Simplified optimization method for efficient molding process of high-quality component made from fiber fabric-reinforced resin matrix composite material

Abstract

The present invention relates to the technical field of molding of components made from fiber fabric-reinforced resin matrix composite materials, and specifically relates to a simplified optimization method for an efficient molding process of a high-quality component made from a fiber fabric-reinforced resin matrix composite material. In the present invention, a finite element is used to perform a thermo-chemical analysis of a macroscopic model and a thermo-mechanical analysis of a microscopic model of a component, achieving simulation of the temperature gradient, curing degree, and residual stress of the component in a molding process; then, a genetic algorithm is used to simultaneously minimize the temperature gradient, molding time, and residual stress as optimization objectives, with a curing process curve as a parameter variable, to perform multi-objective optimization of molding process parameters, and obtain the efficient molding process of the high-quality component. The method of the present invention can reduce the temperature gradient and residual stress of the component in the molding process, shorten the molding time, and scientifically and effectively reduce the manufacturing cost while improving the quality of the component, and is more effective and simpler.

Description

Streamlining and Optimization Methods for High-Quality Molding Processes of Fiber Fabric Reinforced Resin Matrix Composites

[0001] Cross-reference to related applications

[0002] This application claims priority to Chinese Patent Application No. 202410791899.7, filed on June 19, 2024, entitled "Simplified and Optimized Method for High-Quality Molding Process of Fiber Fabric Reinforced Resin Matrix Composite Materials", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This invention belongs to the field of molding technology for fiber fabric reinforced resin matrix composite parts, specifically relating to a simplified and optimized method for efficient molding process of high-quality fiber fabric reinforced resin matrix composite parts. Background Technology

[0004] Rapid technological advancements have led to a sustained expansion in the market demand and application areas of fiber-reinforced resin matrix composites. However, despite years of development, cost and performance remain bottlenecks limiting the widespread application and development of fiber / resin composites. In terms of cost, raw material prices and manufacturing costs are the determining factors, especially manufacturing costs, which account for over 75% of the total cost of fiber-reinforced resin matrix composites. Regarding performance, the properties of fiber-reinforced resin matrix composites primarily depend on material composition and manufacturing technology (including molding processes, curing conditions, and post-processing), which collectively determine the composite's layered structure and final properties. Therefore, improving the manufacturing quality of fiber-reinforced resin matrix composite parts while reducing production costs is a pressing issue that needs to be addressed in this field.

[0005] Currently, the optimization of molding processes for fiber-reinforced resin matrix composites mainly relies on engineer experience and trial-and-error methods, both of which suffer from a lack of theoretical support, high costs, and long development cycles. Therefore, developing economical and efficient methods for optimizing the molding processes of fiber-reinforced resin matrix composites, reducing manufacturing costs while improving the quality of molded parts, has significant practical importance and application value.

[0006] Summary of the Invention

[0007] This invention aims to optimize the molding process of fiber-reinforced resin matrix composites, reduce manufacturing costs, and improve the quality of molded parts. It provides a simplified and optimized method for the efficient molding process of high-quality fiber-reinforced resin matrix composites. This invention utilizes finite element method (FEM) to perform thermo-chemical analysis in the macroscopic model and thermo-mechanical analysis in the microscopic model of the part, simulating temperature gradient, degree of curing, and residual stress during the molding process. Subsequently, a genetic algorithm is used to simultaneously minimize the temperature gradient, molding time, and residual stress as optimization objectives, with the curing process curve as a parameter variable. This multi-objective optimization of molding process parameters yields an efficient molding process for high-quality parts.

[0008] This invention is achieved through the following technical solution: a simplified and optimized method for efficient molding of high-quality parts made of fiber-reinforced resin matrix composites, comprising the following steps:

[0009] S1. Import the Python libraries required for the calculation process. The Python libraries include abaqus and a genetic algorithm library. The abaqus library includes abaquesConstants, caeModules, and odbAccess.

[0010] S2. Set the optimization objective as simultaneously minimizing molding time, temperature gradient, and residual stress;

[0011] S3. Set the variable range for the holding temperature, heating rate and cooling rate in the molding process parameters, and assign values ​​using random function variables;

[0012] S4. Based on the dimensions and molding environment of the fiber-reinforced resin matrix composite part, establish its macroscopic thermo-chemical analysis model using the finite element method, assign material properties, and use the heat transfer analysis module to analyze the evolution of temperature and degree of curing of the composite material during the molding process; write the macroscopic thermo-chemical analysis model as a function of molding process parameters, where the application of temperature boundaries is to pass randomly generated curing process parameters to the subroutine; import the subroutine and submit the job; extract the minimum degree of curing from the macroscopic model from the calculation results; calculate the maximum temperature gradient during the molding process; and export the temperature-time data at the center of the composite part.

[0013] S5. Based on the proportion of each component in the composite material and the size of the fiber fabric, a two-dimensional volumetric representative element model is established using a Python script; the resin modulus change of the composite material during the molding process is analyzed using a curing-hardening instantaneous linear elastic model, and the stress and strain transformations during the molding process are analyzed using a static general module; the micro-thermodynamic analysis is written as a function of curing process parameters, and the temperature-time data exported in step S4 is added as a predefined field; the Abaqus user subroutine is imported and the job is submitted; the result analysis is to extract the residual stress at the end of molding.

[0014] S6. Calculate the total time of the molding process, including heating time, holding time, and cooling time;

[0015] S7. Write a penalty function and use the dynamic penalty function to evaluate the molding process parameters that violate the constraints;

[0016] S8. Write the main function of the genetic algorithm, defining the population size, number of generations, crossover index, and mutation index; call the thermo-chemical model function, thermodynamic model function, and dynamic penalty function to evaluate the fitness of individuals;

[0017] S9. Based on individual fitness, draw the Pareto optimal solution set after multi-objective optimization. Based on the various molding process parameters provided by the Pareto optimal solution set, select the composite material molding process parameters that meet the actual needs to obtain an efficient molding process for high-quality parts of fiber fabric reinforced resin matrix composites.

[0018] As a further improvement to the technical solution of the present invention, in step S4, the subroutines are DISP, HETVAL, and USDFLD.

[0019] As a further improvement to the technical solution of the present invention, in step S5, the imported Abaqus user subroutines are UMAT, USDFLD, and UEXPAN.

[0020] As a further improvement to the technical solution of the present invention, the genetic algorithm used in step S8 is the NSGA-II genetic algorithm.

[0021] As a further improvement to the technical solution of the present invention, the resin matrix of the composite material is selected from at least one of epoxy resin, furan resin, cyanate ester resin, organosilicon resin and phenolic resin.

[0022] As a further improvement to the technical solution of the present invention, the fiber fabric of the composite material is selected from at least one of carbon fiber fabric, quartz fiber fabric, aramid fiber fabric, and metal fiber fabric.

[0023] As a further improvement to the technical solution of the present invention, the molding process is a compression molding process or a resin transfer molding process.

[0024] The technical solution provided by this invention has the following advantages compared with the prior art:

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1) This invention effectively combines finite element method and genetic algorithm to efficiently optimize the molding process of fiber fabric reinforced resin matrix composite parts, reduce the temperature gradient and residual stress of the parts during molding, and shorten the molding time. This can scientifically and effectively reduce manufacturing costs while improving the quality of the parts.

[0027] 2) Compared with the engineer experience method and experimental trial and error method commonly used to optimize the molding process of fiber fabric reinforced resin matrix composite parts, the method described in this invention is more effective and simpler, and can effectively solve the problems of lack of theoretical support, high cost and long cycle of the current method. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 is a flowchart illustrating the specific implementation of the present invention.

[0031] Figure 2 shows the molding process curve of the quartz fiber cloth reinforced cyanate ester resin composite board before optimization.

[0032] Figure 3 shows the transverse residual stress cloud diagram of the quartz fiber cloth reinforced cyanate ester resin composite plate before optimization.

[0033] Figure 4 shows the optimal molding process curve for quartz fiber cloth reinforced cyanate ester resin composite panels after optimization.

[0034] Figure 5. Horizontal residual stress cloud diagram of the optimized quartz fiber cloth reinforced cyanate ester resin composite plate. Detailed Implementation

[0035] To better understand the above-mentioned objectives, features, and advantages of the present invention, the solutions of the present invention will be further described below. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0036] Many specific details are set forth in the following description in order to provide a full understanding of the invention, but the invention may also be practiced in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of the invention, and not all embodiments.

[0037] The specific embodiments of the present invention will be described in detail below.

[0038] Example

[0039] This invention employs a compression molding process. The materials used to mold the composite material parts are mainly cyanate ester resin and quartz fiber cloth. The part dimensions are 500×500×15mm, the volume fraction of the quartz fiber cloth is 51.0%, and the diameter of a single fiber in the quartz fiber cloth is 0.75μm. The original molding process for the composite material parts was: holding at 150℃ for 55min, holding at 185℃ for 60min, and holding at 230℃ for 60min, then cooling to 50℃. The heating and cooling rates were both 3℃ / min.

[0040] Figure 1 is a flowchart illustrating a specific implementation of an embodiment of the present invention. The specific implementation steps of the method are as follows:

[0041] S1. Import the Python libraries required for the calculation process. The Python libraries include Abaqus and a genetic algorithm library. The Abaqus library includes AbaqusConstants, CaeModules, and ODBAccess.

[0042] S2. The optimization objective is to simultaneously minimize molding time, temperature gradient, and residual stress.

[0043] S3. Set the variable range for the holding temperature, heating rate and cooling rate in the molding process parameters. The parameter transformation range is shown in Table 1. Use random function variables to assign values.

[0044] Table 1. Range of values ​​for multi-objective optimization variables.

[0045] Where: r1, r2, r3 are heating rates, r4 is cooling rate, t1, t2, t3 are holding times, and T1, T2, T3 are holding temperatures.

[0046] S4. Based on the dimensions of the composite material part and the molding environment used, establish a macroscopic thermo-chemical analysis model using the finite element method and assign material properties, the specific values ​​of which are shown in Table 2. Use the heat transfer analysis module to analyze the evolution of temperature and degree of curing of the composite material during the molding process. Write the macroscopic thermo-chemical model as a function of molding process parameters, where the application of temperature boundaries is to pass randomly generated curing process parameters to the subroutine. Import the subroutines DISP, HETVAL, and USDFLD and submit the job. Extract the minimum degree of curing from the calculation results in the macroscopic model. Calculate the maximum temperature gradient during the molding process. Export the temperature-time data at the center of the part.

[0047] Table 2 Thermochemical parameters of quartz fiber cloth reinforced cyanate resin composites

[0048] Where: ρ represents the density of the composite material; C represents the specific heat capacity of the composite material; K x K y K z , respectively, are the thermal conductivity of the composite material along the x, y, and z directions.

[0049] S5. Based on the proportions of each component (resin matrix and fiber fabric) in the composite material and the dimensions of the fiber fabric, a two-dimensional volumetric representative element model is established using a Python script; the resin modulus change during the molding process is analyzed using the curing instantaneous linear elastic model (CHILE(Tg)), and the stress and strain transformations during the part molding process are analyzed using the static general module; the micro-thermodynamic analysis is written as a function of curing process parameters, and the temperature-time data exported in step S4 is added as a predefined field; the Abaqus user subroutines UMAT, UEXPAN, and USDFLD are imported and the job is submitted; the result analysis is to extract the residual stress at the end of molding.

[0050] Table 3 Thermodynamic parameters of quartz fiber reinforced cyanate ester resin composites

[0051] in: and These represent the resin modulus before and after complete curing, respectively. r T represents the Poisson's ratio of the resin. g The glass transition temperature (CTE) of a resin indicates its glass transition temperature. r T represents the coefficient of thermal expansion of the resin, β represents the coefficient of curing shrinkage, and T represents the coefficient of thermal expansion of the resin. C1 and T C2 This represents the difference between two critical temperatures. Within this critical temperature range, the resin modulus undergoes a non-linear change. E 1f and E 2fG represents the modulus along and perpendicular to the fiber direction. 12 G 23 G 13f CTE represents the shear modulus of the fiber in the xy, yz, and xz directions, respectively. 1f CTE 2f and CTE 3f V represents the coefficients of thermal expansion of the fiber in the x, y, and z directions, respectively. f This represents the Poisson's ratio of the fiber.

[0052] S6. Calculate the total time of the molding process, including heating time, holding time, and cooling time.

[0053] S7. Write a penalty function and use the dynamic penalty function to evaluate the molding process parameters that violate the constraints.

[0054] S8. Use the NSGA-II algorithm to write the main function of the genetic algorithm, define the population size, number of generations, crossover index and mutation index, and the parameter values ​​are shown in Table 4; call the thermo-chemical model function, thermo-dynamic model function and dynamic penalty function to evaluate the fitness of individuals.

[0055] Table 4 NSGA-II Algorithm Parameters

[0056] S9. Obtain the Pareto optimal solution set after multi-objective optimization.

[0057] Based on the actual requirements of the composite material, the optimal molding process parameters obtained from the Pareto optimal solution set are: holding at 158.8℃ for 37.4 min, holding at 180.2℃ for 91.2 min, and holding at 217.9℃ for 10.6 min; the first heating rate is 4.5℃ / min, the second heating rate is 4.6℃ / min, the third heating rate is 2.0℃ / min, and the cooling rate is 3.1℃ / min.

[0058] As shown in Figures 2-5, compared with the parts manufactured by the molding process before optimization, the parts manufactured by the molding process parameters obtained in this embodiment of the invention have reduced the maximum temperature gradient and residual stress by 41.4% and 25.2%, respectively. Simultaneously, the curing cycle is shortened to 246.6 min, and the curing time is reduced by 20.0%. This indicates that the simplified and optimized molding process method for fiber-reinforced resin-based composite parts of the present invention can efficiently mold high-quality parts.

[0059] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the present invention. Although detailed descriptions have been provided with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments, and they should all be covered within the protection scope of the claims.

Claims

1. A simplified and optimized method for efficient molding of high-quality parts made of fiber-reinforced resin matrix composites, characterized in that, Includes the following steps: S1. Import the Python libraries required for the calculation process. The Python libraries include abaqus and a genetic algorithm library. The abaqus library includes abaquesConstants, caeModules, and odbAccess. S2. Set the optimization objective as simultaneously minimizing molding time, temperature gradient, and residual stress; S3. Set the variable range for the holding temperature, heating rate and cooling rate in the molding process parameters, and assign values ​​using random function variables; S4. Based on the dimensions and molding environment of the fiber-reinforced resin matrix composite parts, a macroscopic thermo-chemical analysis model is established using the finite element method, and material properties are assigned. The heat transfer analysis module is used to analyze the evolution of temperature and curing degree of the composite material during the molding process. The macroscopic thermo-chemical analysis model is written as a function of molding process parameters, where the application of temperature boundary is to pass randomly generated curing process parameters to the subroutine. Import subroutines and submit the job; extract the minimum degree of solidification from the macroscopic model from the calculation results; Calculate the maximum temperature gradient during the molding process; export the temperature-time data at the center of the composite part. S5. Based on the proportion of each component in the composite material and the size of the fiber fabric, a two-dimensional volumetric representative element model is established using a Python script; the resin modulus change of the composite material during the molding process is analyzed using a curing-hardening instantaneous linear elastic model, and the stress and strain transformations during the molding process are analyzed using a static general module; the micro-thermodynamic analysis is written as a function of curing process parameters, and the temperature-time data exported in step S4 is added as a predefined field; the Abaqus user subroutine is imported and the job is submitted; the result analysis is to extract the residual stress at the end of molding. S6. Calculate the total time of the molding process, including heating time, holding time, and cooling time; S7. Write a penalty function and use the dynamic penalty function to evaluate the molding process parameters that violate the constraints; S8. Write the main function of the genetic algorithm, defining the population size, number of generations, crossover index, and mutation index; call the thermo-chemical model function, thermodynamic model function, and dynamic penalty function to evaluate the fitness of individuals; S9. Based on individual fitness, plot the Pareto optimal solution set after multi-objective optimization. Using the various molding process parameters provided by the Pareto optimal solution set, select the composite material molding process parameters that meet the actual needs to obtain the fiber... Efficient molding process for high-quality parts made of woven fabric-reinforced resin matrix composites.

2. The method for simplifying and optimizing the efficient molding process of high-quality parts made of fiber-reinforced resin matrix composites according to claim 1, characterized in that, In step S4, the subroutines are DISP, HETVAL, and USDFLD.

3. The method for simplifying and optimizing the efficient molding process of high-quality parts made of fiber-reinforced resin matrix composites according to claim 1, characterized in that, In step S5, the imported Abaqus user subroutines are UMAT, USDFLD, and UEXPAN.

4. The method for simplifying and optimizing the efficient molding process of high-quality parts made of fiber-reinforced resin matrix composites according to claim 1, characterized in that, In step S8, the genetic algorithm used is the NSGA-II genetic algorithm.

5. The method for simplifying and optimizing the efficient molding process of high-quality parts made of fiber-reinforced resin matrix composites according to claim 1, characterized in that, The resin matrix of the composite material is selected from at least one of epoxy resin, furan resin, cyanate ester resin, silicone resin and phenolic resin.

6. The method for simplifying and optimizing the efficient molding process of high-quality parts made of fiber-reinforced resin matrix composites according to claim 1, characterized in that, The fiber fabric of the composite material is selected from at least one of carbon fiber fabric, quartz fiber fabric, aramid fiber fabric, and metal fiber fabric.

7. The method for simplifying and optimizing the efficient molding process of high-quality parts made of fiber-reinforced resin matrix composites according to claim 1, characterized in that, The molding process is either compression molding or resin transfer molding.

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