Method for simulating temperature field and curing deformation of mold through finite element and mold optimization design method based on method

Abstract

The invention discloses a method for simulating a mold temperature field and curing deformation through finite elements and a mold optimization design method based on the method. The finite element analogue simulation method comprises the following steps: firstly, modeling and simulating a mold grid; secondly, calculating equivalent thermal and mechanical parameters of the composite material by adopting a multi-scale homogenization method; then, a heat conduction model of the mold and the composite material blade is constructed and coupled with the curing dynamic model through temperature, and mold temperature distribution is simulated; and finally, establishing a mold thermal expansion and curing stress coupling model, and predicting thermal deformation and stress change of the mold. By identifying the weak area of the mold in the simulation process, the mold is optimally designed. By improving a mold temperature field distribution simulation method, optimizing a grid modeling process and introducing an accurate calculation method of equivalent material parameters, the precision of mold design and the controllability of a curing process are effectively improved, so that the problem of inaccurate mold temperature field distribution and curing deformation prediction is solved.

Description

Technical Field

[0001] The present invention relates to a simulation molding of a composite material blade, and in particular to a method for simulating a mold temperature field and solidification deformation by using a finite element simulation method and a mold optimization design method based on the method. Background Art

[0002] Composite blades are increasingly used in aviation, wind energy, and other fields. Precise control of the curing process during their manufacturing is crucial. During the composite material curing process, the mold's temperature distribution and curing deformation directly impact the blade's quality and performance. Traditional mold design typically uses empirical methods, focusing on mold shape and temperature control, but ignoring the non-uniform temperature distribution within the mold and the thermal stress and deformation that occur during the curing process.

[0003] Currently, existing technologies for simulating mold temperature fields rely heavily on simple heat conduction analysis models. A common approach is to predict the mold temperature field based on an assumed uniform temperature distribution. This involves estimating the mold temperature distribution using empirical formulas or simplified heat conduction models and then experimentally measuring deformation. While this approach is simple, it ignores the thermal expansion characteristics of the mold material, the complex heat exchange process between the mold and the heat source, and the geometric deformation of the mold itself. Furthermore, traditional methods for calculating equivalent material parameters for composite materials often rely on simple assumptions, fail to fully consider the impact of different layouts and fiber orientations on the material's mechanical properties, and neglect the complex thermal-curing-mechanical coupling effects within the composite material. This results in low prediction accuracy, large errors in simulation results, and difficulty in accurately predicting the mold's actual performance during the curing process. Equivalent material parameters for complex woven structures, such as quad-axial fabric, are typically calculated using empirical values ​​or simplified models. This lack of precise theoretical calculation and verification leads to large errors in deformation prediction during the curing process. Therefore, in the manufacturing of composite blades, the mold temperature field distribution and curing deformation directly impact the quality of the final product.

[0004] The existing technology has obvious deficiencies in the prediction of mold temperature field and curing deformation, which are mainly manifested in the following aspects: (1) Empirical formulas or simplified models fail to fully consider the anisotropic thermal physical properties of quad-axial cloth materials, resulting in low prediction accuracy; (2) The mold design lacks refined thermal-curing-mechanical coupling analysis, making it difficult to accurately predict the temperature field uniformity and curing shrinkage deformation during the curing process; (3) The existing finite element analysis method is relatively rough in processing the equivalent parameters of composite materials, and fails to establish an accurate equivalent calculation method for woven structures such as quad-axial cloth, which affects the simulation accuracy. Summary of the Invention

[0005] In response to the problem that the inaccurate prediction of mold temperature field distribution and curing deformation in the existing technology, as well as the inaccurate calculation of equivalent material parameters of composite materials, lead to a large gap between design and actual operation, which in turn affects the mold usage effect and production efficiency, the present invention provides a finite element simulation method for simulating mold temperature field and curing deformation and a mold optimization design method based on the method. By improving the mold temperature field distribution simulation method, optimizing the grid modeling process, and introducing an accurate calculation method for equivalent material parameters, the accuracy of mold design and the controllability of the curing process can be effectively improved, thereby solving the problem of inaccurate prediction of mold temperature field distribution and curing deformation in the existing technology, and providing an accurate and reliable simulation prediction method for composite blade mold segmentation.

[0006] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0007] A method for simulating mold temperature field and curing deformation by finite element simulation includes the following steps:

[0008] (1) Mold mesh modeling and simulation method: Based on the HyperMesh fine mesh division method, efficient modeling is performed for the mold's geometric shape and complex contact boundaries;

[0009] A fine meshing method based on HyperMesh was used, with particular emphasis on the mold contact surface and internal temperature gradient areas. This improved mesh captures more accurate temperature distribution and deformation information, resulting in more precise simulation results for heat transfer and solidification deformation of the mold. This improves the accuracy of mold performance predictions and reduces the cost and time of test commissioning. Simulation accuracy was also enhanced by optimizing the mesh generation algorithm in HyperMesh software, particularly at the mold contact surface and internal temperature gradient areas.

[0010] The fine meshing method selects the appropriate mesh type and density based on the stress and temperature field distribution in different areas of the mold. It also uses locally refined meshes to capture temperature changes and stress concentrations in key areas such as joints, chamfers, and heating points, ensuring sufficiently accurate simulation results. Furthermore, the optimal mesh density is determined through step-by-step convergence analysis and comparison of simulation results at different densities.

[0011] The efficient modeling improves simulation efficiency by streamlining the calculation process while ensuring the accuracy of key areas.

[0012] (2) Calculation of equivalent material parameters: Based on the braided structure of the quad-axial cloth, the equivalent thermal and mechanical parameters of the quad-axial cloth and its composite materials are calculated using a multi-scale homogenization method to improve the accuracy of the material parameters;

[0013] The multi-scale homogenization method comprises:

[0014] ① Based on the selected fiber, matrix performance parameters and fiber volume fraction, the basic performance parameters of unidirectional glass fiber / epoxy resin composites can be obtained by equations (1) to (6);

[0015] E 11 =E f ×V f +E m (1-V f )(1)

[0016]

[0017] v 12 =v 13 =v f ×V f +v m (1-V f )(3)

[0018]

[0019] ②The thermal expansion coefficient in the fiber direction and the thermal expansion coefficient in the perpendicular fiber direction can be calculated by formulas (7) to (8):

[0020]

[0021] α transverse =(1+v f )α ) V f +(1+v m )α m V m -α longitudinel v 12 (8)

[0022] ③The thermal conductivity in the fiber direction (in-plane direction) can be calculated by the parallel model:

[0023] k c_longitudinal =k f V f +k m V m (9)

[0024] The thermal conductivity perpendicular to the fiber direction (out-of-plane direction) can be calculated using the series model:

[0025]

[0026] ④ The weight percentage of fiber and matrix in the composite material can be calculated by formula (11) to (12):

[0027]

[0028] Based on the calculation results of formulas (11) and (12), the specific heat capacity of the composite material can be calculated by formula (13):

[0029] C cp =C f W f +C m W m (13);

[0030] E 11 Represents the modulus in one direction (fiber direction), E 22 Represents the 2-direction (perpendicular to the fiber direction) modulus, E 33 Represents the modulus in three directions (perpendicular to the fiber direction), E f represents the fiber modulus, E m represents the matrix modulus, V f represents the fiber volume fraction, V m represents the matrix volume fraction, v f represents the Poisson's ratio of the fiber, v m represents the matrix Poisson's ratio, v 12 Represents the Poisson's ratio in the 12 directions, v 13 represents the Poisson's ratio in the 13 direction, v 23 represents the Poisson's ratio in the 23 direction, G 12 Represents the shear modulus in 12 directions, G 23 represents the shear modulus in the 23 direction, G 13 represents the shear modulus in the 13 direction, G m represents the shear modulus of the matrix, G f represents the shear modulus of the fiber, α f represents the thermal expansion coefficient of the fiber, α m represents the thermal expansion coefficient of the matrix, ρ f represents the density of the fiber, ρ m represents the density of the matrix, C f represents the specific heat capacity of the fiber, C m represents the specific heat capacity of the matrix;

[0031] The multi-scale homogenization method used in this step improves the calculation method of the equivalent material parameters of the four-axis cloth. By introducing precise calculation formulas, the material parameters used in ABAQUS simulation are more realistic, avoiding simulation errors caused by inaccurate material parameters, resulting in more reliable simulation results and improving the accuracy of mold design.

[0032] (3) Simulation of temperature field distribution during mold curing: Based on the thermal and mechanical parameters obtained in step (2), a heat conduction model of the mold and the composite blade is constructed in ABAQUS software. Combined with the curing kinetics equation, the heat conduction model and the curing kinetics model are coupled through temperature to accurately simulate the temperature distribution of the mold under different working conditions during the curing process, especially the heat exchange behavior between the mold and the external heat source and the blade during the composite curing process;

[0033] In step (3), the innovative integrated thermo-solid coupling analysis process is implemented: the dynamic coupling of the curing reaction kinetics model (including the epoxy resin curing degree equation) and the thermoelastic constitutive model is realized in Abaqus;

[0034] (4) Deformation analysis of the mold during curing: Based on the temperature field data simulated in step (3), combined with the curing shrinkage and thermal stress calculation of the composite material, the mold curing deformation analysis was performed using ABAQUS finite element software. A coupling model of mold thermal expansion and curing stress was established, which can accurately predict the thermal deformation and stress changes of the mold during high-temperature curing;

[0035] The improved thermal-mechanical coupling model accurately simulates the heat exchange behavior inside and outside the mold, especially the interaction between the mold and the external heat source during the curing process.

[0036] Furthermore, in step (4), a unique residual stress quantitative evaluation method is used to establish a mold joint deformation prediction model. Specifically, by superimposing the curing shrinkage strain and thermal expansion strain, a mathematical model for predicting the mold joint deformation is established, which can more accurately evaluate the deformation problems that may occur in the mold during the curing process and improve the accuracy of mold design.

[0037] Specifically, during the curing process of composite molds, the material undergoes curing shrinkage—that is, as the temperature drops, the material's volume changes, inducing strain. In high-temperature environments, the mold material itself also undergoes thermal expansion—that is, the mold expands due to heating and contracts when the temperature cools. The interaction between these two strains can lead to localized deformation of the mold, particularly in the joint area, where stress concentration and deformation are likely to occur. By superimposing the curing shrinkage strain and thermal expansion strain, and taking into account the complexity of the interaction between these two strains, a joint deformation prediction model was established. This model can calculate the superposition effect of strain through simulation, and then accurately predict the deformation of the joint area, providing a scientific basis for optimizing mold design and improving manufacturing processes.

[0038] (5) Experimental method verification: Temperature sensors and deformation measurement equipment are used to verify the experimental results to calibrate the finite element model and optimize the simulation accuracy;

[0039] The specific verification method is: use temperature sensors to detect temperature field changes during the curing process, use deformation measurement equipment to detect strain field changes during the process, and compare them with finite element results.

[0040] Furthermore, the present invention also provides a mold optimization design method based on the above simulation method, comprising: identifying weak areas of the mold during the above simulation process, and optimizing the design of the composite blade mold, thereby improving the curing effect and production efficiency of the mold;

[0041] The weak areas of the mold are areas with high curing deformation and strain;

[0042] The mold optimization design includes optimizing the mold's geometric structure, material selection, and heat source distribution.

[0043] Furthermore, optimizing mold geometry primarily involves adjusting mold thickness, achieved by optimizing the heat conduction and deformation characteristics of various mold regions. For areas subject to drastic temperature fluctuations, such as seams and chamfers, the thickness can be reduced to improve heat conduction and avoid thermal stress concentration. In areas with significant deformation, increasing the thickness helps improve rigidity and control deformation during curing.

[0044] Furthermore, when optimizing mold material selection, materials with moderate thermal conductivity are preferred to ensure uniform temperature distribution within the mold. The thermal expansion coefficient of the material should match that of the composite material to avoid additional stress caused by inconsistent expansion. Highly heat- and corrosion-resistant alloys, such as steel or aluminum alloys, are often used to ensure mold stability at high temperatures.

[0045] Furthermore, the heat source distribution optimization achieves uniform heating through a multi-point heating design, avoiding temperature unevenness caused by a single heating point. Based on temperature field distribution simulation, the heating source position and power settings are optimized to cover key areas of the mold. An adaptive control system adjusts the heat source power based on real-time temperature, ensuring a stable temperature throughout the mold during the curing process and preventing stress concentration and deformation caused by temperature unevenness.

[0046] The present invention optimizes the mold's geometry and material selection by analyzing simulation results. This improvement allows for identification and adjustment of weaknesses in the mold design, significantly improving the mold's manufacturing efficiency and service life while reducing errors and material waste during production.

[0047] Because the improved temperature field simulation is more accurate, it can better predict the temperature distribution and thermal stress of the mold, making the mold design more optimized, reducing defects caused by uneven temperature fields, and improving the stability of the mold and the quality of composite products.

[0048] The present invention not only focuses on the thermal stress of the mold, but also combines the mold geometry and material properties to provide more accurate deformation prediction.

[0049] Furthermore, the structural optimization design method of the mold joint seam includes:

[0050] ① Joint seam topology optimization criteria based on simulation results: association rules between the distribution of residual stress extreme points and the curvature characteristics of the mold surface;

[0051] The joint seam topology optimization criteria based on simulation results mainly optimize mold design by analyzing the relationship between the distribution of extreme points of residual stress and the curvature characteristics of the mold surface. Specifically, during the curing process of composite materials, large residual stresses will be generated at the joint seam due to temperature changes and curing shrinkage. Especially near the stress extreme points, when the curvature of the mold surface is large, it is easy to cause stress concentration. Finite element analysis simulation can accurately obtain the distribution of temperature and stress fields. Combined with the curvature characteristics of the mold surface, corresponding optimization criteria can be formulated to adjust the joint seam design, thereby reducing stress concentration and improving mold performance and product quality.

[0052] ② Dynamic correction algorithm of joint geometric parameters: reversely infer the optimal chamfer radius and step height ratio based on the temperature field simulation results.

[0053] The dynamic correction algorithm for the geometric parameters of the splicing seam reversely infers the optimal chamfer radius and step height ratio through the temperature field simulation results. The reasonable setting of the chamfer radius and step height ratio directly affects the stress distribution and heat transfer efficiency at the splicing seam, which in turn affects the curing deformation of the mold. Based on the temperature field and stress field, the algorithm uses reverse engineering technology to dynamically adjust these geometric parameters according to the simulation results to optimize the splicing seam design. Through continuous iterative optimization, it can ensure that the stress distribution of the mold is uniform during the curing process, reduce deformation, and improve production efficiency and the quality of the final product. This process not only improves the design accuracy of the mold, but also provides more reliable simulation support for the manufacture of composite blades.

[0054] Compared with the prior art, the present invention has the following advantages:

[0055] The present invention solves the problem of inaccurate mold performance prediction in the prior art by accurately calculating the equivalent material parameters of the four-axis cloth, optimizing the mold design, and improving the temperature control and deformation prediction accuracy during the curing process.

[0056] (1) Improve prediction accuracy: The present invention adopts an accurate four-axis cloth equivalent material parameter calculation method, which makes the finite element simulation results closer to reality and improves the accuracy of temperature field and deformation prediction.

[0057] (2) Optimizing mold design: The present invention optimizes the mold structure through simulation, reduces thermal stress and deformation during the curing process, and improves the manufacturing quality of the blade.

[0058] (3) Reduced test costs: The present invention reduces dependence on experimental measurements, reduces the time and economic costs of repeated tests, and accelerates the product development cycle.

[0059] (4) Improved production efficiency: The present invention improves the uniformity of the curing process by optimizing mold temperature control, thereby improving production efficiency and blade consistency. DETAILED DESCRIPTION

[0060] The present invention will be further described in detail below with reference to the examples, but the embodiments of the present invention are not limited thereto.

[0061] Example

[0062] A method for simulating mold temperature field and curing deformation by finite element simulation includes the following steps:

[0063] (1) Mold mesh modeling and simulation method: Based on the HyperMesh fine mesh division method, efficient modeling is performed for the mold's geometric shape and complex contact boundaries;

[0064] HyperMesh-based fine meshing was used to select the appropriate mesh type and density based on the stress and temperature field distribution in different areas of the mold. Local mesh refinement was used to capture temperature changes and stress concentrations in key areas such as joints, chamfers, and heating points, ensuring sufficient accuracy in simulation results. Furthermore, the optimal mesh density was determined through step-by-step convergence analysis and comparison of simulation results at different densities.

[0065] (2) Calculation of equivalent material parameters: Based on the braided structure of the quad-axial cloth, the equivalent thermal and mechanical parameters of the quad-axial cloth and its composite materials are calculated using a multi-scale homogenization method to improve the accuracy of the material parameters;

[0066] The multi-scale homogenization method comprises:

[0067] ① Based on the selected fiber, matrix performance parameters and fiber volume fraction, the basic performance parameters of unidirectional glass fiber / epoxy resin composites can be obtained by equations (1) to (6);

[0068] E 11 =E f ×V f +E m (1-V f ) (1)

[0069]

[0070] v 12 =v 13 =v f ×V f +v m (1-V f ) (3)

[0071]

[0072] ②The thermal expansion coefficient in the fiber direction and the thermal expansion coefficient in the perpendicular fiber direction can be calculated by formulas (7) to (8):

[0073]

[0074] α transverse =(1+v f )α f V f +(1+v m )α m V m -α longitudinal v 12 (8)

[0075] ③The thermal conductivity in the fiber direction (in-plane direction) can be calculated by the parallel model:

[0076] k c_longitudinal =k f V f +k m V m (9)

[0077] The thermal conductivity perpendicular to the fiber direction (out-of-plane direction) can be calculated using the series model:

[0078]

[0079] ④ The weight percentage of fiber and matrix in the composite material can be calculated by formula (11) to (12):

[0080]

[0081] Based on the calculation results of formulas (11) and (12), the specific heat capacity of the composite material can be calculated by formula (13):

[0082] C cp =C f W f +C m W m (13);

[0083] E 11 Represents the modulus in one direction (fiber direction), E 22Represents the 2-direction (perpendicular to the fiber direction) modulus, E 33 Represents the modulus in three directions (perpendicular to the fiber direction), E f represents the fiber modulus, E m represents the matrix modulus, V f represents the fiber volume fraction, V m represents the matrix volume fraction, v f represents the Poisson's ratio of the fiber, v m represents the matrix Poisson's ratio, v 12 Represents the Poisson's ratio in the 12 directions, v 13 represents the Poisson's ratio in the 13 direction, v 23 represents the Poisson's ratio in the 23 direction, G 12 Represents the shear modulus in 12 directions, G 23 represents the shear modulus in the 23 direction, G 13 represents the shear modulus in the 13 direction, G m represents the shear modulus of the matrix, G f represents the shear modulus of the fiber, α f represents the thermal expansion coefficient of the fiber, α m represents the thermal expansion coefficient of the matrix, ρ f represents the density of the fiber, ρ m represents the density of the matrix, C f represents the specific heat capacity of the fiber, C m represents the specific heat capacity of the matrix;

[0084] (3) Simulation of temperature field distribution during mold curing: Based on the thermal and mechanical parameters obtained in step (2), a heat conduction model of the mold and the composite blade is constructed in ABAQUS software. Combined with the curing kinetics equation, the heat conduction model and the curing kinetics model are coupled through temperature to accurately simulate the temperature distribution of the mold under different working conditions during the curing process, especially the heat exchange behavior between the mold and the external heat source and the blade during the composite curing process;

[0085] In step (3), the innovative integrated thermo-solid coupling analysis process is implemented: the dynamic coupling of the curing reaction kinetics model (including the epoxy resin curing degree equation) and the thermoelastic constitutive model is realized in Abaqus;

[0086] (4) Deformation analysis of the mold during curing: Based on the temperature field data simulated in step (3), combined with the curing shrinkage and thermal stress calculation of the composite material, the mold curing deformation analysis was performed using ABAQUS finite element software. A coupling model of mold thermal expansion and curing stress was established, which can accurately predict the thermal deformation and stress changes of the mold during high-temperature curing;

[0087] The improved thermal-mechanical coupling model accurately simulates the heat exchange behavior inside and outside the mold, especially the interaction between the mold and the external heat source during the curing process.

[0088] In step (4), a unique residual stress quantitative evaluation method is used to establish a mold joint deformation prediction model. Specifically, by superimposing the curing shrinkage strain and the thermal expansion strain, a mathematical model for predicting the mold joint deformation is established, so as to more accurately evaluate the deformation problems that may occur in the mold during the curing process and improve the accuracy of the mold design.

[0089] (5) Experimental method verification: The temperature field changes during the curing process are detected by temperature sensors, and the strain field changes during the process are detected by deformation measurement equipment. The results are compared with the finite element results to calibrate the finite element model and optimize the simulation accuracy.

[0090] The present invention also provides a mold optimization design method based on the above simulation method, comprising: identifying weak areas of the mold during the above simulation process, and optimizing the design of the composite blade mold, thereby improving the curing effect and production efficiency of the mold;

[0091] The weak areas of the mold are areas with high curing deformation and strain;

[0092] The mold optimization design includes optimizing the mold's geometric structure, material selection, and heat source distribution;

[0093] The optimization of the mold geometry mainly includes adjusting the mold thickness and optimizing the joint structure. The thickness adjustment is mainly achieved by optimizing the heat conduction and deformation characteristics of each area of ​​the mold. That is, for areas with drastic temperature changes, such as joints and chamfers, the thickness can be appropriately reduced to improve heat conduction and avoid thermal stress concentration. In areas with more obvious deformation, increasing the thickness helps to improve rigidity and control deformation during the curing process.

[0094] The structural optimization design method of the mold joint seam includes:

[0095] ① Joint seam topology optimization criteria based on simulation results: association rules between the distribution of residual stress extreme points and the curvature characteristics of the mold surface;

[0096] ② Dynamic correction algorithm for joint geometry parameters: inversely deduce the optimal chamfer radius and step height ratio based on the temperature field simulation results;

[0097] The material of the optimized mold is aluminum alloy.

[0098] The heat source distribution optimization achieves uniform heating through a multi-point heating design, avoiding temperature unevenness caused by a single heating point.

[0099] The present invention adopts a precise four-axis cloth equivalent material parameter calculation method to make the finite element simulation results closer to reality and improve the accuracy of temperature field and deformation prediction; through simulation optimization of the mold structure, the thermal stress and deformation during the curing process are reduced, and the blade manufacturing quality is improved; by optimizing the mold temperature control, the uniformity of the curing process is improved, thereby improving production efficiency and blade consistency.

Claims

1. A method for simulating mold temperature field and curing deformation by finite element simulation, characterized in that: The following steps are involved: (1) Mold mesh modeling and simulation method: Based on the HyperMesh fine mesh division method, efficient modeling is performed for the mold's geometric shape and complex contact boundaries; (2) Calculation of equivalent material parameters: Based on the woven structure of the quad-axial cloth, the equivalent thermal and mechanical parameters of the quad-axial cloth and the composite materials based on the quad-axial cloth are calculated using a multi-scale homogenization method; (3) Simulation of temperature field distribution during mold curing: Based on the thermal and mechanical parameters obtained in step (2), a heat conduction model of the mold and the composite blade is constructed in ABAQUS software. Combined with the curing kinetics equation, the heat conduction model and the curing kinetics model are coupled through temperature to accurately simulate the temperature distribution of the mold under different working conditions during the curing process. (4) Deformation analysis of the mold during curing: Based on the temperature field data simulated in step (3), combined with the curing shrinkage and thermal stress calculation of the composite material, the mold curing deformation analysis was performed using ABAQUS finite element software. A coupling model of mold thermal expansion and curing stress was established, which can accurately predict the thermal deformation and stress changes of the mold during high-temperature curing; (5) Verification of experimental method: Temperature sensors and deformation measurement equipment are used to verify the experimental results to calibrate the finite element model and optimize the simulation accuracy.

2. The method for simulating mold temperature field and curing deformation by finite element simulation according to claim 1, characterized in that: The fine meshing method is to select the appropriate mesh type and density according to the stress and temperature field distribution in different areas of the mold, and to capture the temperature changes and stress concentration in key areas by locally refining the mesh; The areas where the local mesh is encrypted include the mold contact surface and the internal temperature gradient area, and the key areas include the joints, chamfers and heating points.

3. The method for simulating mold temperature field and curing deformation by finite element simulation according to claim 2, characterized in that: The optimal grid density in the fine grid division method is determined by stepwise convergence analysis and comparison of simulation results under different densities.

4. The method for simulating mold temperature field and curing deformation by finite element simulation according to claim 1, characterized in that: The multi-scale homogenization method in step (2) includes: ① Based on the selected fiber, matrix performance parameters and fiber volume fraction, the basic performance parameters of unidirectional glass fiber / epoxy resin composites can be obtained by equations (1) to (6); E 11 =E f ×V f +E m (1-V f ) (1) v 12 =v 13 =v f ×V f +v m (1-V f ) (3) ②The thermal expansion coefficient in the fiber direction and the thermal expansion coefficient in the perpendicular fiber direction can be calculated by formulas (7) to (8): α transverse =(1+v f )α f V f +(1+v m )α m V m -α longitudinal v 12 (8) ③The thermal conductivity in the fiber direction can be calculated by the parallel model: k c_longitudinal =k f V f +k m V m (9) The thermal conductivity in the direction perpendicular to the fiber can be calculated using the series model: ④ The weight percentage of fiber and matrix in the composite material can be calculated by formula (11) to (12): Based on the calculation results of formulas (11) and (12), the specific heat capacity of the composite material can be calculated by formula (13): C cp =C f ·W f +C m ·W m (13); In the above formulas, E 11 Represents the modulus in 1 direction, E 22 Represents the 2-direction modulus, E 33 Represents the 3-direction modulus, E f represents the fiber modulus, E m represents the matrix modulus; V f represents the fiber volume fraction, V m represents the matrix volume fraction, v f represents the Poisson's ratio of the fiber, v m represents the matrix Poisson's ratio, v 12 Represents the Poisson's ratio in the 12 directions, v 13 represents the Poisson's ratio in the 13 direction, v 23 Represents the Poisson's ratio in the 23 direction; G 12 Represents the shear modulus in 12 directions, G 23 represents the shear modulus in the 23 direction, G 13 represents the shear modulus in the 13 direction, G m represents the shear modulus of the matrix, G f represents the shear modulus of the fiber; α f represents the thermal expansion coefficient of the fiber, α m represents the thermal expansion coefficient of the matrix, ρ f represents the density of the fiber, ρ m represents the density of the matrix, C f represents the specific heat capacity of the fiber, C m Represents the specific heat capacity of the matrix; direction 1 is the fiber direction, and directions 2 and 3 are both perpendicular to the fiber direction.

5. The method for simulating mold temperature field and curing deformation by finite element simulation according to claim 1, characterized in that: In step (4), a mold joint deformation prediction model is established by adopting a unique residual stress quantitative evaluation method. That is, by superimposing the curing shrinkage strain and thermal expansion strain, a mathematical model for predicting the mold joint deformation is established to more accurately evaluate the deformation of the mold during the curing process.

6. A mold optimization design method based on the simulation method according to any one of claims 1 to 5, characterized in that: include: During the above simulation process, the weak areas of the mold are identified and the composite blade mold is optimized; The weak areas of the mold are areas with high curing deformation and strain; The mold optimization design includes optimizing the mold's geometric structure, material selection, and heat source distribution.

7. The mold optimization design method according to claim 6, characterized in that: The optimization of the mold geometry mainly includes adjusting the thickness of the mold, which is mainly achieved by optimizing the heat conduction and deformation characteristics of each area of ​​the mold; By reducing the mold thickness in areas where the temperature changes drastically at the joints and chamfers, and increasing the mold thickness in areas where deformation is more obvious.

8. The mold optimization design method according to claim 6, characterized in that: Regarding the material selection of the optimized mold, a material with moderate thermal conductivity is selected, and the thermal expansion coefficient of the material should match that of the composite material.

9. The mold optimization design method according to claim 6, characterized in that: The heat source distribution optimization achieves uniform heating through a multi-point heating design, optimizes the heating source position and power setting based on the temperature field distribution simulation to cover the key areas of the mold, and adjusts the heat source power according to the real-time temperature through an adaptive control system.

10. The mold optimization design method according to claim 6, characterized in that: The optimized mold geometry also includes a structural optimization design of the mold joint, and the design method includes: ① The topology optimization criteria for the joint seam based on the simulation results, i.e., by analyzing the association rules between the distribution of the extreme points of residual stress and the curvature characteristics of the mold surface, the design of the joint seam is adjusted; ② Dynamic correction algorithm for joint seam geometric parameters, that is, the optimal chamfer radius and step height ratio are inferred through temperature field simulation results, and the joint seam geometric parameters are dynamically adjusted according to the simulation results to optimize the joint seam design.

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