Production verification method of metal pipe joint cold extrusion die

Abstract

The invention provides a production verification method of a metal pipe joint cold extrusion die, and relates to the technical field of metal processing, the method comprises the following steps: obtaining a pipe joint extrusion feature group of a first extrusion die; generating a target three-way pressure stress distribution characteristic and a target blank flow characteristic; initial performance detection is conducted on the first extrusion die, and a first die performance index set is generated; generating a first predicted service duration; and carrying out quality monitoring verification on the first extrusion die based on the first predicted service duration. According to the method and the device, the technical problem of poor mold quality monitoring accuracy caused by lack of mold loss analysis under actual operation in the prior art can be solved, and the three-direction pressure stress distribution and blank flow characteristics of the first extrusion mold during actual extrusion operation are analyzed; and in combination with the mold performance, service duration prediction is performed, so that quality monitoring is performed, and the technical effect of improving the mold quality monitoring efficiency and accuracy is achieved.

Description

Technical Field

[0001] This application relates to the field of metal processing technology, and in particular to a production verification method for a cold extrusion die for metal pipe fittings. Background Technology

[0002] Cold extrusion is a commonly used processing method in the manufacturing of metal pipe fittings. It involves applying high pressure to cause plastic deformation of the metal material within a die, thereby obtaining the desired shape and size. The quality of the cold extrusion die directly affects the quality of the pipe fittings, production efficiency, and cost. However, during long-term extrusion, the die's performance can deteriorate due to wear and fatigue, thus affecting product quality. Therefore, monitoring the quality of cold extrusion dies and predicting their service life in advance is crucial for ensuring product quality and improving production efficiency.

[0003] Currently, existing mold quality monitoring technologies often test the dimensions and performance of molds produced on the production line. However, molds will experience wear and tear after actual operation, and even if the quality meets the requirements at the factory, the subsequent service life may not be up to standard, resulting in poor mold quality monitoring results.

[0004] In summary, the existing technology suffers from a lack of analysis on mold wear under actual operation, resulting in poor accuracy in mold quality monitoring. Summary of the Invention

[0005] The purpose of this application is to provide a production verification method for cold extrusion dies for metal pipe fittings, in order to solve the technical problem in the prior art that the lack of die wear analysis under actual operation leads to poor accuracy in die quality monitoring.

[0006] In view of the above problems, this application provides a production verification method for a cold extrusion die for metal pipe fittings. The method includes: obtaining a pipe fitting extrusion feature set of a first extrusion die, wherein the pipe fitting extrusion feature set includes basic die information, target pipe fitting geometric structure information, pipe fitting material information, and basic information of the blank to be processed; performing finished product quality feature distribution simulation based on the pipe fitting extrusion feature set to generate target triaxial compressive stress distribution features and target blank flow features; performing initial performance testing on the first extrusion die to generate a first die performance index set; performing die stress resistance analysis by combining the target triaxial compressive stress distribution features, the target blank flow features, and the first die performance index set to generate a first predicted service life; and performing quality monitoring and verification on the first extrusion die based on the first predicted service life.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: A pipe joint extrusion feature set of a first extrusion die is obtained, wherein the pipe joint extrusion feature set includes basic die information, target pipe joint geometric structure information, pipe joint material information, and basic information of the blank to be processed; based on the pipe joint extrusion feature set, the finished product quality feature distribution is simulated to generate target triaxial compressive stress distribution features and target blank flow features; initial performance testing is performed on the first extrusion die to generate a first die performance index set; die stress resistance analysis is performed by combining the target triaxial compressive stress distribution features, the target blank flow features, and the first die performance index set to generate a first predicted service life; the first extrusion die is quality monitored and verified based on the first predicted service life. By analyzing the triaxial compressive stress distribution and blank flow features of the first extrusion die during actual extrusion operations, and combining the die performance to predict the service life, quality monitoring is performed, thereby achieving the technical effect of improving the efficiency and accuracy of die quality monitoring.

[0008] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating the production verification method for a cold extrusion die for a metal pipe fitting according to this application. Figure 2 This is a schematic diagram of the process for generating the target triaxial compressive stress distribution characteristics and the target billet flow characteristics in the production verification method of a cold extrusion die for a metal pipe joint according to this application. Detailed Implementation

[0011] This application provides a production verification method for cold extrusion dies for metal pipe fittings, solving the technical problem in the prior art where the lack of die wear analysis under actual operation leads to poor accuracy in die quality monitoring. By analyzing the triaxial compressive stress distribution and billet flow characteristics of the first extrusion die during actual extrusion operations, and combining this with die performance to predict service life, quality monitoring is performed, thereby improving the efficiency and accuracy of die quality monitoring.

[0012] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0013] Please see the appendix Figure 1 This application provides a production verification method for cold extrusion dies for metal pipe fittings, the method specifically including the following steps: Step 1: Obtain the pipe joint extrusion feature group of the first extrusion die, wherein the pipe joint extrusion feature group includes basic die information, target pipe joint geometric structure information, pipe joint material information, and basic information of the blank to be processed.

[0014] Specifically, the first extrusion die refers to the die used for extruding pipe fittings. Extrusion is a metal processing method that uses extrusion to deform metal material within the cavity of a die, thereby obtaining a workpiece of the desired shape and size. The pipe fitting extrusion feature set refers to a set of information parameters describing the various information parameters required by the extrusion die during the pipe fitting extrusion process, including basic die information, target pipe fitting geometry information, pipe fitting material information, and basic information of the blank to be processed.

[0015] Basic mold information includes mold dimensions, structure, and manufacturing materials. Target pipe fitting geometry information includes the pipe fitting's shape, dimensions, and wall thickness. Pipe fitting material information includes material type, hardness, and elongation. Basic information about the blank to be processed includes the blank's shape, dimensions, and material. By obtaining the pipe fitting extrusion feature set of the first extrusion mold, a foundation is provided for subsequent mold quality monitoring, facilitating quality monitoring in conjunction with the extrusion scenario and improving the accuracy of quality monitoring.

[0016] Step 2: Based on the extrusion feature group of the pipe joint, simulate the distribution of finished product quality features to generate the target triaxial compressive stress distribution features and the target billet flow features.

[0017] Specifically, a mathematical model is constructed using information from the pipe fitting extrusion feature set. This model simulates the behavior of the pipe fitting during the extrusion process. This model is typically a finite element model, taking into account factors such as die geometry, material properties, and extrusion process parameters. After model construction, simulation analysis is performed to obtain the stress, strain, and temperature distribution of the pipe fitting during extrusion. The target triaxial compressive stress distribution characteristic refers to the distribution of compressive stress in three directions of the pipe fitting during extrusion. In cold extrusion, the material is placed in the first extrusion die, and then the force of the extruder forces the material to flow and form within the cavity of the first extrusion die. Due to the closed nature of the die, the material is subjected to pressure from three directions during flow, which helps the material flow uniformly within the die, reduces defects, and improves the density and mechanical properties of the finished product. Triaxial compressive stress typically refers to the three main stresses experienced by the material during extrusion: principal stress (axial stress), transverse stress (radial stress), and shear stress.

[0018] Meanwhile, the target billet flow characteristics refer to the flow behavior of the billet in the die cavity during extrusion, including the billet flow rate, streamlines, and filling conditions. These flow characteristics are crucial for ensuring the dimensional accuracy and internal quality of the pipe fittings. The target triaxial compressive stress distribution characteristics and target billet flow characteristics obtained through simulation can be used to monitor die quality.

[0019] Step 3: Perform initial performance testing on the first extrusion die to generate a set of first die performance indicators.

[0020] Specifically, initial performance testing refers to testing the material properties of the first extrusion die, such as hardness and toughness. For example, the hardness, toughness, tensile strength, yield strength, and fatigue strength of the first extrusion die can be tested to generate a set of performance indicators for the first die, reflecting the die's performance and expected life during the extrusion process, and assisting in die quality monitoring.

[0021] Step 4: Combine the target triaxial compressive stress distribution characteristics, the target billet flow characteristics, and the first mold performance index set to perform mold stress resistance analysis and generate the first predicted service life.

[0022] Specifically, by combining the first set of mold performance indicators, the hardness, toughness and other performance indicators of the mold are combined with the target triaxial compressive stress distribution characteristics and the target billet flow characteristics to evaluate the mold's resistance to various stresses under actual working conditions, predict the service life of the mold under specific working conditions, and generate the first predicted service life, that is, the expected service life of the mold under specific working conditions. The service life can reflect the quality of the mold and ensure the accuracy of mold quality monitoring.

[0023] Step 5: Perform quality monitoring and verification on the first extrusion die based on the first predicted service life.

[0024] Specifically, when a die leaves the factory, the manufacturer specifies a standard service life. The predicted service life is then used to determine if it meets this standard. If it does, the quality monitoring and verification pass; otherwise, it fails. By analyzing the triaxial compressive stress distribution and billet flow characteristics of the first extrusion die during actual extrusion operations, and combining this with die performance data, service life prediction is performed, thereby improving the efficiency and accuracy of die quality monitoring.

[0025] Further details are attached. Figure 2 As shown, step two of this application includes: Based on the pipe joint material information, material parameters are converted to generate a material parameter set; finite element modeling is performed using the mold basic information, the target pipe joint geometric structure information, the blank to be processed basic information, and the material parameter set to generate a first cold extrusion finite element model; finite boundary conditions are constructed; the first cold extrusion finite element model is driven by the finite boundary conditions, and a triaxial compressive stress distribution dataset and a blank flow characteristic dataset are recorded; mold stress resistance boundary analysis is performed on the triaxial compressive stress distribution dataset and the blank flow characteristic dataset to generate the target triaxial compressive stress distribution characteristics and the target blank flow characteristics.

[0026] Specifically, material parameter conversion based on pipe fitting material information is used to generate a material parameter set. This is to convert the actual properties of the material into parameters usable for finite element analysis. Material parameters include the material's elastic modulus, yield strength, Poisson's ratio, and coefficient of thermal expansion, which can be obtained from the material information using existing technologies and will not be elaborated upon here. Next, finite element modeling is performed using the basic information of the die, the geometric structure of the target pipe fitting, the basic information of the blank to be processed, and the material parameter set to generate the first cold extrusion finite element model. Finite element modeling is a numerical analysis method that simulates the overall behavior of a structure by dividing a complex geometry into small elements and applying physical laws to these elements. In this process, the geometry of the die and the blank is accurately modeled, and material parameters are assigned to these elements to simulate the actual extrusion process.

[0027] Finite boundary conditions are constructed to simulate the influence of extrusion parameters, such as extrusion speed and extrusion ratio, on the interaction between the die and the billet during the extrusion process. Finite boundary conditions, including parameters such as extrusion speed and extrusion ratio, ensure the accuracy of the simulation. Multiple driving simulations are performed on the first cold extrusion finite element model using finite boundary conditions. During the simulation, the flow and deformation of the billet in the die cavity, and the resulting stress distribution, are calculated. The triaxial compressive stress distribution data obtained from each simulation are recorded to form a triaxial compressive stress distribution dataset, and the billet flow characteristic data obtained from each simulation are used to form a billet flow characteristic dataset.

[0028] Finally, a die stress resistance boundary analysis was performed on the triaxial compressive stress distribution dataset and the billet flow characteristic dataset to evaluate the stress level borne by the die and the flow behavior of the billet during the extrusion process, thereby generating target triaxial compressive stress distribution characteristics and target billet flow characteristics, thus ensuring the accuracy of die quality monitoring.

[0029] Furthermore, this application also includes the following steps: The finite boundary conditions include die extrusion parameters and extrusion environment; a preset application scenario of the first extrusion die is obtained, and historical extrusion parameters and historical extrusion environments are retrieved in the preset application scenario to generate an extrusion parameter record dataset and an extrusion environment record dataset; the extrusion parameter record dataset and the extrusion environment record dataset are merged in a stepwise manner to generate multiple step extrusion parameters and multiple step extrusion environments; the finite boundary conditions are generated using the multiple step extrusion parameters and the multiple step extrusion environments.

[0030] Specifically, the steps for constructing finite boundary conditions are as follows: First, finite boundary conditions include die extrusion parameters and the extrusion environment. Extrusion parameters involve the forces and displacements applied to the die during the extrusion process, such as extrusion speed, extrusion ratio, and coefficient of friction. The extrusion environment includes temperature and lubrication conditions during the extrusion process. To generate finite boundary conditions, it is first necessary to obtain the preset application scenario of the first extrusion die, that is, the scenario in which the first extrusion die is used for cold extrusion after passing quality inspection. The specific scenario needs to be determined based on actual conditions, such as extruding pipe fittings of a specific shape and material. From this, historical extrusion parameters and historical extrusion environments under the preset application scenario can be obtained, which can be obtained from production records, generating an extrusion parameter record dataset and an extrusion environment record dataset. It should be noted that the extrusion parameter record dataset and the extrusion environment record dataset each contain multiple sets of different extrusion parameters and extrusion environments.

[0031] Next, the extrusion parameter recording dataset and the extrusion environment recording dataset are merged in a stepped manner. Stepped merging refers to organizing different extrusion parameters or extrusion environments according to a certain logical relationship to form a series of stepped datasets. For example, the extrusion parameter recording dataset and the extrusion environment recording dataset can be divided into steps according to a preset step division threshold. The mean of the data in each step is calculated to obtain one extrusion parameter or extrusion environment for each step, thereby generating multiple stepped extrusion parameters and multiple stepped extrusion environments.

[0032] Then, multiple stepped extrusion parameters and multiple stepped extrusion environments are used to generate finite boundary conditions. That is, during the finite element simulation through the first cold extrusion finite element model, the finite boundary conditions will change according to different extrusion parameters and extrusion environments in the extrusion process to more realistically reflect the dynamic changes in the actual extrusion process. This drives the first cold extrusion finite element model to simulate and predict the die performance and billet behavior under different extrusion parameters and environments, thereby predicting the die service life, ensuring the adaptability of die quality analysis to actual operation scenarios, and thus improving the accuracy of die quality monitoring.

[0033] Furthermore, this application also includes the following steps: Based on the triaxial compressive stress distribution dataset, a die resistance loss maximization analysis is performed to generate the target triaxial compressive stress distribution characteristics; based on the billet flow characteristic dataset, a die resistance loss maximization analysis is performed to generate the target billet flow characteristics.

[0034] Furthermore, this application also includes the following steps: Distributed mold stress resistance index identification is performed on the triaxial compressive stress distribution dataset to generate a distributed mold resistance index set; based on the distributed mold resistance index set, the index maximization mapping is performed on the triaxial compressive stress distribution dataset to generate the target triaxial compressive stress distribution feature.

[0035] Specifically, the steps for performing mold stress resistance boundary analysis on the triaxial compressive stress distribution dataset and the billet flow characteristic dataset are as follows: The purpose of performing die resistance loss maximization analysis based on a triaxial compressive stress distribution dataset is to determine the maximum stress the die can withstand during extrusion, thereby generating target triaxial compressive stress distribution characteristics. Specifically, the triaxial compressive stress distribution dataset is first analyzed to identify the maximum stress point the die experiences during extrusion. Then, die resistance loss maximization analysis is performed, which assesses the die's losses under maximum stress, including die wear, fatigue life, and potential structural damage, to determine if the die design is sufficiently robust to withstand the stresses during actual extrusion. Finally, based on the analysis results, target triaxial compressive stress distribution characteristics are generated. These characteristics include the maximum stress value the die experiences during extrusion and the stress distribution characteristics.

[0036] Similarly, maximizing die resistance loss analysis based on a billet flow characteristic dataset aims to determine the impact of billet flow behavior on the die during extrusion, thereby generating target billet flow characteristics. First, the billet flow characteristic dataset is analyzed to assess the influence of billet flow behavior on die wear and fatigue life, including evaluating shear stress, friction, and temperature changes caused by billet flow. Then, based on the analysis results, target billet flow characteristics are generated. These target billet flow characteristics include the flow velocity distribution of the billet within the die cavity. Through these analyses, a better understanding of die performance and potential problems during extrusion can be achieved, providing strong support for die quality monitoring and improving its accuracy.

[0037] Based on the aforementioned triaxial compressive stress distribution dataset, the specific steps for performing mold resistance loss maximization analysis and generating the target triaxial compressive stress distribution characteristics are as follows: First, distributed mold stress resistance index identification is performed on the triaxial compressive stress distribution dataset to extract indices reflecting the mold's stress resistance capability. These indices constitute a distributed mold resistance index set. Specifically, the stress peak values ​​at different locations are identified as distributed mold stress resistance indices. Next, based on the distributed mold resistance index set, the triaxial compressive stress distribution dataset is mapped to maximize the index. This involves comparing the stress distribution in the triaxial compressive stress distribution dataset with the distributed mold resistance index set to identify the stress distribution characteristics in the triaxial compressive stress distribution dataset corresponding to the maximum resistance index. In other words, the maximum stress that different areas of the mold may bear is determined, forming the target triaxial compressive stress distribution characteristics. This facilitates the prediction of service life under specific operating conditions and improves the accuracy of mold quality monitoring.

[0038] Similarly, the steps for obtaining the flow characteristics of the target billet are the same as those for obtaining the triaxial compressive stress distribution characteristics of the target billet, and will not be elaborated here.

[0039] Furthermore, step four of this application includes: Based on the target triaxial compressive stress distribution characteristics, the target billet flow characteristics, and the first mold performance index set, continuous mold wear analysis is performed to generate a wear index time series; a preset wear index threshold is constructed, wherein the preset wear index threshold is the wear index when the mold quality is unqualified; combining the preset wear index threshold and the wear index time series, a service life that meets the preset wear index threshold is generated as the first predicted service life.

[0040] Furthermore, this application also includes the following steps: Define a mold loss index; construct a mold loss continuous identification module based on the mold loss index; use the mold loss continuous identification module to perform continuous mold loss analysis on the target triaxial compressive stress distribution characteristics, the target billet flow characteristics and the first mold performance index set, and output the time series of the loss index.

[0041] Specifically, continuous die wear analysis based on the target triaxial compressive stress distribution characteristics, target billet flow characteristics, and the first die performance index set is conducted to assess the wear of the first extrusion die during long-term use and predict its service life. First, using the target triaxial compressive stress distribution characteristics and target billet flow characteristics, combined with the first die performance index set, continuous die wear analysis is performed. Specifically, the wear rate and wear degree of the first extrusion die during the extrusion process are identified, generating a wear index time series, i.e., the change of die wear over time. Then, a preset wear index threshold is constructed. This preset wear index threshold represents the wear index when the die quality is unqualified; that is, when the die wear reaches or exceeds the preset wear index threshold, the die performance will no longer meet production requirements. Next, by combining the preset loss index threshold and the loss index time series, the service life that meets the preset loss index threshold is generated. That is, the loss index time series is analyzed to find the time point when the mold wear reaches the preset threshold. This time point is the predicted service life of the mold, that is, the time when the mold is expected to work normally before reaching a quality unqualified state. In this way, the service life of the mold can be predicted based on the actual working conditions and performance indicators of the mold, thereby realizing the quality analysis of the first extrusion mold and improving the accuracy of mold quality monitoring.

[0042] The specific steps for generating the timing sequence of wear indicators are as follows: First, define the die wear indicators. Defining die wear indicators is to quantify the wear of the die during the extrusion process. These indicators will be used to evaluate the performance of the die and predict its service life. Die wear indicators may include indicators such as wear rate, material fatigue, and wear degree. The specific indicators that can reflect the die wear condition are selected by those skilled in the art, and there are no restrictions on this.

[0043] Based on mold wear indicators, a continuous mold wear identification module is constructed. This module is a machine learning model capable of predicting wear based on the input target triaxial compressive stress distribution characteristics, target billet flow characteristics, and a first set of mold performance indicators, outputting a time series of wear indicators. Specifically, those skilled in the art can obtain samples of triaxial compressive stress distribution characteristics, billet flow characteristics, mold performance indicators, and corresponding mold wear indicators from historical mold quality monitoring records. Then, the continuous mold wear identification module is constructed by training an existing machine learning model. Training the machine learning model is a common technique used by those skilled in the art and will not be elaborated upon here. This enables mold wear prediction, facilitating the prediction of service life and thus mold quality monitoring.

[0044] Furthermore, step five of this application includes: Obtain the preset service life of the first extrusion die; determine whether the first predicted service life meets the preset service life; if yes, the quality monitoring verification passes; if no, the quality monitoring verification fails.

[0045] The steps for quality monitoring and verification of the first extrusion die based on the first predicted service life are as follows: First, obtain the preset service life of the first extrusion die, i.e., determine the expected service life of the die under normal operating conditions. The preset service life is usually set by the manufacturer or user. Then, compare the first predicted service life with the preset service life. The first predicted service life is the predicted service life of the die derived from finite element simulation, loss analysis, and other performance evaluation methods. If the first predicted service life meets the preset service life, i.e., the expected service life of the die in actual use is not lower than the standard set by the manufacturer or user, then the quality monitoring and verification can be considered passed. This indicates that the design and manufacture of the die meet the requirements and can be safely used in production. If the first predicted service life does not meet the preset service life, i.e., the expected service life of the die is lower than the set standard, then the quality monitoring and verification fails. This may indicate that the design, material selection, manufacturing process, or usage conditions of the die need improvement. Through this comparison and judgment, it can be ensured that the performance of the die meets the predetermined standards, thereby ensuring the continuity of production and the quality of the product, thus achieving precise die quality monitoring and ensuring the quality of die production.

[0046] In summary, the production verification method for a cold extrusion die for metal pipe fittings provided in this application has the following technical advantages: A pipe joint extrusion feature set of a first extrusion die is obtained, wherein the pipe joint extrusion feature set includes basic die information, target pipe joint geometric structure information, pipe joint material information, and basic information of the blank to be processed; based on the pipe joint extrusion feature set, the finished product quality feature distribution is simulated to generate target triaxial compressive stress distribution features and target blank flow features; initial performance testing is performed on the first extrusion die to generate a first die performance index set; die stress resistance analysis is performed by combining the target triaxial compressive stress distribution features, the target blank flow features, and the first die performance index set to generate a first predicted service life; the first extrusion die is quality monitored and verified based on the first predicted service life. By analyzing the triaxial compressive stress distribution and blank flow features of the first extrusion die during actual extrusion operations, and combining the die performance to predict the service life, quality monitoring is performed, thereby achieving the technical effect of improving the efficiency and accuracy of die quality monitoring.

[0047] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0048] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for production verification of a cold extrusion die for metal pipe fittings, characterized in that, The method includes: Obtain the pipe joint extrusion feature group of the first extrusion die, wherein the pipe joint extrusion feature group includes basic die information, target pipe joint geometric structure information, pipe joint material information and basic information of the blank to be processed; Based on the extrusion feature group of the pipe joint, the distribution of finished product quality features is simulated to generate the target triaxial compressive stress distribution features and the target billet flow features. The first extrusion die is subjected to initial performance testing to generate a first die performance index set. Combining the target triaxial compressive stress distribution characteristics, the target billet flow characteristics, and the first mold performance index set, mold stress resistance analysis is performed to generate a first predicted service life. The first extrusion die is quality monitored and verified based on the first predicted service life.

2. The production verification method for a cold extrusion die for metal pipe fittings as described in claim 1, characterized in that, Based on the aforementioned pipe joint extrusion feature set, the finished product quality feature distribution is simulated to generate target triaxial compressive stress distribution features and target billet flow features, including: Based on the pipe fitting material information, material parameters are converted to generate a material parameter set; Finite element modeling is performed using the basic mold information, the geometric structure information of the target pipe joint, the basic information of the blank to be processed, and the material parameter set to generate a first cold extrusion finite element model. Construct finite boundary conditions; The first cold extrusion finite element model was driven by the finite boundary conditions, and the triaxial compressive stress distribution dataset and billet flow characteristic dataset were recorded. Perform mold stress resistance boundary analysis on the triaxial compressive stress distribution dataset and the billet flow characteristic dataset to generate the target triaxial compressive stress distribution characteristics and the target billet flow characteristics.

3. The production verification method for a cold extrusion die for metal pipe fittings as described in claim 2, characterized in that, Constructing finite boundary conditions includes: The finite boundary conditions include die extrusion parameters and extrusion environment; Obtain the preset application scenario of the first extrusion die, retrieve historical extrusion parameters and historical extrusion environment in the preset application scenario, and generate an extrusion parameter record dataset and an extrusion environment record dataset. The extrusion parameter recording dataset and the extrusion environment recording dataset are merged in a stepwise manner to generate multiple step extrusion parameters and multiple step extrusion environments; The finite boundary conditions are generated using the multiple stepped extrusion parameters and the multiple stepped extrusion environments.

4. The production verification method for a cold extrusion die for metal pipe fittings as described in claim 2, characterized in that, Perform mold stress resistance boundary analysis on the triaxial compressive stress distribution dataset and the billet flow characteristic dataset to generate the target triaxial compressive stress distribution characteristics and the target billet flow characteristics, including: Based on the triaxial compressive stress distribution dataset, a mold resistance loss maximization analysis is performed to generate the target triaxial compressive stress distribution characteristics. Based on the billet flow characteristic dataset, a die resistance loss maximization analysis is performed to generate the target billet flow characteristics.

5. The production verification method for a cold extrusion die for metal pipe fittings as described in claim 4, characterized in that, Based on the aforementioned triaxial compressive stress distribution dataset, a mold resistance loss maximization analysis is performed to generate the target triaxial compressive stress distribution characteristics, including: Distributed mold stress resistance index identification is performed on the triaxial compressive stress distribution dataset to generate a distributed mold resistance index set; Based on the mold distributed adversarial index set, the triaxial compressive stress distribution dataset is mapped to maximize the index, thereby generating the target triaxial compressive stress distribution features.

6. The production verification method for a cold extrusion die for metal pipe fittings as described in claim 1, characterized in that, Combining the target triaxial compressive stress distribution characteristics, the target billet flow characteristics, and the first mold performance index set, a mold stress resistance analysis is performed to generate a first predicted service life, including: Based on the target triaxial compressive stress distribution characteristics, the target billet flow characteristics and the first mold performance index set, continuous mold loss analysis is performed to generate a loss index time series. Construct a preset loss index threshold, wherein the preset loss index threshold is the loss index when the mold quality is unqualified; By combining the preset loss index threshold and the loss index timing, a service duration that satisfies the preset loss index threshold is generated, which is used as the first predicted service duration.

7. The production verification method for a cold extrusion die for metal pipe fittings as described in claim 6, characterized in that, Based on the target triaxial compressive stress distribution characteristics, the target billet flow characteristics, and the first mold performance index set, continuous mold loss analysis is performed to generate a time series of loss indices, including: Define mold wear indicators; A continuous mold loss identification module is constructed based on the mold loss index. The continuous mold loss identification module performs continuous mold loss analysis on the target triaxial compressive stress distribution characteristics, the target billet flow characteristics, and the first mold performance index set, and outputs the time series of the loss index.

8. The production verification method for a cold extrusion die for metal pipe fittings as described in claim 1, characterized in that, Based on the first predicted service life, the quality monitoring and verification of the first extrusion die includes: Obtain the preset service life of the first extrusion die; Determine whether the first predicted service life meets the preset service life; If yes, the quality monitoring and verification has passed; otherwise, the quality monitoring and verification has failed.

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