Method and system for size optimization of extrusion dies for copper bar production

Abstract

The application provides an extrusion die size optimization method and system for copper bar production, and relates to the technical field of copper material manufacturing. The method comprises the following steps: taking target micro-texture and mechanical property parameters as reverse design input, establishing a reverse mapping model, establishing a reverse correlation model, fusing strain energy uniformity indexes and energy dissipation uniformity indexes, generating a comprehensive uniformity optimization target, driving topology optimization, and iteratively solving a three-dimensional geometric configuration of a die internal flow channel and a material component gradient distribution that can achieve the comprehensive uniformity optimization target under the constraints of die structure and manufacturability, and outputting a three-dimensional die flow channel model. The technical problems that the copper bar quality is unstable and the product performance is difficult to reach the expected standard due to the inability to accurately correlate performance and die size in the prior art are solved. The technical effect of accurately optimizing the extrusion die size through micro-texture and mechanical property reverse mapping and improving the quality of copper bar products is achieved.

Description

Technical Field

[0001] This invention relates to the field of copper manufacturing technology, and more specifically to a method and system for optimizing the size of extrusion dies used in copper busbar production. Background Technology

[0002] Copper busbars are copper conductors with a wide cross-section and thin thickness, widely used in the power and electronics industries for current transmission and electrical connections. Their performance directly affects the conductivity and reliability of the system. In actual production, copper busbars are usually manufactured through a hot extrusion process, in which copper billets are extruded into shape at high temperatures through a die, and then drawn or cooled to obtain the required dimensions and mechanical properties. The final performance of copper busbars depends not only on the chemical composition and heat treatment state of the copper material itself, but also on the size design of the extrusion die, the geometry of the flow channels, and the material distribution.

[0003] In existing technologies, mold size design mainly relies on experience or single geometric optimization, often focusing only on the cross-sectional dimensions of the copper busbar or local stress distribution, while lacking comprehensive consideration of the microstructure and macroscopic mechanical properties of the copper busbar. This leads to uneven energy dissipation and strain distribution in the internal flow channels of the mold, and excessive local deformation or stress concentration of the copper busbar during extrusion, resulting in problems such as uneven grain size, fluctuating mechanical properties, and unstable electrical conductivity.

[0004] Existing technologies suffer from technical problems such as the inability to accurately correlate performance with mold dimensions, resulting in unstable copper busbar quality and product performance failing to meet expected standards. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for optimizing the size of extrusion dies for copper busbar production, in order to solve the technical problem that the existing technology cannot accurately correlate performance with die size, resulting in unstable copper busbar quality and product performance that fails to meet expected standards.

[0006] In view of the above problems, this application provides a method and system for optimizing the size of extrusion dies for copper busbar production.

[0007] The first aspect of this application provides a method for optimizing the dimensions of extrusion dies for copper busbar production. This method includes: obtaining the target microstructure and mechanical property parameters of the finished copper busbar to be produced, and using the target microstructure and mechanical property parameters as inputs for reverse design; establishing a reverse mapping model from the target microstructure to the extrusion deformation history based on crystal plasticity theory, quantifying the target microstructure as a strain energy uniformity index of the die exit section; establishing a reverse correlation model from the mechanical property parameters to the extrusion pre-strain, mapping the mechanical property parameters as an energy dissipation uniformity index of the internal flow channel of the die; fusing the strain energy uniformity index and the energy dissipation uniformity index to generate a comprehensive uniformity optimization target for the flow channel design; driving topology optimization with the comprehensive uniformity optimization target, and iteratively solving for the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the die that can achieve the comprehensive uniformity optimization target under die structural constraints and manufacturability constraints, outputting a three-dimensional die flow channel model.

[0008] Optionally, the strain energy uniformity index can quantitatively describe the strain distribution of the blank cross section in the width and thickness directions.

[0009] Optionally, the target microtexture, used as input for reverse design, is analyzed to extract its dominant grain orientation type and orientation distribution function; based on crystal plasticity theory, a reverse mapping model is established that can solve the required extrusion deformation history from the target microtexture; using the reverse mapping model, the dominant grain orientation type and orientation distribution function are reverse mapped to the target strain tensor on the preset discrete grid nodes of the die exit section; based on the target strain tensor, the strain energy density of each grid node is calculated, and the distribution variance of the strain energy density of all discrete points on the entire die exit section is statistically analyzed as an index of strain energy uniformity.

[0010] Optionally, the mechanical performance parameters used as input for reverse design are analyzed to determine the target yield strength and elongation. Based on the constitutive equation and performance evolution law of the material, a reverse correlation model is established that can deduce the required mean equivalent strain and distribution of the copper busbar blank from the target mechanical performance parameters. The target yield strength and elongation are input into the reverse correlation model to solve for the mean equivalent strain and its allowable fluctuation range that the blank should have after extrusion to meet the performance requirements. The mean equivalent strain and its allowable fluctuation range are converted into the target values ​​of plastic deformation work and friction work that should be achieved at each point in the flow channel space inside the mold. The spatial distribution uniformity of the target values ​​of plastic deformation work and friction work is calculated on the key cross-section of the flow channel inside the mold as an indicator of energy dissipation uniformity.

[0011] Optionally, weighting coefficients are assigned to the strain energy uniformity index and the energy dissipation uniformity index, respectively; based on the weighting coefficients, a weighted fit is performed on the strain energy uniformity index and the energy dissipation uniformity index to form a basic optimization objective function; in the basic optimization objective function, a penalty term is added for performance decoupling when uniformity is achieved in the width and thickness directions of the mold flow channel; the penalty term is embedded in the basic optimization objective function and defined as a comprehensive uniformity optimization objective to drive subsequent topology optimization.

[0012] Optionally, the internal flow channel space of the mold is defined as a topology optimization design domain and discretized using finite element methods. A pseudo-density variable field and a material composition identifier variable field are initialized for each element. The overall uniformity optimization objective is used as the objective function for topology optimization, and the maximum equivalent stress, minimum manufacturing wall thickness, and material gradient continuity of the mold are used as the mold structural constraints and manufacturability constraints. In each optimization iteration, a thermo-mechanically coupled extrusion process finite element analysis is performed to calculate the overall uniformity optimization objective value under the current element pseudo-density variable and material composition identifier variable. The sensitivity of the overall uniformity optimization objective value to the element pseudo-density variable and the material composition identifier variable is calculated. Based on the sensitivity, the mold structural constraints, and the manufacturability constraints, the pseudo-density variable and material composition identifier variable of all elements are updated synchronously. Iterative calculations are repeated until the overall uniformity optimization objective value converges, obtaining the optimal element pseudo-density distribution and material composition identifier distribution, i.e., the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the mold. The optimal element pseudo-density distribution and material composition identifier distribution are geometrically reconstructed and smoothed to output a three-dimensional mold flow channel model containing the gradient material distribution.

[0013] Optionally, the topology optimization process simultaneously solves two design variable fields: by updating the unit pseudo-density variable field, the spatial distribution of the flow channel entity and cavity is determined, and the three-dimensional geometric configuration of the flow channel inside the mold is generated; by updating the material component identification variable field, the spatial volume fraction distribution of different functional material phases in the flow channel entity region is determined, and the material component gradient distribution is generated.

[0014] Optionally, along the width direction of the mold flow channel, the thermal conductivity of the material decreases from the center to both sides; along the thickness direction of the mold flow channel, the hardness and wear resistance of the material increase from the neutral layer to the mold wall surface; wherein, the gradient distribution is achieved by controlling the local volume fraction of the reinforcing phase particles through the material component identification variable field.

[0015] Optionally, after outputting the three-dimensional mold flow channel model, it is imported into a high-fidelity process simulation system for full-process digital twin simulation, and the model parameters are corrected according to the simulation results until the digital twin verification is passed.

[0016] A second aspect of this application provides a system for optimizing the dimensions of extrusion dies for copper busbar production. This system includes: a data acquisition module for acquiring the target microstructure and mechanical property parameters of the finished copper busbar to be produced, and using the target microstructure and mechanical property parameters as input for reverse design; a reverse mapping model establishment module for establishing a reverse mapping model from the target microstructure to the extrusion deformation history based on crystal plasticity theory, quantifying the target microstructure as a strain energy uniformity index at the die exit section; and a reverse correlation model establishment module for... A reverse correlation model is established from mechanical performance parameters to extrusion pre-strain, mapping the mechanical performance parameters to an energy dissipation uniformity index for the internal flow channel of the mold. An index fusion module is used to fuse the strain energy uniformity index and the energy dissipation uniformity index to generate a comprehensive uniformity optimization objective for the flow channel design. An iterative solution module is used to drive topology optimization with the comprehensive uniformity optimization objective, and under mold structure constraints and manufacturability constraints, iteratively solves for the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the mold that can achieve the comprehensive uniformity optimization objective, and outputs a three-dimensional mold flow channel model.

[0017] One or more technical solutions provided in this application have at least the following technical effects or advantages: The method provided in this application obtains the target microstructure and mechanical property parameters of the finished copper busbar to be produced, and uses these parameters as inputs for reverse design. Based on crystal plasticity theory, a reverse mapping model from the target microstructure to the extrusion deformation history is established, quantifying the target microstructure as a strain energy uniformity index at the die exit section. A reverse correlation model from the mechanical property parameters to the extrusion pre-strain is established, mapping the mechanical property parameters as an energy dissipation uniformity index of the internal flow channel of the die. The strain energy uniformity index and the energy dissipation uniformity index are fused to generate a comprehensive uniformity optimization target for the flow channel design. Using this comprehensive uniformity optimization target to drive topology optimization, under die structure constraints and manufacturability constraints, the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the die that achieve the comprehensive uniformity optimization target are iteratively solved, outputting a three-dimensional die flow channel model. This achieves the technical effect of accurately optimizing the extrusion die size and improving the quality of copper busbar products through the reverse mapping of microstructure and mechanical properties.

[0018] 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

[0019] 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.

[0020] Figure 1 This is a flowchart illustrating the method for optimizing the size of extrusion dies used in copper busbar production provided in this application.

[0021] Figure 2 This is a schematic diagram of the extrusion die size optimization system for copper busbar production provided in this application.

[0022] Figure labeling: Data acquisition module 11, inverse mapping model establishment module 12, inverse correlation model establishment module 13, index fusion module 14, iterative solution module 15. Detailed Implementation

[0023] This application provides a method and system for optimizing the dimensions of extrusion dies in copper busbar production. It addresses the technical problem in existing technologies where the inability to accurately correlate performance with die dimensions leads to unstable copper busbar quality and product performance failing to meet expected standards. The method achieves the technical effect of precisely optimizing extrusion die dimensions through inverse mapping of microstructure and mechanical properties, thereby improving the quality of copper busbar products.

[0024] The technical solutions of the present invention 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 the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0025] Example 1, as Figure 1As shown, this application provides a method for optimizing the size of extrusion dies used in copper busbar production. The method includes: Obtain the target microstructure and mechanical property parameters of the finished copper busbar to be produced, and use the target microstructure and mechanical property parameters as input for reverse design.

[0026] Specifically, samples of finished copper busbars produced in the past that are similar to the copper busbars to be produced in terms of composition and process route (such as smelting, casting, and rolling) are collected. These samples undergo fine grinding and polishing to achieve a mirror-like surface. The processed samples are then placed in a scanning electron microscope, and the sample surface is scanned using an electron backscatter diffraction (EBSD) attachment. EBSD technology analyzes the diffraction pattern of backscattered electrons excited by the electron beam on the sample surface, which can accurately determine the orientation information of each grain, thereby obtaining the microstructure data of the copper busbar. Microstructure refers to the arrangement, orientation, and distribution of grains in a metallic material, including the dominant grain orientation type and orientation distribution function, reflecting the arrangement and orientation characteristics of the grains inside the copper busbar. By analyzing and summarizing the microstructure data of multiple similar product samples, representative microstructure features are extracted as a reference for the target microstructure of the finished copper busbar to be produced. At the same time, based on the product design and mechanical performance requirements of similar copper busbar products, the mechanical performance parameters of the finished copper busbar to be produced are obtained, such as key parameters like yield strength, elongation, and hardness. Yield strength reflects the magnitude of stress when the copper busbar begins to undergo plastic deformation, and elongation reflects the plastic deformation capacity of the copper busbar.

[0027] The acquired target microstructure and mechanical property parameters are used as inputs for reverse design. Reverse design refers to starting from predetermined product performance, such as microstructure and mechanical properties, and working backward to deduce the required production process and mold design.

[0028] For example, by analyzing the design of the finished copper busbar to be produced and similar copper busbar products, the dominant grain orientation type of the finished copper busbar to be produced is determined to be... <100> The orientation is such that 90% of the grains exhibit this orientation, the yield strength of the copper busbar is 220 MPa, the elongation is 15%, and the hardness is 80.4 HV.

[0029] By accurately acquiring the target microstructure and mechanical property parameters, it is possible to ensure that the mold design process can match the performance requirements required in the production process to the greatest extent, thereby ensuring that the finished copper busbars meet the expected quality standards.

[0030] Based on the theory of crystal plasticity, an inverse mapping model from the target microtexture to the history of extrusion deformation is established, and the target microtexture is quantified as the strain energy uniformity index of the die exit section.

[0031] Furthermore, the strain energy uniformity index can quantitatively describe the strain distribution state of the blank cross section in the width and thickness directions.

[0032] Furthermore, an inverse mapping model from the target microtexture to the extrusion deformation history is established, quantifying the target microtexture as a strain energy uniformity index of the die exit section. This includes: analyzing the target microtexture as the input for inverse design and extracting its dominant grain orientation type and orientation distribution function; based on crystal plasticity theory, establishing an inverse mapping model that can solve the required extrusion deformation history from the target microtexture; using the inverse mapping model, mapping the dominant grain orientation type and orientation distribution function inversely to the target strain tensor on the preset discrete grid nodes of the die exit section; based on the target strain tensor, calculating the strain energy density of each grid node, and statistically analyzing the distribution variance of the strain energy density at all discrete points on the entire die exit section as a strain energy uniformity index.

[0033] Specifically, the dominant grain orientation type and orientation distribution function are extracted from the microtexture data and input according to the format required by finite element analysis software such as ABAQUS. A numerical model is established based on crystal plasticity theory to inversely solve the required extrusion deformation history from the target microtexture. Crystal plasticity theory is a theoretical model for analyzing the deformation behavior of metallic materials under external forces, considering the anisotropic characteristics of crystals and decomposing the material deformation into microscopic mechanisms such as intragranular slip and grain boundary deformation. When establishing the numerical model, the dominant grain orientation type and orientation distribution function are used as input conditions. Combined with crystal plasticity theory, the boundary conditions and loading methods in the numerical simulation of the material extrusion process are clearly defined, including the die inlet velocity, outlet constraints, and friction conditions.

[0034] Combining numerical models, an inverse mapping model is established using finite element analysis software. This model simulates the deformation behavior of the material during extrusion, allowing for the reverse derivation of the strain distribution history of the copper busbar at various locations during extrusion from the target microtexture. Discrete meshes are generated at the die exit section based on actual needs and computational capabilities. The meshes are further refined for the flow channels and regions with drastic strain changes, taking into account the shape and size characteristics of the copper busbar. After mesh generation, the dominant grain orientation type and its orientation distribution function in the target microtexture are input into the inverse mapping model. In this model, each grain is treated as a unit with anisotropic plasticity. Crystal plasticity theory describes the deformation behavior of grains along different slip systems under external forces. Through numerical simulation and iterative calculations, the required local strain state for each mesh node under the target microtexture conditions is solved inversely, ensuring that the grain arrangement and overall texture satisfy the orientation distribution function. This yields the target strain tensor, which describes the deformation state of the material at each discrete point at the die exit section, including normal and shear strain components, reflecting the degree and direction of deformation at that point.

[0035] Based on the energy formula in crystal plasticity theory and the target strain tensor of each grid node, the corresponding strain energy density is calculated. Strain energy density is the internal energy generated per unit volume due to deformation, calculated as W = (1 / 2)σ:ε, where σ is the stress tensor and ε is the target strain tensor. After obtaining the strain energy density of all grid nodes, the variance of the strain energy density distribution at all discrete points on the entire die exit section is statistically analyzed. This variance serves as an index for strain energy uniformity. A smaller variance indicates a more uniform strain energy distribution, meaning a more uniform strain distribution in the width and thickness directions of the blank cross-section. This strain energy uniformity index quantitatively describes the strain distribution state of the blank cross-section in the width and thickness directions, reflecting the strain uniformity of the material throughout the extrusion process.

[0036] For example, the material is highly conductive pure copper (Cu ≥ 99.9%), with a target microstructure where the dominant grain orientation is {111} along the extrusion direction, and the ODF exhibits a unimodal distribution with a full width at half maximum (FWHM) of approximately 30°. First, the grain orientation type and ODF are input in a standardized manner according to the requirements of the finite element method (FEM) software, and an inverse mapping model is established in ABAQUS. This model, based on crystal plasticity theory, decomposes the overall plastic deformation of the material into two parts: intragranular slip and grain boundary deformation. It considers the anisotropic plasticity of each grain and defines the critical shear stress, anisotropic shear modulus, and hardening parameters for each slip system. The stress-strain relationship describes the plastic deformation behavior of grains along different slip systems under external force. In the model, the deformation of each grain unit follows the constitutive equation in crystal plasticity theory. The total strain of the grain is decomposed into elastic strain and plastic strain. The plastic strain accumulates along each slip system, while also considering the grain boundary strain generated by the mutual constraints between grains.

[0037] When establishing the finite element model, the mold exit cross-section dimensions were set to a width of 100 mm and a thickness of 20 mm. A 50×10 mesh was created along both the width and thickness directions, with the mesh size locally refined to 100×20 in the flow channel region and areas with large strain gradients. Each mesh node corresponds to a grain element. The target grain orientation {111} and ODF were input into the ABAQUS user subroutine UMAT. Combined with crystal plasticity parameters, the mold inlet velocity was set to 10 mm / s, and the exit surface was fixed. Iterative numerical simulation was used to solve for the local strain state of each mesh node, ensuring that the simulated grain arrangement matched the target ODF. During the iteration process, the slip strain component of each grain element at each incremental time step was calculated based on crystal plasticity theory. The total plastic strain was obtained by accumulating the plastic strain of each slip system. Simultaneously, the stress tensor was calculated based on the material's elastic response to ensure that the grain orientation distribution matched the preset ODF.

[0038] According to the energy formula in crystal plasticity theory, the strain energy density of each grid node is calculated from the inner product of the stress tensor and the target strain tensor of that node. That is, the strain energy density is equal to the internal energy generated per unit volume due to deformation. The calculation formula is: strain energy density W = (1 / 2)σ:ε, where σ is the stress tensor and ε is the target strain tensor. After obtaining the strain energy density of all grid nodes, the variance of the strain energy density of all nodes on the entire mold exit section is statistically analyzed, and the strain energy uniformity index is obtained as 0.0023 MJ / m. 3 The small variance indicates that the strain distribution at the die exit section is uniform, which can be used to guide die design and process optimization, such as adjusting the flow channel shape, wall thickness distribution and extrusion speed, to achieve uniform plastic deformation of the material in the width and thickness directions and the formation of the target texture, thereby ensuring the uniformity of the microstructure and the consistency of the overall performance of the copper busbar during the extrusion process.

[0039] The specific solution process of the inverse mapping model is as follows: using the orientation distribution function ODF(g) of the target microtexture as a constraint, an optimization inversion problem is constructed: , where ε ij (x) represents the target strain tensor at position x on the mold exit section, ODF sim ODF is the texture distribution function obtained through forward simulation of crystal plasticity using the finite element method. target Let W be the target texture distribution function, N be the total number of discrete grid nodes, and W be the value of the target texture distribution function. kLet λ be the strain energy density of the k-th node, λ be the regularization weighting coefficient used to balance texture matching accuracy and strain energy uniformity, and Var(W) be the statistical variance of the strain energy density of all discrete grid nodes on the mold exit section. The optimization inversion problem is solved using the gradient descent method combined with the adjoint variable method. In each iteration, the texture evolution under the current strain field is calculated forward using the crystal plastic constitutive equation, and the sensitivity of the objective function to the strain tensor of each node is calculated through adjoint analysis. ODF / ε ij And update the target strain tensor of each node in the opposite direction of sensitivity.

[0040] By establishing an inverse mapping model, the extrusion deformation parameters required to achieve the desired copper busbar texture can be derived from the microstructure of the desired copper busbar. This provides a theoretical basis for mold design and extrusion process optimization. Obtaining the strain energy uniformity index provides a quantitative standard for evaluating whether the extrusion deformation is uniform, further improving the quality and performance stability of the copper busbar and ensuring that the produced copper busbar meets the design requirements.

[0041] A reverse correlation model is established from mechanical performance parameters to extrusion pre-strain, and the mechanical performance parameters are mapped to the energy dissipation uniformity index of the internal flow channel of the mold.

[0042] Furthermore, a reverse correlation model is established from mechanical performance parameters to extrusion pre-strain, mapping the mechanical performance parameters to an energy dissipation uniformity index for the internal flow channel of the die. This includes: analyzing the mechanical performance parameters as input for reverse design to determine the target yield strength and elongation; establishing a reverse correlation model based on the material's constitutive equation and performance evolution law, which can deduce the required mean and distribution of equivalent strain of the copper busbar blank from the target mechanical performance parameters; inputting the target yield strength and elongation into the reverse correlation model to solve for the mean equivalent strain and its allowable fluctuation range that the blank should possess after extrusion to meet the performance requirements; converting the mean equivalent strain and its allowable fluctuation range into target values ​​of plastic deformation work and friction work that should be achieved at each point in the internal flow channel space of the die; and calculating the spatial distribution uniformity of the target values ​​of plastic deformation work and friction work at key cross-sections of the internal flow channel of the die, as an energy dissipation uniformity index.

[0043] Specifically, the target mechanical performance parameters are analyzed to clarify the target yield strength and elongation of the finished copper busbar. A suitable constitutive equation for the copper material is selected, such as the Johnson-Cook constitutive model or a crystal plastic constitutive model, to describe the stress-strain relationship of the material. The constitutive equation maps strain, strain rate, and temperature to stress. For example, the Johnson-Cook constitutive model uses the formula... The stress σ of a material is expressed as plastic strain ε, strain rate The expression is a function of temperature T, where A is the yield strength, B is the hardening modulus, n is the strain hardening exponent, C is the strain rate sensitivity coefficient, and m is the thermal softening exponent. The reference strain rate is T0, where T is room temperature, and T is T m This is the melting point temperature.

[0044] The Johnson-Cook constitutive model can simultaneously consider strain hardening, strain rate sensitivity, and temperature effects. The crystal plastic constitutive model establishes grain-level strain-stress relationships by describing the critical shear stress and hardening behavior of grains along each slip system. The overall material stress is obtained from the collective grain response and can be used to reflect the anisotropy and micro-deformation characteristics of the material. Based on the constitutive equations, a theoretical foundation for the inverse correlation model is provided, enabling a mathematical connection between the target mechanical property parameters and the strain state during the billet extrusion process. On this basis, combined with the evolution laws of copper busbar performance, such as strain hardening, dynamic recovery, and grain refinement effects, an inverse correlation model is established. This model can deduce the required equivalent strain mean and distribution of the copper busbar billet from the target mechanical property parameters.

[0045] The key idea behind the inverse correlation model is to iteratively solve the constitutive equation in reverse, mapping the target mechanical performance parameters back to the mean equivalent strain and allowable fluctuation range required by the billet. Given the target yield strength and elongation of the finished copper busbar as input, the constitutive equation is solved iteratively to map the mechanical performance parameters back to the mean equivalent strain and allowable fluctuation range that the material should experience during extrusion. This ensures that the material performance reaches the target value after extrusion. Equivalent strain is a scalar describing the overall degree of plastic deformation of the material, used to quantify the amount of deformation experienced by the billet during extrusion. If the calculated performance deviates from the target value, the mean equivalent strain and fluctuation range are adjusted, and the calculation is repeated, iterating until the simulation results meet the tolerance requirements of the target performance.

[0046] According to the principle of energy conservation in plastic deformation, the energy absorbed by a material during plastic deformation is equal to the plastic strain energy within its volume. The equivalent strain mean and allowable fluctuation range obtained from the inverse solution are mapped onto the nodes of the discrete mesh of the mold flow channel. The stress tensor of each node is calculated using the constitutive equation, and then according to the formula... The target value of plastic deformation work for each node is obtained, where Wp is the target value of plastic deformation work, the integral symbol represents the integral over volume V, the entire mesh element or material volume, indicating that the total plastic work is accumulated from the local plastic strain contribution of each volume element, dεp is the plastic strain increment tensor, representing the irreversible plastic deformation produced by the material in a small time step or loading step, and : represents the double dot product of the tensor, that is, each stress component is multiplied by its corresponding plastic strain increment and then summed to obtain the plastic work increment per unit volume. Friction work is also considered. Where μ is the coefficient of friction and p is the contact pressure, the frictional work is the energy consumed by friction when the mold wall contacts the blank, calculated using the coefficient of friction and contact pressure, and distributed at key cross-sectional nodes. The spatial distribution uniformity of the target values ​​of plastic deformation work and frictional work is calculated at the key cross-sections of the mold's internal flow channels. This spatial distribution uniformity can be measured using the variance index in statistics. The smaller the variance, the more uniform the work distribution, indicating uniform energy dissipation at all points within the mold's flow channels. This is beneficial for maintaining consistent mechanical properties of the copper busbar during extrusion and avoiding localized over-plasticity or defects. Spatial distribution uniformity is used as an indicator of energy dissipation uniformity to quantify the degree of uniformity of energy dissipation within the mold's internal flow channels.

[0047] The specific solution process of the inverse correlation model is as follows: given the target yield strength and elongation, based on the Johnson-Cook constitutive model... Establish the parameter inversion equation: F(ε) eq )=σy(ε eq )-σy goal =0 and G(ε eq )=δ(ε eq )-δ goal =0, where the equivalent strain ε eq Let σy(ε) be the unknown quantity to be determined. eq ) and δ(ε eq σy represents the predicted yield strength and elongation calculated by the constitutive equation for a given equivalent strain value. goal δ represents the target yield strength. goal The target elongation is used. The Newton-Raphson method is used to iteratively solve the above equations. In each step, the current equivalent strain value is substituted into the constitutive equation to calculate the predicted yield strength and elongation. If the difference between the predicted value and the target value is greater than the convergence tolerance, the correction amount is calculated using the ratio of this difference to the derivative of the equation. The equivalent strain is updated and the next iteration begins, until the difference between the predicted value and the target value is less than the preset tolerance. The convergence tolerance refers to the allowable error limit for determining whether the iterative calculation has reached sufficient accuracy. For example, it is set to the absolute value of the difference between the predicted yield strength and the target value not exceeding 2 MPa, and the absolute value of the difference between the predicted elongation and the target value not exceeding 0.2%. The target value refers to the design performance requirement of the finished copper busbar to be produced, i.e., the target yield strength σy. goal =220MPa and target elongation δ goal =15%, the ε obtained after convergence eqThis refers to the mean equivalent strain of the extruded billet required to meet the target mechanical properties. For example, the target copper busbar has a yield strength of 220 MPa and an elongation of 15%. Through inverse correlation model calculations, the mean equivalent strain of the billet at the die exit section is calculated to be 0.35, with an allowable fluctuation range of ±0.05. Further converted into target values ​​for the plastic deformation work and friction work in the die's internal flow channel, simulation results show that the plastic deformation work in the central area of ​​the flow channel is 50 J / cm³, and the friction work near the wall is 10 J / cm³. By statistically analyzing the work distribution of each grid node, the variance is calculated to be 0.02 J² / cm³. 6 As an indicator of energy dissipation uniformity, it indicates that the energy distribution inside the flow channel is relatively uniform, which can ensure the consistency of the mechanical properties of the finished product.

[0048] By establishing a reverse correlation model, starting from the target mechanical performance parameters, the required extrusion pre-strain and the energy target values ​​of the internal flow channel of the die are derived. This provides clear targets and basis for subsequent die design and extrusion process optimization, enabling die design and process formulation to more effectively meet the mechanical performance requirements of the product and improve the quality of copper busbar products.

[0049] By integrating the strain energy uniformity index and the energy dissipation uniformity index, a comprehensive uniformity optimization target for the flow channel design is generated.

[0050] Furthermore, the integration of the strain energy uniformity index and the energy dissipation uniformity index to generate a comprehensive uniformity optimization objective for the flow channel design further includes: assigning weight coefficients to the strain energy uniformity index and the energy dissipation uniformity index respectively; performing a weighted fit on the strain energy uniformity index and the energy dissipation uniformity index based on the weight coefficients to form a basic optimization objective function; adding a penalty term to the basic optimization objective function to address performance decoupling in the width and thickness directions of the mold flow channel when uniformity is achieved; embedding the penalty term into the basic optimization objective function, defining it as a comprehensive uniformity optimization objective used to drive subsequent topology optimization.

[0051] Specifically, based on the influence of strain energy uniformity on the final mechanical properties of the copper busbar, uneven distribution of strain energy during the copper busbar forming process may lead to local stress concentration, thereby affecting key mechanical properties such as yield strength and elongation. The energy dissipation uniformity index reflects the spatial uniformity of plastic deformation work and friction work within the mold flow channel. Uneven energy dissipation can exacerbate local wear of the mold, reduce its service life, and also affect the forming quality of the copper busbar. Based on the importance of the strain energy uniformity index and the energy dissipation uniformity index, weighting coefficients are assigned to each index using expert evaluation and the Analytic Hierarchy Process (AHP) to balance the importance of microstructure and macroscopic mechanical properties in the optimization process. For example, three experts in materials mechanics, extrusion processes, and mold design are invited to score the indicators. An AHP-based judgment matrix is ​​constructed, comparing the indicators pairwise. For instance, if experts consider strain energy uniformity slightly more important than energy dissipation uniformity, a ratio of 3:1 can be assigned. The average of the three experts' scores yields the judgment matrix. The eigenvector normalization process of the judgment matrix yields a strain energy uniformity weighting coefficient of 0.75 and an energy dissipation uniformity weighting coefficient of 0.25. These weighting coefficients reflect that during the optimization process, microtexture uniformity has a significant impact on the final mechanical properties of the copper busbar, while energy dissipation uniformity contributes relatively less to mold life and process stability. This allows for reasonable weighting when constructing the basic optimization objective function, ensuring that the optimization results balance product performance and process reliability.

[0052] Then, based on the determined weighting coefficients, a weighted fit is performed on the strain energy uniformity index and the energy dissipation uniformity index to form the basic optimization objective function, i.e., basic optimization objective function = strain energy uniformity weighting coefficient × strain energy uniformity index + energy dissipation uniformity weighting coefficient + energy dissipation uniformity index. In actual mold flow channel design, performance decoupling may occur when achieving uniformity in the width and thickness directions of the mold flow channel. Performance decoupling means that the uniformity indices in the two directions are independent and have no synergistic effect, resulting in the inability to simultaneously optimize the performance in both directions. To avoid this situation, a penalty term is added to the basic optimization objective function to address performance decoupling in the width and thickness directions of the mold flow channel when achieving uniformity. The penalty term needs to consider the degree of difference between the uniformity indices in the two directions; the greater the difference, the greater the penalty. For example, the penalty term can be defined as the square of the difference between the uniformity indices in the two directions multiplied by a penalty coefficient, which can be set according to actual needs. Embedding the penalty term into the basic optimization objective function, the weighted basic optimization objective function is summed with the penalty term to obtain the comprehensive uniformity optimization objective for topology optimization. For example, we can introduce a penalty term P = α(Uw - Ut). 2Where Uw and Ut are the uniformity indices in the width and thickness directions of the flow channel, respectively, and α is the penalty coefficient. The penalty coefficient typically ranges from 10 to 100 and is adjusted based on the sensitivity of the uniformity difference in the width and thickness directions and the importance of the optimization objective, in order to balance the penalty intensity with the overall optimization convergence. Assume the width uniformity index is 0.02, the thickness uniformity index is 0.03, and the difference is 0.01. Let the penalty coefficient α = 50, then the penalty term P = 50 × (0.01). 2 =0.005. Adding this penalty term to the weighted basic optimization objective function yields the comprehensive uniformity optimization objective. This ensures that if the uniformity difference between the width and thickness directions is significant during optimization, the penalty term will increase, thereby guiding the optimization algorithm to simultaneously consider the uniformity in both directions and achieve collaborative optimization.

[0053] By integrating strain energy uniformity and energy dissipation uniformity indices and considering performance decoupling issues, the generated comprehensive uniformity optimization objective can more comprehensively and accurately reflect the performance requirements of the die flow channel design. It not only provides clear numerical optimization indices but also provides a directly usable objective function for topology optimization, enabling the die design to achieve the optimal geometric configuration while ensuring the consistency of material microstructure and mechanical properties. This improves the quality and efficiency of extrusion die design optimization, thereby enhancing the performance and quality of copper busbar products.

[0054] Driven by the comprehensive uniformity optimization objective, topology optimization is performed iteratively under mold structure constraints and manufacturability constraints to solve for the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the mold that can achieve the comprehensive uniformity optimization objective, and outputs a three-dimensional mold flow channel model.

[0055] Furthermore, driven by the comprehensive uniformity optimization objective, topology optimization is performed iteratively under mold structure constraints and manufacturability constraints to solve for the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the mold that can achieve the comprehensive uniformity optimization objective, outputting a three-dimensional mold flow channel model. This includes: defining the internal flow channel space of the mold as the topology optimization design domain and performing finite element discretization; initializing the element pseudo-density variable field and material composition identifier variable field for each element; using the comprehensive uniformity optimization objective as the objective function of topology optimization; and using the maximum equivalent stress of the mold, the minimum manufacturing wall thickness, and the material gradient continuity as the mold structure constraints and manufacturability constraints; in each optimization iteration, performing a thermo-mechanically coupled extrusion process finite element analysis, and calculating... Calculate the overall uniformity optimization target value under the current unit pseudo-density variable and material composition identification variable; calculate the sensitivity of the overall uniformity optimization target value to the unit pseudo-density variable and the material composition identification variable; based on the sensitivity, the mold structure constraints, and manufacturability constraints, synchronously update the pseudo-density variable and material composition identification variable of all units; repeat the iterative calculation until the overall uniformity optimization target value converges, obtaining the optimal unit pseudo-density distribution and material composition identification distribution, i.e., the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the mold; perform geometric reconstruction and smoothing on the optimal unit pseudo-density distribution and material composition identification distribution, and output a three-dimensional mold flow channel model containing gradient material distribution.

[0056] Specifically, the internal flow channel space of the mold is divided into a topology optimization design domain, serving as the basic spatial range for the entire optimization process. A three-dimensional element mesh is generated through finite element discretization, and two key variable fields are initialized for each element: the element pseudo-density variable field and the material composition identification variable field. The element pseudo-density variable characterizes the presence of material in the element; 0 indicates no material in the element, and 1 indicates that the element contains solid material. The material composition identification variable distinguishes the distribution of different material components within the element; different values ​​correspond to different material components, such as controlling the changes in thermal conductivity or hardness along the width and thickness directions.

[0057] The overall uniformity optimization objective is used as the objective function for topology optimization. Constraints are added to ensure the design results satisfy both physical laws and manufacturability. These constraints include mold structure constraints and manufacturability constraints, such as the maximum equivalent stress constraint, the minimum manufacturing wall thickness constraint, and the material gradient continuity constraint. The maximum equivalent stress constraint ensures the mold will not fail due to excessive stress during operation, guaranteeing its structural safety. The minimum manufacturing wall thickness constraint is based on actual manufacturing process limitations, ensuring the mold can be manufactured smoothly. The material gradient continuity constraint ensures a smooth transition of material components within the mold, avoiding performance discontinuities or stress concentrations. For example, the maximum equivalent stress constraint is set to no more than 350 MPa to ensure the mold will not fail due to excessive stress during extrusion, guaranteeing structural safety. The minimum manufacturing wall thickness constraint is set to 3 mm, considering actual processing technology and precision, ensuring sufficient flow channel wall thickness for manufacturing and preventing cracking. The material gradient continuity constraint limits the rate of change of material components between adjacent units to no more than 0.1 / unit length, ensuring a smooth transition of material components along the width and thickness directions, avoiding stress concentrations or excessive local plasticity caused by abrupt changes in local properties.

[0058] In each optimization iteration, a thermo-mechanical coupled finite element analysis is performed. Specifically, a finite element model is established using the pseudo-density variable and material composition identifier variable of the current element, and a thermo-mechanical coupled analysis is conducted on the die. The thermo-mechanical coupled analysis considers the interaction between the temperature field and stress field of the die during the extrusion process. For example, the elasto-plastic properties of the material change at high temperatures, and local temperature rise occurs in stress concentration areas, thus affecting strain distribution and energy dissipation. Through finite element solution, the local strain, stress, plastic deformation work, and friction work of each element are obtained. Combining the aforementioned strain energy uniformity index and energy dissipation uniformity index, a weighted calculation is performed to obtain the comprehensive uniformity optimization target value under the current iteration, which is used to evaluate the merits of the current design scheme.

[0059] The sensitivity of the overall uniformity optimization objective value to the pseudo-density variable and material composition identifier variable is calculated. Sensitivity reflects how sensitive the objective function is to changes in design variables. By slightly perturbing the pseudo-density variable or material composition identifier variable of each element, the finite element thermo-mechanical coupling analysis is re-executed, and the overall uniformity optimization objective value after perturbation is calculated. The gradient of the objective function with respect to each element variable is obtained using the finite difference method or analytical sensitivity formula, which serves as a sensitivity index. By calculating the sensitivity, it is determined which regions are most sensitive to changes in the objective function.

[0060] Based on sensitivity, mold structure constraints, and manufacturability constraints, optimization algorithms, such as SIMP, MMA, or OC methods, are employed to simultaneously update the pseudo-density variables and material composition identifier variables of all elements. These algorithms, while satisfying the constraints, gradually adjust the variable values, continuously reducing the objective function value. This iterative calculation process is repeated until the overall uniformity optimization objective value converges. Convergence means that the objective function value no longer changes significantly with increasing iterations. The resulting element pseudo-density distribution and material composition identifier distribution represent the optimal solutions, corresponding to the three-dimensional geometric configuration of the internal flow channels and the gradient distribution of the material composition, respectively.

[0061] For example, the SIMP method can be used to optimize the internal flow channels of a mold. First, the pseudo-density variable of each finite element element is initialized to an intermediate value, such as 0.5, indicating a material presence of 50%. The SIMP method achieves the transition from continuous material distribution to binary material distribution by defining the relationship between the effective material stiffness and pseudo-density of the element. In each iteration, a finite element model is constructed using the current pseudo-density distribution and material composition identifier variables. A thermo-mechanical coupling analysis is performed to calculate the stress, strain, and corresponding comprehensive homogeneity optimization target value for each element. Then, the sensitivity of the objective function to the pseudo-density variable and material composition identifier variable of each element is calculated. That is, by slightly perturbing the pseudo-density variable and material composition identifier variable of each element, the finite element thermo-mechanical coupling analysis is re-executed, and the change in the comprehensive homogeneity optimization target value after the perturbation is calculated. This yields the gradient of the objective function with respect to each element variable, reflecting the contribution of each element to the optimization objective. Based on the sensitivity information, the OC update method is used to adjust the pseudo-density variable and the material composition identifier variable. The above iterative calculation process is repeated, and the pseudo-density variable and the material composition identifier variable gradually converge to the optimal value. When the change in the comprehensive uniformity optimization objective function is less than a preset threshold, such as 1×10, the result is achieved. -4 The iteration stops when the time is right. Finally, the optimal pseudo-density distribution and material composition identifier distribution of each unit are obtained. Units with pseudo-density greater than a threshold, such as 0.5, are reconstructed as solid materials, and the material composition distribution is smoothed to generate a three-dimensional mold flow channel geometry and gradient material distribution that meets the requirements of overall uniformity.

[0062] After optimization, the pseudo-density distribution of discrete elements and the material composition identifier distribution are geometrically reconstructed and smoothed. The goal of geometric reconstruction is to transform the pseudo-density distribution of discrete elements into a continuous three-dimensional flow channel geometry while maintaining the continuity of the material gradient. Specifically, elements with pseudo-density greater than a threshold are defined as solids, and elements with pseudo-density less than the threshold are defined as cavities. The threshold is usually determined based on the physical meaning of the pseudo-density variable in topology optimization, typically ranging from 0.4 to 0.6, with 0.5 being a commonly used value. The threshold can be fine-tuned by combining volume fraction constraints and connectivity checks to ensure that the generated flow channel satisfies both the design volume ratio and maintains continuity. Using Marching Cubes or Dual Contouring algorithms, isosurfaces are generated for the pseudo-density field. By comparing the relationship between the node pseudo-density and the threshold on a voxel-by-voxel basis in the three-dimensional mesh, the intersection coordinates are calculated using linear interpolation at locations where the density of adjacent nodes crosses the threshold, and triangular patches are generated within the voxels, thus forming a continuous flow channel boundary surface and obtaining a smooth three-dimensional flow channel geometric profile.

[0063] Simultaneously, the material component identifier variable field is smoothed using filtering or interpolation methods. For example, Gaussian filtering is applied within the cell neighborhood, and a weighted average is performed on each cell based on its neighborhood distance weight, thereby eliminating local abrupt changes and generating a continuous material gradient distribution. This ensures that thermal conductivity, hardness, or wear resistance changes smoothly in the channel width and thickness directions, avoiding stress concentration or abrupt changes in local performance. Through this processing, a three-dimensional mold channel model containing complete channel geometry and gradient material distribution is formed.

[0064] For example, the internal flow channel design domain of the mold was discretized into 50×20×10 finite element elements, totaling 10,000 hexahedral elements. Initially, each element was randomly assigned a pseudo-density variable value, ranging from 0.2 to 0.8, and the material composition identifier variable was set to 0 or 1. The initial value of the overall uniformity optimization objective was 0.85. In the first iteration, a thermo-mechanically coupled extrusion process finite element analysis was performed, and the current overall uniformity optimization objective value was calculated to be 0.82. Sensitivity calculations revealed that the pseudo-density variable and material composition identifier variable of some elements had a significant impact on the objective function value. Based on sensitivity and constraints, the maximum equivalent stress of the mold was limited to 500 MPa, the minimum manufacturing wall thickness was 2 mm, and the material gradient continuity required that the difference in material composition between adjacent elements not exceed 0.3. The moving asymptote method was used to update the pseudo-density variable and material composition identifier variable of all elements. After 10 iterations, the overall uniformity optimization objective value decreased to 0.75, but had not yet converged. Continuing the iteration, on the 25th iteration, the overall uniformity optimization objective value converged to 0.72. The optimal element pseudo-density distribution obtained at this point shows that the element pseudo-density in the central region of the flow channel is close to 1, indicating solid material, while the element pseudo-density in the edge region is close to 0, indicating no material. The optimal material composition identifier distribution shows that the flow channel inlet contains one material composition, the outlet contains another, and the material composition in the middle region exhibits a gradient transition. After geometric reconstruction and smoothing of the optimal element pseudo-density distribution and material composition identifier distribution, a 3D mold flow channel model containing a gradient material distribution is output. This 3D mold flow channel model satisfies the overall uniformity optimization objective, while also satisfying mold structural constraints and manufacturability constraints.

[0065] By combining the comprehensive uniformity optimization objective with element pseudo-density and material composition identification, and through iterative optimization and finite element analysis, the optimal flow channel geometry and material composition gradient distribution are obtained under the premise of ensuring mold structural safety and manufacturability. A three-dimensional mold flow channel model is output, realizing the coordinated optimization of micro-texture and macro-mechanical properties. This provides a precise design basis for mold manufacturing, ensuring that the mold size design is not only theoretically optimal, but also feasible in manufacturing and use, thereby improving the quality and stability of copper busbar production.

[0066] Furthermore, the topology optimization process simultaneously solves two design variable fields: by updating the unit pseudo-density variable field, the spatial distribution of the flow channel entity and cavity is determined, and the three-dimensional geometric configuration of the flow channel inside the mold is generated; by updating the material component identification variable field, the spatial volume fraction distribution of different functional material phases in the flow channel entity region is determined, and the material component gradient distribution is generated.

[0067] Furthermore, the material composition gradient distribution configuration includes: along the width direction of the mold flow channel, the thermal conductivity of the material decreases from the center to both sides; along the thickness direction of the mold flow channel, the hardness and wear resistance of the material increase from the neutral layer to the mold wall surface; wherein, the gradient distribution is achieved by controlling the local volume fraction of the reinforcing phase particles through the material composition identifier variable field.

[0068] Specifically, in the topology optimization solution process, two design variable fields are solved simultaneously: the element pseudo-density variable field and the material composition identification variable field. The element pseudo-density variable field represents the solidity of each finite element in space, typically ranging from 0 to 1. A pseudo-density variable field close to 1 indicates that the element is a solid material region, while a value close to 0 indicates that the element is a cavity region. In the initial optimization stage, each element within the design domain is randomly assigned an initial pseudo-density value. During iterative optimization, based on the comprehensive uniformity optimization objective and the mold structure and manufacturability constraints, the element pseudo-density variable field is continuously updated using optimization algorithms, such as the Moving Asymptote Method (MMA). This gradually determines the spatial distribution of the flow channel solids and cavities, thus forming the three-dimensional geometric configuration of the flow channel inside the mold.

[0069] The material composition identifier variable field is used to represent the local volume fraction of different functional material phases within the flow channel solid region, such as the ratio of metal matrix to reinforcing phase particles. At the start of optimization, each element is also assigned an initial material composition identifier variable value. The material composition gradient distribution has specific configuration requirements: along the width direction of the mold flow channel, the thermal conductivity of the material decreases from the center to both sides; along the thickness direction of the mold flow channel, the hardness and wear resistance of the material increase from the neutral layer to the mold wall surface. This gradient distribution is achieved by controlling the local volume fraction of the reinforcing phase particles; the material composition identifier variable field is used to control the local volume fraction of the reinforcing phase particles.

[0070] During the iterative optimization process, based on the comprehensive uniformity optimization objective and the aforementioned gradient distribution requirements, the material composition identifier variable field is synchronously updated using an optimization algorithm. Specifically, in the channel width direction, the material composition identifier variable field value is made higher in the central region and gradually decreases towards both sides, thus causing the volume fraction of reinforcing phase particles in the material to gradually decrease from the center to the edge. According to the material property mixing principle, such as thermal conductivity, a gradient distribution of thermal conductivity is achieved, decreasing from the center to both sides. Simultaneously, in the channel thickness direction, the material composition identifier variable field gradually increases from the neutral layer towards the mold wall surface, causing the reinforcing phase particles to accumulate near the mold wall, thereby improving the material's hardness and wear resistance, as a higher content of reinforcing phase particles indicates higher hardness and wear resistance. By continuously adjusting the material composition identifier variable value of each unit, the spatial volume fraction distribution of different functional material phases within the channel solid region is determined, thus generating a material composition gradient distribution.

[0071] By simultaneously solving two design variable fields, we can not only accurately determine the geometry of the flow channel inside the mold, ensuring that the flow channel meets the process requirements of copper busbar extrusion molding, but also achieve a gradient distribution of material components, so that the mold has different properties in different parts. For example, the central part has good thermal conductivity, which is conducive to heat transfer, while the thermal conductivity on both sides is slightly worse, which can reduce heat loss. The mold wall surface has high hardness and good wear resistance. We can accurately optimize the extrusion mold size, improve the microstructure and mechanical properties of the copper busbar, and thus improve the forming quality of the copper busbar.

[0072] Furthermore, the method also includes: after outputting the three-dimensional mold flow channel model, importing it into a high-fidelity process simulation system to perform full-process digital twin simulation, and correcting the model parameters based on the simulation results until the digital twin verification is passed.

[0073] Specifically, after outputting the 3D mold runner model, it is imported into a high-fidelity process simulation system to initiate full-process digital twin simulation. The high-fidelity process simulation system is a software system capable of highly accurate simulation of actual production processes. Based on advanced numerical calculation methods and physical models, it meticulously simulates various physical phenomena during the copper busbar extrusion-drawing process within the mold runner, such as heat conduction, stress-strain, and material flow. Digital twins are digital models established in a virtual environment that are highly consistent with the real production system, used to predict production processes and product performance. In the copper busbar extrusion mold, the digital twin creation process includes: First, importing the output 3D mold runner model into the high-fidelity process simulation system and assigning material properties such as elastoplastic constitutive properties, thermal conductivity, friction coefficient, and initial temperature and boundary conditions. These parameters are pre-set based on actual production experience and process requirements. Second, creating a copper busbar blank model in the simulation software, defining its initial microstructure and material uniformity, and setting the simulation process, including all process steps such as mold heating, copper busbar entering the runner, extrusion, drawing, and cooling. Finally, high-precision numerical calculation methods, such as the finite element method and thermo-mechanical coupled solvers, are selected to enable the virtual model to simulate the stress-strain state, temperature distribution, and material flow of the copper busbar during the extrusion process. This allows for the acquisition of processing behavior and microstructure evolution highly consistent with actual production in the virtual environment. The digital twin is not merely a static model but can dynamically update material states and boundary conditions to predict the final product performance of the entire process. A full-process digital twin simulation of extrusion-drawing is performed. During the simulation, finite element analysis technology is used to accurately calculate the deformation, stress distribution, and temperature changes of the copper busbar within the die flow channel. Through continuous iterative calculations, the entire forming process of the copper busbar from the initial blank to the final product is simulated.

[0074] After the simulation, the final microstructure and mechanical properties of the virtual copper busbar are extracted from the simulation results. Microstructure refers to the arrangement and orientation of grains within the material, while mechanical properties include yield strength and elongation. The extracted final microstructure and mechanical properties are compared with the input target microstructure and mechanical property parameters. Deviation thresholds are pre-set based on design specifications, process controllability, and product performance requirements. For example, the microstructure index is set at ±2% to ±3% of the design target value to ensure that the grain uniformity is close to the expectation. The macroscopic mechanical property index is set at ±3% to ±5% based on the material's allowable error range to ensure that the final copper busbar meets strength and toughness requirements during tensile testing and use.

[0075] If the deviation of the comparison results is greater than or equal to the deviation threshold, it indicates that the current 3D mold runner model cannot meet the product's performance requirements. Based on the magnitude and direction of the deviation, model parameter corrections are generated. If local strain or energy dissipation is insufficient, pseudo-density can be added or the runner cross-sectional dimensions adjusted in the corresponding area to increase the solid proportion in that area, thereby enhancing the material's plastic deformation capacity. If local energy is too high, pseudo-density can be reduced or the runner curvature optimized to prevent excessive strain concentration. Based on the mechanical deviation, the material composition identifier variable is modified to increase or decrease the reinforcing phase particles in the required areas. For example, if the mold wall area has insufficient hardness, the material composition identifier variable can be increased to improve local wear resistance; if thermal conductivity is insufficient, the material composition identifier variable can be reduced in the central area to optimize heat conduction. The model parameter corrections are fed back to the inverse mapping model and / or inverse correlation model, and the entire process from model establishment and topology optimization to 3D mold runner output is re-executed. Through multiple iterations of simulation and parameter correction, until digital twin verification is passed, meaning the microstructure and mechanical properties of the simulation output are basically consistent with the design goals, ensuring that the mold runner design can meet the performance requirements of the copper busbar product. By using full-process digital twin simulation, the actual production process can be simulated in a virtual environment, allowing potential problems in mold flow channel design to be identified in advance, thus avoiding trial and error costs in actual production.

[0076] By correcting the model parameters based on the simulation results, the mold flow channel model can be continuously optimized, making the performance of the copper busbar products generated closer to the target value. This ensures that the final mold design meets the material structure requirements, has manufacturability and process reliability, and improves the accuracy of extrusion mold size optimization and production success rate.

[0077] Example 2, based on the same inventive concept as the method for optimizing the size of extrusion dies used in the aforementioned examples for copper busbar production, such as... Figure 2 As shown, this application provides a system for optimizing the size of extrusion dies for copper busbar production, wherein the system for optimizing the size of extrusion dies for copper busbar production includes: The data acquisition module 11 is used to acquire the target microstructure and mechanical performance parameters of the finished copper busbar to be produced, and use the target microstructure and mechanical performance parameters as input for reverse design; the reverse mapping model establishment module 12 is used to establish a reverse mapping model from the target microstructure to the extrusion deformation history based on the crystal plasticity theory, and quantify the target microstructure into the strain energy uniformity index of the die exit section; the reverse correlation model establishment module 13 is used to establish a reverse correlation model from the mechanical performance parameters to the extrusion pre-strain, and map the mechanical performance parameters into the energy dissipation uniformity index of the internal flow channel of the die; the index fusion module 14 is used to fuse the strain energy uniformity index and the energy dissipation uniformity index to generate a comprehensive uniformity optimization target for the flow channel design; the iterative solution module 15 is used to drive topology optimization with the comprehensive uniformity optimization target, and under the constraints of die structure and manufacturability, iteratively solve the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the die that can achieve the comprehensive uniformity optimization target, and output a three-dimensional die flow channel model.

[0078] Furthermore, the inverse mapping model establishment module 12 is also used to: the strain energy uniformity index can quantitatively describe the strain distribution state of the blank cross section in the width and thickness directions.

[0079] Furthermore, the inverse mapping model establishment module 12 is also used to: analyze the target microstructure as input for inverse design, and extract its dominant grain orientation type and orientation distribution function; based on crystal plasticity theory, establish an inverse mapping model that can solve the required extrusion deformation history from the target microstructure; using the inverse mapping model, inversely map the dominant grain orientation type and orientation distribution function to the target strain tensor on the preset discrete grid nodes of the die exit section; based on the target strain tensor, calculate the strain energy density of each grid node, and statistically analyze the distribution variance of the strain energy density of all discrete points on the entire die exit section as a strain energy uniformity index.

[0080] Furthermore, the reverse correlation model establishment module 13 is also used to: analyze the mechanical performance parameters as input for reverse design, and determine the target yield strength and elongation; based on the constitutive equation and performance evolution law of the material, establish a reverse correlation model that can deduce the required mean and distribution of equivalent strain of the copper busbar blank from the target mechanical performance parameters; input the target yield strength and elongation into the reverse correlation model, and solve for the mean equivalent strain and its allowable fluctuation range that the blank should have after extrusion to meet the performance requirements; convert the mean equivalent strain and its allowable fluctuation range into the target values ​​of plastic deformation work and friction work that should be achieved at each point in the flow channel space inside the mold; calculate the spatial distribution uniformity of the target values ​​of plastic deformation work and friction work on the key cross-section of the flow channel inside the mold, as an indicator of energy dissipation uniformity.

[0081] Furthermore, the index fusion module 14 is also used to: assign weight coefficients to the strain energy uniformity index and the energy dissipation uniformity index respectively; perform weighted fitting on the strain energy uniformity index and the energy dissipation uniformity index based on the weight coefficients to form a basic optimization objective function; add a penalty term to the basic optimization objective function to address performance decoupling when uniformity is achieved between the width and thickness directions of the mold flow channel; embed the penalty term into the basic optimization objective function and define it as a comprehensive uniformity optimization objective to drive subsequent topology optimization.

[0082] Furthermore, the iterative solution module 15 is also used to: define the internal flow channel space of the mold as a topology optimization design domain and perform finite element discretization; initialize the pseudo-density variable field and material composition identifier variable field for each element; use the comprehensive uniformity optimization objective as the objective function of topology optimization, and use the maximum equivalent stress of the mold, the minimum manufacturing wall thickness, and the material gradient continuity as the mold structural constraints and manufacturability constraints; in each optimization iteration, perform a thermo-mechanical coupled extrusion process finite element analysis, calculate the comprehensive uniformity optimization objective value under the current element pseudo-density variable and material composition identifier variable; calculate the comprehensive... The sensitivity of the uniformity optimization objective value to the unit pseudo-density variable and the material composition identification variable is determined. Based on the sensitivity, the mold structure constraints, and the manufacturability constraints, the pseudo-density variables and material composition identification variables of all units are updated synchronously. The calculation is repeated iteratively until the comprehensive uniformity optimization objective value converges, obtaining the optimal unit pseudo-density distribution and material composition identification distribution, which are the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the mold. The optimal unit pseudo-density distribution and material composition identification distribution are geometrically reconstructed and smoothed to output a three-dimensional mold flow channel model containing the gradient material distribution.

[0083] Furthermore, the iterative solution module 15 is also used to: simultaneously solve two design variable fields in the topology optimization process: by updating the unit pseudo-density variable field, determine the spatial distribution of the flow channel entity and cavity, and generate the three-dimensional geometric configuration of the flow channel inside the mold; by updating the material component identification variable field, determine the spatial volume fraction distribution of different functional material phases in the flow channel entity region, and generate the material component gradient distribution.

[0084] Furthermore, the iterative solution module 15 is also used to: along the width direction of the mold flow channel, the thermal conductivity of the material decreases from the center to both sides; along the thickness direction of the mold flow channel, the hardness and wear resistance of the material increase from the neutral layer to the mold wall surface; wherein, the gradient distribution is achieved by controlling the local volume fraction of the reinforcing phase particles through the material component identification variable field.

[0085] Furthermore, the system is also used to: after outputting the three-dimensional mold flow channel model, import it into a high-fidelity process simulation system to perform full-process digital twin simulation, and correct the model parameters according to the simulation results until the digital twin verification is passed.

[0086] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The method and specific examples for optimizing the extrusion die size for copper busbar production in the foregoing embodiment 1 are also applicable to the system for optimizing the extrusion die size for copper busbar production in this embodiment. Through the foregoing detailed description of the method for optimizing the extrusion die size for copper busbar production, those skilled in the art can clearly understand the system for optimizing the extrusion die size for copper busbar production in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0087] 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.

[0088] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for optimizing the dimensions of extrusion dies used in copper busbar production, characterized in that, The method includes: Obtain the target microstructure and mechanical property parameters of the finished copper busbar to be produced, and use the target microstructure and mechanical property parameters as input for reverse design; Based on the theory of crystal plasticity, an inverse mapping model from the target microtexture to the history of extrusion deformation is established, and the target microtexture is quantified as the strain energy uniformity index of the die exit section. A reverse correlation model from mechanical performance parameters to extrusion pre-strain is established, and the mechanical performance parameters are mapped to the energy dissipation uniformity index of the internal flow channel of the mold. By integrating the strain energy uniformity index and the energy dissipation uniformity index, a comprehensive uniformity optimization target for the flow channel design is generated; Driven by the comprehensive uniformity optimization objective, topology optimization is performed iteratively under mold structure constraints and manufacturability constraints to solve for the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the mold that can achieve the comprehensive uniformity optimization objective, and outputs a three-dimensional mold flow channel model.

2. The method for optimizing the size of extrusion dies for copper busbar production as described in claim 1, characterized in that, The strain energy uniformity index can quantitatively describe the strain distribution of the blank cross section in the width and thickness directions.

3. The method for optimizing the size of extrusion dies used in copper busbar production as described in claim 2, characterized in that, A reverse mapping model from the target microtexture to the extrusion deformation history is established, quantifying the target microtexture as a strain energy uniformity index at the die exit section, including: The target microtexture, which serves as input for reverse design, is analyzed to extract its dominant grain orientation type and orientation distribution function; Based on the theory of crystal plasticity, an inverse mapping model is established that can solve the required extrusion deformation history from the target microtexture. Using the inverse mapping model, the dominant grain orientation type and orientation distribution function are inversely mapped to the target strain tensor on the preset discrete grid nodes of the mold exit section; Based on the target strain tensor, the strain energy density of each grid node is calculated, and the distribution variance of the strain energy density at all discrete points on the entire mold exit section is statistically analyzed as an index of strain energy uniformity.

4. The method for optimizing the size of extrusion dies used in copper busbar production as described in claim 1, characterized in that, A reverse correlation model is established from mechanical performance parameters to extrusion pre-strain, mapping the mechanical performance parameters to an energy dissipation uniformity index of the internal flow channel of the die, including: The mechanical performance parameters, which are used as inputs for reverse design, are analyzed to determine the target yield strength and elongation. Based on the constitutive equation and performance evolution law of materials, an inverse correlation model is established that can deduce the mean and distribution of the equivalent strain required for copper busbar blanks from the target mechanical performance parameters. The target yield strength and elongation are input into the inverse correlation model to solve for the mean equivalent strain and its allowable fluctuation range that the billet should have after extrusion to meet the performance requirements. The equivalent strain mean and its allowable fluctuation range are converted into the target values ​​of plastic deformation work and friction work that should be achieved at each point in the flow channel space inside the mold. On the key cross-section of the flow channel inside the mold, the spatial distribution uniformity of the target values ​​of plastic deformation work and friction work is calculated as an indicator of energy dissipation uniformity.

5. The method for optimizing the size of extrusion dies used in copper busbar production as described in claim 1, characterized in that, By integrating the strain energy uniformity index and the energy dissipation uniformity index, a comprehensive uniformity optimization objective for the flow channel design is generated, which also includes: Weighting coefficients are assigned to the strain energy uniformity index and the energy dissipation uniformity index, respectively. Based on the weighting coefficients, the strain energy uniformity index and the energy dissipation uniformity index are weighted and fitted to form the basic optimization objective function; In the basic optimization objective function, a penalty term is added for performance decoupling that occurs when uniformity is achieved in the width and thickness directions of the mold flow channel; The penalty term is embedded into the basic optimization objective function and defined as a comprehensive uniformity optimization objective to drive subsequent topology optimization.

6. The method for optimizing the dimensions of extrusion dies used in copper busbar production as described in claim 1, characterized in that, Driven by the comprehensive uniformity optimization objective, topology optimization is performed iteratively under mold structure and manufacturability constraints to solve for the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the mold that achieves the comprehensive uniformity optimization objective. The resulting three-dimensional mold flow channel model includes: The internal flow channel space of the mold is defined as the topology optimization design domain and discretized by finite element method. The pseudo density variable field and material composition identification variable field of each element are initialized. The comprehensive uniformity optimization objective is used as the objective function of topology optimization, and the maximum equivalent stress of the mold, the minimum manufacturing wall thickness, and the material gradient continuity are used as the mold structure constraints and manufacturability constraints. In each optimization iteration, a thermo-mechanically coupled extrusion process finite element analysis is performed to calculate the comprehensive uniformity optimization target value under the current unit pseudo-density variable and material composition identifier variable; Calculate the sensitivity of the overall uniformity optimization target value to the unit pseudo-density variable and the material composition identification variable; Based on the sensitivity, mold structure constraints, and manufacturability constraints, the pseudo-density variables and material composition identification variables of all units are updated synchronously. Repeated iterative calculations are performed until the comprehensive uniformity optimization target value converges, resulting in the optimal unit pseudo-density distribution and material composition identifier distribution, i.e., the three-dimensional geometric configuration of the flow channel inside the mold and the gradient distribution of material composition. The optimal unit pseudo-density distribution and material component identification distribution are geometrically reconstructed and smoothed to output a three-dimensional mold flow channel model containing gradient material distribution.

7. The method for optimizing the dimensions of extrusion dies used in copper busbar production as described in claim 6, characterized in that, The topology optimization process simultaneously solves for two design variable fields: By updating the unit pseudo-density variable field, the spatial distribution of the flow channel entity and the cavity is determined, and the three-dimensional geometric configuration of the flow channel inside the mold is generated. By updating the material component identifier variable field, the spatial volume fraction distribution of different functional material phases within the flow channel solid region is determined, and the material component gradient distribution is generated.

8. The method for optimizing the dimensions of extrusion dies used in copper busbar production as described in claim 7, characterized in that, The material composition gradient distribution configuration includes: Along the width of the mold flow channel, the thermal conductivity of the material decreases from the center to both sides. Along the thickness direction of the mold flow channel, the hardness and wear resistance of the material increase in a gradient from the neutral layer to the mold wall surface. The gradient distribution is achieved by controlling the local volume fraction of the reinforcing phase particles through the material component identifier variable field.

9. The method for optimizing the dimensions of extrusion dies used in copper busbar production as described in claim 1, characterized in that, The method further includes: After outputting the three-dimensional mold flow channel model, it is imported into a high-fidelity process simulation system for full-process digital twin simulation. The model parameters are then corrected based on the simulation results until the digital twin verification is successful.

10. A system for optimizing the dimensions of extrusion dies used in copper busbar production, characterized in that, The step of implementing the method for optimizing the size of extrusion dies for copper busbar production according to any one of claims 1 to 9, wherein the system for optimizing the size of extrusion dies for copper busbar production comprises: The data acquisition module is used to acquire the target microstructure and mechanical performance parameters of the finished copper busbar to be produced, and to use the target microstructure and mechanical performance parameters as input for reverse design. The inverse mapping model establishment module is used to establish an inverse mapping model from the target microtexture to the extrusion deformation history based on the crystal plasticity theory, and quantifies the target microtexture as the strain energy uniformity index of the die exit section. The reverse correlation model establishment module is used to establish a reverse correlation model from mechanical performance parameters to extrusion pre-strain, and map the mechanical performance parameters to the energy dissipation uniformity index of the internal flow channel of the mold. The index fusion module is used to fuse the strain energy uniformity index and the energy dissipation uniformity index to generate a comprehensive uniformity optimization target for the flow channel design. The iterative solution module is used to drive topology optimization with the comprehensive uniformity optimization objective. Under the constraints of mold structure and manufacturability, iteratively solves the three-dimensional geometric configuration and material composition gradient distribution of the internal flow channel of the mold that can achieve the comprehensive uniformity optimization objective, and outputs a three-dimensional mold flow channel model.

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