching die model, calculate the contour disorder corresponding to the optimized punching die model, and formulate the optimal assembly order corresponding to each component in the optimized punching die model based on the contour disorder; The digital modeling module 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 corresponding to the punching die in combination with the optimal assembly order and the optimal assembly path to obtain a modeling result.

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