Co-extrusion processing optimization method, device, equipment, storage medium and program product

Abstract

The invention relates to a co-extrusion processing optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The co-extrusion processing refers to a process of mixing and extruding a plurality of materials through an extrusion die to form a layered structure, and the co-extrusion processing comprises an extrusion process and a cooling process; the method comprises the following steps: acquiring extrusion process parameters of an extrusion process, cooling process parameters of a cooling process, constitutive models of all materials and a runner structure model of an extrusion die; performing finite element analysis on the extrusion process of each material according to the extrusion process parameters, the cooling process parameters, the constitutive model and the runner structure model to obtain physical quantity distribution information of each material in the co-extrusion processing process; and optimizing at least one of the extrusion process parameters, the cooling process parameters, the constitutive model and the runner structure model according to the physical quantity distribution information. By adopting the method, resource waste can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of cable processing, and particularly to a co-extrusion processing optimization method, device, computer device, computer-readable storage medium, and computer program product. Background Art

[0002] As a key infrastructure for power transmission and communication networks, the quality of cables is directly related to the reliability and safety of the entire system. The cable co-extrusion process is a core link in manufacturing cables. By extruding insulating materials or sheath materials onto the outer layer of a metal conductor under high temperature and high pressure, an insulating layer or sheath layer is formed on the outer layer of the metal conductor.

[0003] In traditional technologies, the optimization of the cable co-extrusion process mainly relies on manual experience debugging. That is, technicians estimate process parameters based on material properties, equipment performance, product requirements, etc., and then through multiple trial productions, gradually adjust the parameters until the expected product quality is achieved.

[0004] However, with the increasing requirements for cable quality, the outer protective layer of cables has gradually evolved into a multi-layer composite structure. Not only is the structure becoming more and more complex, but the material properties of each layer also vary greatly. Existing empirical formulas and test methods are difficult to accurately estimate the process parameters that meet the requirements, and often multiple trial productions are required to determine the appropriate process parameters, resulting in a large waste of resources such as manpower, material resources, and time. Summary of the Invention

[0005] Based on this, it is necessary to provide a co-extrusion processing optimization method, device, computer device, computer-readable storage medium, and computer program product that can reduce resource waste for the above technical problems.

[0006] In a first aspect, the present application provides a co-extrusion processing optimization method. Co-extrusion processing refers to the process of mixing and extruding multiple materials through an extrusion die to form a layered structure. The co-extrusion processing includes an extrusion process and a cooling process. The method includes:

[0007] Obtain the extrusion process parameters of the extrusion process, the cooling process parameters of the cooling process, the constitutive model of each material, and the flow channel structure model of the extrusion die;

[0008] Perform finite element analysis on the extrusion process of each material according to the extrusion process parameters, cooling process parameters, constitutive model, and flow channel structure model to obtain the physical quantity distribution information of each material during the co-extrusion process;

[0009] Optimize at least one of the extrusion process parameters, cooling process parameters, constitutive model, and flow channel structure model according to the physical quantity distribution information.

[0010] In one embodiment, obtaining the constitutive model of the material includes:

[0011] Obtaining the performance data of the material at multiple temperatures to be measured;

[0012] According to the performance data, constructing the viscosity-shear rate curves of the material at each temperature to be measured, and taking each viscosity-shear rate curve as the constitutive model of the material.

[0013] In one embodiment, the performance data includes material constants, shear thinning index, critical shear stress, first temperature correlation constant, second temperature correlation constant, and glass transition temperature; obtaining the performance data of the material at multiple temperatures to be measured includes:

[0014] Obtaining the glass transition temperature of the material and determining the glass transition temperature as the temperature to be measured;

[0015] Using a rheometer to detect the zero-shear viscosity of the material at each temperature to be measured, and the viscosity of the material at each temperature to be measured and multiple preset shear rates;

[0016] Determining the zero-shear viscosity corresponding to the glass transition temperature as the material constant of the material;

[0017] Based on the glass transition temperature, each zero-shear viscosity, each temperature to be measured, and a preset first viscoelastic algorithm, performing linear fitting to obtain the first temperature correlation constant and the second temperature correlation constant;

[0018] Based on each viscosity, each temperature to be measured, each preset shear rate, and a preset second viscoelastic algorithm, obtaining the shear thinning index and the critical shear stress of the material by fitting through a solver.

[0019] In one embodiment, obtaining the flow channel structure model of the extrusion die includes:

[0020] Obtaining the flow channel structure information of the extrusion die;

[0021] According to the flow channel structure information, performing flow channel structure modeling on the extrusion die to obtain the flow channel structure model.

[0022] In one embodiment, the flow channel structure information includes material confluence position information and fillet radius information; the physical quantity distribution information includes residence time distribution information; according to the physical quantity distribution information, optimizing at least one of the extrusion process parameters, cooling process parameters, constitutive model, and flow channel structure model, including:

[0023] According to the residence time distribution information, optimizing at least one of the material confluence position information and the fillet radius information.

[0024] In one embodiment, the extrusion process parameters include at least one of the temperature at the inlet, the flow rate, the flow velocity, and the drawing speed at the outlet; the cooling process parameters include the cooling temperature; the physical quantity distribution information includes at least one of the temperature distribution information, the pressure distribution information, the velocity distribution information, and the residence time distribution information.

[0025] According to the physical quantity distribution information, at least one of the extrusion process parameters, the cooling process parameters, the constitutive model, and the flow channel structure model is optimized, including at least one of the following:

[0026] In the case where at least one temperature value is detected to exceed the preset temperature threshold based on the temperature distribution information, at least one of the following is performed: reducing the temperature at the inlet, reducing the flow rate, reducing the flow velocity, increasing the drawing speed at the outlet, and reducing the cooling temperature;

[0027] In the case where at least one pressure value is detected to exceed the preset pressure threshold based on the pressure distribution information, at least one of the following is performed: increasing the temperature at the inlet, reducing the flow rate, reducing the flow velocity, and increasing the drawing speed at the outlet;

[0028] In the case where at least one velocity value is detected to exceed the preset velocity threshold based on the velocity distribution information, at least one of the following is performed: reducing the temperature at the inlet, reducing the flow rate, reducing the flow velocity, and reducing the drawing speed at the outlet;

[0029] In the case where at least one residence time value is detected to exceed the preset residence time threshold based on the residence time distribution information, at least one of the following is performed: increasing the temperature at the inlet, increasing the flow rate, increasing the flow velocity, and increasing the drawing speed at the outlet.

[0030] In a second aspect, the present application further provides a co-extrusion processing optimization device. Co-extrusion processing refers to the process of mixing and extruding multiple materials through an extrusion die to form a layered structure. The co-extrusion processing includes an extrusion process and a cooling process. The device includes:

[0031] An acquisition module for acquiring the extrusion process parameters of the extrusion process, the cooling process parameters of the cooling process, the constitutive model of each material, and the flow channel structure model of the extrusion die;

[0032] An analysis module for performing finite element analysis on the extrusion process of each material according to the extrusion process parameters, the cooling process parameters, the constitutive model, and the flow channel structure model to obtain the physical quantity distribution information of each material in the co-extrusion processing process;

[0033] An optimization module for optimizing at least one of the extrusion process parameters, the cooling process parameters, the constitutive model, and the flow channel structure model according to the physical quantity distribution information.

[0034] In a third aspect, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0035] Obtain the extrusion process parameters of the extrusion process, the cooling process parameters of the cooling process, the constitutive models of various materials, and the flow channel structure model of the extrusion die;

[0036] According to the extrusion process parameters, the cooling process parameters, the constitutive models, and the flow channel structure model, perform finite element analysis on the extrusion process of each material to obtain the physical quantity distribution information of each material during the co-extrusion process;

[0037] According to the physical quantity distribution information, optimize at least one of the extrusion process parameters, the cooling process parameters, the constitutive models, and the flow channel structure model.

[0038] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0039] Obtain the extrusion process parameters of the extrusion process, the cooling process parameters of the cooling process, the constitutive models of various materials, and the flow channel structure model of the extrusion die;

[0040] According to the extrusion process parameters, the cooling process parameters, the constitutive models, and the flow channel structure model, perform finite element analysis on the extrusion process of each material to obtain the physical quantity distribution information of each material during the co-extrusion process;

[0041] According to the physical quantity distribution information, optimize at least one of the extrusion process parameters, the cooling process parameters, the constitutive models, and the flow channel structure model.

[0042] In a fifth aspect, the present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0043] Obtain the extrusion process parameters of the extrusion process, the cooling process parameters of the cooling process, the constitutive models of various materials, and the flow channel structure model of the extrusion die;

[0044] According to the extrusion process parameters, the cooling process parameters, the constitutive models, and the flow channel structure model, perform finite element analysis on the extrusion process of each material to obtain the physical quantity distribution information of each material during the co-extrusion process;

[0045] According to the physical quantity distribution information, optimize at least one of the extrusion process parameters, the cooling process parameters, the constitutive models, and the flow channel structure model.

[0046] The above co-extrusion processing optimization method, device, computer equipment, computer-readable storage medium and computer program product obtain the extrusion process parameters during the extrusion process, the cooling process parameters during the cooling process, the constitutive models of various materials, and the flow channel structure model of the extrusion die. According to the extrusion process parameters, cooling process parameters, constitutive models and flow channel structure model, a finite element analysis is carried out on the extrusion process of each material to obtain the physical quantity distribution information of each material during the co-extrusion process, realizing the simulation of the co-extrusion process of each material. Thus, it can accurately simulate and predict the influence of the extrusion process parameters, cooling process parameters, properties of the material itself and the flow channel structure of the extrusion die on the extrusion behavior. Therefore, based on the simulation results, at least one of the extrusion process parameters, cooling process parameters, constitutive model and flow channel structure model can be optimized, that is, the co-extrusion processing optimization based on simulation is realized. Compared with the method that relies on manual experience debugging and repeated trial production, the co-extrusion processing optimization based on simulation can significantly improve the prediction accuracy of process parameters, reduce the number of trial productions, thereby effectively reducing the waste of resources such as manpower, material resources and time, and at the same time improving the quality and production efficiency of cable products. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0048] Figure 1 It is a schematic flowchart of the co-extrusion processing optimization method in an embodiment;

[0049] Figure 2 It is a schematic structural diagram of the flow channel in the extrusion die in an embodiment;

[0050] Figure 3 It is a schematic flowchart of the co-extrusion processing optimization method in another embodiment;

[0051] Figure 4 It is a schematic diagram of the viscosity-shear rate curve in an embodiment;

[0052] Figure 5 It is a schematic diagram of the flow channel structure model in an embodiment;

[0053] Figure 6 It is a schematic diagram of the temperature contour map in an embodiment;

[0054] Figure 7 It is a schematic diagram of the pressure contour map in an embodiment;

[0055] Figure 8 The structural block diagram of the coextrusion processing optimization device in an embodiment;

[0056] Figure 9 The internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0057] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] In an exemplary embodiment, as Figure 1 shown, a coextrusion processing optimization method is provided. In this embodiment, the method is exemplified by being applied to a terminal. Among them, the terminal can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things device can be a smart speaker, a smart TV, a smart air conditioner, a smart vehicle-mounted device, a projection device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps S10-S30. Among them:

[0059] Step S10, obtain the extrusion process parameters in the extrusion process, the cooling process parameters in the cooling process, the constitutive models of various materials, and the flow channel structure model of the extrusion die.

[0060] Among them, coextrusion processing can refer to the process of mixing and extruding multiple materials through an extrusion die to form a layered structure. Through coextrusion processing, two or more different materials can be extruded simultaneously, so that they are combined together to form a multi-layer composite structure.

[0061] Coextrusion processing at least includes an extrusion process and a cooling process.

[0062] The extrusion process can refer to the process in which the molten material is formed and laminated through an extrusion die, and then extruded from the outlet of the extrusion die. The material extruded from the outlet of the extrusion die initially forms a layered structure, but at this time, the layered structure has a high temperature and poor stability in shape and size.

[0063] Among them, the extrusion die can refer to a die with specific shapes and dimensions, which is used to determine the cross-sectional shape of the final product. That is, the predetermined cross-sectional shape of the product is determined based on the extrusion die.

[0064] The cooling process can refer to the process of cooling and shaping the layered structure extruded from the extrusion die to form a stable layered structure.

[0065] Among them, the cooling method can be one or a combination of air cooling, water cooling, vacuum cooling, etc.

[0066] In some feasible embodiments, before melting and plasticizing the material, pre-treatments such as drying and mixing the material can also be performed.

[0067] In some feasible embodiments, after cooling the multi-layer structure, post-treatments such as cutting, winding, and printing the cooled multi-layer structure can also be performed.

[0068] In some feasible embodiments, the extruded multi-layer structure can also be pulled out of the extrusion die at a certain speed by a traction device. By providing a constant speed to control the moving speed of the extruded material, so that the product is pulled out of the extrusion die at a stable speed, it is possible to avoid product deformation or dimensional deviation caused by uneven speed, and reduce the accumulation of internal stress in the material, thereby improving the product quality.

[0069] In some feasible embodiments, the co-extrusion process includes: various materials enter through different feeding ports of the extruder respectively. Under the action of the screw, all materials are pushed forward and gradually melted and plasticized in the heating cylinder; the melted materials continue to move forward under the pressure of the screw, and are further uniformly mixed and reach the required temperature and viscosity; the melted and plasticized various materials are respectively transported to the corresponding inlets on the extrusion die, and the molten materials are extruded through the extrusion die to form a continuous layered structure with a predetermined cross-sectional shape; the layered structure just extruded from the extrusion die is still in a high-temperature state. Therefore, the extruded multi-layer structure can be rapidly cooled and solidified by a cooling device to ensure the stability of its shape and dimensions; furthermore, the extruded multi-layer structure is pulled out of the extrusion die at a certain speed by a traction device, and post-treatments such as cutting or winding are performed as required.

[0070] The extrusion process parameters can refer to the adjustable variables that affect the flow, deformation, and shaping of the material during the extrusion process. The extrusion process parameters can include at least one of the temperature at the inlet, flow rate, flow velocity, and the traction speed at the outlet, etc.

[0071] The cooling process parameters can refer to the adjustable variables that affect the shaping and performance of the product during the cooling process after extrusion. The cooling process parameters can include at least one of the cooling temperature, cooling type, and cooling time, etc.

[0072] The extrusion process parameters and the cooling process parameters can be initial values set randomly or empirical values set based on experience. This embodiment places no restrictions on this.

[0073] It can be understood that before performing finite element analysis, co - extrusion processing based on the extrusion process parameters and the cooling process parameters may or may not be able to produce products meeting quality requirements, which is uncertain. If after finite element analysis, it is determined that co - extrusion processing based on the extrusion process parameters and the cooling process parameters can produce products meeting quality requirements, then the subsequent optimization process can be omitted; if after finite element analysis, it is determined that co - extrusion processing based on the extrusion process parameters and the cooling process parameters is difficult to produce products meeting quality requirements, then at least one of the extrusion process parameters and the cooling process parameters can be optimized, and finite element analysis can be performed again based on the optimized parameters until products meeting quality requirements can be produced based on the optimized parameters.

[0074] The constitutive model can refer to a mathematical model that describes the mechanical behavior and flow characteristics of materials during the extrusion process. The constitutive model can include at least one of viscosity - shear rate curves, physical properties, and mechanical properties, etc.

[0075] In some feasible embodiments, the material of the cable protective layer can be determined. For example, the customer specifies the processing raw materials for each layer of the protective layer. In this case, the constitutive model of each material can also be determined. Thus, the constitutive model of each material can be pre - constructed before co - extrusion processing optimization or can be constructed during the co - extrusion processing optimization.

[0076] In some other feasible embodiments, the materials of each layer of the cable protective layer can also be optimized by the co - extrusion processing optimization method.

[0077] The runner structure model can refer to a geometric model that describes the flow path of materials in the extrusion die. The runner structure model can include at least one of runner geometry, runner size, and runner surface characteristics, etc.

[0078] In some feasible embodiments, the runner structure of the extrusion die can be determined. For example, existing extrusion equipment is used for processing. In this case, the runner structure model of the extrusion die can also be determined. Thus, the runner structure model of the extrusion die can be pre - constructed before co - extrusion processing optimization or can be constructed during the co - extrusion processing optimization.

[0079] In some other feasible embodiments, when the runner structure of the extrusion die is adjustable, the runner structure of the extrusion die can also be optimized by the co - extrusion processing optimization method.

[0080] In an extrusion die for co-extruding multiple materials, each material first passes through its respective dedicated runner and then converges. The various materials can converge at the same location or at different positions, and this embodiment does not limit this. In this case, the runner structure model can also include at least one of the material convergence position information and the fillet radius information. The material convergence position information is used to characterize the convergence position of two or more materials. The fillet radius information is used to characterize the radius of the arc transition part of the runner at the turning or intersecting points.

[0081] As an example, as Figure 2 shown, different materials are respectively conveyed in the first runner 201, the second runner 202, and the third runner 203. The first runner 201 and the second runner 202 are connected at the first material convergence position 204. The solid line between the first runner 201 and the second runner 202 indicates that they are not connected, and the dashed line between the first runner 201 and the second runner 202 indicates that they are connected. Therefore, the materials in the first runner 201 and the second runner 202 are combined at the first material convergence position 204 to form a double-layer structure; further, the combined runner of the first runner 201 and the second runner 202 and the third runner 203 are connected at the second material convergence position 205. Therefore, this double-layer structure is combined with the third material at the second material convergence position 205 to form a three-layer structure.

[0082] Step S20: According to the extrusion process parameters, cooling process parameters, constitutive model, and runner structure model, perform finite element analysis on the extrusion process of each material to obtain the physical quantity distribution information of each material during the co-extrusion process.

[0083] Among them, finite element analysis can refer to an engineering simulation technology based on numerical methods, which can be used to solve physical phenomena in complex engineering problems. Finite element analysis can discretize a continuous geometric body into a finite number of small elements, establish mathematical equations for each element, and finally obtain the physical quantity distribution of the entire system by solving these equations.

[0084] The physical quantity distribution information can refer to a data set that describes the variation law of a certain physical quantity within its scope of action, where the scope of action can include time or space. The physical quantity can include temperature, pressure, velocity, density, stress, or strain, etc. The physical quantity distribution information can be represented by mathematical functions, charts, or images, etc.

[0085] In some feasible embodiments, if the co-extrusion process optimization requires multiple physical quantities, for each physical quantity, its corresponding physical quantity distribution information can be analyzed and obtained.

[0086] Exemplarily, after obtaining the extrusion process parameters, cooling process parameters, constitutive model, and runner structure model, a geometric model can be created or imported based on the runner structure model, and the physical and mechanical properties of each material can be defined based on the constitutive model. The runner in the extrusion die can be modeled in layers according to the material. Further, based on the runner structure model, fluid meshes can be drawn, and the fluid meshes at the junctions of each material layer can be refined to form boundary layer meshes. Further, the extrusion process parameters and cooling process parameters can be imported, and the physical quantity values of each mesh during the co-extrusion process can be solved through a preset finite element solver to obtain the physical quantity distribution information of each material during the co-extrusion process.

[0087] Step S30, optimize at least one of the extrusion process parameters, cooling process parameters, constitutive model, and runner structure model according to the physical quantity distribution information.

[0088] Exemplarily, after obtaining the physical quantity distribution information, the physical quantity distribution information can be compared with a preset optimization target. If the physical quantity distribution information already meets the preset optimization target, subsequent optimization is not required, and co-extrusion processing can be directly performed based on the extrusion process parameters, the cooling process parameters during the cooling process, the constitutive model of each material, and the runner structure model of the extrusion die. If the physical quantity distribution information does not meet the preset optimization target, at least one of the extrusion process parameters, cooling process parameters, constitutive model, and runner structure model can be optimized according to the preset optimization rule and the physical quantity distribution information that does not meet the preset optimization target.

[0089] Among them, the preset optimization purpose and the preset optimization rule can both be determined in advance based on actual needs or test results, etc., and this embodiment does not limit this. For example, assuming that the physical quantity distribution information includes temperature distribution information, the preset optimization target can be that the highest temperature does not exceed a preset temperature threshold. If it is detected based on the temperature distribution information that the highest temperature is higher than the preset temperature threshold, optimization can be performed based on the optimization rule corresponding to the highest temperature being higher than the preset temperature threshold. For example, the temperature of the material can be adjusted by at least one of the following methods: reducing the temperature at the inlet, reducing the flow rate, reducing the flow velocity, increasing the traction speed at the outlet, and reducing the cooling temperature, so that the material temperature is reduced below the temperature threshold.

[0090] In the above co-extrusion processing optimization method, by obtaining the extrusion process parameters during the extrusion process, the cooling process parameters during the cooling process, the constitutive models of various materials, and the flow channel structure model of the extrusion die, and based on the extrusion process parameters, cooling process parameters, constitutive models, and flow channel structure model, a finite element analysis is performed on the extrusion process of each material to obtain the distribution information of physical quantities of each material during the co-extrusion process, realizing the simulation of the co-extrusion process of each material. Thus, it can accurately simulate and predict the influence of the extrusion process parameters, cooling process parameters, the properties of the material itself, and the flow channel structure of the extrusion die on the extrusion behavior. Therefore, based on the simulation results, at least one of the extrusion process parameters, cooling process parameters, constitutive model, and flow channel structure model can be optimized, that is, the co-extrusion processing optimization based on simulation is realized. Compared with the method that relies on manual experience debugging and repeated trial production, the co-extrusion processing optimization based on simulation can significantly improve the prediction accuracy of process parameters, reduce the number of trial productions, thereby effectively reducing the waste of resources such as manpower, material resources, and time, and at the same time improving the quality and production efficiency of cable products.

[0091] In an exemplary embodiment, obtaining the constitutive model of a material includes steps S101 to S102. Among them:

[0092] Step S101, obtaining the performance data of the material at multiple measured temperatures.

[0093] It should be noted that the protective layer material of the cable is usually a polymer, and the polymer melt usually behaves as a non-Newtonian fluid, whose viscosity changes with the shear rate, and during the extrusion process, the ranges of change of the shear rate and temperature are large. On the other hand, the temperature change significantly affects the viscosity of the polymer melt, and the polymer melt will be compressed under high pressure, which will also cause a change in viscosity. These above-mentioned changes greatly increase the simulation difficulty and easily lead to the deviation of the simulation result from the real co-extrusion process, resulting in that the product still cannot meet the quality requirements after the co-extrusion processing optimization.

[0094] Among them, the performance data can refer to a set of parameters or indicators used to describe the physical, chemical, or mechanical behavior of the material under specific conditions, and can include at least one of rheological performance data, thermal performance data, mechanical performance data, etc. The performance data can be obtained through experimental measurement, theoretical model, or numerical simulation.

[0095] The temperature to be measured can refer to the temperature used to construct the viscosity-shear rate curve. In the process of constructing the viscosity-shear rate curve, it is often necessary to detect rheological data through rheological experiments and perform fitting. When conducting experiments and fitting, it is necessary to determine the experimental temperature, that is, the temperature to be measured. The temperature to be measured can be determined according to the processing temperature of actual co-extrusion processing. For example, if the processing temperature of a certain material is 120 °C, the temperatures to be measured can be 115 °C, 120 °C, 125 °C, and 130 °C, and can be specifically determined according to actual needs and test results, etc. This embodiment does not limit this.

[0096] As an example, the performance data of each material at each temperature to be measured can be queried from the network or database according to the identification information of the material and the temperature to be measured as an index.

[0097] As another example, a suitable viscosity-shear rate model can be determined first, and then according to the parameters required to be determined in the viscosity-shear rate model, the performance of each material is detected at multiple temperatures to be measured, and the performance data of each material at each temperature to be measured is obtained.

[0098] Step S102, according to the performance data, construct the viscosity-shear rate curves of the material at each temperature to be measured, and use each viscosity-shear rate curve as the constitutive model of the material.

[0099] Among them, the viscosity-shear rate curve can be used to represent the relationship between the viscosity of the material and the shear rate at a constant temperature. In some feasible embodiments, the viscosity of the material is negatively correlated with the shear rate.

[0100] As an example, for the viscosity-shear rate curve corresponding to each temperature to be measured, the performance data corresponding to the temperature to be measured can be used as model parameters and directly substituted into the viscosity-shear rate model to construct the viscosity-shear rate curve of the material at the temperature to be measured, and each viscosity-shear rate curve is used as the constitutive model of the material.

[0101] As another example, the performance data can be multiple groups of data of viscosity and shear rate. A suitable viscosity-shear rate model can be selected first, and then based on the data group of viscosity and shear rate, viscosity-shear rate model fitting is performed to obtain the mathematical expression of the change of viscosity with shear rate, and the mathematical expression can be used as the constitutive model of the material.

[0102] In some other feasible embodiments, based on the fitting results, with the shear rate as the horizontal axis and the viscosity as the vertical axis, the viscosity-shear rate curves of the material at each temperature can be plotted, and the viscosity-shear rate curves are used as the constitutive models of the material.

[0103] In some feasible embodiments, the viscosity-shear rate model can be the Cross-WLF model (a model used to describe the viscosity characteristics of thermoplastic materials). The Cross-WLF model can accurately describe the rheological behavior of polymer melts during processing, especially the viscosity changes under temperature, shear rate, and pressure variations, and is applicable to a wide range of shear rate and temperature conditions. Therefore, it can more accurately predict the flow behavior in extrusion processing, optimize the coextrusion processing parameters, and improve product quality.

[0104] In this embodiment, since the flow behavior of the material during extrusion processing highly depends on the shear rate and temperature, through the viscosity-shear rate curve, the viscosity changes of the material at different shear rates can be accurately described, thereby reflecting the non-Newtonian fluid characteristics of the material. In extrusion processing, the polymer melt undergoes complex flow in the screw and die, and the shear rate range is usually very wide. Through the viscosity-shear rate curve, the transition of the material from Newtonian fluid behavior at low shear rates to shear thinning behavior at high shear rates can be captured. In addition, by combining the viscosity-shear rate curve with temperature dependence, the flow characteristics of the material at different temperatures can be further described, which is particularly important for processes with significant temperature changes in extrusion processing. Therefore, using the viscosity-shear rate curves at multiple temperatures as the constitutive model of the material can more realistically simulate the flow behavior in extrusion processing and provide a reliable basis for process optimization and defect prediction.

[0105] In an exemplary embodiment, as Figure 3 shown, the performance data includes material constants, shear thinning index, critical shear stress, first temperature correlation constant, second temperature correlation constant, and glass transition temperature; obtaining the performance data of the material at multiple temperatures to be measured includes steps S1011 to S1015. Among them:

[0106] Step S1011, obtaining the glass transition temperature of the material and determining the glass transition temperature as the temperature to be measured.

[0107] The viscosity-shear rate model can be the Cross-WLF model, and the Cross-WLF model can be expressed as:

[0108]

[0109] Wherein, .

[0110] Wherein, η can be the viscosity; can be the shear rate; f can represent the shear rate function; A can be the material constant; can be the critical shear stress; n can be the shear thinning index; αT can be the temperature dependence factor.

[0111] Among them, according to the WLF equation, .

[0112] Among them, C1 and C2 are the temperature correlation constants of the WLF equation. C1 is used to characterize the sensitivity of viscosity to temperature. The larger C1 is, the more significant the change of viscosity with temperature is. C2 is used to characterize the temperature dependence range of the material. The larger C2 is, the wider the response range of viscosity to temperature change is; T is the temperature to be measured; T ref is the reference temperature, usually taken as the glass transition temperature.

[0113] Among them, the glass transition temperature can refer to the temperature at which the material changes from a hard and brittle glassy state to a soft and elastic high elastic state. The glass transition temperature is an inherent property of the material and can be determined by tests such as differential scanning calorimetry or dynamic mechanical analysis after determining the chemical composition and molecular structure of the material, etc.

[0114] After measuring the material constant, the first temperature correlation constant, the second temperature correlation constant, the shear thinning index and the critical shear stress, substituting the material constant, the first temperature correlation constant, the second temperature correlation constant, the shear thinning index and the critical shear stress into the Cross-WLF model, the viscosity-shear rate curve can be obtained.

[0115] Exemplarily, before the coextrusion processing optimization, the material can be tested first to determine the glass transition temperature of the material and store the data. During the coextrusion processing optimization, the glass transition temperature of the material can be directly obtained; or during the coextrusion processing optimization, the material can be tested to determine the glass transition temperature of the material. Then, the glass transition temperature is determined as the temperature to be measured. It can be understood that the glass transition temperature is only one of the temperatures to be measured, and the temperature to be measured can also include other temperatures besides the glass transition temperature.

[0116] Step S1012, using a rheometer, detect the zero-shear viscosity of the material at each temperature to be measured, and the viscosity of the material at each temperature to be measured and multiple preset shear rates.

[0117] Among them, the zero-shear viscosity can refer to the viscosity value exhibited by the material at an extremely low shear rate. The zero-shear viscosity of the same material at different temperatures may be different. Therefore, for each material, its zero-shear viscosity at each temperature to be measured can be determined separately.

[0118] Exemplarily, at each temperature to be measured, the rheometer can be first used to perform tests at an extremely low shear rate to obtain viscosity data at the extremely low shear rate. Then, the shear rate can be gradually increased from low to high, and the shear stress at each shear rate can be recorded. Furthermore, the viscosity data at the extremely low shear rate can be extrapolated to a shear rate approaching zero to obtain the zero-shear viscosity, and the viscosity at each shear rate can also be calculated based on the shear stress.

[0119] In some feasible embodiments, the extremely low shear rate can be 0.001 s -1 to 0.01 s -1 .

[0120] In some feasible embodiments, the rheometer can be at least one of a rotational rheometer and a capillary rheometer. Among them, the rotational rheometer is applicable to the low to medium shear rate range, while the capillary rheometer is applicable to the high shear rate range. One or more rheometers can be selected for testing according to different shear rate ranges.

[0121] Step S1013, determining the zero-shear viscosity corresponding to the glass transition temperature as the material constant of the material.

[0122] Exemplarily, after measuring the zero-shear viscosity, the zero-shear viscosity corresponding to the glass transition temperature can be queried from the test results, and the zero-shear viscosity corresponding to the glass transition temperature can be determined as the material constant of the material.

[0123] Step S1014, performing linear fitting based on the glass transition temperature, each zero-shear viscosity, each temperature to be measured, and a preset first viscoelastic algorithm to obtain a first temperature correlation constant and a second temperature correlation constant.

[0124] Among them, the preset first viscoelastic algorithm can refer to the WLF equation, which is used to characterize the correlation between viscosity and temperature. The preset first viscoelastic algorithm can be expressed as:

[0125]

[0126] where C1 is the first temperature correlation constant; C2 is the second temperature correlation constant; T is the temperature to be measured; T ref is the reference temperature, usually taken as the glass transition temperature; is the zero-shear viscosity at the temperature to be measured; is the zero-shear viscosity at the reference temperature.

[0127] Exemplarily, the preset first viscoelastic algorithm can be rewritten into a linear form, in which the slope and intercept are both composed of at least one of the first temperature correlation constant and the second temperature correlation constant; then, based on this linear form, the glass transition temperature, each zero-shear viscosity, and each temperature to be measured, linear fitting is performed, and the first temperature correlation constant and the second temperature correlation constant are determined according to the fitting result.

[0128] Step S1015, based on each viscosity, each temperature to be measured, each preset shear rate, and the preset second viscoelastic algorithm, the shear thinning index and the critical shear stress of the material are obtained by fitting through a solver.

[0129] Among them, the preset second viscoelastic algorithm can refer to the Cross-WLF model.

[0130] Exemplarily, through a solver, based on each viscosity, each temperature to be measured, each preset shear rate, and the preset second viscoelastic algorithm, non-linear fitting is performed to minimize the error between the predicted value of the Cross-WLF model and the experimental data, and the shear thinning index and the critical shear stress corresponding to the minimum error are determined.

[0131] In this embodiment, by fitting the experimental data, the key parameters required to construct the viscosity-shear rate curve can be accurately determined, providing a scientific basis for the simulation and optimization of the material flow behavior.

[0132] In an exemplary embodiment, obtaining the flow channel structure model of the extrusion die includes steps S111 to S112. Among them:

[0133] Step S111, obtaining the flow channel structure information of the extrusion die.

[0134] Among them, the flow channel structure information can refer to the detailed information describing the geometric shape, size, and characteristics of the internal flow channels of the extrusion die. The flow channel structure information directly affects the flow behavior, pressure distribution, and the quality of the final product during the extrusion process. Through reasonable flow channel design, the extrusion process can be optimized, and the production efficiency and product quality can be improved.

[0135] As an example, when the extrusion die is determined, the flow channel structure information of the extrusion die can be obtained by obtaining the design drawings of the extrusion die or image recognition, etc.

[0136] As another example, when the extrusion die has not been determined and can be adjusted and optimized according to actual processing requirements, initial values can be set for each item of flow channel structure information based on experience first, and these initial values are determined as the flow channel structure information of the extrusion die. Subsequently, after analyzing and obtaining the physical quantity distribution information, the flow channel structure information can be optimized based on the physical quantity distribution information.

[0137] Step S112: According to the runner structure information, perform runner structure modeling on the extrusion die to obtain a runner structure model.

[0138] Exemplarily, through modeling software, according to the runner structure information, and in accordance with the corresponding relationship between each layer of material and the runner, perform hierarchical modeling on each runner in the extrusion die to obtain a runner structure model.

[0139] In this embodiment, the runner structure information directly affects the flow behavior, pressure distribution, and final product quality of the material during the extrusion process. Through reasonable runner design, the extrusion process can be optimized, and the production efficiency and product quality can be improved.

[0140] In an exemplary embodiment, the runner structure information includes material convergence position information and fillet radius information; the physical quantity distribution information includes residence time distribution information; according to the physical quantity distribution information, at least one of the extrusion process parameters, cooling process parameters, constitutive model, and runner structure model is optimized, including:

[0141] According to the residence time distribution information, at least one of the material convergence position information and the fillet radius information is optimized.

[0142] It should be noted that if the material convergence position is not designed reasonably, it may cause some materials to have too long residence time while other materials have too short residence time. It may also cause the local residence time of some materials at the convergence position to be extended or shortened, resulting in uneven residence time distribution, degradation of product performance, and poor bonding at the interlayer interface, affecting the mechanical properties and appearance quality of the product.

[0143] The size of the fillet radius also affects the flow resistance of the material in the corner or transition region. A smaller fillet radius increases the flow resistance, causing the material to stagnate at the corner and extending the residence time. A larger fillet radius can reduce the flow resistance, making the material flow more smoothly and the residence time distribution more uniform.

[0144] Among them, the material convergence position information may refer to the position where the materials in different runners are re-merged within the extrusion die. The fillet radius information may refer to the size of the fillet radius at the corners, transition regions, or material convergence positions in the runner of the extrusion die. The residence time distribution information can be used to characterize the distribution of the residence time of the material in different regions of the runner.

[0145] For example, after determining the residence time distribution information, the uniformity of the residence time distribution at various locations within the flow channel can be detected. For residence times outside the uniform distribution range, the corresponding flow channel position can be first determined, and then at least one of the material confluence position information and the fillet radius information corresponding to that flow channel position can be optimized. For example, if it is detected that the material dwells too long in a certain section of a flow channel, the fillet radius within that section and a distance preceding it can be increased, and the material confluence position within that section and a distance preceding it can be adjusted to achieve a smoother flow path for that section and a distance preceding it.

[0146] In this embodiment, by optimizing the material by the residence time distribution information and at least one of the position information and fillet radius information, the bonding effect of the multilayer material can be effectively improved, thereby improving the quality of the interlayer interface of the layered structure product and the product quality.

[0147] In an exemplary embodiment, the extrusion process parameters include at least one of the inlet temperature, flow rate, flow velocity, and outlet pulling velocity; the cooling process parameters include the cooling temperature; the physical quantity distribution information includes at least one of the temperature distribution information, pressure distribution information, velocity distribution information, and residence time distribution information;

[0148] Optimizing at least one of the extrusion process parameters, cooling process parameters, constitutive model, and flow channel structure model based on the physical quantity distribution information includes at least one of the following:

[0149] When it is detected based on the temperature distribution information that at least one temperature value exceeds a preset temperature threshold, performing at least one of the following: reducing the inlet temperature, reducing the flow rate, reducing the flow rate, increasing the traction speed at the outlet, and reducing the cooling temperature;

[0150] When it is detected based on the pressure distribution information that at least one pressure value exceeds a preset pressure threshold, performing at least one of the following: increasing the temperature at the inlet, reducing the flow rate, reducing the flow velocity, and increasing the traction velocity at the outlet;

[0151] When it is detected based on the speed distribution information that at least one speed value exceeds a preset speed threshold, performing at least one of the following: reducing the temperature at the inlet, reducing the flow rate, reducing the flow velocity, and reducing the traction velocity at the outlet;

[0152] When it is detected based on the residence time distribution information that at least one residence time value exceeds a preset residence time threshold, at least one of the following is performed: increasing the inlet temperature, increasing the flow rate, increasing the flow velocity, and increasing the traction velocity at the outlet.

[0153] It should be noted that if the temperature of the material in the runner is too high, it may cause thermal decomposition of the protective layer material, resulting in a decline in its physical properties, such as a decrease in tensile strength or an increase in brittleness; it may also cause bubbles, char marks or other surface defects in the product, affecting the appearance and insulation performance of the cable; high temperature will also cause the material to soften excessively, making it difficult to maintain the required dimensional accuracy, and may lead to uneven thickness of the material layer.

[0154] The temperature value exceeding the preset temperature threshold may be caused by one or more of the reasons such as too high inlet temperature, insufficient cooling effect, and excessive heat generation due to material shearing. Therefore, when at least one temperature value exceeds the preset temperature threshold, by reducing the inlet temperature of the material, the overall temperature distribution can be reduced; by reducing the flow rate, the amount of material passing through the die per unit time can be reduced, and the shearing heat can be reduced; by reducing the flow velocity, the material flow velocity can be reduced, and the shearing heat and frictional heat can be reduced; by increasing the traction speed at the outlet, the speed of the product leaving the die can be accelerated, and the residence time of the material in the high-temperature area can be reduced. Therefore, the temperature of the material in the runner can be effectively reduced, and the product quality can be improved.

[0155] It should be noted that if the pressure of the material in the runner is too high, it may cause uneven material flow, resulting in the problem of inconsistent thickness of the material layer, which will in turn affect the electrical and mechanical properties of the cable; and the processing equipment will be under high pressure for a long time, which will accelerate the wear of the die and other mechanical equipment, shorten the service life and increase the maintenance cost; too high pressure will also generate large residual stresses in the cable protective layer, affecting the long-term reliability and even causing cracking.

[0156] The pressure value exceeding the preset pressure threshold may be caused by one or more of the reasons such as too large flow resistance, too high material viscosity, and too fast extrusion speed. Therefore, when at least one pressure value exceeds the preset pressure threshold, by increasing the inlet temperature, the material temperature can be increased, the viscosity can be reduced, and the flow resistance can be reduced; by reducing the flow rate, the amount of material passing through the die per unit time can be reduced, and the pressure can be reduced; by reducing the flow velocity, the material flow velocity can be reduced, and the pressure peak can be reduced; by increasing the traction speed at the outlet, the speed of the product leaving the die can be accelerated, and the residence time of the material in the high-pressure area can be reduced, thereby reducing the pressure in the runner.

[0157] It should be noted that if the speed of the material in the runner is too high, it may cause the material to be not fully plasticized or shaped, affecting the appearance and mechanical properties of the cable protective layer; it may also cause unstable product quality and increase the defective rate; and after the material is rapidly extruded, if it exceeds the cooling capacity of the cooling equipment, it may cause insufficient cooling, resulting in cable deformation or performance decline, especially in application scenarios that require precise dimension control, the impact on cable quality is greater.

[0158] The speed value exceeds the preset speed threshold, which may be caused by one or more reasons such as too fast extrusion speed, too low temperature, etc. Therefore, when at least one speed value exceeds the preset speed threshold, by reducing the temperature at the inlet, the material temperature can be reduced, the viscosity can be increased, and the flow speed can be slowed down; by reducing the flow rate, the amount of material passing through the die per unit time can be reduced, and the flow rate can be lowered; by reducing the flow rate, the extrusion speed can be directly reduced to avoid too high local flow rate; by reducing the traction speed at the outlet, the speed at which the product leaves the die can be slowed down, and the flow speed distribution can be balanced.

[0159] It should be noted that too high residence time of the material in the runner may indicate an abnormal situation of local blockage in the runner, which can easily lead to a decline in product performance, and may also cause poor bonding at the interlayer interface, affecting the mechanical properties and appearance quality of the product.

[0160] Therefore, when at least one residence time value exceeds the preset residence time threshold, by increasing the temperature at the inlet, the material temperature can be increased, the viscosity can be reduced, and the flow speed can be accelerated; by increasing the flow rate, the amount of material passing through the die per unit time can be increased, and the residence time can be shortened; by increasing the flow rate, the extrusion speed can be increased, and the residence time of the material in the die can be reduced; by increasing the traction speed at the outlet, the speed at which the product leaves the die can be accelerated, and the material retention can be reduced.

[0161] In some feasible implementation manners, if the residence time is further prolonged after increasing the flow rate and the flow speed, it may be caused by an unreasonable runner structure. In this case, the runner structure can be optimized.

[0162] In an exemplary embodiment, the required processed layered structure is the protective layer of a cable, and the layered structure is, from the inside to the outside, an inner shielding layer, an XLPE (Cross-linked Polyethylene) layer, and an outer shielding layer. The constitutive models of the materials of each layer include: viscosity shear rate curve, density, specific heat, conductivity, and thermal expansion coefficient. The density, specific heat, conductivity, and thermal expansion coefficient are shown in Table 1:

[0163] Table 1

[0164]

[0165] Furthermore, the viscosity shear rate curves of the materials of each layer at different temperatures to be measured are obtained through a capillary rheometer.

[0166] Taking the XLPE layer as an example, since the processing temperature of the XLPE layer is 120 °C, the temperatures to be measured can be set at 115 °C, 120 °C, 125 °C, and 130 °C, and the parameters in the cross-WLF constitutive model are obtained by fitting through the Solver. Specifically, the freezing temperature of XLPE can be determined to be 395.15 K, the material constant is 6.03 + 27 Pa·s, the shear thinning index is 0.4, the reference shear stress is 164.157 Pa, the first temperature correlation constant is 64.42, the second temperature correlation constant is 51.6, and the glass transition temperature is 195.15 K. The viscosity-shear rate curves at each temperature to be measured can be derived according to the above viscosity constitutive model, as Figure 4 shown.

[0167] Furthermore, the flow channel in the extrusion die is modeled in layers according to each layer of material by using Hypermesh software (a finite element preprocessing software), as Figure 5 shown is a quarter-symmetry model of the flow channel in the extrusion die. Among them, the material flows from the inlet 501 to the outlet 502. The thickness of the outer shielding layer 503 is 0.5 mm, the thickness of the XLPE layer 504 is 11 mm, and the thickness of the inner shielding layer 505 is 0.5 mm. Fluid grids are drawn, and 5 layers of boundary layer grids are drawn at the junction positions of each layer to obtain the grid model of the three-layer flow channel in the extrusion die.

[0168] The inlet temperature is set at 120 °C, the flow rate is 2000 mm 3 / s, and the flow velocity is 1.949 mm / s at the inlet of the inner shielding layer flow channel. The drawing speed at the outlet is 3 m / s. The inlet temperature is set at 120 °C, the flow rate is 108.2 mm 3 / s, and the flow velocity is 1.972 mm / s at the inlet of the XLPE layer flow channel. The drawing speed at the outlet is 3 m / s. The inlet temperature is set at 120 °C, the flow rate is 55.89 mm 3 / s, and the flow velocity is 1.982 mm / s at the inlet of the outer shielding layer flow channel. The drawing speed at the outlet is 3 m / s. Select the cross-section of the cooling channel, and the coolant is selected as air with a temperature of 10 °C.

[0169] Furthermore, through Hyperextrude software, the constitutive models of the inner shielding layer, XLPE layer, and outer shielding layer are input, combined with the grid model of the three-layer flow channel, a finite element comprehensive model is established, and the above process parameters and cooling parameters are input to realize the coextrusion process analysis of the multi-layer coextruded cable. A temperature nephogram as shown in Figure 6 can be obtained. When the maximum temperature in the temperature nephogram is greater than the reference value, the material temperature at the inlet of the flow channel can be reduced, the flow rate can be reduced, the flow velocity can be reduced, the cooling capacity of the cooling equipment can be improved, and the average temperature of the actual product extrusion can be optimized. A result as shown in Figure 7As shown in the pressure contour map, when the maximum pressure in the pressure contour map is greater than the reference parameter, the material temperature at the runner inlet can be increased, the flow rate can be decreased, the flow velocity can be decreased, and the maximum extrusion pressure of the actual product can be optimized.

[0170] It should be understood that although the steps in the flowcharts involved in the above embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0171] Based on the same inventive concept, the embodiments of the present application also provide a co-extrusion processing optimization device for implementing the co-extrusion processing optimization method involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the co-extrusion processing optimization device provided below can refer to the limitations on the co-extrusion processing optimization method in the above text, and will not be repeated here.

[0172] In an exemplary embodiment, as Figure 8 shown, a co-extrusion processing optimization device is provided. Co-extrusion processing refers to the process of mixing and extruding multiple materials through an extrusion die to form a layered structure. Co-extrusion processing includes an extrusion process and a cooling process; the device includes: an acquisition module 802, an analysis module 804, and an optimization module 806, where:

[0173] The acquisition module 802 is used to acquire the extrusion process parameters of the extrusion process, the cooling process parameters of the cooling process, the constitutive models of each material, and the runner structure model of the extrusion die;

[0174] The analysis module 804 is used to perform finite element analysis on the extrusion process of each material according to the extrusion process parameters, the cooling process parameters, the constitutive model, and the runner structure model, and obtain the physical quantity distribution information of each material in the co-extrusion process;

[0175] The optimization module 806 is used to optimize at least one of the extrusion process parameters, the cooling process parameters, the constitutive model, and the runner structure model according to the physical quantity distribution information.

[0176] In an exemplary embodiment, the acquisition module 802 is further used for:

[0177] Obtain the performance data of the material at multiple temperatures to be measured;

[0178] According to the performance data, construct the viscosity-shear rate curve of the material at each temperature to be measured, and use each viscosity-shear rate curve as the constitutive model of the material.

[0179] In an exemplary embodiment, the performance data includes material constants, shear thinning index, critical shear stress, first temperature correlation constant, second temperature correlation constant, and glass transition temperature; the acquisition module 802 is further configured to:

[0180] Obtain the glass transition temperature of the material, and determine the glass transition temperature as the temperature to be measured;

[0181] Through a rheometer, detect the zero-shear viscosity of the material at each temperature to be measured, and the viscosity of the material at each temperature to be measured and multiple preset shear rates;

[0182] Determine the zero-shear viscosity corresponding to the glass transition temperature as the material constant of the material;

[0183] Based on the glass transition temperature, each zero-shear viscosity, each temperature to be measured, and a preset first viscoelastic algorithm, perform linear fitting to obtain the first temperature correlation constant and the second temperature correlation constant;

[0184] Based on each viscosity, each temperature to be measured, each preset shear rate, and a preset second viscoelastic algorithm, obtain the shear thinning index and critical shear stress of the material by fitting through the solver.

[0185] In an exemplary embodiment, the acquisition module 802 is further configured to:

[0186] Obtain the runner structure information of the extrusion die;

[0187] According to the runner structure information, perform runner structure modeling on the extrusion die to obtain a runner structure model.

[0188] In an exemplary embodiment, the runner structure information includes material confluence position information and fillet radius information; the optimization module 806 is further configured to:

[0189] According to the residence time distribution information, optimize at least one of the material confluence position information and the fillet radius information.

[0190] In an exemplary embodiment, the extrusion process parameters include at least one of the temperature at the inlet, flow rate, flow velocity, and draw speed at the outlet; the cooling process parameters include the cooling temperature; the physical quantity distribution information includes at least one of temperature distribution information, pressure distribution information, velocity distribution information, and residence time distribution information; the optimization module 806 is further configured to perform at least one of the following:

[0191] In the case where at least one temperature value is detected to exceed a preset temperature threshold based on temperature distribution information, perform at least one of the following: reduce the temperature at the inlet, reduce the flow rate, reduce the flow velocity, increase the drawing speed at the outlet, and reduce the cooling temperature;

[0192] In the case where at least one pressure value is detected to exceed a preset pressure threshold based on pressure distribution information, perform at least one of the following: increase the temperature at the inlet, reduce the flow rate, reduce the flow velocity, and increase the drawing speed at the outlet;

[0193] In the case where at least one speed value is detected to exceed a preset speed threshold based on speed distribution information, perform at least one of the following: reduce the temperature at the inlet, reduce the flow rate, reduce the flow velocity, and reduce the drawing speed at the outlet;

[0194] In the case where at least one residence time value is detected to exceed a preset residence time threshold based on residence time distribution information, perform at least one of the following: increase the temperature at the inlet, increase the flow rate, increase the flow velocity, and increase the drawing speed at the outlet.

[0195] Each module in the above coextrusion processing optimization device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0196] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 9As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a co-extrusion processing optimization method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0197] Those skilled in the art can understand that Figure 9 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0198] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0199] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0200] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0201] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0202] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0203] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0204] The above embodiments only express several implementation manners of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application should be subject to the appended claims.

Claims

1. An optimized co-extrusion processing method, characterized in that, The co-extrusion process refers to the process of mixing and extruding multiple materials through an extrusion die to form a layered structure. The co-extrusion process includes an extrusion process and a cooling process; the method includes: Obtaining the extrusion process parameters of the extrusion process, the cooling process parameters of the cooling process, the constitutive model of each material, and the flow channel structure model of the extrusion die; Performing finite element analysis on the extrusion process of each material according to the extrusion process parameters, the cooling process parameters, the constitutive model, and the flow channel structure model to obtain the physical quantity distribution information of each material during the co-extrusion process; Optimizing at least one of the extrusion process parameters, the cooling process parameters, the constitutive model, and the flow channel structure model according to the physical quantity distribution information.

2. The method according to claim 1, characterized in that, Obtaining the constitutive model of the material includes: Obtaining the performance data of the material at multiple measured temperatures; According to the performance data, constructing the viscosity-shear rate curve of the material at each measured temperature, and taking each viscosity-shear rate curve as the constitutive model of the material.

3. The method according to claim 2, characterized in that, The performance data includes material constants, shear thinning index, critical shear stress, first temperature correlation constant, second temperature correlation constant, and glass transition temperature; obtaining the performance data of the material at multiple measured temperatures includes: Obtaining the glass transition temperature of the material and determining the glass transition temperature as the measured temperature; Detecting the zero-shear viscosity of the material at each measured temperature and the viscosity of the material at each measured temperature and multiple preset shear rates through a rheometer; Determining the zero-shear viscosity corresponding to the glass transition temperature as the material constant of the material; Performing linear fitting based on the glass transition temperature, each zero-shear viscosity, each measured temperature, and a preset first viscoelastic algorithm to obtain the first temperature correlation constant and the second temperature correlation constant; Based on each viscosity, each measured temperature, each preset shear rate, and a preset second viscoelastic algorithm, obtaining the shear thinning index and the critical shear stress of the material by fitting through a programming solver.

4. The method according to claim 1, characterized in that Obtaining the flow channel structure model of the extrusion die includes: Obtaining the flow channel structure information of the extrusion die; According to the flow channel structure information, performing flow channel structure modeling on the extrusion die to obtain the flow channel structure model.

5. The method according to claim 4, characterized in that The flow channel structure information includes material convergence position information and fillet radius information; the physical quantity distribution information includes residence time distribution information; according to the physical quantity distribution information, optimizing at least one of the extrusion process parameters, the cooling process parameters, the constitutive model, and the flow channel structure model includes: Optimizing at least one of the material convergence position information and the fillet radius information according to the residence time distribution information.

6. The method according to any one of claims 1 to 5, characterized in that The extrusion process parameters include at least one of the temperature at the inlet, the flow rate, the flow velocity, and the drawing speed at the outlet; the cooling process parameters include the cooling temperature; the physical quantity distribution information includes at least one of the temperature distribution information, the pressure distribution information, the velocity distribution information, and the residence time distribution information; Optimizing at least one of the extrusion process parameters, the cooling process parameters, the constitutive model, and the flow channel structure model according to the physical quantity distribution information includes at least one of the following: When it is detected based on the temperature distribution information that at least one temperature value exceeds a preset temperature threshold, perform at least one of the following: reduce the temperature at the inlet, reduce the flow rate, reduce the flow velocity, increase the pulling speed at the outlet, and reduce the cooling temperature; When it is detected based on the pressure distribution information that at least one pressure value exceeds a preset pressure threshold, perform at least one of the following: increase the temperature at the inlet, reduce the flow rate, reduce the flow velocity, and increase the pulling speed at the outlet; When it is detected based on the velocity distribution information that at least one velocity value exceeds a preset velocity threshold, perform at least one of the following: reduce the temperature at the inlet, reduce the flow rate, reduce the flow velocity, and reduce the pulling speed at the outlet; When it is detected based on the residence time distribution information that at least one residence time value exceeds a preset residence time threshold, perform at least one of the following: increase the temperature at the inlet, increase the flow rate, increase the flow velocity, and increase the pulling speed at the outlet.

7. A co-extrusion processing optimization device, characterized in that, The coextrusion process refers to the process of mixing and extruding multiple materials through an extrusion die to form a layered structure. The coextrusion process includes an extrusion process and a cooling process; the device includes: An acquisition module for acquiring the extrusion process parameters of the extrusion process, the cooling process parameters of the cooling process, the constitutive model of each material, and the flow channel structure model of the extrusion die; An analysis module for performing finite element analysis on the extrusion process of each material according to the extrusion process parameters, the cooling process parameters, the constitutive model, and the flow channel structure model to obtain the physical quantity distribution information of each material during the coextrusion process; An optimization module for optimizing at least one of the extrusion process parameters, the cooling process parameters, the constitutive model, and the flow channel structure model according to the physical quantity distribution information.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

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