Method and system for predicting and optimizing thermal deformation in processing of metal-plastic composite products

Abstract

The present invention discloses a method and system for predicting and optimizing thermal deformation in the processing of metal-plastic composite products, including: establishing a geometric model of the composite product according to the material properties and preset boundary conditions of the composite product; using a finite element analysis method and selecting a preset time step and a nonlinear solver to couple and solve the temperature field and stress field according to the geometric model of the composite product; using simulation analysis of the change data of the temperature field and stress field over time and thermomechanical cycles and monitoring the accumulated deformation and interface stress data of the material in a preset number of cycles to generate simulation prediction results; optimizing the heating and cooling rates and the mold loading path according to the simulation prediction results. The method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products of the present invention can be based on multi-physical field coupling and combined with nonlinear finite element solution.

Description

Technical Field

[0001] The invention relates to the technical field of metal-plastic composite products, and in particular to a method and system for predicting and optimizing thermal deformation in the processing of metal-plastic composite products. Background Art

[0002] Metal-plastic composite products are products made of two different materials, metal and plastic, through various processing methods. These products fully combine the strength and durability of metal with the light weight, corrosion resistance and easy processing of plastic. They are widely used in the fields of automobiles, aerospace, electronics, construction, etc. For example, metal-plastic composite parts used extensively in the automotive industry can provide both structural strength and lightweight performance.

[0003] Due to the significant differences in the physical and chemical properties of metals and plastics, thermal deformation is an important issue in the composite processing of metals and plastics. Due to the large differences in the thermal expansion coefficient, thermal conductivity and other physical properties of metals and plastics, temperature changes during the processing will cause uneven expansion or contraction of the material, resulting in stress concentration, warping, deformation, and even interface failure. These problems not only affect the processing accuracy of the product, but also reduce the service life of the product and increase production costs.

[0004] At present, in order to improve the quality of metal-plastic composite products, it is very important to predict the thermal deformation behavior of materials during processing and optimize it. Accurate prediction of thermal deformation can improve product accuracy, reduce scrap rate, extend product life, production efficiency and processing consistency; however, although there are many studies and technologies for predicting the thermal deformation behavior of metal-plastic composite products, they still face the following problems: First, traditional single physical field analysis cannot fully simulate the complex behavior of materials during processing; second, the thermodynamic and mechanical properties of metals and plastics are nonlinear and complex, and more accurate material models are needed to describe their temperature dependence and nonlinear relationship between stress and strain.

[0005] Therefore, there is an urgent need for a method to predict and optimize thermal deformation in the processing of metal-plastic composite products based on multi-physical field coupling and combined with nonlinear finite element solution. Summary of the invention

[0006] Purpose of the invention: In order to overcome the above shortcomings, the purpose of the present invention is to provide a method and system for predicting and optimizing thermal deformation in the processing of metal-plastic composite products.

[0007] In order to solve the above technical problems, the present invention provides a method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products, the method comprising the following steps:

[0008] Step S1: establishing a geometric model of the composite product according to the material properties of the composite product and preset boundary conditions;

[0009] Step S2: according to the geometric model of the composite product, the temperature field and stress field are solved by using the finite element analysis method and selecting a preset time step and a nonlinear solver coupling;

[0010] Step S3: using simulation to analyze the variation data of temperature field and stress field over time and thermomechanical cycles and monitoring the accumulated deformation and interface stress data of the material in a preset number of cycles to generate simulation prediction results;

[0011] Step S4: Optimizing the heating and cooling rates and the mold loading path according to the simulation prediction results.

[0012] In one aspect, in step S1, the method comprises the following steps:

[0013] Step S11: constructing a composite product geometric model and using a grid to divide the composite product geometric model into metal and plastic regions;

[0014] Step S12: setting thermal conductivity, thermal expansion coefficient, and elastic modulus for metal and plastic regions of the composite product geometric model respectively;

[0015] Step S13: applying thermal boundary conditions, mechanical boundary conditions and fluid field boundary conditions to the composite product geometric model.

[0016] In one aspect, in step S1, the method further comprises the following steps:

[0017] Step S101: assigning metal material properties to the metal region of the composite product geometric model according to the physical properties of the metal material, wherein the metal material properties include thermal conductivity, specific heat capacity, thermal expansion coefficient and elastic modulus;

[0018] Step S102: assigning plastic material properties to the plastic region of the composite product geometric model according to the physical properties of the plastic material, wherein the plastic material properties include thermal conductivity, specific heat capacity, thermal expansion coefficient, viscosity and rheological properties;

[0019] Step S103: applying bonding or sliding conditions to the metal-plastic interface of the composite product geometric model to simulate the actual mechanical contact characteristics between the metal and the plastic;

[0020] Step S104: applying thermal boundary conditions including heat flow boundary, convection boundary and radiation boundary to the composite product geometric model, applying mechanical boundary conditions including fixed ports, free boundaries and contact conditions, and further applying fluid field boundary conditions including inlet and outlet boundaries and wall boundaries.

[0021] In one aspect, in step S2, the method comprises the following steps:

[0022] Step S21: performing a preset number of heating and cooling cycles on the composite product geometric model and setting the applied contact pressure or mold load to calculate the stress field changes during forming;

[0023] Step S22: using the heat conduction equation to solve the temperature field distribution of the composite product in the thermomechanical cycle, and then calculating the temperature field in combination with the set boundary conditions;

[0024] Step S23: using the thermal stress equation to simulate the internal stress of the material of the composite product geometric model caused by thermal expansion, and then using the plastic deformation equation and the creep equation to calculate the cumulative deformation process of the material in the thermomechanical cycle, so as to solve the stress field.

[0025] In one aspect, in step S2, the method comprises the following steps:

[0026] Step S201: after obtaining the temperature field of the composite product in the thermomechanical cycle, the temperature field is introduced into the thermoelastic equation to calculate the thermal stress and strain;

[0027] Step S202: Solve the fluid velocity field according to the fluid dynamics equation, and then couple the heat conduction equation to calculate the change of the temperature field during the fluid flow process.

[0028] In one aspect, in step S3, the method comprises the following steps:

[0029] Step S31: after each thermomechanical cycle, generate stress and temperature distribution and record interface stress and deformation data, and then generate a curve of material cumulative stress and deformation over time through a preset number of cycles;

[0030] Step S32: Based on the variation curve and the sum of the load variations that the material can withstand before fracture, fatigue damage is accumulated using the Miner criterion to analyze the material fatigue life and predict the fatigue failure position.

[0031] In one aspect, in step S4, the method comprises the following steps:

[0032] Step S41: setting the objective function according to the target optimization problem;

[0033] Step S42: setting design variables to optimize process parameters of the processing process by adjustment, wherein the design variables include: heating rate, cooling rate, forming pressure, mold loading path and processing time;

[0034] Step S43: setting constraints to limit the value range of the design variables, wherein the constraints include: material strength constraints, temperature constraints, and processing equipment constraints;

[0035] Step S44: Select a preset optimization algorithm and set initial parameters.

[0036] In one aspect, in step S4, the method further comprises the following steps:

[0037] Step S401: During the optimization algorithm iteration process, the process parameters are continuously adjusted and the objective function value is evaluated in combination with the simulation prediction results;

[0038] Step S402: During the iteration of the preset optimization algorithm, the design variables are updated according to the optimization algorithm rules, the matching stress and deformation are calculated, and the fitness value is updated;

[0039] Step S403: Output the optimal design variables through a preset optimization algorithm.

[0040] In one aspect, in step S403, the method further includes the following steps:

[0041] Step S4031: using the optimal design variables to perform actual processing or simulated processing verification to generate optimized processing results;

[0042] Step S4032: Determine whether the optimization processing result meets the preset requirements. If so, stop the iteration. If not, return to readjust the design variables and model parameters and continue the iteration.

[0043] The present invention also provides a system for predicting and optimizing thermal deformation in the processing of metal-plastic composite products, characterized in that the system is used to execute the method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products, comprising:

[0044] A model building module, used to build a geometric model of the composite product according to the material properties of the composite product and preset boundary conditions;

[0045] An analysis and solution module is used to solve the temperature field and stress field by using a finite element analysis method and selecting a preset time step and a nonlinear solver coupling according to the geometric model of the composite product;

[0046] A simulation prediction module is used to use simulation to analyze the change data of temperature field and stress field over time and thermomechanical cycles and monitor the cumulative deformation and interface stress data of the material in a preset number of cycles to generate simulation prediction results;

[0047] The processing optimization module is used to optimize the heating and cooling rates and the mold loading path according to the simulation prediction results.

[0048] Beneficial effects of the method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products in this application:

[0049] 1. It can comprehensively consider the thermal, mechanical, and mechanical coupling effects of metals and plastics during processing, and accurately model the nonlinear behavior of materials, so as to effectively predict and control thermal deformation and improve the overall performance and production quality of metal-plastic composite products.

[0050] 2. Through the coupled calculation of heat conduction equation, thermal stress equation and fluid dynamics equation, the temperature field and stress field distribution of composite products in the thermomechanical cycle can be accurately predicted to ensure the precise control of material thermal deformation during the actual processing. Through simulation analysis, the cumulative deformation and interface stress of the material can be monitored in the preset number of cycles to ensure that the material can maintain stable dimensional accuracy under multiple cycles.

[0051] 3. By optimizing process parameters such as heating and cooling rates, mold loading paths, etc., the stress gradient of composite products during processing can be effectively reduced, thereby reducing the stress concentration phenomenon inside the material. Especially in the bonding or sliding condition treatment at the metal and plastic interface, the mechanical contact characteristics of the material can be better simulated, the stress concentration at the interface can be reduced, and the service life of the material can be extended. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0053] Figure 1 It is a flow chart of a method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products provided by an embodiment of the present invention.

[0054] Figure 2 It is a flow chart of the model building method provided by an embodiment of the present invention.

[0055] Figure 3 It is a flow chart of a method for setting material properties and boundary conditions provided by an embodiment of the present invention.

[0056] Figure 4 It is a flow chart of a method for solving temperature field and stress field provided in an embodiment of the present invention.

[0057] Figure 5 It is a flow chart of the fatigue prediction method provided by an embodiment of the present invention.

[0058] Figure 6 It is a flow chart of the optimization setting method provided by an embodiment of the present invention.

[0059] Figure 7It is a flow chart of the optimal design variable generation method provided by an embodiment of the present invention.

[0060] Figure 8 It is a flow chart of an optimization verification method provided by an embodiment of the present invention.

[0061] Fig. 9 It is a flow chart of the coupling calculation method provided by an embodiment of the present invention.

[0062] Fig.10 It is a connection relationship diagram of the thermal deformation prediction and optimization system in the processing of metal-plastic composite products provided by an embodiment of the present invention.

[0063] Description of the Figures in the Specification:

[0064] 101. Model building module, 102. Analysis and solution module, 103. Simulation prediction module, 104. Processing optimization module. DETAILED DESCRIPTION

[0065] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0066] refer to Figure 1 As shown, in some embodiments, the method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products includes the following steps:

[0067] Step S1: Establishing a geometric model of the composite product according to the material properties of the composite product and preset boundary conditions.

[0068] Specifically, in step S1, the processing of metal-plastic composite products involves multiple physical fields, which interact with each other through material properties and processing conditions to form a coupling effect; the following physical fields are mainly considered:

[0069] Temperature field is used to describe the temperature distribution and heat conduction behavior of materials during processing. Due to the differences in thermal conductivity and heat capacity, metals and plastics will show different temperature field distributions during heating and cooling. Therefore, Fourier heat conduction equation is used to express it: ,in is the density, is the specific heat capacity, is the thermal conductivity, is the temperature, As heat source.

[0070] Force field, which describes the thermal stress and deformation of materials under temperature changes, especially at the interface between metal and plastic. The difference in thermal expansion coefficient may lead to stress concentration and material detachment. Therefore, it is expressed using thermoelastic equations, plastic deformation and creep equations: ,in is the stress component, is the elastic modulus tensor of the material, is the strain component, Coefficient of thermal expansion, Temperature change; ,in is the plastic strain rate, is the plasticity multiplier used to control the plastic flow velocity, is the yield function; , is the creep rate, , is the material constant, is the activation energy, is the gas constant; these equations are combined to describe the deformation process of the material during the thermomechanical cycle.

[0071] Specifically, refer to Figure 2 and Figure 3 As shown, in step S1, the method includes the following steps:

[0072] Step S11: constructing a geometric model of the composite product and using a grid to divide the geometric model of the composite product into metal and plastic areas.

[0073] Among them, a geometric model of metal-plastic composite products is established, and the influence of interface areas and complex geometric shapes is taken into account. The model is constructed through modeling software (such as SolidWorks, CAD, etc.) or directly in finite element analysis software.

[0074] When constructing the model, geometric partitioning or Boolean operations are used to define the metal and plastic areas in the model separately, and special attention is paid to the interface between metal and plastic to ensure accurate geometric processing of the interface part; then, according to the geometric complexity of the material and the area of ​​thermomechanical stress concentration, the appropriate mesh type (such as quadrilateral mesh or hexahedral mesh) is selected, and the mesh is refined in key areas (such as metal areas and plastic areas) to ensure accurate simulation of thermal stress distribution.

[0075] Step S12: respectively setting the thermal conductivity, thermal expansion coefficient and elastic modulus for the metal and plastic regions of the composite product geometric model.

[0076] Furthermore, according to the physical properties of the metal material, metal material properties are assigned to the metal area of ​​the composite product geometric model, and the metal material properties include: thermal conductivity, specific heat capacity, thermal expansion coefficient and elastic modulus; according to the physical properties of the plastic material, plastic material properties are assigned to the plastic area of ​​the composite product geometric model, and the plastic material properties include: thermal conductivity, specific heat capacity, thermal expansion coefficient and viscosity and rheological properties.

[0077] Step S13: applying thermal boundary conditions, mechanical boundary conditions and fluid field boundary conditions to the composite product geometric model.

[0078] Wherein, the thermal boundary conditions include:

[0079] Heat flow boundary: If there is an external heating or cooling source, the corresponding heat flow boundary conditions need to be defined; for example, during induction heating, a certain amount of heat flow or heat flux is applied to the boundary to simulate the effect of the heating device;

[0080] Convection boundary: In the cooling process, convection boundary conditions are usually used to simulate the heat exchange between the external environment and the material; for example, after forming, the cooling is accelerated by circulating the coolant. At this time, the convection heat transfer coefficient is calculated by existing fluid dynamics calculations or the data previously calculated by the test personnel.

[0081] Radiation boundary: In high temperature processing, radiation heat transfer may also affect the temperature field. Especially in metal processing, it is necessary to define the radiation heat transfer conditions according to the Stefan-Boltzmann law.

[0082] Wherein, the mechanical boundary conditions include:

[0083] Fixed ports: For some fixed parts in the processing of composite products (such as mold fixing fixtures or forming molds), set corresponding fixed boundary conditions. These boundary conditions are usually set to zero displacement to simulate the constraint state of the material during processing;

[0084] Free boundary: In some places (such as the edge of freely hanging materials), free boundary conditions are set to allow the material to expand or contract freely without external restrictions;

[0085] Contact conditions: At the metal-plastic interface, define contact mechanics boundary conditions, such as friction contact, no-slip contact, etc., and consider the change of contact area with temperature.

[0086] Wherein, the fluid field boundary conditions include:

[0087] Inlet and outlet boundaries: During the injection molding process, the inlet boundary of the fluid field is set as the injection port, and pressure or velocity boundary conditions are applied. Pressure release conditions are set at the outlet to ensure that the molten material fills the mold smoothly;

[0088] Wall boundary: In the fluid field, the wall boundary condition is used to define the interaction between the fluid and the mold wall. It is usually a no-slip condition to simulate the flow of viscous fluid along the mold wall.

[0089] Step S2: According to the geometric model of the composite product, the temperature field and stress field are solved by using the finite element analysis method and selecting a preset time step and a nonlinear solver coupling.

[0090] Among them, since the material properties (such as elastic modulus, thermal conductivity, etc.) show nonlinear characteristics with temperature changes, and when the plastic region and interface fail, the problem becomes highly nonlinear, the commonly used nonlinear solution methods include: Newton-Raphson iteration method and incremental iteration method. In the embodiment of this application, reference is made to Newton-Raphson iteration method. For dynamic heating and cooling processes, the time step is reasonably selected to ensure the simulation accuracy during rapid heating or cooling. The time step calculation process is: ,in is the minimum grid length, is the maximum thermal conductivity.

[0091] Thus, for example, assuming that the composite product is a rectangular metal-plastic composite plate with a thickness of 5 mm, the metal layer and the plastic layer have different thicknesses, for example, the metal part is 2 mm and the plastic part is 3 mm; use SolidWorks or ANSYS software to establish a geometric model of the composite product, and divide the metal layer and the plastic layer of the model into two regions, respectively, and set the physical properties of each region (such as thermal conductivity, thermal expansion coefficient, etc.) to ensure that the physical properties can be dynamically adjusted with temperature changes. For example, for the metal part, assume that its thermal conductivity is 200 W / m·K and its thermal expansion coefficient is 1.2×10⁻ 5 / K, for the plastic part, the thermal conductivity is 0.2W / m·K, and the thermal expansion coefficient is 8×10⁻ 5 / K; then, during the heating process, a heat flux of 200°C can be applied, and natural convection is used during cooling, assuming a cooling environment temperature of 25°C and a convection heat transfer coefficient of 10W / m²·K boundary conditions.

[0092] Specifically, refer to Figure 4 As shown, in step S2, the method includes the following steps:

[0093] Step S21: performing a preset number of heating and cooling cycles on the composite product geometric model and setting the applied contact pressure or mold load to calculate the stress field changes during forming.

[0094] The preset number of times is set by the test personnel according to actual needs, and 3 times is used as an example; the setting conditions of the heating and cooling cycles are: in the heating stage, an external heat source is introduced through the heat flow boundary conditions; in the cooling stage, natural convection or forced convection cooling is used. During the forming process, the evolution of the stress field in the forming process is simulated by setting the applied contact pressure or mold load.

[0095] Step S22: using the heat conduction equation to solve the temperature field distribution of the composite product in the thermomechanical cycle, and then calculating the temperature field in combination with the set boundary conditions.

[0096] Among them, the initial condition is to set the temperature distribution of the material at the initial moment, usually the temperature field at the beginning of processing, assuming that the material is initially at a uniform temperature or setting an initial gradient. The boundary conditions are: convection boundary conditions, considering the convection heat transfer during the cooling process, the heat flux density can be expressed by the existing Newton cooling formula; radiation boundary conditions: if high temperature is involved, the existing Stefan-Boltzmann law can be used to describe radiation heat dissipation.

[0097] Step S23: using the thermal stress equation to simulate the internal stress of the material of the composite product geometric model caused by thermal expansion, and then using the plastic deformation equation and the creep equation to calculate the cumulative deformation process of the material in the thermomechanical cycle, so as to solve the stress field.

[0098] Among them, the stress field caused by thermal expansion is calculated using the above-mentioned thermal stress equation through the known temperature field distribution, and the stress distribution inside the material, especially the interface area, is analyzed in detail; after each thermomechanical cycle, the plastic deformation equation and creep equation are used to calculate the cumulative plastic deformation and creep effect of the material, especially the deformation after multiple cycles.

[0099] Furthermore, after finite element simulation of thermomechanical cycles, the stress and strain distribution in the metal-plastic composite products was analyzed, with special attention paid to stress concentration and deformation at the interface;

[0100] Interface stress concentration: Due to the difference in thermal expansion coefficients, large stress concentration is likely to occur between metal and plastic. By analyzing the distribution of interface stress, the possibility of interface debonding or failure can be predicted.

[0101] Analyze the deformation of the material, especially the deformation of the surface and the inside. By observing the accumulated deformation after different cycles, predict the dimensional accuracy after the final processing. The calculation formula for thermal expansion deformation is: ,in, is the change in material length, is the initial length, is the coefficient of thermal expansion that varies with temperature, For temperature changes.

[0102] Step S3: Use simulation to analyze the change data of temperature field and stress field over time and thermomechanical cycles and monitor the cumulative deformation and interface stress data of the material in a preset number of cycles to generate simulation prediction results.

[0103] Specifically, in step S3, a temperature field analysis is performed: data on the change of the internal and surface temperature of the material over time is obtained, and the temperature gradient in the heating and cooling stages is analyzed in particular to predict the thermal stress that may be generated in the material, and in particular to analyze the interface temperature difference between metals and plastics to predict interface stress and potential debonding problems. Stress field analysis is performed: by analyzing the thermal stress caused by the temperature field, the cumulative change of stress over time is observed, with special attention paid to stress concentration in the interface area. Deformation accumulation analysis: During the cycle process, deformation gradually accumulates, and by analyzing the change of deformation with the number of cycles, the final material deformation is calculated: ;in, is the total deformation, is the initial length, is the thermal expansion coefficient at the nth cycle, is the temperature change in the nth cycle.

[0104] Therefore, deformation prediction is performed, and the formula is: ;in, is the predicted cumulative deformation, is the number of cycles, is the deformation of each cycle. The stress fatigue prediction formula is: ;in, is the possible failure stress value of the material, is the initial stress, is the stress increment of each cycle. The interface stress is predicted by the formula:

[0105] ;in, is the interface stress, is the elastic modulus at the interface, is the interface temperature difference varying with time.

[0106] For example, the deformation accumulation analysis is as follows: Assuming the initial length of the composite product is , set a 3-cycle process flow, the time step is set to 1 second, then the temperature change after 3 cycles is 200°C→50°C→200°C→25°C, the corresponding thermal expansion coefficient is Deformation in the first cycle as the temperature changes , the second cycle , the third cycle , then the total deformation .

[0107] Specifically, refer to Figure 5 As shown, in step S3, the method includes the following steps:

[0108] Step S31: after each thermomechanical cycle, generate stress and temperature distribution and record interface stress and deformation data, and then generate a curve of material cumulative stress and deformation changing with time through a preset number of cycles.

[0109] Step S32: Based on the variation curve and the sum of the load variations that the material can withstand before fracture, fatigue damage is accumulated using the Miner criterion to analyze the material fatigue life and predict the fatigue failure position.

[0110] Among them, the formula for cumulative fatigue damage using Miner's criterion is: ,in is the cumulative loss factor, is the actual load number of the ith cycle, is the fatigue life under the i-th load; the formula for predicting fatigue failure is: ,in is the cumulative stress, is the initial stress, is the stress increment introduced in the i-th cycle.

[0111] For example, fatigue life prediction is as follows: Assuming the initial stress of the composite material is , the stress increment per cycle , then the total stress is =100+5+8+10=123 , if the predicted value of fatigue life is Cycles, actual load times , then the cumulative damage factor D=3000 / 5000=0.6, indicating that the material damage has reached 60%. Step S4: According to the simulation prediction results, the heating and cooling rates and the mold loading path are optimized.

[0112] Specifically, refer to Figure 6 As shown, before the optimization is performed in step S4, the method includes the following steps:

[0113] Step S41: Setting the objective function according to the target optimization problem.

[0114] Among them, the objective function is used to measure the optimization effect of the process, which is usually based on the thermomechanical performance indicators of the material, such as thermal stress, deformation or fatigue life. The form of the objective function depends on the specific optimization goal. For example, to reduce the maximum thermal stress, the goal is to minimize stress concentration to avoid material failure; to minimize the deformation, the goal is to reduce deformation and ensure the dimensional accuracy of the processed product; to extend the fatigue life, the goal is to maximize the fatigue life of the material.

[0115] Step S42: setting design variables to optimize the process parameters of the processing process through adjustment, wherein the design variables include: heating rate, cooling rate, forming pressure, mold loading path and processing time.

[0116] Among them, the heating rate affects heat conduction and temperature field distribution; the cooling rate affects the residual stress and deformation of the material during the cooling process; the forming pressure is the external force applied during the forming process; and the processing time is the time of the heating, forming or cooling process.

[0117] Step S43: setting constraints to limit the value range of the design variables, wherein the constraints include: material strength constraints, temperature constraints and processing equipment constraints.

[0118] Among them, during the optimization process, constraints ensure that the processing process meets the strength requirements of the material and the equipment capacity, such as the material strength limit is less than or equal to the yield strength of the material, the temperature limit is less than or equal to the maximum allowable temperature of the material or equipment, and the processing equipment limit is less than or equal to the maximum load-bearing capacity of the processing equipment.

[0119] Step S44: Select a preset optimization algorithm and set initial parameters.

[0120] Among them, the preset optimization algorithms include but are not limited to genetic algorithms and particle swarm algorithms. The specific genetic algorithms are: based on the processing conditions of the thermomechanical cycle, a set of initial processing parameter combinations are randomly generated; according to the objective function, the fitness value of each individual is calculated, such as maximizing fatigue life, and the pros and cons of each individual are evaluated through the fitness function; using the existing fitness proportional selection algorithm (Roulette Wheel Selection) or tournament selection algorithm (Tournament Selection), individuals with preset fitness values ​​(such as individuals with the best fitness values) are selected for reproduction; two parent individuals are selected using the uniform crossover method, new offspring individuals are generated through crossover operations, and some individuals are mutated according to the existing Gaussian mutation or random mutation methods to increase the diversity of the population; a preset number of iterations are performed to output the optimal solution.

[0121] The specific particle swarm optimization algorithm is as follows: the processing parameters are regarded as a particle swarm, and the position and speed of the particles are randomly initialized; each particle represents a set of processing parameters, and the fitness (deformation or stress value) of the particle is calculated through thermomechanical cycle simulation; by adjusting the position and speed of the particles, it gradually approaches the optimal solution until it converges to the optimal processing parameters.

[0122] Specifically, refer to Figure 7 As shown, in step S4, the method includes the following steps:

[0123] Step S401: During the iteration of the optimization algorithm, the process parameters are continuously adjusted and the objective function value is evaluated in combination with the simulation prediction results.

[0124] Step S402: During the iteration of the preset optimization algorithm, the design variables are updated according to the optimization algorithm rules, the matching stress and deformation are calculated, and the fitness value is updated.

[0125] Step S403: Output the optimal design variables through a preset optimization algorithm.

[0126] Among them, the objective function when outputting the optimal design variables should be minimized.

[0127] Specifically, refer to Figure 8 As shown, in step S403, the method further includes the following steps:

[0128] Step S4031: Use the optimal design variables to perform actual processing or simulation processing verification to generate optimized processing results.

[0129] Step S4032: determine whether the optimization processing result meets the preset requirements. If so, execute step S40320: stop iteration; if not, execute step S40321: return to readjust the design variables and model parameters and continue iteration.

[0130] During the processing of composite products, multiple physical fields such as temperature field, force field, fluid field, etc. interact with each other and are defined by boundary conditions, material properties (such as thermal expansion coefficient, thermal conductivity) and interface interactions. For example: the temperature field is coupled with the force field. As the temperature changes, the material will expand or contract thermally. By coupling the heat conduction equation and the thermoelastic equation, the thermal stress and strain distribution can be calculated, especially the stress concentration at the interface; the fluid field is coupled with the temperature field. The temperature field affects the viscosity of the fluid and thus changes the flow behavior; the movement of the fluid takes away or transfers heat and changes the temperature distribution; in the molten or flowing state, the shear force of the flow will also affect the molding process of the plastic. By coupling the fluid dynamics equation and the heat conduction equation, the solidification behavior and residual stress of the material can be predicted.

[0131] Among them, the Navier-Stokes equation and the heat conduction equation are used to simulate the coupling of flow and temperature field. The Navier-Stokes equation is: ; where v is the velocity, is the density, is viscosity, p is pressure, and f is gravity.

[0132] In some embodiments, reference Fig. 9 As shown, in step S2, the method includes the following steps:

[0133] Step S201: After obtaining the temperature field of the composite product in the thermomechanical cycle, the temperature field is introduced into the thermoelastic equation to calculate the thermal stress and strain.

[0134] Step S202: Solve the fluid velocity field according to the fluid dynamics equation, and then couple the heat conduction equation to calculate the change of the temperature field during the fluid flow process, and calculate the influence of the fluid flow on the temperature field through repeated iterations until convergence.

[0135] Among them, the convective heat transfer of the fluid will change the temperature field, and the coupled heat conduction equation is:

[0136] ,in, is the convective heat transfer term of the fluid.

[0137] In the experimental test of the present application, common metal-plastic composite products, such as aluminum alloy-polycarbonate (PC) composite products, are selected, and then several composite product samples with consistent sizes are prepared for experimental testing.

[0138] First, a model is established according to the geometric dimensions of the composite product, and the metal and plastic regions are divided. The thermophysical properties of the materials (such as thermal conductivity, specific heat capacity, thermal expansion coefficient, etc.) are assigned respectively, and the heating and cooling boundary conditions are set. Different heating rates (such as rapid heating, slow heating) and cooling rates (such as air cooling, forced cooling) are simulated, and the temperature field and stress field distribution are calculated by coupling the heat conduction equation and the thermal stress equation.

[0139] Then, the samples are actually processed under the same heating and cooling process conditions, an infrared thermal imager is used to monitor the changes in the sample surface temperature field, and a strain gauge or laser speckle measurement instrument is used to obtain the stress and strain data of the material during the processing.

[0140] Then, data is collected and compared, namely: the distribution data of temperature field and stress field over time are obtained from the simulation, and the stress gradient changes under different heating and cooling rates are analyzed; the actual temperature field and stress field changes over time are obtained from the experimental test, focusing on the stress concentration area at the metal and plastic interface.

[0141] Then, by adjusting the heating and cooling rates, multiple groups of simulation comparisons are performed to optimize the process parameter combination, and the optimized process conditions are used for experimental verification to ensure that the desired effect is achieved.

[0142] For example, referring to the table below, the comparison between the experiment and the simulation shows that, under the same heating and cooling rates, the temperature field distribution predicted by the simulation is highly consistent with the actual measurement results.

[0143]

[0144] For example, as shown in the table below, the comparison between the experiment and the simulation shows that under the conditions of rapid heating and cooling, the stress peaks measured by the simulation and the experiment appear at the interface between the metal and the plastic, and the difference between the stress peaks is less than 10%. As the cooling rate slows down, the stress peaks drop significantly. The simulation results show that by optimizing the cooling rate and the heating path, the distribution of the stress field is more uniform, especially the stress gradient in the interface area is significantly reduced.

[0145]

[0146] Through simulation and experimental comparison, the accuracy of the predicted temperature field and stress field was verified. The optimized process parameters can effectively reduce the stress gradient, especially in the metal-plastic interface area. After optimizing the heating and cooling rates, the stress distribution of the material is more uniform and the service life of the material is significantly extended.

[0147] In some embodiments, reference Fig.10 As shown, the present application also provides a system for predicting and optimizing thermal deformation in the processing of metal-plastic composite products. The system is used to execute the above-mentioned method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products, including:

[0148] The model building module is used to build a composite product geometry model based on the material properties and preset boundary conditions of the composite product. It is also used to construct the composite product geometry model and use grids to divide the metal and plastic regions for the composite product geometry model; set the thermal conductivity, thermal expansion coefficient, and elastic modulus for the metal and plastic regions of the composite product geometry model respectively; and apply thermal boundary conditions, mechanical boundary conditions, and fluid field boundary conditions to the composite product geometry model. It is also used to assign metal material properties to the metal area of ​​the composite product geometric model according to the physical properties of the metal material, and the metal material properties include: thermal conductivity, specific heat capacity, thermal expansion coefficient and elastic modulus; assign plastic material properties to the plastic area of ​​the composite product geometric model according to the physical properties of the plastic material, and the metal material properties include: thermal conductivity, specific heat capacity, thermal expansion coefficient and viscosity and rheological properties; apply bonding or slip conditions to the metal and plastic interface of the composite product geometric model to simulate the actual mechanical contact characteristics between the metal and the plastic; apply thermal boundary conditions including heat flow boundaries, convection boundaries and radiation boundaries to the composite product geometric model, apply mechanical boundary conditions including fixed ports, free boundaries and contact conditions, and then apply fluid field boundary conditions including inlet and outlet boundaries and wall boundaries.

[0149] The analysis and solution module is used to solve the temperature field and stress field according to the geometric model of the composite product by using the finite element analysis method and selecting the preset time step and the nonlinear solver coupling. It is also used to perform a preset number of heating and cooling cycles on the geometric model of the composite product and set the applied contact pressure or mold load to calculate the stress field changes during forming; the heat conduction equation is used to solve the temperature field distribution of the composite product in the thermomechanical cycle, and then the temperature field is calculated in combination with the set boundary conditions; the thermal stress equation is used to simulate the internal stress of the material caused by thermal expansion of the geometric model of the composite product, and then the plastic deformation equation and the creep equation are used to calculate the cumulative deformation process of the material in the thermomechanical cycle to solve the stress field. It is also used to introduce the temperature field into the thermoelastic equation to calculate the thermal stress and strain after obtaining the temperature field of the composite product in the thermomechanical cycle; the fluid velocity field is solved according to the fluid dynamics equation, and then the heat conduction equation is coupled to calculate the change of the temperature field during the fluid flow process.

[0150] The simulation prediction module is used to use simulation to analyze the change data of temperature field and stress field over time and thermomechanical cycles and monitor the cumulative deformation and interface stress data of the material in a preset number of cycles to generate simulation prediction results. It is also used to generate stress and temperature distribution and record interface stress and deformation data after each thermomechanical cycle, and then generate a curve of the cumulative stress and deformation of the material over time through a preset number of cycles; based on the change curve and the sum of the load changes that the material can withstand before breaking, the fatigue damage is accumulated using the Miner criterion, the fatigue life of the material is analyzed, and the fatigue failure position is predicted.

[0151] The processing optimization module is used to optimize the heating and cooling rates and the mold loading path according to the simulation prediction results; it is also used to set the objective function according to the target optimization problem; set the design variables to optimize the process parameters of the processing process by adjustment; set the constraints to limit the value range of the design variables, and the constraints include: material strength limit, temperature limit and processing equipment limit; select the preset optimization algorithm and set the initial parameters. It is also used to continuously adjust the process parameters and evaluate the objective function value in combination with the simulation prediction results during the iteration of the optimization algorithm; in the iteration of the preset optimization algorithm, update the design variables according to the optimization algorithm rules, calculate the matching stress and deformation and update the fitness value; output the optimal design variables through the preset optimization algorithm. It is also used to use the optimal design variables for actual processing or simulation processing verification to generate optimized processing results; judge whether the optimized processing results meet the preset requirements, if so, stop the iteration, if not, return to readjust the design variables and model parameters, and continue the iteration.

[0152] In some embodiments, the present application further provides a computer medium having a computer program stored thereon, and the computer program is executed by a processor to implement the method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products.

[0153] In some embodiments, the present application also provides a computer, comprising the computer medium described above.

[0154] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0155] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products, characterized in that: The method comprises the following steps: Step S1: establishing a geometric model of the composite product according to the material properties of the composite product and preset boundary conditions; Step S2: according to the geometric model of the composite product, the temperature field and stress field are solved by using the finite element analysis method and selecting a preset time step and a nonlinear solver coupling; Step S3: using simulation to analyze the variation data of temperature field and stress field over time and thermomechanical cycles and monitoring the accumulated deformation and interface stress data of the material in a preset number of cycles to generate simulation prediction results; Step S4: optimizing the heating and cooling rates and the mold loading path according to the simulation prediction results; Wherein, in step S2, the method comprises the following steps: Step S21: performing a preset number of heating and cooling cycles on the composite product geometric model and setting the applied contact pressure or mold load to calculate the stress field changes during forming; Step S22: using the heat conduction equation to solve the temperature distribution of the composite product in the thermomechanical cycle, and then calculating the temperature field in combination with the set boundary conditions; Step S23: using the thermal stress equation to simulate the internal stress of the material caused by thermal expansion of the composite product geometric model, and then using the plastic deformation equation and the creep equation to calculate the cumulative deformation process of the material in the thermomechanical cycle, so as to solve the stress field; In step S2, the method comprises the following steps: Step S201: after obtaining the temperature field of the composite product in the thermomechanical cycle, the temperature field is introduced into the thermoelastic equation to calculate the thermal stress and strain; Step S202: Solve the fluid velocity field according to the fluid dynamics equation, and then couple the heat conduction equation to calculate the change of the temperature field during the fluid flow process.

2. The method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products according to claim 1, characterized in that: In step S1, the method comprises the following steps: Step S11: constructing a composite product geometric model and using a grid to divide the composite product geometric model into metal and plastic regions; Step S12: setting thermal conductivity, thermal expansion coefficient, and elastic modulus for metal and plastic regions of the composite product geometric model respectively; Step S13: applying thermal boundary conditions, mechanical boundary conditions and fluid field boundary conditions to the composite product geometric model.

3. The method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products according to claim 2, characterized in that: In step S1, the method further comprises the following steps: Step S101: assigning metal material properties to the metal region of the composite product geometric model according to the physical properties of the metal material, wherein the metal material properties include thermal conductivity, specific heat capacity, thermal expansion coefficient and elastic modulus; Step S102: assigning plastic material properties to the plastic region of the composite product geometric model according to the physical properties of the plastic material, wherein the metal material properties include: thermal conductivity, specific heat capacity, thermal expansion coefficient, viscosity and rheological properties; Step S103: applying bonding or sliding conditions to the metal-plastic interface of the composite product geometric model to simulate the actual mechanical contact characteristics between the metal and the plastic; Step S104: applying thermal boundary conditions including heat flow boundary, convection boundary and radiation boundary to the composite product geometric model, applying mechanical boundary conditions including fixed ports, free boundaries and contact conditions, and further applying fluid field boundary conditions including inlet and outlet boundaries and wall boundaries.

4. The method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products according to claim 1, characterized in that: In step S3, the method comprises the following steps: Step S31: after each thermomechanical cycle, generate stress and temperature distribution and record interface stress and deformation data, and then generate a curve of material cumulative stress and deformation over time through a preset number of cycles; Step S32: Based on the variation curve and the sum of the load variations that the material can withstand before fracture, fatigue damage is accumulated using the Miner criterion to analyze the material fatigue life and predict the fatigue failure position.

5. The method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products according to claim 4, characterized in that: In step S4, the method comprises the following steps: Step S41: setting the objective function according to the target optimization problem; Step S42: setting design variables to optimize process parameters of the processing process by adjustment, wherein the design variables include: heating rate, cooling rate, forming pressure, mold loading path and processing time; Step S43: setting constraints to limit the value range of the design variables, wherein the constraints include: material strength constraints, temperature constraints, and processing equipment constraints; Step S44: Select a preset optimization algorithm and set initial parameters.

6. The method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products according to claim 5, characterized in that: In step S4, the method further comprises the following steps: Step S401: During the optimization algorithm iteration process, the process parameters are continuously adjusted and the objective function value is evaluated in combination with the simulation prediction results; Step S402: During the iteration of the preset optimization algorithm, the design variables are updated according to the optimization algorithm rules, the matching stress and deformation are calculated, and the fitness value is updated; Step S403: Output the optimal design variables through a preset optimization algorithm.

7. The method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products according to claim 6, characterized in that: In step S403, the method further includes the following steps: Step S4031: using the optimal design variables to perform actual processing or simulated processing verification to generate optimized processing results; Step S4032: Determine whether the optimization processing result meets the preset requirements. If so, stop the iteration. If not, return to readjust the design variables and model parameters and continue the iteration.

8. A system for predicting and optimizing thermal deformation in the processing of metal-plastic composite products, characterized in that: The system is used to implement a method for predicting and optimizing thermal deformation in the processing of metal-plastic composite products as described in any one of claims 1 to 7, comprising: A model building module, used to build a geometric model of the composite product according to the material properties of the composite product and preset boundary conditions; An analysis and solution module is used to solve the temperature field and stress field by using a finite element analysis method and selecting a preset time step and a nonlinear solver coupling according to the geometric model of the composite product; A simulation prediction module is used to use simulation to analyze the change data of temperature field and stress field over time and thermomechanical cycles and monitor the cumulative deformation and interface stress data of the material in a preset number of cycles to generate simulation prediction results; The processing optimization module is used to optimize the heating and cooling rates and the mold loading path according to the simulation prediction results.

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