Method and system for predicting porosity of composite material curing process based on flow-compaction simulation

Abstract

The application relates to the field of composite material curing forming, and discloses a composite material curing process porosity prediction method and system based on flow-compaction simulation, which comprises the following steps: constructing a finite element model based on component geometry, lay-up, material and process parameters; performing thermal-chemical curing coupling analysis through a heat conduction equation and a resin curing dynamics equation to obtain a temperature field and a curing degree field, then updating resin viscosity and fiber permeability, calculating seepage stress by using Darcy's law, and respectively obtaining compaction counterforce and pore compression counterforce by combining a fiber bed compaction model and a pore compression model; based on the balance relationship between external curing pressure and the three stresses, iteratively solving the compaction strain, and dynamically updating the fiber volume fraction, pore pressure and porosity; outputting the time history change and spatial distribution of the porosity, evaluating the curing process scheme and component forming quality. The application can predict the porosity distribution before actual manufacturing, and provides a basis for process design and quality control.

Description

Technical Field

[0001] This invention relates to the field of composite material curing and manufacturing process quality prediction technology, and in particular to a method and system for predicting the porosity of composite material curing process based on flow-compaction simulation. Background Technology

[0002] Fiber-reinforced resin matrix composites possess excellent properties such as high specific strength, high specific stiffness, high temperature resistance, corrosion resistance, and fatigue resistance, and have been widely used in high-end manufacturing fields such as aerospace. Among them, autoclave molding is one of the important methods for manufacturing high-performance thermosetting resin matrix composite components because it can provide a stable temperature and pressure field.

[0003] Autoclave molding typically involves cutting prepreg to the required size and laying it layer by layer onto a mold surface according to a pre-defined layup sequence. After vacuum sealing and pre-compacting, the prepreg is placed in an autoclave along with the mold. Under set temperature and pressure, the resin flows, compacts, and cross-links, curing to obtain a composite component. However, for complex-shaped composite components, the curing process is influenced by a combination of factors, including layup structure, curing temperature, curing pressure, resin viscosity evolution, fiber bed compaction, interlaminar slippage, and mold constraints. This can easily lead to defects such as delamination, cracks, wrinkles, debonding, and porosity after molding. Among these, porosity is one of the key defects affecting the mechanical properties and molding quality of composite components. Porosity weakens the load transfer capacity between the fibers and the resin matrix and causes localized stress concentration, thereby reducing the tensile, compressive, shear, and fatigue properties of the component. Therefore, high-quality composite components typically require a porosity of no more than 2%, while critical load-bearing components in aerospace and other fields typically require a porosity of less than 1%.

[0004] Currently, porosity control in composite material components mainly relies on process experiments and engineering experience. This involves repeatedly adjusting process parameters such as curing temperature, curing pressure, holding time, and heating rate, and optimizing the process based on test results. However, this method suffers from problems such as long testing cycles, high material and equipment costs, and heavy reliance on personnel experience, making it difficult to meet the development needs of low-cost, high-efficiency composite material manufacturing.

[0005] To improve the accuracy of porosity prediction, existing methods employ multi-physics coupled models, including thermo-chemical, resin flow, fiber bed compaction, and pore compression, to perform finite element simulations of porosity evolution during the curing process. These methods can reflect the coupling relationships between temperature, degree of cure, resin viscosity, fiber volume fraction, pore pressure, and molding pressure. However, existing models often have the following shortcomings: First, some models do not fully consider the impact of real-time changes in resin viscosity with temperature and degree of cure on the flow-compaction process; second, some models do not fully consider the influence of nonlinear compaction behavior of the fiber bed on pore compression; third, the influence of interlayer friction and restricted interlayer slip on the local porosity distribution in complex curved components has not been fully described; and fourth, for complex components such as L-shaped and U-shaped structures, there are significant differences in pressure transmission and compaction behavior between straight and rounded regions, leading to uneven spatial distribution of porosity. Summary of the Invention

[0006] To address the aforementioned problems, the purpose of this invention is to provide a method and system for predicting the porosity of composite material curing processes based on flow-compaction simulation. This method can predict the porosity distribution of composite material components under different layup structures, geometries, and curing process conditions before actual manufacturing, providing a basis for process design and quality control.

[0007] To achieve the above objectives, in a first aspect, the technical solution adopted by the present invention is as follows: a method for predicting the porosity of composite material curing process based on flow-compaction simulation, comprising: acquiring the geometric parameters, layup parameters, material parameters, and curing process parameters of the composite material component, and establishing a finite element model; performing thermo-chemical curing coupling analysis on the finite element model based on the heat conduction equation and the resin curing kinetic equation, and calculating the temperature field and resin curing degree field inside the component at the current time step; updating the resin viscosity according to the current temperature field and resin curing degree field, and updating the fiber permeability according to the current fiber volume fraction; calculating the seepage stress generated by resin flow based on Darcy's law according to the updated resin viscosity and fiber permeability; and calculating the seepage stress based on the current compaction stress. The system employs a variable fiber bed compaction model to calculate the fiber bed compaction reaction force; and calculates the pore compression reaction force based on the current pore pressure and pore compression model. It establishes a material constitutive relation based on the pressure balance relationship between the external curing pressure and the seepage stress, fiber bed compaction reaction force, and pore compression reaction force, iteratively solving for the compaction strain increment at the current time step and updating the compaction strain. Based on the updated compaction strain, it updates the fiber volume fraction, pore pressure, and porosity. This process continues until the set termination conditions are met, outputting the porosity change over time during curing and the spatial distribution of porosity after curing. The curing process scheme and component molding quality are evaluated based on the overall average porosity, local maximum porosity, and porosity differences between different regions of the cured component.

[0008] In some embodiments, the heat conduction control equation for the internal temperature field of the composite material in the thermo-chemical curing coupling analysis is: ; Where T is temperature and t is time; , , These are the thermal conductivity coefficients of the composite material in the x-axis, y-axis, and z-axis directions, respectively. Resin density; Density of the composite material; Specific heat capacity of composite materials; This represents the fiber volume fraction. The heat released during the curing reaction of resin per unit mass; This refers to the degree of resin curing.

[0009] In some embodiments, the resin curing field is calculated using an autocatalytic curing kinetic model: ; ; in, For the first The reaction rate constant of a curing-like reaction; For frequency factors; Activation energy; It is the ideal gas constant; , , These are the fitting parameters.

[0010] In some embodiments, updating the resin viscosity based on the current temperature field and resin curing field, and updating the fiber permeability based on the current fiber volume fraction, includes: Calculate the equivalent stress generated by resin seepage based on the current compaction strain rate, resin viscosity, and fiber permeability: ; η=C η1 exp(-C η2 T); in, The equivalent stress generated by resin permeation; Resin viscosity; The characteristic distance of the pores; For fiber permeability; C is the compaction strain rate. η1 C is the viscosity fitting coefficient; η2 The temperature sensitivity coefficient is T; T is the temperature. The fiber permeability is dynamically updated based on the current fiber volume fraction: ; Where K is the fiber permeability. These are fitting constants related to the fiber arrangement. The fiber radius; This represents the maximum fiber volume fraction.

[0011] In some embodiments, the fiber bed compaction model is: ; in, For the compaction reaction force of the fiber bed; The fitting coefficients for the fiber bed stiffness; This represents the fiber volume fraction in the non-porous state.

[0012] In some embodiments, the pore compression reaction force is calculated as follows: ; in, This is the pore compression reaction force; The initial pore pressure; The initial temperature; The current temperature; The initial porosity; This represents the compressive strain in the thickness direction.

[0013] In some embodiments, the set termination condition is: the resin reaches the gel point, the curing termination condition is met, or the preset curing time ends.

[0014] Secondly, the technical solution adopted by this invention is: a porosity prediction system for the curing process of composite materials based on flow-compaction simulation, comprising: The finite element module obtains the geometric parameters, layup parameters, material parameters, and curing process parameters of composite material components, and establishes a finite element model. The thermo-chemical curing analysis module performs thermo-chemical curing coupling analysis on the finite element model based on the heat conduction equation and the resin curing kinetic equation, and calculates the temperature field and resin curing degree field inside the component at the current time step; The fiber permeability update module updates the resin viscosity based on the current temperature field and resin curing degree field, and updates the fiber permeability based on the current fiber volume fraction. The seepage stress calculation module calculates the seepage stress generated by resin flow based on Darcy's law, according to the updated resin viscosity and fiber permeability. The compaction reaction force and pore compression reaction force calculation module calculates the fiber bed compaction reaction force based on the current compaction strain and fiber bed compaction model; and calculates the pore compression reaction force based on the current pore pressure and pore compression model. The parameter update module establishes a material constitutive relationship based on the pressure balance relationship between the external curing pressure and the seepage stress, fiber bed compaction reaction force and pore compression reaction force, iteratively solves the compaction strain increment at the current time step, and updates the compaction strain; it updates the fiber volume fraction, pore pressure and porosity based on the updated compaction strain. The prediction module continues until the set termination conditions are met, outputting the porosity change over time during the curing process and the spatial distribution of porosity after curing. Based on the overall average porosity, local maximum porosity, and porosity differences between different regions of the cured component, the curing process scheme and the component molding quality are evaluated.

[0015] Thirdly, the technical solution adopted by the present invention is: a computer-readable storage medium for storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device performs any of the methods described above.

[0016] Fourthly, the technical solution adopted by the present invention is: a computing device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0017] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention establishes a porosity prediction model for the curing process of composite materials that couples thermo-chemical processes, resin permeation, fiber bed compaction, and pore compression. This model can reflect the coupling relationship between temperature, degree of curing, resin viscosity, fiber volume fraction, pore pressure, and porosity during the curing process.

[0018] 2. This invention takes into account the influence of resin viscosity changes with temperature and degree of curing on the flow-compaction process, and can more accurately describe the resin flow behavior of thermosetting resin-based composite materials during the curing process.

[0019] 3. This invention takes into account the influence of nonlinear compaction behavior of fiber beds on pore compression, and can predict the porosity changes under different fiber bed stiffness and different layup structures.

[0020] 4. This invention takes into account the influence of interlayer friction and interlayer slip capability on the local porosity of complex curved components, and is particularly suitable for porosity prediction of complex composite material components such as L-shaped, U-shaped, and curved surfaces.

[0021] 5. This invention can output the spatial distribution of porosity and the porosity evolution process, providing a basis for optimizing process and structural parameters such as curing pressure, pressurization timing, layup scheme, component thickness and radius of curvature.

[0022] 6. This invention can predict the molding quality of composite material components before actual manufacturing, reduce the number of trial and error experiments, reduce manufacturing costs, and improve the quality control capability of composite material components.

[0023] In summary, this invention can be widely applied to the curing process design, molding quality assessment, defect control, and process parameter optimization of composite material components in fields such as aerospace, rail transportation, automobiles, and energy equipment. Attached Figure Description

[0024] Figure 1 This is a flowchart of the porosity prediction method for the curing process of composite materials based on flow-compaction simulation in an embodiment of the present invention. Detailed Implementation

[0025] To address the shortcomings of existing models, this invention provides a method and system for predicting porosity during the curing process of composite materials based on flow-compaction simulation. Through coupled simulation of thermo-chemical analysis, resin permeation calculation, fiber bed compaction calculation, and pore compression calculation, and embedding these simulations into a finite element model, the invention achieves simultaneous calculation of the temperature field, degree of cure field, resin viscosity, fiber volume fraction, pore pressure, and porosity evolution during the curing process of composite components. This invention can predict the porosity distribution of composite components under different layup structures, geometries, and curing processes before actual manufacturing, providing a basis for process design and quality control.

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0027] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0028] In one embodiment of the present invention, a method for predicting the porosity of composite material curing process based on flow-compaction simulation is provided. This method, based on multiphysics coupling simulation of flow-compaction, is particularly suitable for numerically predicting the porosity evolution and final spatial distribution of thermosetting resin-based composite materials during autoclave curing. In this embodiment, the core of the invention lies in establishing a multiphysics coupling simulation model of resin flow, fiber bed compaction, and pore compression during the composite material curing process, and obtaining the porosity evolution of the composite material component during curing and the porosity distribution after curing through finite element analysis. Specifically, as shown... Figure 1 As shown, the method includes the following steps: 1) Obtain the geometric parameters, layup parameters, material parameters and curing process parameters of the composite material component, and establish a finite element model.

[0029] 2) Based on the heat conduction equation and the resin curing kinetic equation, a thermo-chemical curing coupling analysis is performed on the finite element model. The thermo-chemical curing coupling analysis is solved by sequential decoupling to calculate the temperature field and resin curing degree field inside the component at the current time step.

[0030] 3) Update the resin viscosity based on the current temperature field and resin curing field, and update the fiber permeability based on the current fiber volume fraction.

[0031] 4) Based on the updated resin viscosity and fiber permeability, calculate the seepage stress generated by resin flow using Darcy's law.

[0032] 5) Calculate the fiber bed compaction reaction force based on the current compaction strain and fiber bed compaction model; and calculate the pore compression reaction force based on the current pore pressure and pore compression model.

[0033] 6) Establish the material constitutive relationship based on the pressure balance relationship between external curing pressure and seepage stress, fiber bed compaction reaction force and pore compression reaction force. The finite element solver iteratively solves the compaction strain increment of the current time step based on the global equilibrium equation and the material tangent stiffness matrix, and updates the compaction strain. Based on the updated compaction strain, update the fiber volume fraction, pore pressure and porosity.

[0034] 7) Until the set termination conditions are met, output the porosity change over time during the curing process and the spatial distribution of porosity after curing. Evaluate the curing process scheme and component molding quality based on the overall average porosity, local maximum porosity, and porosity differences between different regions of the cured component.

[0035] In this embodiment, the specific evaluation method for the curing process and the component molding quality is as follows: The overall average porosity and the local maximum porosity are compared with the preset allowable porosity thresholds. When the overall average porosity and the local maximum porosity do not exceed the corresponding preset thresholds, and the porosity difference between different regions meets the preset uniformity requirements, the curing process scheme is determined to meet the component molding quality requirements. When comparing multiple curing process schemes, the curing process scheme with the smallest overall average porosity, local maximum porosity, and regional porosity difference is selected first.

[0036] In one possible implementation, in step 1) above, a finite element model is established based on the geometry, ply structure and mold constraints of the composite component, defining the component thickness, radius of curvature, ply angle, ply sequence, interlayer contact relationship and boundary conditions.

[0037] In one possible implementation, in step 2) above, the temperature field and resin curing degree field of the composite component during the curing process are calculated by thermo-chemical curing coupling analysis, and real-time state variables are provided for subsequent calculations of resin viscosity, pore pressure and flow-compaction.

[0038] During the curing process, the internal temperature change of the composite material is jointly determined by the convective heat transfer from the external autoclave, the heat transfer from the mold, and the exothermic reaction of the resin crosslinking. The internal temperature field of the composite material is determined by the internal heat conduction and the exothermic reaction of the resin crosslinking. The convective heat transfer from the external autoclave and the heat transfer from the mold are applied through appropriate thermal boundary conditions. The heat conduction governing equation for the internal temperature field of the composite material is: (1) Where T is temperature and t is time; , , These are the thermal conductivity coefficients of the composite material in the x-axis, y-axis, and z-axis directions, respectively. Resin density; Density of the composite material; Specific heat capacity of composite materials; This represents the fiber volume fraction. The heat released during the curing reaction of resin per unit mass; This refers to the degree of resin curing.

[0039] In this embodiment, the resin curing degree field is calculated using an autocatalytic curing kinetic model: (2) (3) in, For the first The reaction rate constant for the curing-like reaction, i = 1, 2; For frequency factors; Activation energy; It is the ideal gas constant; , , These are the fitting parameters.

[0040] In this embodiment, the temperature and degree of curing distribution inside the composite material at each time step can be obtained through thermo-chemical curing coupling analysis, and the material state parameters such as resin viscosity can be updated in real time based on the temperature and degree of curing.

[0041] In one possible implementation, step 3) above updates the resin viscosity based on the current temperature field and resin curing field, and updates the fiber permeability based on the current fiber volume fraction, including the following steps: 3.1) Calculate the equivalent stress generated by resin seepage based on the current compaction strain rate, resin viscosity, and fiber permeability: (4) η=C η1 exp(-C η2 T); (5) in, The equivalent stress generated by resin permeation; Resin viscosity; The characteristic distance of the pores; For fiber permeability; The compaction strain rate; These are the viscosity fitting coefficients; This is the temperature sensitivity coefficient. During the finite element method solution, the current temperature is read at each time increment, and the resin viscosity is recalculated according to the above formula. The updated resin viscosity is then used to calculate the resin percolation stress.

[0042] The resin viscosity is updated in real time based on temperature and degree of curing, thereby reflecting the influence of the resin's transition from a low-viscosity flow state to a gel state on flow-pressure behavior during the curing process.

[0043] 3.2) Fiber permeability is dynamically updated based on the current fiber volume fraction: (6) in, These are fitting constants related to the fiber arrangement. The fiber radius; The maximum fiber volume fraction; This represents fiber permeability.

[0044] In one possible implementation, step 4) above calculates the seepage stress generated by resin flow based on Darcy's law, using the updated resin viscosity and fiber permeability. The specific implementation method is as follows: Under external pressure, the fiber bed undergoes nonlinear compaction deformation, and the fiber volume fraction increases with increasing compressive strain in the thickness direction. The relationship between fiber volume fraction and compressive strain in the thickness direction can be expressed as: (7) (8) in, This represents the current fiber volume fraction. This represents the initial fiber volume fraction. This represents the compressive strain in the thickness direction, which is negative during compression. This refers to the seepage stress generated by the resin flowing relative to the fiber bed; The viscosity of the resin at the current time step; This represents the characteristic distance at which the resin flows within the fiber bed. The compaction strain rate in the thickness direction of the composite material; This represents the fiber permeability corresponding to the current fiber volume fraction. The higher the resin viscosity, the higher the compaction strain rate, or the larger the characteristic distance, the greater the seepage stress generated by the resin flow; the higher the fiber permeability, the easier it is for the resin to flow inside the fiber bed, and the smaller the seepage stress generated.

[0045] In one possible implementation, in step 5) above, the fiber bed compaction model is used to describe the compressive deformation of the fiber reinforcement in the thickness direction under curing pressure and its effect on porosity changes.

[0046] Based on the current compaction strain and fiber bed compaction model, the fiber bed compaction model adopts the existing nonlinear fiber bed compaction model based on fiber volume fraction, and its expression is: (9) in, For the compaction reaction force of the fiber bed; The fitting coefficients for the fiber bed stiffness; The fiber volume fraction represents the fiber volume in the non-porous state. The effects of different layup structures, fiber arrangements, and fiber bed stiffness on compaction deformation and porosity reduction are characterized by the fiber bed compaction reaction force.

[0047] In one possible implementation, step 5) above calculates pore compression and porosity evolution to describe the compression, shrinkage, and porosity reduction process of the initial pores under the combined effects of curing pressure, resin pressure, and temperature changes. Under curing pressure, the composite prepreg undergoes compressive deformation along its thickness, and the initial pores gradually shrink during the compaction process. The relationship between porosity and compressive strain in the thickness direction is as follows: (10) in, This represents the current porosity. The initial porosity; This represents the compressive strain in the thickness direction.

[0048] The pore compression reaction force is calculated as follows: (11) in, This is the pore compression reaction force; The initial pore pressure; The initial temperature; The current temperature; The initial porosity; This represents the compressive strain in the thickness direction.

[0049] In this embodiment, the changes in pore pressure, pore volume, and porosity at different time steps and spatial locations can be effectively calculated.

[0050] In one possible implementation, step 6) above establishes a material constitutive relation based on the pressure balance relationship between external curing pressure and seepage stress, fiber bed compaction reaction force, and pore compression reaction force. The compaction strain increment at the current time step is then calculated, and the compaction strain is iteratively updated. Based on the updated compaction strain, the fiber volume fraction, pore pressure, and porosity are then updated. The specific implementation method is as follows: The relationship between curing pressure and load can be expressed as: (12) in, External curing pressure; The equivalent stress generated by resin permeation; For the compaction reaction force of the fiber bed; This is the pore compression reaction force.

[0051] In this embodiment, a user material subroutine or a user-defined subroutine can be used to store and update state variables such as temperature, degree of cure, resin viscosity, fiber volume fraction, pore pressure, compaction strain, and porosity at each integration point.

[0052] In one possible implementation, the termination condition set in step 7) above is: the resin reaches the gel point, the curing termination condition is met, or the preset curing time ends.

[0053] In this embodiment, the output results in step 7) include, but are not limited to: overall porosity after curing, porosity of flat areas, porosity of rounded corner areas, porosity of local areas, porosity spatial distribution cloud map, porosity evolution curve with curing time, and distribution of state variables such as temperature, degree of curing, resin viscosity, fiber volume fraction and pore pressure.

[0054] By comparing simulation results under different layup methods, component thicknesses, radii of curvature, curing pressures, pressurization timings, heating rates, and insulation regimes, the influence of each factor on porosity can be obtained, providing a basis for optimizing the curing process of composite material components.

[0055] In one embodiment of the present invention, a porosity prediction system for the curing process of composite materials based on flow-compaction simulation is provided, which includes: The finite element module obtains the geometric parameters, layup parameters, material parameters, and curing process parameters of composite material components, and establishes a finite element model. The thermo-chemical curing analysis module performs thermo-chemical curing coupling analysis on the finite element model based on the heat conduction equation and the resin curing kinetic equation, and calculates the temperature field and resin curing degree field inside the component at the current time step; The fiber permeability update module updates the resin viscosity based on the current temperature field and resin curing degree field, and updates the fiber permeability based on the current fiber volume fraction. The seepage stress calculation module calculates the seepage stress generated by resin flow based on Darcy's law, according to the updated resin viscosity and fiber permeability. The compaction reaction force and pore compression reaction force calculation module calculates the fiber bed compaction reaction force based on the current compaction strain and fiber bed compaction model; and calculates the pore compression reaction force based on the current pore pressure and pore compression model. The parameter update module establishes a material constitutive relationship based on the pressure balance relationship between the external curing pressure and the seepage stress, fiber bed compaction reaction force and pore compression reaction force, iteratively solves the compaction strain increment at the current time step, and updates the compaction strain; it updates the fiber volume fraction, pore pressure and porosity based on the updated compaction strain. The prediction module continues until the set termination conditions are met, outputting the porosity change over time during the curing process and the spatial distribution of porosity after curing. Based on the overall average porosity, local maximum porosity, and porosity differences between different regions of the cured component, the curing process scheme and the component molding quality are evaluated.

[0056] In one possible implementation, the heat conduction governing equation for the internal temperature field of the composite material in the thermo-chemical curing coupled analysis is: ; Where T is temperature and t is time; , , These are the thermal conductivity coefficients of the composite material in the x-axis, y-axis, and z-axis directions, respectively. Resin density; Density of the composite material; Specific heat capacity of composite materials; This represents the fiber volume fraction. The heat released during the curing reaction of resin per unit mass; This refers to the degree of resin curing.

[0057] In one possible implementation, the resin cure field is calculated using an autocatalytic curing kinetic model: ; ; in, For the first The reaction rate constant of a curing-like reaction; For frequency factors; Activation energy; It is the ideal gas constant; , , These are the fitting parameters.

[0058] In one possible implementation, the resin viscosity is updated based on the current temperature field and resin curing field, and the fiber permeability is updated based on the current fiber volume fraction, including: Calculate the equivalent stress generated by resin seepage based on the current compaction strain rate, resin viscosity, and fiber permeability: ; η=C η1 exp(-C η2 T); in, The equivalent stress generated by resin permeation; Resin viscosity; The characteristic distance of the pores; For fiber permeability; C is the compaction strain rate. η1 C is the viscosity fitting coefficient; η2 The temperature sensitivity coefficient is T; T is the temperature. Fiber permeability is dynamically updated based on the current fiber volume fraction: ; in, These are fitting constants related to the fiber arrangement. The fiber radius; denoted as the maximum fiber volume fraction; K represents the fiber permeability.

[0059] In one possible implementation, the fiber bed compaction model is as follows: ; in, For the compaction reaction force of the fiber bed; The fitting coefficients for the fiber bed stiffness; This represents the fiber volume fraction in the non-porous state.

[0060] In one possible implementation, the pore compression reaction force is calculated as follows: ; in, This is the pore compression reaction force; The initial pore pressure; The initial temperature; The current temperature; The initial porosity; This represents the compressive strain in the thickness direction.

[0061] In one possible implementation, the set termination condition is: the resin reaches the gel point, the curing termination condition is met, or the preset curing time ends.

[0062] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0063] In one embodiment of the present invention, a computing device is also provided. This computing device can be a terminal, and may include a processor, a communication interface, memory, a display screen, and an input device. The processor, communication interface, and memory communicate with each other via a communication bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. When the computer programs are executed by the processor, they implement the methods described in the above embodiments. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, a management network, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display or an e-ink display. The input device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the casing of the computing device, or an external keyboard, touchpad, or mouse. The processor can call logical instructions in the memory to execute the methods described in the above embodiments.

[0064] In one embodiment of the present invention, a computer program product including instructions is also provided. This computer program product may be a software or program product including instructions and capable of running on a computing device or stored on any available medium. When the computer program product is run on at least one computing device, it causes the at least one computing device to perform the methods and functions of any of the foregoing embodiments.

[0065] In one embodiment of the invention, a computer-readable storage medium is also provided. The computer-readable storage medium can be any available medium accessible to a computing device, or a data storage device (e.g., a data center) that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, or magnetic tapes), optical media (e.g., DVDs), semiconductor media (e.g., solid-state drives), etc. The computer-readable storage medium includes instructions, and the instructions instruct the computing device to perform the methods and functions in any of the foregoing embodiments.

[0066] Computer-readable media can be any tangible medium that includes or stores a program for executing instructions on or associated with a system, apparatus, or device, or a data storage device, such as a data center, including one or more available media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, any suitable combination thereof. More detailed examples of computer-readable storage media include electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0067] Furthermore, although the operations of the methods disclosed in this invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations need to be performed in a specific order, or that all the operations shown need to be performed to achieve the desired result. Rather, the order of execution of the operations shown in the flowcharts can be changed. Additionally or alternatively, some operations may be omitted, multiple operations may be combined into one operation for execution, and / or one operation may be decomposed into multiple operations for execution. It should also be noted that the features and functions of two or more devices disclosed in this invention may be specific in one device. Conversely, the features and functions of the aforementioned one device may be further specific in multiple devices.

[0068] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for predicting porosity during the curing process of composite materials based on flow-compaction simulation, characterized in that, include: Obtain the geometric parameters, layup parameters, material parameters, and curing process parameters of the composite material component, and establish a finite element model; Based on the heat conduction equation and the resin curing kinetics equation, a thermo-chemical curing coupling analysis is performed on the finite element model to calculate the temperature field and resin curing degree field inside the component at the current time step. The resin viscosity is updated based on the current temperature field and resin curing degree field, and the fiber permeability is updated based on the current fiber volume fraction. Based on the updated resin viscosity and fiber permeability, the seepage stress generated by resin flow is calculated using Darcy's law. Calculate the compaction reaction force of the fiber bed based on the current compaction strain and fiber bed compaction model; Based on the current pore pressure and pore compression model, the pore compression reaction force is calculated; The material constitutive relation is established based on the pressure balance relationship between the external curing pressure and the seepage stress, fiber bed compaction reaction force and pore compression reaction force. The compaction strain increment at the current time step is solved iteratively and the compaction strain is updated. The fiber volume fraction, pore pressure and porosity are updated based on the updated compaction strain. Until the set termination conditions are met, the results of porosity change over time during the curing process and the spatial distribution of porosity after curing are output. The curing process scheme and component molding quality are evaluated based on the overall average porosity, local maximum porosity and porosity differences between different regions of the cured component.

2. The method for predicting porosity during the curing process of composite materials based on flow-compaction simulation as described in claim 1, characterized in that, In the thermo-chemical curing coupled analysis, the heat conduction control equation for the internal temperature field of the composite material is: ； Where T is temperature and t is time; , , These are the thermal conductivity coefficients of the composite material in the x-axis, y-axis, and z-axis directions, respectively. Resin density; Density of the composite material; Specific heat capacity of composite materials; This represents the fiber volume fraction. The heat released during the curing reaction of resin per unit mass; This refers to the degree of resin curing.

3. The method for predicting porosity during the curing process of composite materials based on flow-compaction simulation as described in claim 2, characterized in that, The resin curing degree field was calculated using an autocatalytic curing kinetic model: ； ； in, For the first The reaction rate constant of a curing-like reaction; For frequency factors; Activation energy; It is the ideal gas constant; , , These are the fitting parameters.

4. The method for predicting porosity during the curing process of composite materials based on flow-compaction simulation as described in claim 1, characterized in that, The resin viscosity is updated based on the current temperature field and resin curing degree field, and the fiber permeability is updated based on the current fiber volume fraction, including: Calculate the equivalent stress generated by resin seepage based on the current compaction strain rate, resin viscosity, and fiber permeability: ； η=C η1 exp(-C η2 T); in, The equivalent stress generated by resin permeation; Resin viscosity; The characteristic distance of the pores; For fiber permeability; C is the compaction strain rate. η1 C is the viscosity fitting coefficient; η2 The temperature sensitivity coefficient is T; T is the temperature. The fiber permeability is dynamically updated based on the current fiber volume fraction: ； Where K is the fiber permeability. These are fitting constants related to the fiber arrangement. The fiber radius; This represents the maximum fiber volume fraction.

5. The method for predicting porosity during the curing process of composite materials based on flow-compaction simulation as described in claim 1, characterized in that, The fiber bed compaction model is as follows: ； in, For the compaction reaction force of the fiber bed; The fitting coefficients for the fiber bed stiffness; This represents the fiber volume fraction in the non-porous state.

6. The method for predicting porosity during the curing process of composite materials based on flow-compaction simulation as described in claim 1, characterized in that, The pore compression reaction force is calculated as follows: ； in, This is the pore compression reaction force; The initial pore pressure; The initial temperature; The current temperature; The initial porosity; This represents the compressive strain in the thickness direction.

7. The method for predicting porosity during the curing process of composite materials based on flow-compaction simulation as described in claim 1, characterized in that, The set termination conditions are: the resin reaches the gel point, the curing termination condition is met, or the preset curing time ends.

8. A porosity prediction system for the curing process of composite materials based on flow-compaction simulation, characterized in that, include: The finite element module obtains the geometric parameters, layup parameters, material parameters, and curing process parameters of composite material components, and establishes a finite element model. The thermo-chemical curing analysis module performs thermo-chemical curing coupling analysis on the finite element model based on the heat conduction equation and the resin curing kinetic equation, and calculates the temperature field and resin curing degree field inside the component at the current time step; The fiber permeability update module updates the resin viscosity based on the current temperature field and resin curing degree field, and updates the fiber permeability based on the current fiber volume fraction. The seepage stress calculation module calculates the seepage stress generated by resin flow based on Darcy's law, according to the updated resin viscosity and fiber permeability. The compaction reaction force and pore compression reaction force calculation module calculates the fiber bed compaction reaction force based on the current compaction strain and fiber bed compaction model. Based on the current pore pressure and pore compression model, the pore compression reaction force is calculated; The parameter update module establishes a material constitutive relationship based on the pressure balance relationship between the external curing pressure and the seepage stress, fiber bed compaction reaction force and pore compression reaction force, iteratively solves the compaction strain increment at the current time step, and updates the compaction strain; it updates the fiber volume fraction, pore pressure and porosity based on the updated compaction strain. The prediction module continues until the set termination conditions are met, outputting the porosity change over time during the curing process and the spatial distribution of porosity after curing. Based on the overall average porosity, local maximum porosity, and porosity differences between different regions of the cured component, the curing process scheme and the component molding quality are evaluated.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 7.

10. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 7.

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