A numerical simulation-based copper-phosphorus brazing filler wire extrusion production process parameter optimization method

Abstract

The application discloses a copper-phosphorus brazing filler metal wire extrusion production process parameter optimization method based on numerical simulation, and aims to solve the technical problems that the existing brazing filler metal extrusion process research and development relies on experience and trial and error, has a long research and development cycle, high cost and lacks a systematic multi-target optimization system. The application comprises the following steps: establishing a neural network constitutive model and a simulation model of the brazing filler metal to be extruded, and performing simulation; comparing simulation output results with actual measurement results, and iteratively correcting the simulation model; designing an orthogonal test scheme of multiple sets of processing parameter combinations based on different production requirements, and performing simulation output by using the corrected simulation model; establishing a process response relationship based on the simulation results of multiple parameters, establishing a multi-target evaluation function of the constraint condition, and selecting processing parameters in combination with actual working conditions to obtain an optimized extrusion process scheme. The application effectively guarantees the size consistency and surface integrity of the brazing filler metal welding wire, and realizes fast, efficient and low-cost multi-process parameter coordination optimization.

Description

Technical Field

[0001] This invention belongs to the field of alloy extrusion production technology, and in particular relates to a method for optimizing the process parameters of copper-phosphorus brazing wire extrusion production based on numerical simulation. Background Technology

[0002] Copper-phosphorus brazing filler metals, as key materials for reliable connections between various metals, are widely used in critical fields such as electronic packaging, aerospace, and refrigeration and heating systems. Brazing filler wire is the most commonly used product form in industrial automated flame or high-frequency induction brazing operations. Hot extrusion is the core process in the preparation of copper-phosphorus brazing filler wire. Process parameters such as billet preheating temperature, die preheating temperature, extrusion speed, and die structure directly determine the microstructure and mechanical properties of the brazing filler wire, while also affecting production efficiency, die life, and equipment operating safety. However, the hot extrusion process for brazing filler wire has unique complexities that are drastically different from those of conventional structural metals. To achieve wetting and melting at specific temperatures, copper-phosphorus brazing filler alloys have a high phosphorus content and contain a large amount of Cu3P hard and brittle intermetallic compounds. These filler metals are not only extremely hard and brittle at room temperature but also have a very narrow hot working window. Their rheological softening and hardening behavior under high temperature and high strain exhibits a high degree of nonlinearity, making it difficult for traditional empirical constitutive models to accurately characterize their thermal deformation behavior. Furthermore, brazing filler metal extrusion often involves extremely high extrusion ratios, easily leading to severe frictional temperature rises at the die exit. Excessive temperature rise poses a risk of grain coarsening. Simultaneously, if the extrusion parameters fall into the rheological instability zone of the metal, it will cause adiabatic shear bands or microcracks within the filler metal, severely affecting its plasticity. With the development of numerical simulation technology, finite element simulation is increasingly applied to the analysis of metal extrusion processes. However, there is a lack of simulation optimization schemes for filler metal alloy extrusion, especially a lack of customized solutions that balance product surface quality and dimensional accuracy, production efficiency and equipment safety, making it difficult to obtain the optimal process scheme truly meeting the needs of precision brazing wire production. Therefore, existing technologies suffer from technical problems such as reliance on experience-based trial and error in filler metal extrusion process development, long development cycles and high costs, and a lack of a systematic multi-objective optimization system. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a method for optimizing the process parameters of copper-phosphorus brazing wire extrusion production based on numerical simulation, thereby resolving the issues present in the prior art.

[0004] In a first aspect, to achieve the above objectives, the present invention provides a method for optimizing the process parameters of copper-phosphorus solder wire extrusion production based on numerical simulation, comprising the following steps: A neural network constitutive model of the brazing filler metal to be extruded is established, and a finite element simulation model is established based on the design parameters of the extrusion equipment and the brazing filler metal ingot to obtain the initial simulation model; The actual extrusion production test results based on the initial process scheme are used to perform iterative correction with the simulation results of the initial simulation model to obtain the corrected simulation model; The level range of orthogonal experimental factors is determined based on the material's thermal processing diagram and actual production requirements, and an orthogonal experimental scheme is designed. The orthogonal experimental scheme was simulated using the corrected simulation model to obtain multiple sets of simulation result data. Response relationship analysis is performed on the multiple sets of simulation results data to obtain the response relationship between each optimization index and process parameter; Based on the aforementioned response relationship, multi-objective optimization is performed under constraints to obtain the optimal process scheme.

[0005] Optionally, the process of establishing a neural network constitutive model of the brazing filler metal to be extruded includes: obtaining rheological stress-strain experimental data of the material through hot compression experiments and constructing a training dataset; constructing a neural network model consisting of an input layer, a hidden layer, and an output layer, where the input is strain experimental data and the output is the physical parameters of the Arrhenius constitutive model; substituting the physical parameters output by the neural network into the Arrhenius constitutive model, and calculating the predicted rheological stress value in combination with temperature and strain rate; and iteratively optimizing the neural network weights with the goal of minimizing the error between the predicted value and the experimentally measured value to obtain the neural network constitutive model of the brazing filler metal to be extruded.

[0006] Optionally, the iterative correction process using the actual extrusion production test results based on the initial process scheme and the simulation results of the initial simulation model includes: acquiring the measured curves of load, speed, and temperature changes over time during the extrusion process through sensors on the production equipment; performing multi-dimensional comparative analysis of the measured curves with the initial simulation results under the same process conditions; adjusting the interfacial friction coefficient, heat transfer coefficient, and constitutive parameters in the simulation model based on the comparative analysis results, and repeating the simulation calculation and comparative verification; stopping the iteration when the root mean square error between the simulation results and the measured results is less than or equal to 5%, and obtaining the corrected simulation model.

[0007] Optionally, the process of determining the level range of orthogonal experimental factors based on the material's heat treatment diagram and actual production needs includes: calculating the power dissipation coefficient and rheological instability coefficient based on the rheological stress-strain experimental data of the material at different temperatures and strain rates, drawing a rheological instability diagram, and superimposing the power dissipation diagram and the rheological instability diagram to obtain the heat treatment diagram; using the die preheating temperature, billet preheating temperature, and extrusion speed as orthogonal experimental factors; and limiting the level range of the orthogonal experimental factors to the rheological stability region of the heat treatment diagram.

[0008] Optionally, the process of performing response relationship analysis on the multiple sets of simulation results data includes: eliminating process combinations that exceed the equipment's processing range by using the maximum load limit of the equipment as a constraint; performing variance analysis on the simulation results of the remaining effective process combinations; selecting the two factors that have the most significant impact on the optimization index as factors for response surface analysis; determining the value range of the response surface factors based on the orthogonal experiment results; designing response surface experiments and completing simulation calculations based on the corrected simulation model; drawing three-dimensional response surface diagrams and two-dimensional contour maps of each optimization index and factor; and analyzing the influence trend and interaction of each factor on the optimization index based on the graphs.

[0009] Optionally, the optimization indicators include peak extrusion load, maximum temperature at the extrusion outlet, and uniformity of metal flow.

[0010] Optionally, the process of multi-objective optimization under constraints based on the response relationship includes: constructing a multi-objective evaluation function with production efficiency, equipment load, and product surface quality and dimensional accuracy as optimization objectives using a weighted comprehensive scoring method; setting a penalty function in the evaluation function with the constraint of controlling the maximum temperature to avoid matrix grain growth; applying a penalty function to the evaluation function when the simulation-predicted maximum instantaneous temperature of the ingot is greater than or equal to the difference between the set constraint temperature and the safety margin; and searching for the Pareto optimal solution set with the highest comprehensive score within the optimal process parameter range to obtain the optimal process scheme.

[0011] Optionally, the process of obtaining the initial simulation model also includes mesh generation and convergence verification: in regions where the stress gradient or temperature gradient is greater than a preset threshold, the mesh is locally refined; mesh convergence is verified by comparing the load-stroke curves under different mesh densities; when the relative error of the load-stroke curve of the finer mesh is less than 3% or the change in peak load is less than 2% compared with the previous mesh, the mesh used is confirmed as the final calculation mesh.

[0012] Secondly, the present invention also provides a computer terminal device, comprising: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the method for optimizing the process parameters of copper-phosphorus brazing wire extrusion production based on numerical simulation in the first aspect above.

[0013] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the method for optimizing the extrusion process parameters of copper-phosphorus brazing filler wire based on numerical simulation in the first aspect described above.

[0014] Compared with the prior art, the present invention has the following advantages and technical effects: This invention provides a method for optimizing process parameters in the extrusion production of copper-phosphorus brazing filler wire based on numerical simulation. It utilizes a neural network to construct a constitutive model, accurately describing the actual stress-strain response of the brazing filler wire within an extremely narrow hot working window. This invention integrates a hot working diagram based on the Prasad rheological instability criterion into an orthogonal experimental design, eliminating process parameters that easily induce micropores and adiabatic shear bands in the brazing filler wire from the source. This achieves effective fragmentation and dispersion of brittle phases, significantly improving the processing plasticity of the wire. The multi-objective evaluation function of this invention sets a penalty function with a safety margin for the highest temperature, effectively avoiding the risk of grain coarsening caused by local temperature rise. Under the premise of ensuring absolute reliability of wire quality, it explores the Pareto optimal solution for production efficiency and equipment load. This invention effectively ensures the dimensional consistency and surface integrity of the brazing filler wire, achieving rapid, efficient, and low-cost coordinated optimization of multiple process parameters. Attached Figure Description

[0015] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating the method for optimizing the extrusion process parameters of copper-phosphorus brazing filler wire based on numerical simulation, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of an isothermal compression test according to an embodiment of the present invention; Figure 3 The strain rate condition for this embodiment of the invention is 0.001 s. -1 True stress-true strain curves under the given conditions; Figure 4 The strain rate condition for this embodiment of the invention is 0.01 s. -1 True stress-true strain curves under the given conditions; Figure 5 The strain rate condition for this embodiment of the invention is 0.1 s. -1 True stress-true strain curves under the given conditions; Figure 6 The strain rate condition for this embodiment of the invention is 1 s. -1 True stress-true strain curves under the given conditions; Figure 7 The strain rate condition for this embodiment of the invention is 10 s. -1 True stress-true strain curves under the given conditions; Figure 8 This is a full-size three-dimensional geometric model schematic diagram of the extrusion cylinder, extrusion die, and brazing filler ingot according to an embodiment of the present invention, wherein 1-extrusion pad; 2-die; 3-billet; Figure 9 This is a schematic diagram of the parametric neural network model structure according to an embodiment of the present invention; Figure 10 This is a simulation model of extrusion production after mesh generation, as described in an embodiment of the present invention. Figure 11 This is a schematic diagram of the simulation results of an embodiment of the present invention; Figure 12 This is a thermal processing diagram under a strain of 0.1 according to an embodiment of the present invention; Figure 13 This is a thermal processing diagram under a strain of 0.2 according to an embodiment of the present invention; Figure 14 This is a thermal processing diagram under a strain of 0.3 according to an embodiment of the present invention; Figure 15 This is a thermal processing diagram under a strain of 0.4 according to an embodiment of the present invention. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0018] Example 1 like Figure 1 As shown, this embodiment provides a method for optimizing the process parameters of copper-phosphorus solder wire extrusion production based on numerical simulation, including: A neural network constitutive model of the brazing filler metal to be extruded is established, and a finite element simulation model is established based on the design parameters of the extrusion equipment and the brazing filler metal ingot to obtain the initial simulation model; The actual extrusion production test results based on the initial process scheme are used to perform iterative correction with the simulation results of the initial simulation model to obtain the corrected simulation model; The level range of orthogonal experimental factors is determined based on the material's thermal processing diagram and actual production requirements, and an orthogonal experimental scheme is designed. The orthogonal experimental scheme was simulated using the corrected simulation model to obtain multiple sets of simulation result data. Response relationship analysis is performed on the multiple sets of simulation results data to obtain the response relationship between each optimization index and process parameter; Based on the aforementioned response relationship, multi-objective optimization is performed under constraints to obtain the optimal process scheme.

[0019] Further, step S1: Determine the initial process scheme.

[0020] The design parameters of the extrusion equipment and the brazing ingot, as well as the processing parameters for the production of brazing wire, are obtained to arrive at the initial process plan.

[0021] The design parameters for the extrusion equipment and brazing ingots include dimensional parameters and performance parameters; Dimensions: The inner diameter of the extrusion cylinder is 60 mm, the die aperture is 2 mm, the diameter of the cylindrical ingot is 57 mm, and the height is 100 mm.

[0022] Performance parameters: The specific heat capacity of copper-phosphorus brazing filler metal is 0.7 J / (g). K), Poisson's ratio is 0.33; the stress-strain response relationship of the brazing filler metal thermal deformation was obtained by isothermal compression experiments on the brazing filler metal ingot, the experimental scheme is as follows: Figure 2 As shown in the figure, the true stress-strain curves of the copper-phosphorus alloy at different temperatures and strain rates were obtained experimentally. Figures 3-7 As shown.

[0023] Based on engineering experience and the actual working conditions of the copper-phosphorus brazing filler metal production line, the initial processing parameters were determined as follows: billet preheating temperature 325℃, die preheating temperature 325℃, extrusion speed 2mm / s, and no lubrication. This constituted the initial process plan.

[0024] Furthermore, the process of establishing a neural network constitutive model for the brazing filler metal to be extruded includes: obtaining rheological stress-strain experimental data of the material through hot compression experiments and constructing a training dataset; constructing a neural network model consisting of an input layer, a hidden layer, and an output layer, where the input is strain experimental data and the output is the physical parameters of the Arrhenius constitutive model; substituting the physical parameters output by the neural network into the Arrhenius constitutive model, and calculating the predicted rheological stress value in combination with temperature and strain rate; and iteratively optimizing the neural network weights with the goal of minimizing the error between the predicted value and the experimentally measured value to obtain the neural network constitutive model of the brazing filler metal to be extruded.

[0025] Specifically, the implementation process of this embodiment includes: Step S2: Initial finite element simulation model construction and simulation.

[0026] Based on the initial process scheme described in step S1, a finite element simulation model adapted to the brazing filler metal extrusion characteristics is constructed, and simulation is performed to obtain initial simulation results. The specific implementation process is as follows: Based on the geometric feature parameters of the extrusion equipment, die, and ingot determined in step S1, a full-size 3D geometric model of the extrusion cylinder, extrusion die, and brazing filler ingot is created using the 3D modeling software SOLIDWORKS to recreate the geometric boundaries of the actual extrusion production. The modeling results are as follows: Figure 8 As shown.

[0027] The Arrhenius constitutive model based on neural networks is constructed in the following steps: The experimental data obtained in step S1 is preprocessed to remove nonlinear data caused by non-ideal factors of the test system and the specimen itself in the initial stage. True stress-strain data at different temperatures and strain rates are interpolated at equal intervals of 0.002 strain intervals. The interpolation method used is linear interpolation to obtain a stress dataset with a sufficient amount of data. A parameterized neural network model is constructed, which consists of an input layer with one neuron, two hidden layers with 16 neurons each, and an output layer with four neurons, as shown below. Figure 9 As shown, the input is strain experimental data, and the output is the physical parameters of the Arrhenius constitutive model. The activation function used is the ReLU function; The set of physical parameters output by the neural network is substituted into the Arrhenius constitutive model. Combined with the corresponding temperature and strain rate, the predicted value of the rheological stress is calculated. A loss function is constructed, and the weights of the neural network are iteratively optimized by minimizing the error between the predicted value of the rheological stress and the measured value of the stress in the experimental data. The Adam optimizer is used and the learning rate is set to 0.001 to obtain the trained neural network constitutive model.

[0028] The established three-dimensional geometric model was imported into the finite element simulation software DEFORM, and the simulation parameters were set. Specifically, the Arrhenius constitutive model of the copper-phosphorus brazing filler metal was imported, and the die preheating temperature, billet preheating temperature, and extrusion speed were set to 325℃ and 325℃, respectively. To match the unlubricated working condition, the interface friction model was set to shear friction with a friction factor of 0.5. The billet-air heat transfer coefficient was set to 0.002 N / (s·mm·℃), and the billet-die interface heat transfer coefficient was set to 20 N / (s·mm·℃).

[0029] Tetrahedral meshes were used to mesh the 3D model. Based on the absolute mesh size method, the global mesh size was set to 3 mm. For critical deformation areas with large stress and temperature gradients, such as the die inlet, working area, and die fillets, local mesh refinement windows were set, with a mesh size of 0.05 mm in the refined areas. The extrusion production simulation model after meshing was completed, as shown below. Figure 10 As shown. Simultaneously, mesh convergence verification was performed. The mesh size was changed, and it was confirmed that under this mesh scheme, the relative error of the load-stroke curve was less than 3%, and the peak load variation was less than 2%, meeting the mesh convergence requirements and making it suitable for subsequent simulation calculations.

[0030] The simulation solver employs a direct iterative conjugate gradient method implicit solver, with a penalty function method for the contact algorithm. The time step is controlled to ensure that the deformation in a single step is less than 1 / 5 of the minimum mesh size. The iteration convergence tolerance is set to a relative velocity error norm less than 1e-3 and a relative force error norm less than 1e-2 to guarantee computational convergence and result accuracy. Iterative calculations are executed to obtain initial simulation results, including the metal flow process during extrusion, distribution contour maps of macroscopic physical field variables such as temperature, stress, and strain fields, and extrusion load-stroke curves, such as... Figure 11 As shown.

[0031] Furthermore, the iterative correction process using the actual extrusion production test results based on the initial process scheme and the simulation results of the initial simulation model includes: acquiring the measured curves of load, speed, and temperature changes over time during the extrusion process through sensors on the production equipment; performing multi-dimensional comparative analysis of the measured curves with the initial simulation results under the same process conditions; adjusting the interface friction coefficient, heat transfer coefficient, and constitutive parameters in the simulation model based on the comparative analysis results, and repeating the simulation calculation and comparative verification; stopping the iteration when the root mean square error between the simulation results and the measured results is less than or equal to 5%, and obtaining the corrected simulation model.

[0032] Specifically, the implementation process of this embodiment includes: Step S3: Iterative calibration of the simulation model The actual experimental results of brazing filler metal extrusion production based on the initial process scheme are obtained from multiple dimensions. The experimental results are then compared and analyzed with the initial simulation results under the same process. The simulation model is iteratively corrected to obtain the corrected simulation model. The specific implementation process is as follows: Based on the initial process scheme in step S1, an extrusion production test of copper-phosphorus brazing filler metal was conducted on an actual horizontal extrusion production equipment. Through the sensors integrated on the production equipment, measured data on the load, extrusion speed, and die temperature changes with time / stroke during the extrusion process were collected.

[0033] The measured results under the same process conditions are compared and analyzed from multiple dimensions with the initial simulation results obtained in step S2. The focus is on quantifying the deviation between the simulated and measured values ​​of the extrusion load-stroke curve and temperature change trend.

[0034] Based on the comparative analysis results, the key parameters of the simulation model (including the interface friction coefficient, interface heat transfer coefficient, thermal boundary conditions, etc.) are checked and iteratively adjusted, and the simulation calculation and comparative verification are repeated. When the root mean square error between the simulation result and the measured result is less than or equal to 5%, or the difference between the root mean square error of two consecutive iterations is less than 0.5%, the iteration is stopped, the model is corrected, and the corrected simulation model adapted to the working conditions of this embodiment is obtained.

[0035] Furthermore, the process of determining the level range of orthogonal experimental factors based on the material's heat treatment diagram and actual production needs includes: calculating the power dissipation coefficient and rheological instability coefficient based on the rheological stress-strain experimental data of the material at different temperatures and strain rates, drawing the rheological instability diagram, and superimposing the power dissipation diagram and the rheological instability diagram to obtain the heat treatment diagram; using the die preheating temperature, billet preheating temperature, and extrusion speed as orthogonal experimental factors; and limiting the level range of the orthogonal experimental factors to the rheological stability region of the heat treatment diagram.

[0036] Specifically, the implementation process of this embodiment includes: Step S4: Orthogonal Experimental Design Based on process requirements, the preheating temperature of the die, the preheating temperature of the billet, and the extrusion speed were selected as factors for the orthogonal experiment. A thermal processing diagram of the material is established based on the rheological stress-strain experimental data of the material at different temperatures and strain rates, as shown below. Figures 12-15 As shown, the specific steps are as follows: 1) Calculate the strain rate sensitivity coefficient ; 2) Calculate the power dissipation factor ; 3) According to Prasad's rheological instability criterion To assess the instability of the material; 4) Based on the instability coefficient Plotting temperature T and logarithmic strain rate The contour map is the rheological instability map. Overlaying the power dissipation map and the rheological instability map yields the thermal processing map.

[0037] The factor levels for orthogonal experiments are determined based on the material's heat treatment diagram and actual production needs, avoiding their appearance in the instability zone of the heat treatment diagram.

[0038] Design a three-factor, three-level orthogonal experimental design, with the level settings for each factor as follows: Mold preheating temperature: 275~475℃; Billet preheating temperature: 275~475℃; Extrusion speed: 1~10 mm / s.

[0039] Based on the above factors and level settings, an orthogonal experimental table was constructed, containing 25 different combinations of process parameters. The experimental scheme is shown in Table 1.

[0040] Table 1 Further, step S5: Orthogonal experimental group simulation calculation Based on the high-precision simulation model corrected in step S3, finite element simulations were performed on the 25 combinations of process parameters in the above orthogonal test table. During the simulation, single variables were strictly controlled to ensure the comparability of the test results. After completing the iterative calculations of all test groups, simulation results corresponding to different process schemes for the 25 groups were obtained, and data such as the peak extrusion load, the highest temperature at the extrusion outlet, and the uniformity of metal flow for each group were extracted.

[0041] Furthermore, the process of analyzing the response relationship of the multiple sets of simulation results includes: eliminating process combinations that exceed the equipment's processing range by using the maximum load limit of the equipment as a constraint; performing variance analysis on the simulation results of the remaining effective process combinations to select the two factors that have the most significant impact on the optimization indicators as factors for response surface analysis; determining the value range of the response surface factors based on the orthogonal experimental results; designing response surface experiments and completing simulation calculations based on the corrected simulation model; drawing three-dimensional response surface plots and two-dimensional contour plots of each optimization indicator and factor; and analyzing the influence trend and interaction of each factor on the optimization indicators based on the plots. The optimization indicators include the peak extrusion load, the highest temperature at the extrusion outlet, and the uniformity of metal flow.

[0042] Specifically, the implementation process of this embodiment includes: Step S6: Response Relationship Analysis Multi-index correlation analysis and significance analysis were performed on the multiple sets of simulation results data to obtain the response relationship between each optimization index and process parameters. The specific implementation process is as follows: Using the maximum load limit of the equipment as a constraint, process combinations that are obviously too fast or too cold will cause the load to exceed the processing range of the equipment. Analysis of variance was performed on the simulation results of the remaining effective process combinations to quantify the influence weight and significance of each process factor on optimization indicators such as peak extrusion load, maximum temperature at the extrusion outlet, and uniformity of metal flow. The two factors with the most significant impact on the optimization indicators were selected as variables for subsequent response surface analysis. Based on the analysis results of the orthogonal experiment, combined with the hot working diagram of the copper-phosphorus alloy and the processing capacity of the extrusion equipment, the range of values ​​for the factors in the response surface analysis was determined. Based on the screened factors, the response surface experiment was designed, and the corresponding simulation calculation was completed through the high-precision simulation model corrected in step S3 to obtain the simulation results under each parameter combination.

[0043] Based on the results of the response surface methodology experiment, three-dimensional response surface plots and two-dimensional contour plots were drawn for each optimization index and process factor. The influence trend of each single factor on the optimization index and the interaction between multiple factors were analyzed based on the plots to clarify the range of the optimal process parameters.

[0044] Furthermore, the process of multi-objective optimization under constraints based on the aforementioned response relationship includes: constructing a multi-objective evaluation function with production efficiency, equipment load, and product surface quality and dimensional accuracy as optimization objectives using a weighted comprehensive scoring method; setting a penalty function in the evaluation function with the constraint of controlling the maximum temperature to avoid matrix grain growth; applying a penalty function to the evaluation function when the simulation-predicted maximum instantaneous temperature of the ingot is greater than or equal to the difference between the set constraint temperature and the safety margin; and searching for the Pareto optimal solution set with the highest comprehensive score within the optimal process parameter range to obtain the optimal process scheme.

[0045] Specifically, the implementation process of this embodiment includes: Step S7: Multi-objective optimization and optimal solution determination under constraints Based on the aforementioned response relationship, multi-objective optimization under constraints is performed to determine the optimal process scheme. Specifically, a weighted comprehensive scoring method is used to construct a multi-objective evaluation function with production efficiency, equipment load, and product surface quality and dimensional accuracy as optimization objectives. The constraint condition is controlling the maximum temperature to prevent rapid growth of matrix grains that could lead to deterioration of product surface quality and dimensional accuracy. The form is as follows: In the formula, X is a decision variable vector, which includes the die preheating temperature, the billet preheating temperature and the extrusion speed; For each normalized optimization objective sub-function, The weight coefficients for each optimization objective (satisfying) P(X) is a penalty function used to handle temperature constraints.

[0046] When i=1, the objective subfunction Let v be the extrusion speed. ; When i=2, the objective subfunction Peak load P max , ; When i=3, the objective subfunction Temperature difference at the outlet section , .

[0047] The penalty function is defined as follows: In the formula The simulation predicts the highest instantaneous temperature of the ingot. The set constraint temperature is M, the penalty operator is set to 500℃, and ΔT is the safety margin, set to 10℃.

[0048] By traversing the weight combinations and setting target thresholds, the Pareto optimal solution set with the best overall performance is obtained. Combining the actual production equipment capacity and mass production requirements, the final optimal brazing filler metal extrusion production process scheme is determined from the solution set.

[0049] Example 2 In this embodiment, a computer terminal device is provided, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the above-described method for optimizing the extrusion process parameters of copper-phosphorus brazing wire based on numerical simulation.

[0050] In this embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the above-described method for optimizing the extrusion process parameters of copper-phosphorus brazing filler wire based on numerical simulation.

[0051] This invention provides a method for optimizing process parameters in the extrusion production of copper-phosphorus brazing filler wire based on numerical simulation. It utilizes a neural network to construct a constitutive model, accurately describing the actual stress-strain response of the brazing filler wire within an extremely narrow hot working window. This invention integrates a hot working diagram based on the Prasad rheological instability criterion into an orthogonal experimental design, eliminating process parameters that easily induce micropores and adiabatic shear bands in the brazing filler wire from the source. This achieves effective fragmentation and dispersion of brittle phases, significantly improving the processing plasticity of the wire. The multi-objective evaluation function of this invention sets a penalty function with a safety margin for the highest temperature, effectively avoiding the risk of grain coarsening caused by local temperature rise. Under the premise of ensuring absolute reliability of wire quality, it explores the Pareto optimal solution for production efficiency and equipment load. This invention effectively ensures the dimensional consistency and surface integrity of the brazing filler wire, achieving rapid, efficient, and low-cost coordinated optimization of multiple process parameters.

[0052] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing process parameters in the extrusion production of copper-phosphorus solder wire based on numerical simulation, characterized in that, Includes the following steps: A neural network constitutive model of the brazing filler metal to be extruded is established, and a finite element simulation model is established based on the design parameters of the extrusion equipment and the brazing filler metal ingot to obtain the initial simulation model; The actual extrusion production test results based on the initial process scheme are used to perform iterative correction with the simulation results of the initial simulation model to obtain the corrected simulation model; The level range of orthogonal experimental factors is determined based on the material's thermal processing diagram and actual production requirements, and an orthogonal experimental scheme is designed. The orthogonal experimental scheme was simulated using the corrected simulation model to obtain multiple sets of simulation results data. Response relationship analysis is performed on the multiple sets of simulation results data to obtain the response relationship between each optimization index and process parameter; Based on the aforementioned response relationship, multi-objective optimization is performed under constraints to obtain the optimal process scheme.

2. The method according to claim 1, characterized in that, The process of establishing a neural network constitutive model for the brazing filler metal to be extruded includes: obtaining rheological stress-strain experimental data of the material through hot compression experiments and constructing a training dataset; constructing a neural network model consisting of an input layer, a hidden layer, and an output layer, where the input is strain experimental data and the output is the physical parameters of the Arrhenius constitutive model; substituting the physical parameters output by the neural network into the Arrhenius constitutive model, and calculating the predicted rheological stress value in combination with temperature and strain rate; and iteratively optimizing the neural network weights with the goal of minimizing the error between the predicted value and the experimentally measured value to obtain the neural network constitutive model of the brazing filler metal to be extruded.

3. The method according to claim 1, characterized in that, The iterative correction process using the actual extrusion production test results based on the initial process scheme and the simulation results of the initial simulation model includes: acquiring the measured curves of load, speed, and temperature changes over time during the extrusion process through sensors on the production equipment; performing multi-dimensional comparative analysis of the measured curves with the initial simulation results under the same process conditions; adjusting the interfacial friction coefficient, heat transfer coefficient, and constitutive parameters in the simulation model based on the comparative analysis results, and repeating the simulation calculation and comparative verification; stopping the iteration when the root mean square error between the simulation results and the measured results is less than or equal to 5%, and obtaining the corrected simulation model.

4. The method according to claim 1, characterized in that, The process of determining the level range of orthogonal experimental factors based on the material's heat treatment diagram and actual production needs includes: calculating the power dissipation coefficient and rheological instability coefficient based on the rheological stress-strain experimental data of the material at different temperatures and strain rates, drawing the rheological instability diagram, and superimposing the power dissipation diagram and the rheological instability diagram to obtain the heat treatment diagram; using the die preheating temperature, billet preheating temperature, and extrusion speed as orthogonal experimental factors; and limiting the level range of the orthogonal experimental factors to the rheological stability region of the heat treatment diagram.

5. The method according to claim 1, characterized in that, The process of analyzing the response relationship of the multiple sets of simulation results includes: eliminating process combinations that exceed the processing range of the equipment by using the maximum load limit of the equipment as a constraint; performing variance analysis on the simulation results of the remaining effective process combinations to select the two factors that have the most significant impact on the optimization index as factors for response surface analysis; determining the value range of the response surface factors based on the orthogonal experiment results; designing response surface experiments and completing simulation calculations based on the corrected simulation model; drawing three-dimensional response surface plots and two-dimensional contour plots of each optimization index and factor; and analyzing the influence trend and interaction of each factor on the optimization index based on the plots.

6. The method according to claim 5, characterized in that, The optimization indicators include peak extrusion load, maximum temperature at the extrusion outlet, and uniformity of metal flow.

7. The method according to claim 1, characterized in that, The process of multi-objective optimization under constraints based on the aforementioned response relationship includes: constructing a multi-objective evaluation function with production efficiency, equipment load, and product surface quality and dimensional accuracy as optimization objectives using a weighted comprehensive scoring method; setting a penalty function in the evaluation function with the constraint of controlling the maximum temperature to avoid matrix grain growth; applying a penalty function to the evaluation function when the simulation-predicted maximum instantaneous temperature of the ingot is greater than or equal to the difference between the set constraint temperature and the safety margin; and searching for the Pareto optimal solution set with the highest comprehensive score within the optimal process parameter range to obtain the optimal process scheme.

8. The method according to claim 1, characterized in that, The process of obtaining the initial simulation model also includes mesh generation and convergence verification: local mesh refinement is used in regions where the stress gradient or temperature gradient is greater than a preset threshold; mesh convergence is verified by comparing the load-stroke curves under different mesh densities; when the relative error of the load-stroke curve of the finer mesh is less than 3% or the change in peak load is less than 2% compared with the previous mesh, the mesh used is confirmed as the final calculation mesh.

9. A computer terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the steps of the method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-8.

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