Three-dimensional die flow simulation optimization method and device for aluminum profile extrusion die

Abstract

The invention discloses a three-dimensional mold flow simulation optimization method and device for an aluminum profile extrusion mold. The method comprises the steps that a three-dimensional model, a flow field, a temperature field, a stress field and a welding field corresponding to the aluminum profile extrusion mold are constructed; performing multi-physical field simulation calculation on a flow field, a temperature field, a stress field and a welding field to generate a flow velocity distribution cloud picture, a stress distribution cloud picture, a mold core displacement cloud picture, a temperature distribution cloud picture and a welding quality cloud picture of the aluminum profile extrusion mold; for a target area of the aluminum profile extrusion die, selecting a cloud picture of a corresponding type under each time sequence to generate a target cloud picture, and according to the target cloud picture under each time sequence, generating a target cloud picture sequence of the target area under each time sequence stage; and according to the target cloud picture sequence in each time sequence stage, determining the defect of the target area in the aluminum profile extrusion die, and performing optimization adjustment on the aluminum profile extrusion die according to the defect. By implementing the method, the optimization efficiency of the aluminum profile extrusion die can be improved, and the development period can be shortened.

Description

Technical Field

[0001] This invention relates to the field of aluminum alloy extrusion manufacturing technology, and in particular to a three-dimensional mold flow simulation optimization method and apparatus for aluminum profile extrusion dies. Background Technology

[0002] Aluminum profiles, with their advantages of lightweight, high strength, and ease of processing, are widely used in construction, rail transportation, aerospace, and other fields. Extrusion molding is the core process in aluminum profile production, and the structure of the extrusion die directly determines the forming quality, dimensional accuracy, and production efficiency of the profile. Traditional die design relies on engineers' experience, resulting in numerous trial runs, long development cycles, and high costs. Summary of the Invention

[0003] This invention provides a three-dimensional mold flow simulation optimization method and device for aluminum profile extrusion dies, in order to solve the problems of existing mold design relying on engineers' experience, resulting in numerous trial runs, long development cycles, and high costs.

[0004] To achieve the above objectives, a first aspect of this application provides a three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies, comprising: Construct a three-dimensional model of the aluminum profile extrusion die based on the die parameters; Construct a flow field to characterize the rheological behavior of aluminum alloys in aluminum profile extrusion dies, a temperature field to characterize the temperature change law, a stress field to characterize the stress distribution and deformation law during pressurization, and a welding field to measure the diffusion welding quality of multiple metal flows in the welding chamber. Based on the three-dimensional model, multi-physics field simulation calculations are performed on the flow field, temperature field, stress field, and welding field to generate flow velocity distribution cloud map, stress distribution cloud map, core displacement cloud map, temperature distribution cloud map, and welding quality cloud map of aluminum profile extrusion die. For the target area of ​​an aluminum profile extrusion die, a target cloud map is generated at each time step by selecting the corresponding type of cloud map. Based on the target cloud maps at each time step, a sequence of target cloud maps for the target area at each time step is generated. The target area includes: the working zone, the welding chamber, the die core, and the flow guide bridge. When the target area is the working zone, the temperature distribution cloud map and the flow velocity distribution cloud map of the corresponding area are fused to generate the target cloud map. When the target area is the welding chamber, the welding quality cloud map is selected as the target cloud map. When the target area is the flow guide bridge, the temperature distribution cloud map and the stress distribution cloud map of the corresponding area are fused to generate the target cloud map. When the target area is the die core, the die core displacement cloud map is selected as the target cloud map. Based on the target cloud map sequence at each time stage, the defects in the target area of ​​the aluminum profile extrusion die are determined, and the aluminum profile extrusion die is optimized and adjusted according to the defects. Furthermore, a flow field is constructed to characterize the rheological behavior of aluminum alloys within an aluminum profile extrusion die, including: Based on the rate of change of mass of the infinitesimal element and the difference in mass flux between the inflow and outflow of the infinitesimal element, the following mass conservation equation is constructed: ; Based on the relationship between the rate of change of momentum of a micro-element and the flow pressure gradient, viscous stress of the aluminum alloy, gravity, and viscous stress of the oxide layer when aluminum alloy flows in the mold channel, the following momentum conservation equation is constructed: ; The stress-dominant term is constructed based on the flow stress, the material coefficient of the aluminum alloy, the stress coefficient, and the strain hardening exponent; the temperature correction term is constructed based on the deformation activation energy, the gas constant, and the temperature. Based on the coupling relationship between aluminum alloy oxide layer and time and temperature, an oxide layer growth model describing the dynamic growth law of aluminum alloy oxide layer thickness is constructed:

[0005] Based on the oxide layer growth model and the preset oxide layer influence coefficient, an oxide layer correction term is constructed to quantify the oxide layer's inhibitory effect on deformation. Based on the coupling relationship between the equivalent plastic strain rate of aluminum alloy and the stress-dominant term, temperature correction term, and oxide layer correction term, the following rheological constitutive equation is constructed: ; The flow field is determined based on the mass conservation equation, the momentum conservation equation, and the rheological constitutive equation. in, t represents the density of the aluminum alloy; t represents time. For the flow velocity vector of the aluminum alloy; For flow pressure; For aluminum alloy viscous stress tensor; This is the vector of gravitational acceleration; For the viscous stress tensor of the oxide layer; The thickness of the aluminum alloy oxide layer; This is the oxidation rate constant; It is the activation energy for oxidation; It is the activation energy for the deformation of aluminum alloys; It is the gas constant; This refers to the actual absolute temperature. denoted as the equivalent plastic strain rate of the aluminum alloy; A is a material constant. Stress coefficient; For flow stress; The strain hardening index; This is the oxide layer correction factor, and ; This represents the influence coefficient of the oxide layer.

[0006] Furthermore, a temperature field characterizing the temperature change pattern is constructed, including: A heat conduction term is constructed based on the heat exchange between the micro-element and the environment; The basic internal heat source term is constructed based on the heat energy of plastic work during metal deformation in the extrusion process and the heat generated by friction between the metal and the die interface. An additional heat generation term for the oxide layer is constructed based on the additional heat generated by interfacial friction and viscous dissipation of the aluminum alloy oxide layer when it flows on the metal surface. Based on the heat conduction term, the basic internal heat source term, the additional heat generation term of the oxide layer, and the rate of change of thermodynamic energy of the micro-element, the following Fourier heat conduction equation is constructed to obtain the temperature field; ; in, The specific heat capacity of aluminum alloy; Thermal conductivity of aluminum alloy; Based on the intensity of the internal heat source, and , For the thermal strength of plastic deformation, The frictional heat intensity between the aluminum alloy and the mold; The coefficient of plastic work-heat conversion. The coefficient of friction between the aluminum alloy and the mold; The heat generation intensity of the oxide layer, and , The coefficient of frictional heat generation of the oxide layer.

[0007] Furthermore, a stress field characterizing the stress distribution and deformation patterns during the pressurization process is constructed, including: A stress equilibrium equation is constructed based on the equilibrium relationship between the stress divergence of the infinitesimal element, gravity, and the additional volume forces of the oxide layer: ; Based on the elastic strain rate and the plastic strain rate, construct the elastoplastic constitutive equation: ; By incorporating an oxide thickness correction factor and a temperature correction term into the pre-defined Drucker-Prager model, a yield criterion is generated. ; The stress field is constructed based on the stress balance equation, the elastoplastic constitutive equation, and the yield criterion. in, Let be the flow stress tensor, and its equivalent scalar is: ; Add a volume force vector to the oxide layer; For the total strain rate tensor of aluminum alloy; For the elastic strain rate tensor of aluminum alloy; Let be the plastic strain rate tensor of aluminum alloy, and its equivalent scalar is . ; This represents the yield function value. These are the parameters for the Drucker-Prager model; It is the first invariant of stress; It is the second invariant of stress; The basic yield parameter; The yield influence coefficient of the oxide layer; The temperature yield influence coefficient; This is the preset reference absolute temperature.

[0008] Furthermore, a welding field is constructed and used to measure the diffusion welding quality of multiple metal streams in the welding chamber, including: Welding criteria are constructed based on the oxide layer growth model, welding chamber pressure, and the residence time of the metal in the welding chamber, and the welding field is obtained. The welding criteria include: The welding is deemed effective if the thickness of the aluminum alloy oxide layer is less than or equal to the first preset thickness value, the pressure in the welding chamber is greater than or equal to the first preset pressure value, and the residence time of the metal in the welding chamber is greater than the first time threshold. The welding is deemed effective when the aluminum alloy oxide layer thickness is greater than the first preset thickness value, the welding chamber pressure is greater than or equal to the second preset pressure value, and the metal residence time in the welding chamber is greater than or equal to the first time threshold value; the first preset pressure value is less than the second preset pressure value. In all other cases, the weld is deemed invalid.

[0009] Furthermore, when the target area is a working zone, a first temperature field cloud map and a first velocity distribution cloud map of the working zone area are extracted; based on the average velocity corresponding to the first velocity distribution cloud map, the velocity uniformity value corresponding to each velocity is calculated; a first velocity uniformity distribution cloud map is generated based on the velocity uniformity value; the first temperature field cloud map and the first velocity distribution cloud map are fused to generate a first fused cloud map, and the locations in the first fused cloud map where the temperature exceeds a first preset temperature value and the velocity uniformity is less than a first velocity uniformity threshold are marked with a first color, and the remaining locations are marked with a second color to obtain the corresponding target cloud map; When the target area is a welding chamber, the locations where welding is invalid are marked with the third color and the remaining locations are marked with the fourth color according to the welding quality cloud map, thus obtaining the corresponding target cloud map; When the target area is the mold core, the points with displacements greater than the preset displacement value are marked with the fifth color according to the mold core displacement cloud map, and the remaining points are marked with the sixth color to obtain the corresponding target cloud map. When the target area is a flow guide bridge, the temperature distribution cloud map and stress distribution cloud map of the corresponding area are fused to generate a second fused cloud map. The locations in the second fused cloud map where the temperature exceeds the second preset temperature value and the stress exceeds the preset stress value are marked with the seventh color, and the remaining locations are marked with the eighth color to obtain the corresponding target cloud map.

[0010] Furthermore, each of the time-series stages includes any one or a combination of the following stages: preheating stage, start-up stage, stable extrusion stage, and finishing stage; The step of determining defects in the target area of ​​the aluminum profile extrusion die based on the target cloud map sequence at each time stage includes: If the target area is a working zone, and during the stable extrusion stage, the area of ​​the continuous first color region in the target cloud map is larger than the first preset area and continues to expand during the finishing stage, then it is determined that there is a local overheating wear defect in the corresponding area of ​​the working zone; if during the start-up stage, the target cloud map shows scattered first color spots that do not disappear during the stable extrusion stage, then it is determined that there is a metal flow unevenness defect in the corresponding area of ​​the working zone. If the target area is a welding chamber, and during the stable extrusion stage, the area of ​​the third color region in the target cloud map is larger than the second preset area, then it is determined that there is a welding defect in the corresponding area of ​​the welding chamber. If the target area is the mold core, and a seventh color area appears during the stable extrusion stage and remains stable during the finishing stage, it is determined that there is a mold core thermal deformation deviation defect. If the target area is a guide bridge, and a seventh-colored area that extends in the thickness direction of the guide bridge and appears as a strip during the stable extrusion stage, and the color deepens during the final stage, then it is determined that there is a cracking risk defect in the area corresponding to the guide bridge.

[0011] Furthermore, the aluminum profile extrusion die is optimized and adjusted based on the aforementioned defects, including: If localized overheating and wear defects are identified, shorten the working zone corresponding to the defect area; if uneven metal flow defects are identified, increase the working zone length in areas with excessively high flow rates and shorten the working zone length in areas with excessively low flow rates; if poor welding defects are identified, increase the welding chamber volume; if excessive thermal deformation defects of the mold core are identified, add reinforcing ribs; if cracking risk defects are identified, increase the thickness of the guide bridge.

[0012] Furthermore, optimizing and adjusting the aluminum profile extrusion die based on the aforementioned defects also includes: A micro-mesh of a preset size is used to divide the defect area into micro-mesh domains to obtain the micro-computation domain. Given the existence of localized overheating and wear defects in the micro-computation domain, the abnormal grain growth rate and microhardness within the micro-computation domain are extracted; based on the abnormal grain growth rate and microhardness, the shortening of the working band is calculated. When it is determined that there is a defect of uneven metal flow in the micro-computation domain, the grain size gradient is calculated based on the maximum and minimum grain sizes in the micro-computation domain; the micro-shear stress in the micro-computation domain is extracted; and the working band length adjustment value is determined based on the grain size gradient and the micro-shear stress. When it is determined that there are poor welding defects in the micro-computation domain, the porosity and grain boundary fusion rate of the crystal in the micro-computation domain are extracted, and the volume expansion of the welding chamber is determined based on the porosity and grain boundary fusion rate. When it is determined that there is a core thermal deformation defect in the micro-computation domain, the micro-stress concentration factor is determined based on the ratio of the local maximum micro-stress to the average micro-stress in the micro-computation domain; the grain orientation deviation in the region is extracted; and the number of reinforcing ribs to be added is determined based on the micro-stress concentration factor and the grain orientation deviation. When it is determined that there is a risk of cracking in the micro-computation domain, the micro-crack propagation rate and intergranular stress within the micro-computation domain are extracted, and the increase in the thickness of the flow bridge is determined based on the micro-crack propagation rate and intergranular stress.

[0013] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments; One embodiment of the present invention provides a three-dimensional mold flow simulation optimization device for aluminum profile extrusion dies, including: a three-dimensional model construction module, a multi-coupling field construction module, a cloud map construction module, a target cloud map sequence construction module, and a die adjustment module; The 3D model building module is used to build a 3D model of the aluminum profile extrusion die based on the die parameters of the aluminum profile extrusion die. The multi-coupling field construction module is used to construct a flow field to characterize the rheological behavior of aluminum alloy in aluminum profile extrusion die, a temperature field to characterize the temperature change law, a stress field to characterize the stress distribution and deformation law during pressurization, and a welding field to measure the diffusion welding quality of multiple metal flows in the welding chamber. The cloud map construction module is used to perform multi-physics field simulation calculations of flow field, temperature field, stress field and welding field based on the three-dimensional model, and generate flow velocity distribution cloud map, stress distribution cloud map, core displacement cloud map, temperature distribution cloud map and welding quality cloud map of aluminum profile extrusion die; The target cloud map sequence construction module is used to select the corresponding type of cloud map to generate a target cloud map for the target area of ​​the aluminum profile extrusion die at each time sequence, and generate a target cloud map sequence for the target area at each time sequence stage based on the target cloud maps at each time sequence stage. The target area includes: the working zone, the welding chamber, the die core, and the flow guide bridge. When the target area is the working zone, the temperature distribution cloud map and the flow velocity distribution cloud map of the corresponding area are selected and fused to generate the target cloud map. When the target area is the welding chamber, the welding quality cloud map is selected as the target cloud map. When the target area is the flow guide bridge, the temperature distribution cloud map and the stress distribution cloud map of the corresponding area are selected and fused to generate the target cloud map. When the target area is the die core, the die core displacement cloud map is selected as the target cloud map. The mold adjustment module is used to determine the defects in the target area of ​​the aluminum profile extrusion mold according to the target cloud map sequence at each time stage, and to optimize and adjust the aluminum profile extrusion mold according to the defects.

[0014] The following benefits can be obtained by implementing the present invention: This invention first constructs a three-dimensional model based on mold parameters, and then specifically constructs four core physical fields: flow field, temperature field, stress field, and welding field. Through multi-physics simulation calculations, it generates quantitative cloud maps of flow velocity, stress, displacement, temperature, and welding quality, completely reproducing the entire extrusion molding process in a virtual environment. This transforms previously vague, experience-based guesswork into precise, data-visualized judgments, reducing trial failures caused by design errors from the outset. Secondly, it aggregates different cloud maps in space to identify the main defects and corresponding influencing factors that are prone to occur in different areas of aluminum profile extrusion dies. By associating multi-physics causality and restoring the true coupling relationship, the accuracy of defect identification is improved. In terms of time, combining time-series sequences to capture dynamic changes during the extrusion process reconstructs the dynamic formation path of defects, allowing for more accurate determination of defect causes. For example, the core defects in the working zone are localized overheating wear and uneven metal flow leading to surface defects such as stripes and burrs. Temperature is a key factor affecting wear, and flow velocity distribution directly reflects the frictional intensity and uniformity between the metal and the working zone. Therefore, fusing temperature and flow velocity distribution cloud maps, and then combining the fused cloud map sequences at various time series, can quickly locate defects in the working zone. In summary, this invention directly optimizes mold parameters in a 3D model, avoiding blindly modifying the mold during optimization. The entire process requires no physical mold trials, only completing the process in a virtual environment, significantly reducing the number of physical mold trials. Simultaneously, the optimization direction is entirely driven by simulation data, avoiding wasted time and cost due to ineffective modifications, ultimately shortening the development cycle and reducing overall costs. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a three-dimensional mold flow simulation optimization device for aluminum profile extrusion dies provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] like Figure 1 As shown, an embodiment of the present invention provides a three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies, comprising the following steps: S1. Construct a three-dimensional model of the aluminum profile extrusion die based on the die parameters of the aluminum profile extrusion die.

[0018] Specifically, the mold parameters collected in this invention include, but are not limited to: the number of diversion holes, the diameter of the diversion holes, the distribution angle of the diversion holes, the width of the guide bridge, the height of the guide bridge, the shape of the welding chamber, the height of the welding chamber, the taper of the welding chamber, the diameter of the mold core, the eccentricity of the mold core, the length of the working zone, the number of support ribs, the cross-sectional dimensions of the support ribs, and the mold material, etc. Based on the obtained mold parameters, the aluminum profile extrusion mold was modeled in three dimensions using existing 3D integrated software, resulting in the aforementioned 3D model.

[0019] Preferably, in constructing the aforementioned 3D model, an adaptive meshing method combining a global coarse mesh and a fine mesh for key areas is adopted. Specifically, the core areas such as the working zone, guide bridge, welding chamber, mold core, and flow divider holes are meshed using a hexahedral structured mesh, while other areas are meshed using a tetrahedral unstructured mesh. Furthermore, the mesh size of the flow divider holes and the working zone is smaller than that of the guide bridge, welding chamber, and mold core support ribs. For example, the flow divider holes and the working zone can be meshed using a fine mesh of 0.3–1 mm, while the guide bridge, welding chamber, and mold core support ribs can be meshed using a coarse mesh of 2–4 mm. Through this differentiated meshing method, simulation accuracy and computational efficiency can be balanced.

[0020] S2. Construct a flow field to characterize the rheological behavior of aluminum alloys in aluminum profile extrusion dies, a temperature field to characterize the temperature change law, a stress field to characterize the stress distribution and deformation law during pressurization, and a welding field to measure the diffusion welding quality of multiple metal flows in the welding chamber. Unlike other metals, aluminum alloy billets are easily oxidized during heating, transport, and extrusion. To more accurately correct the physical boundary conditions and material property parameters of each field, eliminate the interference of the oxide layer on metal flow, heat transfer, stress transmission, and welding quality, and make the simulation results more consistent with actual production conditions, this invention innovatively introduces the oxide layer for correction when constructing the flow field, temperature field, stress field, and welding field. Therefore, an oxide layer growth model for the dynamic growth law of the aforementioned aluminum alloy oxide layer thickness is constructed: To improve the accuracy of simulation calculations, the construction of each field will be explained in detail below.

[0021] For the flow field, in a preferred embodiment, a flow field is constructed to characterize the rheological behavior of aluminum alloys within an aluminum profile extrusion die, including: Based on the rate of change of mass of the infinitesimal element and the difference in mass flux between the inflow and outflow of the infinitesimal element, the following mass conservation equation is constructed: ; Based on the relationship between the rate of change of momentum of a micro-element and the flow pressure gradient, viscous stress of the aluminum alloy, gravity, and viscous stress of the oxide layer when aluminum alloy flows in the mold channel, the following momentum conservation equation is constructed: ; The stress-dominant term is constructed based on the flow stress, the material coefficient of the aluminum alloy, the stress coefficient, and the strain hardening exponent; the temperature correction term is constructed based on the deformation activation energy, the gas constant, and the temperature. Based on the coupling relationship between aluminum alloy oxide layer and time and temperature, an oxide layer growth model describing the dynamic growth law of aluminum alloy oxide layer thickness is constructed:

[0022] Based on the oxide layer growth model and the preset oxide layer influence coefficient, an oxide layer correction term is constructed to quantify the oxide layer's inhibitory effect on deformation. Based on the coupling relationship between the equivalent plastic strain rate of aluminum alloy and the stress-dominant term, temperature correction term, and oxide layer correction term, the following rheological constitutive equation is constructed: ; The flow field is determined based on the mass conservation equation, the momentum conservation equation, and the rheological constitutive equation. in, t represents the density of the aluminum alloy; t represents time. For the flow velocity vector of the aluminum alloy; For flow pressure; For aluminum alloy viscous stress tensor; This is the vector of gravitational acceleration; For the viscous stress tensor of the oxide layer; The thickness of the aluminum alloy oxide layer; This is the oxidation rate constant; It is the activation energy for oxidation; It is the activation energy for the deformation of aluminum alloys; It is the gas constant; This refers to the actual absolute temperature. denoted as the equivalent plastic strain rate of the aluminum alloy; A is a material constant. Stress coefficient; For flow stress; The strain hardening index; This is the oxide layer correction factor, and ; This represents the influence coefficient of the oxide layer.

[0023] Specifically, the flow field is used to describe the flow behavior of high-temperature aluminum alloys within the mold flow channel, and its core components include velocity distribution, pressure transmission, and metal streamline trajectory. In this invention, based on the law of mass conservation in a closed system, within a closed mold flow channel, the metal mass neither increases nor decreases out of thin air; the mass flowing into a certain region is equal to the mass flowing out of that region. Based on the above principle, a small element within the mold flow channel is considered, and the balance relationship between the inflow and outflow mass flux and the rate of mass change within the small element is analyzed. After simplification, the aforementioned mass conservation equation is obtained. In the aforementioned mass conservation equation... This represents the rate of change of local mass, i.e., the rate of change of mass of the infinitesimal element. The divergence of mass flux represents the difference in mass flux between the inflow and outflow of the infinitesimal element.

[0024] Based on the application of Newton's second law in continuous media (aluminum alloy melt), the rate of change of momentum of the molten metal is equal to the sum of all external forces acting on the melt, thus constructing the above momentum conservation equation. Unlike the traditional momentum conservation equation, because aluminum alloys are easily oxidized, the oxide layer adheres to the metal surface, increasing flow resistance, equivalent to applying additional viscous stress. Therefore, this invention adds viscous stress from the oxide layer to improve accuracy. In the above momentum conservation equation… The rate of change of momentum of the infinitesimal element; For the flow pressure gradient, For aluminum alloy viscous stress divergence; This is the term related to gravity. This is a unique oxide layer viscous stress divergence term in this invention.

[0025] Next, to describe the rheological properties of the aluminum alloy melt during extrusion, a corresponding rheological constitutive equation was constructed. Since the oxide layer thickness inhibits the strain rate, this invention adds an oxide layer correction coefficient to the basic Zener-Hollomon (ZH) equation to characterize the inhibitory effect of oxide layer thickness on the strain rate. In the aforementioned rheological constitutive equation, Let be the equivalent plastic strain rate of aluminum alloy, and be the unknown quantity to be determined. Stress-dominant term This is a temperature correction term; For oxide layer correction,

[0026] Among the above parameters , A , , , , , All of these are known quantities; the rest are unknown quantities. , The flow field was obtained through experimental fitting. The above-mentioned mass conservation equation, momentum conservation equation, and rheological constitutive equation together form the flow field described above.

[0027] For the temperature field, in a preferred embodiment, constructing a temperature field characterizing the temperature change pattern includes: A heat conduction term is constructed based on the heat exchange between the micro-element and the environment; The basic internal heat source term is constructed based on the heat energy of plastic work during metal deformation in the extrusion process and the heat generated by friction between the metal and the die interface. An additional heat generation term for the oxide layer is constructed based on the additional heat generated by interfacial friction and viscous dissipation of the aluminum alloy oxide layer when it flows on the metal surface. Based on the heat conduction term, the basic internal heat source term, the additional heat generation term of the oxide layer, and the rate of change of thermodynamic energy of the micro-element, the following Fourier heat conduction equation is constructed to obtain the temperature field; ; in, The specific heat capacity of aluminum alloy; Thermal conductivity of aluminum alloy; Based on the intensity of the internal heat source, and , For the thermal strength of plastic deformation, The frictional heat intensity between the aluminum alloy and the mold; The coefficient of plastic work-heat conversion. The coefficient of friction between the aluminum alloy and the mold; The heat generation intensity of the oxide layer, and , The coefficient of frictional heat generation of the oxide layer.

[0028] Specifically, the temperature field is constructed based on the Fourier heat conduction equation. Since the oxide layer is an adhesion layer on the metal surface, it generates interfacial friction with the metal flow. At the same time, the oxidation reaction itself releases a small amount of heat. Therefore, this invention adds an additional heat generation term for the oxide layer to the traditional Fourier heat conduction equation to form the above-mentioned temperature field. In the above temperature field, The rate of change of the thermodynamic energy of the infinitesimal element; For heat conduction, The quantity is known; The basic internal heat source intensity is the aforementioned basic internal heat source term, which is the sum of heat generated by plastic deformation and heat generated by friction, quantifying the process of energy conversion into heat in metal flow. Heat generated during plastic deformation, i.e., the heat intensity of plastic deformation; The heat generated by the flow friction is the frictional heat intensity between the aluminum alloy and the mold. The additional heat generation term for the oxide layer is specifically designed for the thermal effect of the oxide layer, quantifying the additional heat generation from interfacial friction and oxidation reaction. Among the above parameters, , , , , Given quantities This was obtained through experimental testing.

[0029] For the stress field, in a preferred embodiment, constructing a stress field characterizing the stress distribution and deformation patterns during pressurization includes: A stress equilibrium equation is constructed based on the equilibrium relationship between the stress divergence of the infinitesimal element, gravity, and the additional volume forces of the oxide layer: ; Based on the elastic strain rate and the plastic strain rate, construct the elastoplastic constitutive equation: ; By incorporating an oxide thickness correction factor and a temperature correction term into the pre-defined Drucker-Prager model, a yield criterion is generated. ; The stress field is constructed based on the stress balance equation, the elastoplastic constitutive equation, and the yield criterion. in, Let be the flow stress tensor, and its equivalent scalar is: ; Add a volume force vector to the oxide layer; For the total strain rate tensor of aluminum alloy; For the elastic strain rate tensor of aluminum alloy; Let be the plastic strain rate tensor of aluminum alloy, and its equivalent scalar is . ; This represents the yield function value. These are the parameters for the Drucker-Prager model; It is the first invariant of stress; It is the second invariant of stress; The basic yield parameter; The yield influence coefficient of the oxide layer; The temperature yield influence coefficient; This is the preset reference absolute temperature.

[0030] In this invention, the presence of an oxide layer alters the stress state of the metal surface, generating pressure directed inwards. Therefore, based on fundamental static equilibrium, this invention adds an additional volume force from the oxide layer to ensure a complete stress equilibrium description in the constructed stress equilibrium equation. In the stress equilibrium equation... For the above stress divergence, Since the basic volume force vector is mainly gravity, it is used... To express, This invention features an additional volume force term for the oxide layer.

[0031] The essence of the elastoplastic constitutive equation is that the total strain rate is still the sum of the elastic and plastic strain rates. Let be the total strain rate tensor of the aluminum alloy. Let be the elastic strain rate tensor of the aluminum alloy. For the plastic strain rate tensor of aluminum alloy; Regarding the yield criterion, this invention retains the core of the Drucker-Prager model and adds an oxide layer thickness correction factor. and temperature correction item The oxide layer reduces the yield strength of the metal surface, and increased temperature weakens the material's yield characteristics. Correction coefficients are obtained through experimental fitting to make the yield criterion more closely match actual working conditions with oxide layers. It should be noted that in a stress field... , , , as well as All are known quantities, for illustrative purposes only. It can be set to 25℃.

[0032] For the welding field, in a preferred embodiment, the welding field described above for constructing and measuring the diffusion welding quality of multiple metal flows in the welding chamber includes: Welding criteria are constructed based on the oxide layer growth model, welding chamber pressure, and the residence time of the metal in the welding chamber, and the welding field is obtained. The welding criteria include: The welding is deemed effective when the aluminum alloy oxide layer thickness is less than or equal to a first preset thickness value, the welding chamber pressure is greater than or equal to a first preset pressure value, and the metal residence time in the welding chamber is greater than a first time threshold value; the welding is deemed effective when the aluminum alloy oxide layer thickness is greater than a first preset thickness value, the welding chamber pressure is greater than or equal to a second preset pressure value, and the metal residence time in the welding chamber is greater than or equal to a first time threshold value; the first preset pressure value is less than the second preset pressure value; otherwise, the welding is deemed invalid.

[0033] The first preset thickness value can be 0.1 mm; the first preset pressure value can be 100 MPa; the first time threshold can be 10 ms; and the second preset pressure value can be 120 MPa. In this invention, when the oxide layer is thin, it has little impact on the weld, so a pressure threshold of 100 MPa is maintained. When the oxide layer is too thick, the oxide film will hinder atomic diffusion, and the pressure needs to be increased to 120 MPa to break the oxide film. Therefore, the above adaptive pressure threshold criterion is constructed to obtain the welding criterion to determine whether it is effective. The specific formula of the welding criterion is as follows: Where W represents the weld quality, 1 indicates a valid weld, and 0 indicates an invalid weld; The pressure in the welding chamber; This refers to the residence time of the metal in the welding chamber.

[0034] This invention combines the effects of aluminum alloy oxidation to construct a flow field, temperature field, stress field, and welding field, forming a four-field coupling for accurate three-dimensional mold flow simulation calculations. The coupling relationships of each field are as follows: The flow field provides the basic internal heat source, while the metal flow drives the oxide layer movement, generating heat from the oxide layer, which in turn drives the temperature field change; the temperature rise in the temperature field accelerates oxide layer growth, reduces the metal strain rate through a correction coefficient, and simultaneously changes the metal viscosity, indirectly affecting the flow velocity distribution in the flow field; the pressure and viscous stress in the flow field are the core loads of the stress field, while the additional volume forces generated by the oxide layer supplement the stress field, exacerbating local stress concentration; the mold should... Force deformation (such as core yaw) alters the flow channel dimensions, affecting metal flow; simultaneously, the yield strength of the stress field changes with the oxide layer and temperature, indirectly adjusting flow resistance; the flow field determines the welding pressure and residence time within the welding field, and both the flow and temperature fields jointly drive oxide layer growth, triggering adaptive adjustment of the welding pressure threshold (100MPa→120MPa); the mechanical properties of the effective welding area in the welding field are homogenized, reducing flow resistance fluctuations; the loose structure of the ineffective welding area leads to abrupt changes in local flow resistance, slightly adjusting the flow velocity distribution; uneven temperature distribution generates thermal stress, and rising temperature accelerates oxide layer growth, which is addressed through the temperature-yield criterion. The oxide layer coupling term reduces the material's yield strength and alleviates stress concentration in the stress field; plastic deformation in the stress concentration area releases a small amount of heat, supplementing the heat source of the temperature field; at the same time, mold stress deformation changes the contact state between the oxide layer and the metal, affecting the heat generation of the oxide layer; increased temperature promotes atomic diffusion and improves welding efficiency, but at the same time accelerates oxide layer growth, requiring increased welding pressure to offset the influence of the oxide film; there is no significant heat change during weld formation, but the heat conduction efficiency of the effective weld area is higher than that of the ineffective weld area, slightly altering the local temperature transfer path; mold stress deformation changes the actual size of the welding chamber, affecting pressure and dwell time; at the same time, the mold core wobble caused by stress concentration will cause uneven local welding pressure, which, combined with the difference in oxide layer distribution, exacerbates the fluctuation of weld quality; the weld strength of effective welds is close to that of the base material, the metal stress is transmitted evenly, and the mold stress distribution is gentle; the weld strength of ineffective welds is low, and local stress concentration is easy to occur, changing the mold stress distribution.

[0035] Step S3: Based on the three-dimensional model, perform multi-physics field simulation calculations of the flow field, temperature field, stress field, and welding field to generate flow velocity distribution cloud map, stress distribution cloud map, core displacement cloud map, temperature distribution cloud map, and welding quality cloud map of the aluminum profile extrusion die.

[0036] Specifically, after constructing the aforementioned flow field, temperature field, stress field, and welding field, boundary constraints are set. For the flow field: an extrusion velocity is applied to the inlet surface of the model blank, and a pressure boundary (atmospheric pressure) is applied to the outlet surface of the profile. For the temperature field: convective heat transfer is applied to the outer wall of the model mold, and a high-temperature boundary is applied to the wall of the welding chamber. For the stress field: displacement constraints (Ux=Uy=Uz=0) are applied to the model, and a metal flow pressure load is applied to the surface of the mold core. Then, initial conditions are set, such as: mold preheating to 480~520℃, blank preheating to 450~480℃, and the initial temperature of the oxide layer being consistent with that of the blank; the environmental convective heat transfer coefficient is taken as 30~50W / (m²). 2 K).

[0037] Then, simulation calculations are performed to obtain the aforementioned flow velocity distribution cloud map, stress distribution cloud map, temperature distribution cloud map, core displacement cloud map, and weld quality cloud map. Among them, the generation of the core displacement cloud map is based on the stress at the core position determining the corresponding strain, and then the corresponding displacement is obtained after changing the strain. This conversion is existing technical content and will not be elaborated here. Step S4: For the target area of ​​the aluminum profile extrusion die, select the corresponding type of cloud map to generate a target cloud map at each time sequence, and generate a target cloud map sequence for the target area at each time sequence stage based on the target cloud maps at each time sequence stage; wherein, the target area includes: working zone, welding chamber, die core, and flow guide bridge; when the target area is the working zone, select the temperature distribution cloud map and flow velocity distribution cloud map of the corresponding area and fuse them to generate a target cloud map; when the target area is the welding chamber, select the welding quality cloud map as the target cloud map; when the target area is the flow guide bridge, select the temperature distribution cloud map and stress distribution cloud map of the corresponding area and fuse them to generate a target cloud map; when the target area is the die core, select the die core displacement cloud map as the target cloud map; In a preferred embodiment, when the target area is a working zone, a first temperature field cloud map and a first velocity distribution cloud map of the working zone area are extracted; based on the average velocity corresponding to the first velocity distribution cloud map, the velocity uniformity value corresponding to each velocity is calculated; a first velocity uniformity distribution cloud map is generated based on the velocity uniformity value; the first temperature field cloud map and the first velocity distribution cloud map are fused to generate a first fused cloud map, and the locations in the first fused cloud map where the temperature exceeds a first preset temperature value and the velocity uniformity is less than a first velocity uniformity threshold are marked with a first color, and the remaining locations are marked with a second color to obtain the corresponding target cloud map. When the target area is the welding chamber, the locations where welding is invalid are marked with the third color and the remaining locations are marked with the fourth color, based on the welding quality cloud map, to obtain the corresponding target cloud map. When the target area is the mold core, according to the mold core displacement cloud map, the positions with displacement greater than the preset displacement value are marked with the fifth color, and the remaining positions are marked with the sixth color to obtain the corresponding target cloud map; When the target area is a flow guide bridge, the temperature distribution cloud map and stress distribution cloud map of the corresponding area are fused to generate a second fused cloud map. The locations in the second fused cloud map where the temperature exceeds the second preset temperature value and the stress exceeds the preset stress value are marked with the seventh color, and the remaining locations are marked with the eighth color to obtain the corresponding target cloud map.

[0038] S5. Based on the target cloud map sequence under each time stage, determine the defects in the target area of ​​the aluminum profile extrusion die, and optimize and adjust the aluminum profile extrusion die according to the defects.

[0039] In a preferred embodiment, each timing stage includes any one or a combination of the following stages: a preheating stage, a startup stage, a stable extrusion stage, and a finishing stage; The step of determining defects in the target area of ​​the aluminum profile extrusion die based on the target cloud map sequence at each time stage includes: If the target area is a working zone, and during the stable extrusion stage, the area of ​​the continuous first color region in the target cloud map is larger than the first preset area and continues to expand during the finishing stage, then it is determined that there is a local overheating wear defect in the corresponding area of ​​the working zone; if during the start-up stage, the target cloud map shows scattered first color spots that do not disappear during the stable extrusion stage, then it is determined that there is a metal flow unevenness defect in the corresponding area of ​​the working zone. If the target area is a welding chamber, and during the stable extrusion stage, the area of ​​the third color region in the target cloud map is larger than the second preset area, then it is determined that there is a welding defect in the corresponding area of ​​the welding chamber. If the target area is the mold core, and a seventh color area appears during the stable extrusion stage and remains stable during the finishing stage, it is determined that there is a mold core thermal deformation deviation defect. If the target area is a guide bridge, and a seventh-colored area that extends in the thickness direction of the guide bridge and appears as a strip during the stable extrusion stage, and the color deepens during the final stage, then it is determined that there is a cracking risk defect in the area corresponding to the guide bridge.

[0040] In a preferred embodiment, optimizing the aluminum profile extrusion die according to the defects includes: If localized overheating and wear defects are identified, shorten the working zone corresponding to the defect area; if uneven metal flow defects are identified, increase the working zone length in areas with excessively high flow rates and shorten the working zone length in areas with excessively low flow rates; if poor welding defects are identified, increase the welding chamber volume; if excessive thermal deformation defects of the mold core are identified, add reinforcing ribs; if cracking risk defects are identified, increase the thickness of the guide bridge.

[0041] Specifically, for the working zone area, the core defects are localized overheating wear and profile surface defects caused by uneven metal flow. Temperature is a key factor affecting wear, and the flow velocity distribution directly reflects the frictional intensity and uniformity between the metal and the working zone. Therefore, in this invention, the temperature field cloud map and flow velocity distribution cloud map of the working zone area are extracted from the simulation results, namely the first temperature field cloud map and the first flow velocity distribution cloud map mentioned above, and cropped to the same coordinate system and region. Based on engineering experience, the influence weight of temperature on working zone wear is 0.6, and the weight of flow velocity distribution uniformity is 0.4 (the larger the flow velocity difference, the more concentrated the frictional stress, and the higher the wear risk). For the first flow velocity distribution cloud map, the average flow velocity of the entire first flow velocity distribution cloud map is first calculated, and then according to the following formula: The flow velocity uniformity at each location point is calculated, and a corresponding first flow velocity uniformity distribution cloud map is generated. Next, the first temperature field cloud map and the first flow velocity uniformity distribution cloud map are normalized. Then, the normalized first temperature field cloud map and the first flow velocity uniformity distribution cloud map are superimposed pixel-level according to weighting coefficients to obtain the aforementioned first fused cloud map. Next, a color mapping rule is set for the fused cloud map. A first color (e.g., red) is used to mark locations where the temperature exceeds a first preset temperature value (e.g., 550℃) and the flow velocity uniformity is less than a first flow velocity uniformity threshold (e.g., 0.7), indicating risk areas with high temperature and uneven flow velocity. The remaining areas are marked with a second color (e.g., green), thus obtaining the target cloud map corresponding to the working zone. For the welding chamber, based directly on the welding quality cloud map, the invalid welding points (the welding quality of 0) are marked with the third color (e.g., yellow), and the remaining points (the welding quality of 1) are marked with the fourth color (e.g., blue), thus obtaining the corresponding target cloud map; For the mold core, directly based on the mold core displacement cloud map, the positions with displacement greater than the preset displacement value (e.g., 50μm) are marked with the fifth color (e.g., cyan), and the remaining positions are marked with the sixth color (e.g., orange) to obtain the corresponding target cloud map; For the flow guide bridge, the core defect is cracking caused by local overheating. Excessive temperature reduces the strength of the mold material, and stress concentration easily leads to crack propagation. Both factors need to be characterized to assess the risk. Therefore, temperature distribution cloud maps and stress distribution cloud maps of the flow guide bridge area are extracted. Then, the corresponding temperature distribution cloud maps and stress distribution cloud maps are normalized and weighted and fused with equal weights of 0.5 to generate a second fused cloud map. Then, the locations in the second fused cloud map that exceed the second preset temperature value (e.g., 550℃) and the stress exceeds the preset stress value (e.g., 500 MPa) are marked with the seventh color (e.g., purple), and the remaining locations are marked with the eighth color (e.g., red).

[0042] In an optional embodiment, the colors gradually deepen to represent that the corresponding values ​​are deviating further and further from the normal values.

[0043] Because the extrusion process is a dynamic, time-series process, the die temperature and metal flow state differ significantly at different stages (preheating, start-up, stable extrusion, and finish-up). It is necessary to capture these dynamic changes using a sequence of time-series cloud maps to avoid the limitations of single-moment simulations. First, each time-series stage is determined: based on extrusion process parameters (such as extrusion speed, billet preheating temperature, and die preheating temperature), key time-series nodes are divided, typically including: ① Preheating stage (0-10s, die not in contact with high-temperature billet); ② Start-up stage (10-30s, billet begins to enter the die, metal flow is unstable); ③ Stable extrusion stage (30-150s, metal flow and temperature tend to stabilize, die heating and cooling are balanced); ④ Finish-up stage (150-200s, billet extrusion is about to be completed, metal quantity decreases, flow rate fluctuates). For each region, in chronological order across the above four time-series stages, different time-series target cloud maps for the same region are combined into a sequence, thus visually presenting the risk change trend of the component throughout the entire extrusion process.

[0044] After obtaining the corresponding target cloud map sequence, the corresponding defects can be determined based on the changes in the target cloud map over time; Specifically, if, during the stable extrusion stage, the area of ​​the continuous first color region in the target cloud map is larger than the first preset area (which can be 15% of the total area of ​​the working belt), and continues to expand during the finishing stage, then it is determined that there is a local overheating and wear defect in the corresponding area of ​​the working belt; if, during the start-up stage, the target cloud map shows scattered first color spots, and these spots do not disappear during the stable extrusion stage, then it is determined that there is a metal flow unevenness defect in the corresponding area of ​​the working belt. In the case of a welding chamber, if during the stable extrusion stage, the area of ​​the third color region in the target cloud map is larger than the second preset area (e.g., 20% of the total area of ​​the welding chamber), then it is determined that there is a welding defect in the corresponding area of ​​the welding chamber. For the die core, if a seventh color area appears during the stable extrusion stage and remains stable during the finishing stage, it is determined that there is a die core thermal deformation exceeding the tolerance. If, during the stable extrusion stage, a seventh-colored area extends in the thickness direction of the guide bridge and appears as a strip, and the color deepens during the final stage, then the area corresponding to the guide bridge is determined to have a cracking risk defect.

[0045] When localized overheating and wear defects are identified, the working zone corresponding to the defect area is shortened to reduce frictional heat generation. When uneven metal flow defects are identified, the working zone length is increased in areas with excessively high flow rates to increase flow resistance, while the working zone length is shortened in areas with excessively low flow rates to reduce flow resistance. When poor welding defects are identified, the welding chamber volume is increased to increase metal residence time and promote effective welding. When excessive thermal deformation of the die core is identified, reinforcing ribs are added to improve die core rigidity. When cracking risk defects are identified, the thickness of the guide bridge is increased to reduce stress concentration, thereby achieving optimized adjustment of the aluminum profile extrusion die.

[0046] The aforementioned optimization and adjustment methods can only achieve qualitative adjustments and cannot perform quantitative adjustments. For example, when a local overheating wear defect is identified, the specific amount of shortening of the working zone cannot be determined. The only option is to attempt gradual reduction at preset intervals, making the optimization and adjustment process relatively slow. To further improve the efficiency of optimization and adjustment, this invention, after qualitatively determining the defect and its adjustment method in the macroscopic domain, embeds calculations in the microscopic domain to further calculate the precise adjustment amount, thereby improving the efficiency of mold optimization and adjustment.

[0047] Specifically, in one optional embodiment, optimizing and adjusting the aluminum profile extrusion die according to the defects further includes: A micro-mesh of a preset size is used to divide the defect area into micro-mesh domains to obtain the micro-computation domain. Given the existence of localized overheating and wear defects in the micro-computation domain, the abnormal grain growth rate and microhardness within the micro-computation domain are extracted; based on the abnormal grain growth rate and microhardness, the shortening of the working band is calculated.

[0048] When it is determined that there is a defect of uneven metal flow in the micro-computation domain, the grain size gradient is calculated based on the maximum and minimum grain sizes in the micro-computation domain; the micro-shear stress in the micro-computation domain is extracted; and the working band length adjustment value is determined based on the grain size gradient and the micro-shear stress. When it is determined that there are poor welding defects in the micro-computation domain, the porosity and grain boundary fusion rate of the crystal in the micro-computation domain are extracted, and the volume expansion of the welding chamber is determined based on the porosity and grain boundary fusion rate.

[0049] When it is determined that there is a core thermal deformation defect in the micro-computation domain, the micro-stress concentration factor is determined based on the ratio of the local maximum micro-stress to the average micro-stress in the micro-computation domain; the grain orientation deviation in the region is extracted; and the number of reinforcing ribs to be added is determined based on the micro-stress concentration factor and the grain orientation deviation.

[0050] When it is determined that there is a risk of cracking in the micro-computation domain, the micro-crack propagation rate and intergranular stress within the micro-computation domain are extracted, and the increase in the thickness of the flow bridge is determined based on the micro-crack propagation rate and intergranular stress.

[0051] Specifically, a fine mesh of 1-5μm is used as the micro-mesh of the preset size, and then the defect region is divided using this micro-mesh to obtain the micro-computation domain; the size of this micro-mesh matches the size of aluminum alloy grains (typical aluminum alloy grain size is 20-100μm, and the fine mesh can accurately capture grain evolution and micro-pores). The essence of localized overheating wear is that the friction between the working strip and the aluminum alloy generates heat, leading to excessively high local temperatures, abnormal grain growth, and consequently, a decrease in the surface hardness of the mold, thus exacerbating wear. Therefore, by extracting the abnormal grain growth rate and microhardness from the micro-domain and establishing a correlation model between them and the shortening of the working strip, the specific shortening value can be calculated.

[0052] To address this, the phase-field method was first used to simulate the grain evolution process, and the abnormal grain growth rate in the microscopic computational domain was extracted. Abnormal growth rate of granules =Area of ​​abnormally grown grains / Total area of ​​defect region. Preferably, abnormally grown grains are defined as grains with a size greater than twice that of normal grains. The microhardness HV of the microcomputation domain is obtained through nanoindentation simulation. The microhardness of the normal operating zone is... Calculate the hardness reduction rate The shortening of the working zone, ΔL, is determined by the abnormal grain growth rate. With hardness reduction rate This is a joint decision. Based on illustrative data simulation and fitting, the following correlation formula can be used: ΔL = × + × + constant term Based on the amount of shortening, and The coefficients are those obtained from fitting (for 6063 aluminum alloy). It can be 0.3. It can be 0.2; It can be 0.1; other types of alloys are determined based on the actual fitting results.

[0053] The essence of uneven metal flow is that the difference in flow velocity in different regions of the working zone leads to an excessively large grain size gradient and concentration of microscopic shear stress. By extracting the grain size gradient and microscopic shear stress in the regions with different flow velocities through microscopic domains, the increase in the working zone in the region with excessively high flow velocity and the shortening in the region with excessively low flow velocity are calculated respectively.

[0054] Extracting grain size gradient With micro shear stress For areas with excessively high flow rates, the increase in the working zone is as follows: ΔL + = × + × ; and The coefficients are those obtained from fitting (for 6063 aluminum alloy). It can be 0.02. It can be 0.01; for other types of alloys, the value should be determined based on the actual fitting results. For the shortening of the working zone in areas with excessively slow flow rates: ΔL - = × + × ; and The coefficients are those obtained from fitting (for 6063 aluminum alloy). It can be 0.015. It can be 0.008; for other types of alloys, the value should be determined based on the actual fitting results. The essence of poor welding is insufficient pressure in the welding chamber, which prevents multiple metal streams from fully diffusing and fusing, manifesting microscopically as high porosity and low grain boundary fusion rate. By extracting the porosity and grain boundary fusion rate of the ineffective welding region through microscopic domain analysis, a correlation model between these values ​​and the increase in welding chamber volume is established.

[0055] Given the presence of welding defects in the microscopic computational domain, diffusion theory is used to simulate the hydrogen diffusion and pore evolution process, and the average porosity of the defective region is extracted. Grain boundary fusion rate was extracted through grain boundary analysis. ( = (Fused grain boundary length / Total grain boundary length). Then, the increase in weld chamber volume ΔV is calculated using the following formula: ΔV = × - ×( -0.5) + ; and The coefficients are those obtained through fitting. The constant term is the base expansion (for 6063 aluminum alloy). It can be 5. It can be 2; It can be 2; other types of alloys are determined based on the actual fitting results). The essence of excessive thermal deformation of the mold core is localized micro-stress concentration and excessive grain orientation deviation, leading to macroscopic displacement exceeding the preset value. By extracting the micro-stress concentration coefficient and grain orientation deviation of the deformed area of ​​the mold core in the micro-domain, and combining this with the mold core structural dimensions, the number and arrangement of reinforcing ribs are determined.

[0056] Given the existence of excessive thermal deformation defects in the mold core within the microscopic computational domain, the microscopic stress distribution is simulated using the crystal plasticity finite element method to extract the microscopic stress concentration factor. Grain orientation deviations within the region were extracted using electron backscatter diffraction simulation. Calculate the required number of reinforcing ribs using the following formula. : = round( × + × In the formula, round() is the rounding function, ensuring... It is an integer. and The coefficients are those obtained from fitting (for 6063 aluminum alloy). It can be 0.2. It can be 0.01; for other types of alloys, the value should be determined based on the actual fitting results. The essence of the cracking risk in flow guide bridges is excessively high local temperature and stress, leading to the initiation and propagation of microcracks. Microscopically, this manifests as rapid crack propagation rate and high intergranular stress. By extracting the microcrack propagation rate and intergranular stress in the strip-shaped defect region through micro-domain analysis, a correlation model between these values ​​and the increase in flow guide bridge thickness is established.

[0057] Given the existence of cracking risk defects in the microscopic computational domain, the extended finite element method is used to simulate the evolution of micro-cracks and extract the micro-crack propagation rate. Intergranular stress within the extraction region The increase in the thickness of the guide bridge, Δt, is calculated using the following formula: Δt = × + × ; and The coefficients are those obtained from fitting (for 6063 aluminum alloy). It can be 0.5. It can be 0.003; for other types of alloys, the value should be determined based on the actual fitting results. Through the simulation calculations of the micro-domain described above, the adjustment amount and the corresponding constant in the micro-domain are correlated in the form of a corresponding linear combination, thereby clearly calculating the specific adjustment amount and improving the efficiency of optimizing and adjusting aluminum profile extrusion dies.

[0058] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments; like Figure 2 The present invention provides a three-dimensional mold flow simulation optimization device for aluminum profile extrusion dies, comprising: a three-dimensional model construction module, a multi-coupling field construction module, a cloud map construction module, a target cloud map sequence construction module, and a die adjustment module; The 3D model building module is used to build a 3D model of the aluminum profile extrusion die based on the die parameters of the aluminum profile extrusion die. The multi-coupling field construction module is used to construct a flow field to characterize the rheological behavior of aluminum alloy in aluminum profile extrusion die, a temperature field to characterize the temperature change law, a stress field to characterize the stress distribution and deformation law during pressurization, and a welding field to measure the diffusion welding quality of multiple metal flows in the welding chamber. The cloud map construction module is used to perform multi-physics field simulation calculations of flow field, temperature field, stress field and welding field based on the three-dimensional model, and generate flow velocity distribution cloud map, stress distribution cloud map, core displacement cloud map, temperature distribution cloud map and welding quality cloud map of aluminum profile extrusion die; The target cloud map sequence construction module is used to select the corresponding type of cloud map to generate a target cloud map for the target area of ​​the aluminum profile extrusion die at each time sequence, and generate a target cloud map sequence for the target area at each time sequence stage based on the target cloud maps at each time sequence stage. The target area includes: the working zone, the welding chamber, the die core, and the flow guide bridge. When the target area is the working zone, the temperature distribution cloud map and the flow velocity distribution cloud map of the corresponding area are selected and fused to generate the target cloud map. When the target area is the welding chamber, the welding quality cloud map is selected as the target cloud map. When the target area is the flow guide bridge, the temperature distribution cloud map and the stress distribution cloud map of the corresponding area are selected and fused to generate the target cloud map. When the target area is the die core, the die core displacement cloud map is selected as the target cloud map. The mold adjustment module is used to determine the defects in the target area of ​​the aluminum profile extrusion mold according to the target cloud map sequence at each time stage, and to optimize and adjust the aluminum profile extrusion mold according to the defects.

[0059] In a preferred embodiment, the system further includes a micro-computation module, which is used to divide the defect region into micro-mesh regions using a micro-mesh of a preset size to obtain a micro-computation domain. Given the existence of localized overheating and wear defects in the micro-computation domain, the abnormal grain growth rate and microhardness within the micro-computation domain are extracted; based on the abnormal grain growth rate and microhardness, the shortening of the working band is calculated.

[0060] When it is determined that there is a defect of uneven metal flow in the micro-computation domain, the grain size gradient is calculated based on the maximum and minimum grain sizes in the micro-computation domain; the micro-shear stress in the micro-computation domain is extracted; and the working band length adjustment value is determined based on the grain size gradient and the micro-shear stress. When it is determined that there are poor welding defects in the micro-computation domain, the porosity and grain boundary fusion rate of the crystal in the micro-computation domain are extracted, and the volume expansion of the welding chamber is determined based on the porosity and grain boundary fusion rate.

[0061] When it is determined that there is a core thermal deformation defect in the micro-computation domain, the micro-stress concentration factor is determined based on the ratio of the local maximum micro-stress to the average micro-stress in the micro-computation domain; the grain orientation deviation in the region is extracted; and the number of reinforcing ribs to be added is determined based on the micro-stress concentration factor and the grain orientation deviation.

[0062] When it is determined that there is a risk of cracking in the micro-computation domain, the micro-crack propagation rate and intergranular stress within the micro-computation domain are extracted, and the increase in the thickness of the flow bridge is determined based on the micro-crack propagation rate and intergranular stress.

[0063] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the drug storage management device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0064] One embodiment of this application provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a three-dimensional mold flow simulation optimization method for aluminum profile extrusion molds as described above.

[0065] One embodiment of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a three-dimensional mold flow simulation optimization method for an aluminum profile extrusion die as described above.

[0066] The computer device may be a smartphone, tablet, desktop computer, or cloud server, among other computing devices. This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the figures are merely examples of computer devices and do not constitute a limitation on the computer device. It may include more or fewer components than illustrated, or a combination of certain components, or different components, such as input / output devices, network access devices, etc.

[0067] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0068] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0069] This application provides a computer program product that, when run on a computer device, enables the computer device to execute the steps described in the various method embodiments above.

[0070] In the several embodiments provided in this application, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0071] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies, characterized in that, include: Construct a three-dimensional model of the aluminum profile extrusion die based on the die parameters; Construct a flow field to characterize the rheological behavior of aluminum alloys in aluminum profile extrusion dies, a temperature field to characterize the temperature change law, a stress field to characterize the stress distribution and deformation law during pressurization, and a welding field to measure the diffusion welding quality of multiple metal flows in the welding chamber. Based on the three-dimensional model, multi-physics field simulation calculations are performed on the flow field, temperature field, stress field, and welding field to generate flow velocity distribution cloud map, stress distribution cloud map, core displacement cloud map, temperature distribution cloud map, and welding quality cloud map of aluminum profile extrusion die. For the target area of ​​an aluminum profile extrusion die, a target cloud map is generated at each time step by selecting the corresponding type of cloud map. Based on the target cloud maps at each time step, a sequence of target cloud maps for the target area at each time step is generated. The target area includes: the working zone, the welding chamber, the die core, and the flow guide bridge. When the target area is the working zone, the temperature distribution cloud map and the flow velocity distribution cloud map of the corresponding area are fused to generate the target cloud map. When the target area is the welding chamber, the welding quality cloud map is selected as the target cloud map. When the target area is the flow guide bridge, the temperature distribution cloud map and the stress distribution cloud map of the corresponding area are fused to generate the target cloud map. When the target area is the die core, the die core displacement cloud map is selected as the target cloud map. Based on the target cloud map sequence at each time stage, the defects in the target area of ​​the aluminum profile extrusion die are determined, and the aluminum profile extrusion die is optimized and adjusted according to the defects.

2. The three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies as described in claim 1, characterized in that, Constructing a flow field to characterize the rheological behavior of aluminum alloys within an aluminum profile extrusion die includes: Based on the rate of change of mass of the infinitesimal element and the difference in mass flux between the inflow and outflow of the infinitesimal element, the following mass conservation equation is constructed: ； Based on the relationship between the rate of change of momentum of a micro-element and the flow pressure gradient, viscous stress of the aluminum alloy, gravity, and viscous stress of the oxide layer when aluminum alloy flows in the mold channel, the following momentum conservation equation is constructed: ; The stress-dominant term is constructed based on the flow stress, the material coefficient of the aluminum alloy, the stress coefficient, and the strain hardening exponent; the temperature correction term is constructed based on the deformation activation energy, the gas constant, and the temperature. Based on the coupling relationship between aluminum alloy oxide layer and time and temperature, an oxide layer growth model describing the dynamic growth law of aluminum alloy oxide layer thickness is constructed: Based on the oxide layer growth model and the preset oxide layer influence coefficient, an oxide layer correction term is constructed to quantify the oxide layer's inhibitory effect on deformation. Based on the coupling relationship between the equivalent plastic strain rate of aluminum alloy and the stress-dominant term, temperature correction term, and oxide layer correction term, the following rheological constitutive equation is constructed: ； The flow field is determined based on the mass conservation equation, the momentum conservation equation, and the rheological constitutive equation. in, t represents the density of the aluminum alloy; t represents time. For the flow velocity vector of the aluminum alloy; For flow pressure; For aluminum alloy viscous stress tensor; This is the vector of gravitational acceleration; For the viscous stress tensor of the oxide layer; The thickness of the aluminum alloy oxide layer; This is the oxidation rate constant; It is the activation energy for oxidation; It is the activation energy for the deformation of aluminum alloys; It is the gas constant; This refers to the actual absolute temperature. denoted as the equivalent plastic strain rate of the aluminum alloy; A is a material constant. Stress coefficient; For flow stress; The strain hardening index; This is the oxide layer correction factor, and ; This represents the influence coefficient of the oxide layer.

3. The three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies as described in claim 2, characterized in that, Constructing a temperature field characterizing the temperature change pattern includes: A heat conduction term is constructed based on the heat exchange between the micro-element and the environment; The basic internal heat source term is constructed based on the heat energy of plastic work during metal deformation in the extrusion process and the heat generated by friction between the metal and the die interface. An additional heat generation term for the oxide layer is constructed based on the additional heat generated by interfacial friction and viscous dissipation of the aluminum alloy oxide layer when it flows on the metal surface. Based on the heat conduction term, the basic internal heat source term, the additional heat generation term of the oxide layer, and the rate of change of thermodynamic energy of the micro-element, the following Fourier heat conduction equation is constructed to obtain the temperature field; ； in, The specific heat capacity of aluminum alloy; Thermal conductivity of aluminum alloy; Based on the intensity of the internal heat source, and , For the thermal strength of plastic deformation, The frictional heat intensity between the aluminum alloy and the mold; The coefficient of plastic work-heat conversion. The coefficient of friction between the aluminum alloy and the mold; The heat generation intensity of the oxide layer, and , The coefficient of frictional heat generation of the oxide layer.

4. The three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies as described in claim 3, characterized in that, Constructing a stress field characterizing the stress distribution and deformation patterns during the pressurization process includes: A stress equilibrium equation is constructed based on the equilibrium relationship between the stress divergence of the infinitesimal element, gravity, and the additional volume forces of the oxide layer: ; Based on the elastic strain rate and the plastic strain rate, construct the elastoplastic constitutive equation: ; By incorporating an oxide thickness correction factor and a temperature correction term into the pre-defined Drucker-Prager model, a yield criterion is generated. ; The stress field is constructed based on the stress balance equation, the elastoplastic constitutive equation, and the yield criterion. in, Let be the flow stress tensor, and its equivalent scalar is: ; Add a volume force vector to the oxide layer; For the total strain rate tensor of aluminum alloy; For the elastic strain rate tensor of aluminum alloy; Let be the plastic strain rate tensor of aluminum alloy, and its equivalent scalar is . ; This represents the yield function value. These are the parameters for the Drucker-Prager model; It is the first invariant of stress; It is the second invariant of stress; The basic yield parameter; The yield influence coefficient of the oxide layer; The temperature yield influence coefficient; This is the preset reference absolute temperature.

5. The three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies as described in claim 4, characterized in that, Constructing and measuring the diffusion welding quality of multiple metal streams in the welding chamber includes: Welding criteria are constructed based on the oxide layer growth model, welding chamber pressure, and the residence time of the metal in the welding chamber, and the welding field is obtained. The welding criteria include: The welding is deemed effective if the thickness of the aluminum alloy oxide layer is less than or equal to the first preset thickness value, the pressure in the welding chamber is greater than or equal to the first preset pressure value, and the residence time of the metal in the welding chamber is greater than the first time threshold. The welding is deemed effective when the aluminum alloy oxide layer thickness is greater than the first preset thickness value, the welding chamber pressure is greater than or equal to the second preset pressure value, and the metal residence time in the welding chamber is greater than or equal to the first time threshold value; the first preset pressure value is less than the second preset pressure value. In all other cases, the weld is deemed invalid.

6. The three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies as described in claim 5, characterized in that, When the target area is a working zone, the first temperature field cloud map and the first velocity distribution cloud map of the working zone area are extracted; based on the average velocity corresponding to the first velocity distribution cloud map, the velocity uniformity value corresponding to each velocity is calculated; based on the velocity uniformity value, a first velocity uniformity distribution cloud map is generated; the first temperature field cloud map and the first velocity distribution cloud map are fused to generate a first fused cloud map, and the locations in the first fused cloud map where the temperature exceeds a first preset temperature value and the velocity uniformity is less than a first velocity uniformity threshold are marked with a first color, and the remaining locations are marked with a second color to obtain the corresponding target cloud map. When the target area is the welding chamber, the locations where welding is invalid are marked with the third color and the remaining locations are marked with the fourth color, based on the welding quality cloud map, to obtain the corresponding target cloud map. When the target area is the mold core, according to the mold core displacement cloud map, the positions with displacement greater than the preset displacement value are marked with the fifth color, and the remaining positions are marked with the sixth color to obtain the corresponding target cloud map; When the target area is a flow guide bridge, the temperature distribution cloud map and stress distribution cloud map of the corresponding area are fused to generate a second fused cloud map. The locations in the second fused cloud map where the temperature exceeds the second preset temperature value and the stress exceeds the preset stress value are marked with the seventh color, and the remaining locations are marked with the eighth color to obtain the corresponding target cloud map.

7. The three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies as described in claim 6, characterized in that, Each of the time-series stages includes any one of the following stages or a combination thereof: preheating stage, start-up stage, stable extrusion stage, and finishing stage; The step of determining defects in the target area of ​​the aluminum profile extrusion die based on the target cloud map sequence at each time stage includes: If the target area is a working zone, and during the stable extrusion stage, the area of ​​the continuous first color region in the target cloud map is larger than the first preset area and continues to expand during the finishing stage, then it is determined that there is a local overheating wear defect in the corresponding area of ​​the working zone; if during the start-up stage, the target cloud map shows scattered first color spots that do not disappear during the stable extrusion stage, then it is determined that there is a metal flow unevenness defect in the corresponding area of ​​the working zone. If the target area is a welding chamber, and during the stable extrusion stage, the area of ​​the third color region in the target cloud map is larger than the second preset area, then it is determined that there is a welding defect in the corresponding area of ​​the welding chamber. If the target area is the mold core, and a seventh color area appears during the stable extrusion stage and remains stable during the finishing stage, it is determined that there is a mold core thermal deformation deviation defect. If the target area is a guide bridge, and a seventh-colored area that extends in the thickness direction of the guide bridge and appears as a strip during the stable extrusion stage, and the color deepens during the final stage, then it is determined that there is a cracking risk defect in the area corresponding to the guide bridge.

8. The three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies as described in claim 7, characterized in that, The aluminum profile extrusion die is optimized and adjusted according to the aforementioned defects, including: If localized overheating and wear defects are identified, shorten the working zone corresponding to the defect area; if uneven metal flow defects are identified, increase the working zone length in areas with excessively high flow rates and shorten the working zone length in areas with excessively low flow rates; if poor welding defects are identified, increase the welding chamber volume; if excessive thermal deformation defects of the mold core are identified, add reinforcing ribs; if cracking risk defects are identified, increase the thickness of the guide bridge.

9. The three-dimensional mold flow simulation optimization method for aluminum profile extrusion dies as described in claim 8, characterized in that, Optimizing and adjusting the aluminum profile extrusion die based on the aforementioned defects also includes: A micro-mesh of a preset size is used to divide the defect area into micro-mesh domains to obtain the micro-computation domain. Given the existence of localized overheating and wear defects in the micro-computation domain, the abnormal grain growth rate and microhardness within the micro-computation domain are extracted; based on the abnormal grain growth rate and microhardness, the shortening of the working band is calculated. When it is determined that there is a defect of uneven metal flow in the micro-computation domain, the grain size gradient is calculated based on the maximum and minimum grain sizes in the micro-computation domain; the micro-shear stress in the micro-computation domain is extracted; and the working band length adjustment value is determined based on the grain size gradient and the micro-shear stress. When it is determined that there are poor welding defects in the micro-computation domain, the porosity and grain boundary fusion rate of the crystal in the micro-computation domain are extracted, and the volume expansion of the welding chamber is determined based on the porosity and grain boundary fusion rate. When it is determined that there is a core thermal deformation defect in the micro-computation domain, the micro-stress concentration factor is determined based on the ratio of the local maximum micro-stress to the average micro-stress in the micro-computation domain; the grain orientation deviation in the region is extracted; and the number of reinforcing ribs to be added is determined based on the micro-stress concentration factor and the grain orientation deviation. When it is determined that there is a risk of cracking in the micro-computation domain, the micro-crack propagation rate and intergranular stress within the micro-computation domain are extracted, and the increase in the thickness of the flow bridge is determined based on the micro-crack propagation rate and intergranular stress.

10. A three-dimensional mold flow simulation optimization device for aluminum profile extrusion dies, comprising: The module includes a 3D model building module, a multi-coupling field building module, a cloud map building module, a target cloud map sequence building module, and a mold adjustment module. The 3D model building module is used to build a 3D model of the aluminum profile extrusion die based on the die parameters of the aluminum profile extrusion die. The multi-coupling field construction module is used to construct a flow field to characterize the rheological behavior of aluminum alloy in aluminum profile extrusion die, a temperature field to characterize the temperature change law, a stress field to characterize the stress distribution and deformation law during pressurization, and a welding field to measure the diffusion welding quality of multiple metal flows in the welding chamber. The cloud map construction module is used to perform multi-physics field simulation calculations of flow field, temperature field, stress field and welding field based on the three-dimensional model, and generate flow velocity distribution cloud map, stress distribution cloud map, core displacement cloud map, temperature distribution cloud map and welding quality cloud map of aluminum profile extrusion die; The target cloud map sequence construction module is used to select the corresponding type of cloud map to generate a target cloud map for the target area of ​​the aluminum profile extrusion die at each time sequence, and generate a target cloud map sequence for the target area at each time sequence stage based on the target cloud maps at each time sequence stage. The target area includes: the working zone, the welding chamber, the die core, and the flow guide bridge. When the target area is the working zone, the temperature distribution cloud map and the flow velocity distribution cloud map of the corresponding area are selected and fused to generate the target cloud map. When the target area is the welding chamber, the welding quality cloud map is selected as the target cloud map. When the target area is the flow guide bridge, the temperature distribution cloud map and the stress distribution cloud map of the corresponding area are selected and fused to generate the target cloud map. When the target area is the die core, the die core displacement cloud map is selected as the target cloud map. The mold adjustment module is used to determine the defects in the target area of ​​the aluminum profile extrusion mold according to the target cloud map sequence at each time stage, and to optimize and adjust the aluminum profile extrusion mold according to the defects.

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