Mold temperature optimization design method for mold filling stage of VARI process

Abstract

The invention relates to the technical field of mold temperature optimization design, and discloses a mold temperature optimization design method for a VARI process mold filling stage. The method comprises the following steps: establishing a mathematical model containing resin viscosity, curing reaction and an energy equation; establishing a finite element model based on the geometric dimension of the part; setting the initial temperature of the mold to be the room temperature of 25 DEG C, and tracking the viscosity and curing degree change; obtaining low viscosity time and gelation time of the resin; calculating the mold filling time and comparing the mold filling time with the former two; and judging whether the current temperature meets the condition that the mold filling time is less than two key times, and if not, adjusting the temperature and repeatedly calculating. According to the method, the mathematical model comprehensively considering the dynamic factors is established, and the numerical simulation and optimization algorithm is combined, so that rapid optimization design of the mold temperature of workpieces with different shapes and sizes is realized, and the success rate of the VARI process and the quality of the workpieces are improved.

Description

Technical Field

[0001] The present application relates to the technical field of mold temperature optimization design, and in particular to a mold temperature optimization design method for the mold filling stage of a VARI process. Background Art

[0002] Vacuum Assisted Resin Infusion (VARI) is an advanced composite material manufacturing process widely used in aerospace, wind power generation, shipbuilding, automotive and other fields. The VARI process uses vacuum assistance to allow the resin to infiltrate the fiber preform to form high-performance fiber-reinforced composite components. In the VARI process, the mold filling stage is the key link in the entire molding process, among which the mold temperature is an important process parameter that affects the infiltration effect. The traditional VARI process mold temperature design method is mainly based on the static viscosity curve of the resin, that is, the viscosity data of the resin at a fixed temperature. The mold temperature is determined by empirical formulas or lookup tables. This method has achieved certain success in the processing of simple shapes and small-sized parts.

[0003] However, traditional mold temperature design methods have obvious shortcomings. Since the resin temperature is constantly changing during the mold filling stage, traditional temperature design methods do not take into account dynamic factors such as the curing heat release of the resin itself, the heat exchange between the resin and the fiber and the mold, resulting in difficulty in ensuring the rationality of the determined mold temperature. Especially for large or complex-shaped composite components, the viscosity of thermosetting resins changes continuously with temperature and time. When the mold temperature is set unreasonably, it is very easy to cause the resin viscosity to be too high or to gel prematurely, resulting in process failure and waste, which not only increases production costs, but also affects product quality and production efficiency. In addition, traditional methods lack systematicity and universality, and it is difficult to meet the diverse needs of different types of resins and parts of different shapes. Summary of the Invention

[0004] The present application provides a mold temperature optimization design method for the filling stage of the VARI process, which is used to achieve rapid optimization design of mold temperature for parts of different shapes and sizes by establishing a mathematical model that comprehensively considers dynamic factors, combining numerical simulation and optimization algorithms, thereby improving the success rate of the VARI process and the quality of the parts.

[0005] The present application provides a mold temperature optimization design method for the mold filling stage of the VARI process, and the mold temperature optimization design method for the mold filling stage of the VARI process includes:

[0006] S1. Establishing a mathematical model including a resin viscosity equation, a curing reaction equation, and an energy equation, wherein the mathematical model takes into account factors such as heat release during resin curing and heat exchange between the resin, the fiber, and the mold;

[0007] S2. Establish a finite element model based on the part geometry, and import the finite element model into software developed based on the OpenFoam platform;

[0008] S3. The mold temperature calculated in the initial cycle is set to room temperature (25° C.), and the equations in the mathematical model are solved to track and record the changes in the maximum viscosity and the maximum degree of cure of the part over time in real time to obtain change data.

[0009] S4. Obtaining the time during which the resin is in a low viscosity range and the resin gel time at a set mold temperature according to the change data, thereby obtaining the resin low viscosity time and the resin gel time;

[0010] S5. Calculating a mold filling time for the part based on a ratio of the part volume to the resin injection amount, and comparing the mold filling time with the resin low viscosity time and the resin gel time to obtain a comparison result;

[0011] S6. Determine whether the current mold temperature meets the requirements based on the comparison result. If the mold filling time is less than the resin low viscosity time and less than the resin gel time, output the current mold temperature as the optimized mold temperature. If not, adjust the mold temperature and re-execute steps S1-S5 until a mold temperature that meets the requirements is found.

[0012] The technical solution provided in this application establishes a mathematical model encompassing the resin viscosity equation, the curing reaction equation, and the energy equation. This model fully accounts for the heat release during resin curing and the heat exchange between the resin, the fiber, and the mold. This overcomes the drawback of traditional methods that fail to account for the dynamic characteristics of the resin, making temperature design more scientific and reasonable. The finite element model, based on the part geometry, accurately reflects the geometric characteristics and material distribution of complex parts, providing a reliable foundation for numerical simulation. The mold temperature for initial cycle calculations is set to room temperature (25°C). By solving the equations in the mathematical model, the changes in the maximum viscosity and maximum cure value within the part over time are tracked in real time, providing comprehensive data on the changes in resin properties during the filling process. By analyzing this data, the resin's low viscosity time and gel time are determined, accurately capturing the critical time points for resin process performance. The filling time is calculated based on the ratio of part volume to resin injection volume and compared and analyzed, establishing a scientific process feasibility assessment standard. Finally, by evaluating the comparative results and implementing temperature adjustments and cycle optimization, problems such as excessive resin viscosity or gelation during the filling phase are effectively avoided. This method combines artificial intelligence algorithms with traditional composite materials processes, utilizing numerical simulation and optimization algorithms to automatically find the optimal mold temperature, enabling intelligent design of VARI process parameters. In particular, the algorithm's numerical discretization method and iterative solution strategy efficiently handle complex nonlinear coupled equations, enabling accurate prediction of resin rheological and curing behavior. The temperature iterative adjustment algorithm automatically optimizes mold temperature based on process conditions, significantly improving design efficiency. This method breaks away from the limitations of traditional empirical design and achieves precise optimization of mold temperature through intelligent algorithms, enhancing the success rate of the VARI process and part quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 Schematic diagram of an embodiment of a mold temperature optimization design method for the mold filling stage of the VARI process in an embodiment of the present application. DETAILED DESCRIPTION

[0015] An embodiment of the present application provides a method for optimizing the mold temperature design during the mold filling stage of the VARI process. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0016] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, an embodiment of a mold temperature optimization design method for the mold filling stage of the VARI process includes:

[0017] S1. Establish a mathematical model that includes a resin viscosity equation, a curing reaction equation, and an energy equation. The mathematical model takes into account the heat release during resin curing and the heat exchange between the resin, the fiber, and the mold.

[0018] Specifically, a mathematical analysis was conducted on the relationship between resin viscosity and temperature and degree of cure. A mathematical relationship was established using exponential and power function terms to obtain a resin viscosity equation that reflects the influence of temperature and degree of cure. The resin curing reaction kinetics was analyzed, and an expression in the form of a differential equation was established based on the relationship between reaction rate, temperature and degree of cure, resulting in a curing reaction equation that describes the curing rate.

[0019] A curing reaction rate function is constructed and expressed as a product of temperature dependence and curing degree dependence based on the curing reaction characteristics to obtain the curing reaction rate expression; the heat transfer process of the system is analyzed, considering heat conduction and curing heat release, and a partial differential equation containing time derivatives and spatial second-order derivatives is established to obtain an energy equation describing the temperature field distribution; the density, specific heat capacity and thermal conductivity coefficient of the resin and fiber materials are integrated into the energy equation to construct a complete energy equation containing material parameters and curing heat release terms; the resin viscosity equation, curing reaction equation and energy equation are combined into a system of equations to establish a mathematical model considering resin curing heat release and heat exchange factors.

[0020] The resin viscosity equation is based on the physical properties of resin viscosity changing with temperature and degree of cure. The resin viscosity equation is in the following form:

[0021]

[0022] Among them, μ resin represents the viscosity of the liquid resin (Pa·s), μ0 represents the initial viscosity of the liquid resin (Pa·s), E u represents activation energy (J / mol), R represents ideal gas constant, T represents temperature (K), a represents the degree of curing reaction, and g represents the gel point, d1 and d2 are material-related constants. This equation fully considers the combined effects of temperature and degree of cure on resin viscosity, reflecting the temperature dependence through an exponential function and the degree of cure dependence through a power function.

[0023] Secondly, the curing reaction equation is used to describe the change of the resin curing reaction rate with temperature and curing degree. The curing reaction equation is:

[0024]

[0025] in, represents the rate of change of curing degree with time, G(a,T) is the curing reaction rate function, A1 and A2 are the reaction rate constants (s -1 ), E1 and E2 are the activation energies (J / mol), and m1 and m2 are material-dependent exponential constants. This equation reflects the effect of temperature on the reaction rate through the Arrhenius relationship and the effect of degree of cure on the reaction rate through a power function.

[0026] Finally, the energy equation is used to describe the evolution of the system temperature field, taking into account the effects of heat conduction and curing heat release. The energy equation is:

[0027]

[0028] in, represents the porosity, ρ fluid and ρ fiber Represents the density of fluid (resin) and fiber (kg / m 3 ), c pfluid and c pfiber represents the specific heat capacity of the fluid and fiber (J / kg·K), k represents the thermal conductivity (W / m·K), ΔH represents the heat released by the curing reaction per unit volume (J / m 3 The left side of the equation represents the product of the system's heat capacity per unit volume and the temperature-time rate of change. The first term on the right side represents heat conduction, and the second term represents the heat release from the curing reaction.

[0029] These three equations form a coupled system of partial differential equations, where the viscosity equation depends on temperature and the degree of cure, the curing reaction equation depends on temperature and the degree of cure, and the energy equation depends on the curing reaction rate and the temperature field distribution. Solving this system of equations provides information on temperature, viscosity, and degree of cure at any time and location during the mold filling process.

[0030] S2. Establish a finite element model based on the part geometry and import the finite element model into software developed based on the OpenFoam platform;

[0031] Specifically, the external contour information of the part is obtained based on the part geometry data to obtain the dimensional parameters of the workpiece, such as length, width, and height; the special geometric shape of the part is identified, and the injection port position and the exhaust port position are marked to obtain geometric description information; a three-dimensional digital model is established based on the geometric description information to obtain a geometric model for numerical calculation; the geometric model is meshed, and the mesh type and unit size are selected to obtain a finite element mesh composed of multiple units; the finite element mesh is imported into the software developed on the OpenFoam platform, and the material parameters and calculation parameters of the model are set to obtain a calculation model; boundary conditions and initial conditions are defined in the calculation model, including mold surface temperature conditions and injection port flow conditions, to obtain a finite element model that can be solved.

[0032] The starting point for building a finite element model is obtaining the part's external contour information based on its geometric shape data. Basic dimensional parameters such as the part's length, width, and height are acquired through CAD software or 3D scanning technology. For composite parts used in the VARI process, precise measurements of the external contours are often required, along with recording features such as corners and thickness variations to form a complete geometric description dataset.

[0033] The unique geometry of the part is identified, and the locations of the injection and vent ports are marked. This step is particularly important for the VARI process, as their location directly impacts the resin flow path and mold filling uniformity. During the identification process, unique geometric features such as thickness variations and curved transition areas must be considered, as these areas often present challenges for resin flow. For example, an injection port may be located at one end of the part, and a vent at the other. By marking these locations, geometric descriptions are generated. Based on this acquired geometric description, a 3D digital model is constructed. This step is typically performed using CAD software, integrating all measured data and feature information into a complete 3D model. For the VARI process, the model also requires distinguishing between the fiber preform region and the flow channel region to facilitate the subsequent setting of different material properties. The resulting 3D digital model contains complete part geometry information, providing the geometric foundation for meshing and numerical calculations. Meshing is the process of discretizing a continuous geometric model into a finite number of computational elements. For composite parts used in the VARI process, tetrahedral or hexahedral meshes are typically used. The appropriate mesh type and element size are selected based on the required computational accuracy and geometric complexity. During the partitioning process, the mesh needs to be denser in areas with large resin flow and temperature gradients to improve calculation accuracy. In areas with less pronounced geometric changes, the mesh can be thinned out to reduce the computational effort. For example, a part can be divided into approximately 50,000 computational cells, with a higher mesh density near the injection port and exhaust port, with a cell size of approximately 1 mm. The mesh density is lower in the flat central area of the part, with a cell size of approximately 5 mm.

[0034] Importing the pre-divided finite element mesh into software developed on the OpenFoam platform is a key step in implementing numerical calculations. OpenFoam is an open-source computational fluid dynamics (CFD) software platform with powerful solving capabilities and high customizability. During the import process, format conversion is required to ensure the integrity of the geometric and mesh information. Simultaneously, the model's material and calculation parameters are set, including parameters such as the resin's initial viscosity, activation energy, and gel point, as well as the fiber's density, thermal conductivity, and specific heat capacity. For the material used in the part, parameters such as the resin's initial viscosity, activation energy, and gel point are set accordingly. Boundary and initial conditions are defined in the imported calculation model. Boundary conditions include the mold surface temperature (initially set to 25°C), the injection port flow rate, and the exhaust port pressure. Initial conditions include the initial temperature and pressure distribution within the part. The setting of these conditions directly impacts the accuracy of the calculation results and requires appropriate configuration based on the actual process parameters.

[0035] S3. Set the mold temperature calculated in the initial cycle to room temperature (25°C). By solving the equations in the mathematical model, track and record the changes in the maximum viscosity and maximum curing degree inside the part over time in real time to obtain change data.

[0036] Specifically, the initial value of the mold temperature is set to room temperature 25°C, and the initial temperature of the resin and the resin injection flow rate are set at the same time to obtain the initial calculation conditions; based on the initial calculation conditions, the equations in the mathematical model are numerically discretized, and the continuous equations are converted into discrete equations that can be solved by a computer to obtain a discretized numerical model; the discretized numerical model is subjected to time-advancing calculation, and the temperature field, viscosity field and curing degree field of each time step are solved using an appropriate numerical algorithm to obtain the change data of each field quantity over time; the calculation results of each time step are post-processed, the resin viscosity values of each point inside the part are extracted, and the maximum value is found to obtain the change curve of the highest viscosity value over time; the calculation results of each time step are post-processed, the resin curing degree values of each point inside the part are extracted, and the maximum value is found to obtain the change curve of the highest curing degree value over time; the maximum viscosity value change curve and the maximum curing degree value change curve are saved as change data.

[0037] The initial mold temperature needs to be set to room temperature (25°C), and other initial conditions need to be set. This process includes setting the initial resin temperature, which is usually the same as the ambient temperature, at 31°C; setting the resin injection flow rate, which can be set to 1.5×10 -5 m 3 / s, 1.7×10 -5 m 3 / s or 1.9×10 -5 m 3 The initial temperature of the fiber preform is set, which is usually the same as the ambient temperature. Furthermore, resin material parameters such as initial viscosity, activation energy, and gel point, as well as fiber material parameters such as density, thermal conductivity, and specific heat capacity, must be set. These initial conditions and material parameters together form the starting point for the numerical simulation.

[0038] Based on the initial calculation conditions, the equations in the mathematical model established above are numerically discretized. Numerical discretization is the process of converting continuous differential equations into a system of algebraic equations that can be solved by a computer. For the viscosity equation, curing reaction equation, and energy equation in this scheme, the finite volume method is used for spatial discretization and the implicit Euler method is used for temporal discretization. During spatial discretization, the computational domain is divided into a finite number of control volumes. Within each control volume, the distribution of physical quantities is assumed to satisfy a specific functional form. The differential equations are then integrated over each control volume to obtain a system of algebraic equations. For temporal discretization, the implicit Euler method is used, expressing the time derivative as the difference between the physical quantity at the current time step and the previous time step divided by the time step length. Although the implicit format has high computational complexity, it has good numerical stability and is suitable for solving the multi-physics coupling problem in the VARI process. Time-marching calculations on the discretized numerical model are the core of the solution process. This step uses an iterative solution method to solve the discretized algebraic equation system at each time step. Due to the strong coupling between the viscosity equation, the curing reaction equation, and the energy equation, a separate iteration strategy is typically employed: first, the viscosity field is calculated based on the temperature field and the curing degree field of the current time step; then, the curing reaction rate is calculated based on the updated temperature field, and the curing degree field is updated; finally, the temperature field is updated based on the updated curing degree field and reaction rate. Each equation solution requires a convergence criterion; when the residual is less than a preset threshold, the iteration is considered converged and the calculation proceeds to the next time step. This time-marching calculation can reveal how the temperature, viscosity, and curing degree fields change over time from the start to the end of mold filling.

[0039] Post-processing the calculation results for each time step is a key step in extracting valid data. This process first extracts the resin viscosity value at each point within the part. By traversing all mesh nodes within the computational domain, the viscosity value at each node is recorded. Then, using a comparison function, the maximum value is found and used as the maximum viscosity value for the current time step. Similarly, for the curing degree field, the curing degree value is recorded for each node by traversing all mesh nodes within the computational domain. The maximum value is then found and used as the maximum curing degree value for the current time step. This method of extracting the maximum value effectively captures critical states that may occur during mold filling, providing a basis for subsequent optimization of mold temperature.

[0040] The maximum viscosity and cure values extracted at each time step are organized into time-varying data series, forming two curves. These curves typically use time as the independent variable and viscosity or cure value as the dependent variable, recording the entire mold filling process from the start to the end. The data is typically saved as a two-dimensional array or time series file to facilitate subsequent analysis and processing. These curves provide a direct basis for subsequent assessments of the resin's low viscosity and gel time.

[0041] S4. Obtaining the time during which the resin is in the low viscosity range and the resin gel time at the set mold temperature based on the change data, thereby obtaining the resin low viscosity time and the resin gel time;

[0042] Specifically, according to the characteristics of the resin material, the viscosity threshold is set to 0.3Pa·s as the requirement standard for resin viscosity in the VARI process, and the viscosity judgment standard is obtained; the maximum viscosity value change curve is analyzed to find the time point when the resin viscosity value exceeds the viscosity judgment standard for the first time, and the resin low viscosity time is obtained; according to the characteristics of the resin material, the gel point is set to 0.63 as the critical value of the curing degree at which the resin starts to gel, and the gel judgment standard is obtained; the maximum curing degree value change curve is analyzed to find the time point when the resin curing degree value first reaches the gel judgment standard, and the resin gel time is obtained; the resin low viscosity time and the resin gel time are recorded to obtain the key time points of the resin process characteristics under the current mold temperature conditions, and the feasibility of filling the part at the current mold temperature is evaluated based on the key time points, and the basis for judging the process time is obtained.

[0043] Based on the resin material properties, a viscosity threshold of 0.3 Pa·s is set as the resin viscosity requirement for the VARI process. This threshold is based on the practical requirements of the VARI process. During vacuum-assisted resin infusion, the resin must flow smoothly within the preform and completely impregnate the fibers. When the resin viscosity exceeds 0.3 Pa·s, fluidity is significantly reduced, impregnation becomes less effective, and this hinders a smooth filling process. The setting of the viscosity threshold directly impacts subsequent process timing and may vary slightly for different resin and fiber combinations. Analyzing the peak viscosity curve is a key step in determining the time when the resin reaches low viscosity. This analysis first requires reading the viscosity-time data series obtained in the previous step. A comparison function is then used to identify the time when the viscosity first exceeds the threshold of 0.3 Pa·s. The specific data processing involves iterating through each data point in the viscosity-time data series, comparing the viscosity value of each data point with the threshold of 0.3 Pa·s, and recording the time when the viscosity first exceeds the threshold. If higher accuracy is required, data interpolation methods (such as linear interpolation or spline interpolation) can be used to interpolate between adjacent data points to obtain more accurate time points. The time point obtained in this way is the low viscosity time of the resin, which means the duration that the resin viscosity remains below 0.3 Pa·s from the beginning of injection. Setting the gel point to 0.63 as the critical value of the curing degree for the resin to start gelling according to the properties of the resin material is also a key setting. The gel point is the critical point where the thermosetting resin changes from a liquid state to a colloidal state. At this point, the resin forms a three-dimensional network structure and loses fluidity. The value of the gel point is closely related to the type of resin. For the Ciba-Geigy epoxy resin system, the gel point is 0.63. This value is obtained through experimental determination or from the material parameter table provided by the resin supplier.

[0044] The process for analyzing the maximum cure value curve to determine the resin gel time is similar to analyzing the viscosity curve. First, read the cure value-time data series obtained in the previous step. Then, using a comparison function, find the time point at which the cure value first reaches the gel point of 0.63. The specific data processing procedure is to iterate through each data point in the cure value-time data series, comparing the cure value at each data point with the gel point of 0.63. Whenever the cure value first reaches or exceeds the gel point, the corresponding time point is recorded. Similarly, if higher accuracy is required, data interpolation can be used to interpolate between adjacent data points. The resulting time point is the resin gel time, representing the time from the start of resin injection to the resin gelling. Recording the resin low viscosity time and resin gel time is a crucial step in providing a basis for subsequent process evaluation. This recording method typically creates a data structure containing the mold temperature, resin low viscosity time, and resin gel time, such as a triple (T, tv, tg), where T represents the mold temperature, tv represents the resin low viscosity time, and tg represents the resin gel time. This recording method facilitates subsequent querying and analysis of process characteristics under different mold temperature conditions. The goal of this analysis is to assess the feasibility of filling a part at the current mold temperature based on these critical time points. The fundamental principle of this assessment is that the filling time must be shorter than both the resin's low viscosity period and the resin's gel time. This means that the resin maintains good fluidity and does not gel until the filling process is complete. This assessment directly determines whether and how the mold temperature should be adjusted.

[0045] S5. Calculate the mold filling time of the part based on the ratio of the part volume to the resin injection amount, and compare the mold filling time with the resin low viscosity time and the resin gel time to obtain a comparison result;

[0046] Specifically, the resin volume required to fill the part is calculated based on the part geometry and fiber porosity to obtain the resin injection volume; the resin injection volume is divided by the set resin injection flow rate to obtain the theoretical filling time; the theoretical filling time is corrected to take into account the flow path and flow resistance factors to obtain a more accurate part filling time; the part filling time is compared with the resin low viscosity time, and the difference between the two is calculated to obtain the viscosity time margin; the part filling time is compared with the resin gel time, and the difference between the two is calculated to obtain the gel time margin; the feasibility of the filling process is judged based on the viscosity time margin and the gel time margin, and a comparison result is obtained to determine whether the mold temperature meets the process requirements.

[0047] Among them, calculating the volume of resin required to fill the part based on the part's geometry and fiber porosity is the basis for determining the amount of resin injected. This calculation process first requires obtaining the total volume of the part, which is obtained by performing volume calculations on the three-dimensional geometric model. For parts with complex shapes, they are usually decomposed into multiple simple geometric bodies, and the volumes of each part are calculated separately and then summed up. After obtaining the total volume, it is necessary to consider the filling effect of the fiber preform, which is specifically reflected by the fiber porosity parameter. Fiber porosity indicates the volume ratio of pores in the fiber preform, which is the proportion of space that can be filled with resin. The porosity of fiber materials =0.81, meaning that 81% of the part volume needs to be filled with resin. Therefore, the formula for calculating the resin injection volume is: Resin volume = Total part volume × Fiber porosity. Dividing the calculated resin injection volume by the set resin injection rate is a direct method for obtaining the theoretical filling time. The resin injection rate is a key process parameter in the VARI process and is typically determined by the process designer based on part size and complexity. Once the injection rate is selected, the theoretical filling time can be calculated directly: Theoretical filling time = Resin volume ÷ Resin injection rate. This calculation assumes a constant and uniform resin flow rate during the filling process under ideal conditions. Correcting the theoretical filling time is essential for achieving a more accurate filling time. In the actual VARI process, due to factors such as complex part shapes, varying flow paths, and uneven fiber preform structure, the resin flow rate within the part is not constant and typically slows down as the flow path increases. Therefore, a correction factor is required to adjust the theoretical filling time. This correction method is typically based on empirical formulas or flow simulation results, taking into account factors such as flow path length, cross-sectional variations, and fiber alignment. The typical correction formula is: Corrected filling time = theoretical filling time × correction coefficient, where the correction coefficient is determined according to the characteristics of the part and is usually greater than 1.

[0048] Comparing the corrected part filling time with the resin low viscosity time obtained in the previous step and calculating the difference between the two is key to assessing process feasibility in terms of viscosity. The viscosity time margin is calculated as: Viscosity Time Margin = Resin Low Viscosity Time - Part Filling Time. This margin reflects whether the resin maintains a sufficiently low viscosity to achieve complete impregnation during the filling process. A positive margin indicates that the resin maintains good fluidity throughout the filling process; a negative margin indicates that the resin viscosity exceeds the process tolerance before the end of the filling process, making the filling process difficult.

[0049] Comparing the part filling time with the resin gel time and calculating the difference between the two is an important method for assessing the feasibility of the gel process. The formula for calculating the gel time margin is: Gel time margin = Resin gel time - Part filling time. This margin reflects whether the resin will gel prematurely during the filling process. A positive margin indicates that the resin will not gel before filling is complete; a negative margin indicates that the resin will gel before the filling process is complete, preventing the filling process from being completed. Assessing the feasibility of the filling process based on the viscosity time margin and gel time margin is the ultimate basis for determining whether the mold temperature meets the process requirements. The judgment criteria are clear: only when both the viscosity time margin and the gel time margin are positive does the currently set mold temperature meet the process requirements. If either margin is negative, the current mold temperature is unsuitable and needs to be adjusted. A satisfactory mold temperature typically requires a safety margin. This means that both the viscosity time margin and the gel time margin must not only be positive but also meet a certain threshold, such as no less than 20% of the filling time.

[0050] S6. Determine whether the current mold temperature meets the requirements based on the comparison results. If the mold filling time is less than the resin low viscosity time and less than the resin gel time, the current mold temperature is output as the optimized mold temperature. If not, adjust the mold temperature and re-execute steps S1-S5 until a mold temperature that meets the requirements is found.

[0051] Specifically, based on the comparison results, it is judged whether the filling time is less than the resin low viscosity time and less than the resin gel time, and a judgment result is obtained that the current mold temperature meets the requirements; when the judgment result is that the requirements are met, the current mold temperature is recorded as the optimized mold temperature, the temperature design process is completed, and the final optimization result is obtained; when the judgment result is that the requirements are not met and the filling time is greater than the resin low viscosity time, the mold temperature is reduced by 5°C to obtain a new mold temperature value; when the judgment result is that the requirements are not met and the filling time is greater than the resin gel time, the mold temperature is reduced by 5°C to obtain a new mold temperature value; the new mold temperature value is re-input into the calculation model, and the entire process from numerical simulation to comparative analysis is calculated to obtain an evaluation result of the adjusted mold temperature; the temperature adjustment and evaluation calculation are repeated until a mold temperature that meets the conditions that the filling time is less than the resin low viscosity time and less than the resin gel time is found, and the final optimized mold temperature design value is obtained.

[0052] Among them, according to the comparison result, it is judged whether the filling time is simultaneously less than the resin low-viscosity time and the resin gel time, and the judgment result that the current mold temperature meets the requirements is obtained. This judgment process is simple and clear: obtain three key time parameters of the filling time (ta), the resin low-viscosity time (tv), and the resin gel time (tg) from the previous step, and then perform a logical comparison operation. The judgment condition is: if ta < tv and ta < tg, then the current mold temperature meets the process requirements; otherwise, the current mold temperature does not meet the process requirements and needs to be adjusted. The judgment condition here directly reflects the core requirement of the VARI process: the filling process must be completed within the time window when the resin remains at a sufficiently low viscosity and has not gelled. When the judgment result shows that the current mold temperature meets the process requirements, directly record the current mold temperature as the optimized mold temperature and output this result to complete the entire temperature optimization design process. The recording method usually includes generating a report file to record the mold temperature value and the corresponding key process time parameters for reference in actual production. At the same time, charts such as temperature-time curves, viscosity-time curves, and degree of cure-time curves can also be generated to visually show the rationality of the optimization results.

[0053] When the judgment result shows that the current mold temperature does not meet the process requirements and the filling time is greater than the resin low-viscosity time, it means that at the current temperature, the resin viscosity increases too fast and exceeds the process allowable range before the filling is completed, and the filling process will face difficulties. At this time, the mold temperature needs to be lowered, and the specific adjustment range is 5°C. The purpose of lowering the mold temperature is to slow down the resin curing reaction rate and extend the time when the resin remains at a low viscosity. When the filling time is greater than the resin low-viscosity time, adjust the mold temperature calculated in the cycle and increase it by 5°C to continue the cycle calculation. However, it should be noted that the "increase by 5°C" here refers to the initial set temperature of 25°C, and in actual operation, it should be determined whether to increase or decrease the temperature according to the specific non-compliance conditions. When the resin viscosity increases too fast, the mold temperature should be lowered to slow down the curing reaction rate.

[0054] Similarly, when the judgment result shows that the current mold temperature does not meet the process requirements and the filling time is greater than the resin gel time, it means that at the current temperature, the resin curing reaction is too fast and gelation occurs before the filling is completed, and the filling process cannot be completed. The mold temperature also needs to be lowered, and the adjustment range is also 5°C. The purpose of lowering the temperature is still to slow down the curing reaction rate and extend the resin gel time. In actual situations, it is rare that only one condition is not met. Usually, both conditions are not met or both are met simultaneously.

[0055] Re-entering the adjusted new die temperature value into the calculation model and performing the full-process calculation from numerical simulation to comparative analysis is the key link to achieve cyclic optimization. This process specifically includes: taking the new die temperature value as the initial condition, re-executing the previous numerical simulation calculation to obtain the data of the resin viscosity and degree of cure changing with time under the new temperature condition; then re-evaluating the resin low-viscosity time and gel time;接着重新计算充模时间并与新的树脂低黏度时间和凝胶时间进行对比;最后根据对比结果判断新的模具温度是否满足工艺要求。这一全过程计算实际上是对前面步骤的完整重复,每次调整温度后都需要重新评估所有关键参数。

[0056] Repeatedly executing temperature adjustment and evaluation calculations until a die temperature that meets the conditions is found is a necessary means to achieve the optimization goal. During the cyclic process, the die temperature will be continuously adjusted according to the judgment result, with each adjustment being 5°C until a temperature value is found that makes the filling time less than both the resin low-viscosity time and the gel time. This cyclic optimization method is simple and effective and can find the die temperature that meets the process requirements within a limited number of adjustments. To improve efficiency, the temperature values of each adjustment and the corresponding evaluation results can be recorded to avoid repeated calculations. Assuming the initial set die temperature is 25°C, through the previous numerical simulation and evaluation calculations, the filling time ta = 147 minutes, the resin low-viscosity time tv = 120 minutes, and the resin gel time tg = 180 minutes are obtained. Since ta > tv, it indicates that under the condition of 25°C, the resin viscosity increases too fast and does not meet the process requirements. Therefore, the die temperature is adjusted, reduced by 5°C to 20°C, and then the numerical simulation and evaluation calculations are re-executed. Under the condition of 20°C, the data of the resin viscosity and degree of cure change are obtained by re-executing the numerical simulation, and the new resin low-viscosity time tv = 160 minutes and the resin gel time tg = 220 minutes are evaluated. The filling time is still ta = 147 minutes (assuming the injection flow rate remains unchanged). At this time, ta < tv and ta < tg, and the die temperature of 20°C meets the process requirements. Therefore, 20°C is recorded as the optimized die temperature for the filling stage of the VARI process of this part, and the optimization design process is completed.

[0057] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A mold temperature optimization design method for the mold filling stage of the VARI process, characterized in that: The method comprises: S1. Establishing a mathematical model including a resin viscosity equation, a curing reaction equation, and an energy equation, wherein the mathematical model takes into account factors such as heat release during resin curing and heat exchange between the resin, the fiber, and the mold; S2. Establish a finite element model based on the part geometry, and import the finite element model into software developed based on the OpenFoam platform; S3. The mold temperature calculated in the initial cycle is set to room temperature (25° C.), and the equations in the mathematical model are solved to track and record the changes in the maximum viscosity and the maximum degree of cure of the part over time in real time to obtain change data. S4. Obtaining the time during which the resin is in a low viscosity range and the resin gel time at a set mold temperature according to the change data, thereby obtaining the resin low viscosity time and the resin gel time; S5. Calculating a mold filling time for the part based on a ratio of the part volume to the resin injection amount, and comparing the mold filling time with the resin low viscosity time and the resin gel time to obtain a comparison result; S6. Determine whether the current mold temperature meets the requirements based on the comparison result. If the mold filling time is less than the resin low viscosity time and less than the resin gel time, output the current mold temperature as the optimized mold temperature. If not, adjust the mold temperature and re-execute steps S1-S5 until a mold temperature that meets the requirements is found.

2. The mold temperature optimization design method for the mold filling stage of the VARI process according to claim 1 is characterized in that: Step S1 includes: The relationship between resin viscosity and temperature and degree of cure was mathematically analyzed, and a relationship was established using a mathematical form containing exponential terms and power function terms to obtain a resin viscosity equation that reflects the effects of temperature and degree of cure. The resin curing reaction kinetics were analyzed, and an expression in the form of a differential equation was established based on the relationship between the reaction rate, temperature and curing degree, resulting in a curing reaction equation describing the curing rate. The curing reaction rate function is constructed and expressed as a product of temperature dependence and curing degree dependence based on the curing reaction characteristics, thus obtaining the curing reaction rate expression. The heat transfer process of the system is analyzed, heat conduction and curing heat release are considered, and a partial differential equation including time derivative and space second-order derivative is established to obtain the energy equation describing the temperature field distribution; Integrate the density, specific heat capacity and thermal conductivity of resin and fiber materials into the energy equation to construct a complete energy equation including material parameters and curing heat release terms; The resin viscosity equation, the curing reaction equation and the energy equation are combined into an equation group to establish a mathematical model that takes into account resin curing heat release and heat exchange factors.

3. The mold temperature optimization design method for the mold filling stage of the VARI process according to claim 1 is characterized in that: Step S2 includes: Obtain the external contour information of the part based on the part's geometric shape data, and obtain the length, width, height and other dimensional parameters of the part; Identify the special geometric shape of the part, mark the injection port position and exhaust port position, and obtain geometric description information; Establishing a three-dimensional digital model based on the geometric description information to obtain a geometric model for numerical calculation; Meshing the geometric model, selecting a mesh type and a cell size, and obtaining a finite element mesh consisting of a plurality of cells; Importing the finite element mesh into software developed on the OpenFoam platform, and setting the material parameters and calculation parameters of the model to obtain a calculation model; Boundary conditions and initial conditions, including mold surface temperature conditions and injection port flow conditions, are defined in the calculation model to obtain a finite element model that can be solved.

4. The mold temperature optimization design method for the mold filling stage of the VARI process according to claim 1 is characterized in that: The S3 step includes: The initial mold temperature is set to room temperature (25°C), and the initial resin temperature and resin injection flow rate are set to obtain the initial calculation conditions. numerically discretizing the equations in the mathematical model based on the initial calculation conditions, converting the continuous equations into discrete equations that can be solved by a computer, and obtaining a discretized numerical model; Performing time-marching calculations on the discretized numerical model, using appropriate numerical algorithms to solve the temperature field, viscosity field, and curing degree field at each time step, and obtaining data on the changes of each field quantity over time; Post-process the calculation results of each time step, extract the resin viscosity value of each point inside the part, find the maximum value, and obtain the curve of the maximum viscosity value over time; Post-process the calculation results of each time step, extract the resin curing degree value of each point inside the part, find the maximum value among them, and obtain the curve of the maximum curing degree value changing with time; The maximum viscosity value change curve and the maximum curing degree value change curve are saved as change data.

5. The mold temperature optimization design method for the mold filling stage of the VARI process according to claim 1 is characterized in that: Step S4 includes: According to the characteristics of the resin material, the viscosity threshold is set to 0.3 Pa·s as the requirement standard for resin viscosity in the VARI process, and the viscosity judgment standard is obtained; Analyze the maximum viscosity value change curve to find the time point when the resin viscosity value exceeds the viscosity judgment standard for the first time, and obtain the resin low viscosity time; According to the characteristics of the resin material, the gel point is set to 0.63 as the critical value of the curing degree at which the resin begins to gel, and the gel judgment standard is obtained; Analyze the maximum curing degree value change curve to find the time point when the resin curing degree value first reaches the gel judgment standard to obtain the resin gel time; The resin low viscosity time and the resin gel time are recorded to obtain the key time points of the resin process characteristics under the current mold temperature conditions. The feasibility of filling the mold at the current mold temperature is evaluated based on the key time points to obtain the basis for judging the process time.

6. The mold temperature optimization design method for the mold filling stage of the VARI process according to claim 1 is characterized in that: Step S5 includes: Calculate the resin volume required to fill the part based on the part's geometry and fiber porosity to obtain the resin injection volume; The theoretical mold filling time is obtained by dividing the resin injection amount by the set resin injection flow rate; The theoretical mold filling time is corrected to take into account the flow path and flow resistance factors to obtain a more accurate mold filling time for the part; Comparing the mold filling time of the part with the low viscosity time of the resin, calculating the difference between the two, and obtaining the viscosity time margin; Comparing the mold filling time of the part with the resin gel time, calculating the difference between the two, and obtaining the gel time margin; The feasibility of the mold filling process is judged based on the viscosity time margin and the gel time margin, and a comparison result of whether the mold temperature meets the process requirements is obtained.

7. The mold temperature optimization design method for the mold filling stage of the VARI process according to claim 1 is characterized in that: Step S6 includes: According to the comparison result, it is determined whether the mold filling time is less than the resin low viscosity time and the resin gel time, and a determination result is obtained that the current mold temperature meets the requirements; When the judgment result is that the requirements are met, the current mold temperature is recorded as the optimized mold temperature, the temperature design process is completed, and the final optimization result is obtained; If the result is that the requirements are not met and the mold filling time is longer than the resin low viscosity time, the mold temperature is lowered by 5°C to obtain a new mold temperature value; If the result is that the requirements are not met and the mold filling time is greater than the resin gel time, the mold temperature is lowered by 5°C to obtain a new mold temperature value; Re-inputting the new mold temperature value into the calculation model, performing the entire calculation process from numerical simulation to comparative analysis, and obtaining an evaluation result of the adjusted mold temperature; Repeat the temperature adjustment and evaluation calculation until the mold temperature is found that satisfies the conditions that the filling time is less than the resin low viscosity time and less than the resin gel time, and the final optimized mold temperature design value is obtained.

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