Resin processing technology optimization method and system

Abstract

The invention provides a resin processing technology optimization method and system, and relates to the technical field of data processing, and the method comprises the following steps: constructing a finite element model of a resin transfer molding technology process; performing unit division on the finite element model; setting boundary conditions of the finite element model; setting description equations of the flowing process and the curing process of the resin fluid in the finite element model; performing flow front tracking in the finite element model by adopting a flow front tracking algorithm; initializing process parameters of a resin transfer molding process; loading the process parameters into the finite element model, and simulating a resin processing and molding process under various process parameters; according to the simulation result of the resin processing molding process, determining the filling time, the volume of an unfilled area, the length of a weld line and the resin waste amount under various process parameters; the optimal process parameters are determined through a butterfly optimization algorithm by taking the minimum filling time, the volume of an unfilled area, the length of a weld line and the waste amount of resin as targets.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for optimizing resin processing technology. Background Technology

[0002] Resin transfer molding (RTM) is a closed-mold molding process that involves injecting liquid resin into a closed mold containing fiber reinforcement materials for impregnation, followed by curing to produce high-strength, lightweight composite material products. This process is characterized by high production efficiency, applicability to complex shapes, excellent surface quality, low cost, and environmental friendliness. It is widely used in the automotive, aerospace, wind power, and sporting goods industries, and is one of the important methods for manufacturing high-performance composite material products.

[0003] To improve production efficiency, current resin transfer molding processes often employ multi-injection ports. In this injection method, resin fluids flowing in different directions collide head-on, frequently generating air bubbles in the resin product at the collision zone. This leads to a decrease in the local density of the resin product, resulting in minute structural defects that affect the strength and rigidity of the resin product. Summary of the Invention

[0004] To address the technical problem that traditional multi-injection injection often generates air bubbles in the resin product in the collision area, leading to a decrease in local density of the resin product, creating micro-structural defects, and thus affecting the strength and rigidity of the resin product, this invention provides a resin processing optimization method and system.

[0005] The technical solutions provided by the embodiments of the present invention are as follows: The first aspect of this invention provides a method for optimizing resin processing, comprising: S1: Construct a finite element model of the resin transfer molding process; S2: Divide the finite element model into elements; S3: Set the boundary conditions for the finite element model; S4: Set the descriptive equations for the flow process of the resin fluid and the curing process in the finite element model; S5: The flow front tracking algorithm is used to track the flow front in the finite element model; S6: Initialize the process parameters for the resin transfer molding process; S7: Load the process parameters into the finite element model to simulate the resin processing and molding process under various process parameters; S8: Based on the simulation results of the resin processing and molding process, determine the filling time, unfilled area volume, weld line length, and resin waste under various process parameters; S9: With the goal of minimizing filling time, unfilled area volume, weld line length, and resin waste, the optimal process parameters are determined using the butterfly optimization algorithm.

[0006] A second aspect of the present invention provides a resin processing optimization system, comprising: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the resin processing optimization method as described in the first aspect.

[0007] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this invention, finite element analysis technology is used to simulate the resin processing and molding process under various process parameters. The goal is to minimize filling time, unfilled area volume, weld line length, and resin waste. The optimal process parameters are determined by the butterfly optimization algorithm. This can reduce filling time and improve production efficiency, reduce unfilled area volume and weld line length, thereby reducing the generation of bubbles in the weld collision area, increasing the local density of the resin product, improving the strength and rigidity of the resin product, and also reducing resin waste and lowering material costs. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a resin processing optimization method provided in an embodiment of the present invention.

[0010] Figure 2 This is a schematic diagram of a resin processing optimization method provided in an embodiment of the present invention.

[0011] Figure 3 This is a geometric model schematic diagram of a resin transfer molding process provided in an embodiment of the present invention.

[0012] Figure 4 This is a schematic diagram of a resin processing optimization system provided in an embodiment of the present invention. Detailed Implementation

[0013] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0014] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0015] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0016] Reference manual attached Figure 1 The diagram shows a flow chart of a resin processing optimization method provided by an embodiment of the present invention.

[0017] Reference manual attached Figure 2 The diagram shows a structural schematic of a resin processing optimization method provided in an embodiment of the present invention.

[0018] This invention provides a method for optimizing resin processing technology, which may include the following steps: S1: Construct a finite element model of the resin transfer molding process.

[0019] Specifically, a geometric model of the resin transfer molding process can be constructed using software such as ANSYS and COMSOL, and then a finite element model can be further formed based on the geometric model.

[0020] Reference manual attached Figure 3 The diagram shows a geometric model of a resin transfer molding process provided in an embodiment of the present invention.

[0021] The geometric model mainly contains the geometric models of the mold and the fiber preform. The mold includes an upper mold and a lower mold, forming a closed cavity for resin flow. The mold has an injection port, an vent, and flow channels. The fiber reinforcement material placed inside the mold cavity is abstracted as a porous medium, and its structure and shape are consistent with the mold cavity.

[0022] Furthermore, fiber preforms, acting as reinforcing materials, improve the mechanical properties of the final composite material (such as tensile strength, flexural strength, and stiffness). Fiber preforms typically use glass fibers, carbon fibers, or aramid fibers, which possess high strength and low density characteristics.

[0023] S2: Divide the finite element model into elements.

[0024] Specifically, when meshing a finite element model, the element type and mesh density should be selected appropriately based on the complexity of the geometric model, the analysis objectives, and computational resources. Smaller elements (such as triangular or tetrahedral elements) are used in complex geometric regions to improve accuracy, while larger elements (such as quadrilateral or hexahedral elements) can be used in regular regions to reduce computational load. Critical areas (such as inlets, boundaries, and corners) require local refinement to capture stress concentrations or flow variations, while ensuring that the shape and size of the elements are as regular as possible to avoid numerical errors. Appropriate element meshing can improve solution efficiency while maintaining computational accuracy.

[0025] S3: Set the boundary conditions for the finite element model.

[0026] In one possible implementation, S3 specifically includes sub-steps S301 to S303: S301: Set the pressure at the mold injection port to be equal to the resin injection pressure.

[0027] It should be noted that setting the pressure at the mold inlet to equal the resin injection pressure defines the driving force for the resin to enter the mold cavity, ensuring that the resin is injected into the mold cavity at the set pressure. The magnitude of the injection pressure directly affects the resin flow rate and filling efficiency. A reasonable pressure setting can avoid incomplete filling or air bubble problems, while improving production efficiency.

[0028] S302: Set the pressure at the vent and resin flow interface to be equal to atmospheric pressure.

[0029] It should be noted that setting the pressure at the vent and resin flow interface to atmospheric pressure allows air in the mold cavity to escape freely during resin filling, preventing air bubbles or unfilled areas from forming due to gas retention. Furthermore, setting the pressure at the resin flow interface to atmospheric pressure ensures the boundary accuracy and physical consistency of the resin flow simulation.

[0030] S303: Set the normal mass flux of resin fluid at the mold boundary to zero. ; in, The vector representing the normal to the mold boundary, u, represents the velocity. ρ represents the velocity vector. r This indicates the resin density.

[0031] It should be noted that setting the normal mass flux of the resin fluid at the mold boundary to zero indicates that the mold wall is an impermeable solid boundary, and the resin fluid will not pass through the mold wall, thus ensuring that the resin flow only occurs inside the mold cavity. This boundary condition can accurately simulate the flow behavior of resin within the mold cavity, ensuring that the calculation results are consistent with actual physical phenomena.

[0032] S4: Set the descriptive equations for the flow process of the resin fluid and the curing process in the finite element model.

[0033] In one possible implementation, S4 specifically includes sub-steps S401 to S404: S401: Darcy's law is used to describe the flow process of resin fluid in porous media. ; Where u represents velocity, Let K represent the velocity vector, K represent the fiber permeability tensor, μ represent the resin viscosity, and P represent the pressure. This represents the gradient operator.

[0034] It should be noted that Darcy's law is a fundamental equation used to describe the permeation and flow behavior of resin in fiber-reinforced materials (porous media). It can simulate the flow velocity and direction of resin in the mold cavity and is an important basis for calculating filling time and optimizing the process.

[0035] S402: The flow mass balance equation is used to describe the mass balance of the resin fluid during the flow process. ; in, ρ represents the fiber porosity. r represents the resin density, t represents time, and ∂ represents the partial derivative operator.

[0036] It should be noted that the flow mass balance equation ensures that the resin satisfies the mass conservation condition during flow. It reflects the flow dynamics of the resin within the mold cavity, including how the resin occupies space in the porous medium during filling. This equation is crucial for simulating complete resin filling of the mold cavity and avoiding unfilled areas or bubble formation.

[0037] S403: The transient energy balance equation is used to describe the heat transfer process of resin fluid during curing in the mold. ; Among them, C Pr C represents the specific heat capacity of the resin. Pf ρ represents the specific heat capacity of the fiber. f The value represents fiber density, T represents temperature, and kJ represents temperature. c Indicates the thermal conductivity of the composite material. α indicates that the curing reaction is exothermic, and α represents the degree of curing.

[0038] Optionally, the thermal conductivity of the composite material is as follows: ; Where, k r k represents the thermal conductivity of the resin. fThis indicates the thermal conductivity of the fiber.

[0039] It should be noted that the transient energy balance equation describes the heat transfer process between the resin and the fiber in the mold cavity. This equation can be used to simulate the temperature field in the mold cavity during the curing process, ensuring uniform curing and thus avoiding problems such as local overheating or incomplete curing, which would affect the performance of the final product.

[0040] S404: The curing kinetics equation is used to describe the chemical transformation process of the resin fluid during curing in the mold. ; Where A represents the frequency factor, E represents the activation energy, R represents the ideal gas constant, T represents the temperature, and m and n represent the reaction order.

[0041] It should be noted that the curing kinetics equation describes the chemical reaction process of the resin from liquid to solid, determining the reaction rate of the curing process. This equation can accurately simulate the chemical transformation at different locations during curing, thereby optimizing curing time and temperature distribution, and ensuring the mechanical properties and dimensional stability of the product.

[0042] S5: The flow front tracking algorithm is used to track the flow front in the finite element model.

[0043] In one possible implementation, the flow front tracking algorithm is specifically the level set algorithm.

[0044] Specifically, the level set algorithm is used for flow front tracking: ; Where θ represents the fluid volume function, d represents the differential operator, γ represents the initialization factor, and ε represents the interface thickness.

[0045] It should be noted that the level set algorithm is a numerical method for tracking dynamic interfaces. It introduces a fluid volume function θ to represent the interface position, where the variation of the value (from 0 to 1) describes the unfilled region, flow front, and filled region. This algorithm can handle complex interface shape variations (such as bending, splitting, or merging) and avoids numerical instability problems by smoothing the interface.

[0046] Optionally, the fluid volume function is specifically: ; In this invention, the flow front is tracked using a level set algorithm, which accurately captures the filling state and dynamic changes of the resin within the mold cavity, exhibiting particularly excellent performance in complex geometries and multi-inlet processes. It provides a smooth and accurate interface representation, reduces numerical errors, ensures a high degree of consistency between simulation results and the actual resin transfer process, and provides a reliable basis for process optimization and defect prediction.

[0047] However, while the level set algorithm describes the interface through a smooth transition of volume functions when tracking the flow front, this can lead to a loss of fill volume or non-physical volume growth, thus violating mass conservation. Furthermore, the level set algorithm may introduce numerical diffusion when solving the convection equations, causing interface ambiguity and reducing the positional accuracy of the flow front. Therefore, this invention further employs a flow analysis network (FAN) algorithm for flow front tracking. The FAN algorithm overcomes the shortcomings of the level set algorithm by introducing a precise update mechanism for sub-control volume cells and node fill rates.

[0048] In one possible implementation, the flow front tracking algorithm is specifically a flow analysis network algorithm.

[0049] Specifically, S5 includes sub-steps S501 to S503: S501: Employs a flow analysis network algorithm to calculate the flow volume between adjacent nodes. ; Among them, I i Let P represent the flow volume at the i-th node, K represent the permeability, and P represent the flow volume at the i-th node. in P represents the internal pressure of the unit. out This indicates the pressure of adjacent units.

[0050] The flow volume describes the amount of resin flowing between these nodes and determines the resin's expansion and filling process.

[0051] Furthermore, each node has an associated fill score, indicating whether the node is fully filled with resin (0 indicates unfilled, 1 indicates fully filled).

[0052] S502: Update the fill fraction for each node based on the flow volume: ; in, This represents the fill score of the i-th node at the (n+1)-th time step. V represents the fill score of the i-th node at the n-th time step. iThis represents the control volume of the i-th node (i.e., the spatial volume corresponding to the node). This represents the adaptive step size at the nth time step.

[0053] It should be noted that the filling state of each node is dynamically calculated to ensure the quality conservation during the flow interface update process, and the calculation accuracy is adjusted by adaptive time step.

[0054] Optionally, the adaptive step size is determined as follows: Calculate the optimal time step for each node: ; in, This represents the optimal time step for the i-th node.

[0055] The minimum of the optimal time steps for each node is used as the adaptive step size for the next iteration.

[0056] In this invention, by calculating the optimal time step for each node and selecting the minimum value as the adaptive step size, it can be ensured that all nodes meet the flow accuracy requirements during the iteration process, especially nodes with high fill ratios or low flow rates, which can be accurately updated. The benefits of this approach include: avoiding numerical errors and ensuring that the fill fraction does not exceed physical limitations (e.g., not exceeding the 0-1 range) across all nodes; dynamically optimizing calculations by adapting to the most complex flow regions with the minimum step size, thus improving the overall stability and accuracy of the simulation; and enhancing efficiency and robustness, preventing interface tracking deviations due to excessively large step sizes while avoiding unnecessary computational costs caused by excessively small step sizes. This method strikes a balance between accuracy and efficiency.

[0057] S503: Tracks the flow front based on the fill fraction and adaptive step size.

[0058] It should be noted that by calculating changes in volumetric flow rate and filling volume, precise tracking of the flow front can be achieved, ensuring the comprehensiveness and accuracy of filling in complex flow paths.

[0059] In this invention, by assigning multiple sub-control volumetric units to each node and employing a flow analysis network algorithm to calculate the volumetric flow rate and update the fill fraction, high-precision dynamic tracking of the flow front can be achieved. The advantages of this method include: ensuring mass conservation by accurately calculating the volumetric flow rate and fill status to avoid numerical errors; improving resolution by refining local calculations through the division of sub-control volumetric units; adaptively adjusting the time step to improve computational efficiency and stability; and accurately tracking complex flow paths, especially in cases of complex geometry and multi-injection port injection, ensuring the physical consistency and simulation accuracy of the flow simulation results, thereby optimizing process design and improving production quality.

[0060] S6: Initialize the process parameters for the resin transfer molding process.

[0061] Optionally, the process parameters may include: injection pressure, injection speed, injection time, number and location of injection ports, number and location of venting ports, mold temperature, resin material, and curing time.

[0062] S7: Load process parameters into the finite element model to simulate the resin processing and molding process under various process parameters.

[0063] S8: Based on the simulation results of the resin processing and molding process, determine the filling time, unfilled area volume, weld line length, and resin waste under various process parameters.

[0064] Optionally, the volume of the unfilled region is specifically: ; Among them, V u θ represents the volume of the unfilled region. i V represents the fluid volume function value of the i-th cell. i Let represent the volume of the i-th unit, and n represent the total number of units.

[0065] Alternatively, the weld line is a linear region formed by the meeting of different flow fronts, and its length can be calculated by simulating the intersection of resin flow paths.

[0066] Optionally, the amount of resin wastage can be calculated by simulating the volume of residual resin during the flow process, including residual resin in the injection port and runner, as well as excess resin injected into the mold cavity.

[0067] S9: With the goal of minimizing filling time, unfilled area volume, weld line length, and resin waste, the optimal process parameters are determined using the butterfly optimization algorithm.

[0068] In one possible implementation, S9 specifically includes: S901: Constructing a process optimization objective function with the goals of minimizing filling time, unfilled area volume, weld line length, and resin waste: ; Where f represents the process optimization objective function, X represents the set of process parameters, and T f V represents the fill time. u L represents the volume of the unfilled region. r R represents the length of the fusion splice. w λ1 represents the amount of resin wasted, λ2 represents the weighting factor of filling time, λ3 represents the weighting factor of unfilled area volume, λ4 represents the weighting factor of weld line length, and λ5 represents the weighting factor of resin wasted amount.

[0069] Those skilled in the art can set the weighting coefficients for filling time, unfilled area volume, weld line length, and resin waste according to actual conditions; this invention does not impose any limitations.

[0070] Optionally, before calculating the objective function for process optimization, the filling time, unfilled area volume, weld line length, and resin waste can be normalized to unify the dimensions to between 0 and 1.

[0071] In this invention, minimizing the volume of unfilled areas and the length of weld lines can improve the structural integrity and mechanical properties of the product; reducing filling time increases production efficiency; and reducing resin waste helps save material costs. Integrating filling time, unfilled area volume, weld line length, and resin waste into a single objective function allows for the simultaneous consideration of multiple process performance indicators, rather than a single objective, thereby achieving comprehensive optimization.

[0072] S902: The optimal process parameters are determined by minimizing the process optimization objective function using the butterfly optimization algorithm.

[0073] Specifically, the reciprocal of the process optimization objective function can be used as the fitness function of the butterfly optimization algorithm.

[0074] The butterfly individuals are initialized using a Sine chaotic mapping. Each butterfly individual represents a feasible set of process parameters, and each butterfly individual consists of multiple dimensional components, with each component representing a process parameter. ; ; Where, x i Let lb represent the initial position of the i-th butterfly individual. i Let ub represent the lower bound of the i-th feasible solution. i Let y denote the upper bound of the i-th feasible solution. i Let y represent the chaos number corresponding to the i-th butterfly individual. i-1 This represents the chaos number corresponding to the (i-1)th butterfly individual, and μ represents the chaos parameter, which is typically taken as 0.99.

[0075] In this invention, the introduction of chaotic sequences enhances the global search capability of the optimization algorithm. The initial solutions generated by chaotic mapping exhibit high randomness and a wide distribution, thus avoiding the problem of getting trapped in local optima in traditional optimization methods. The nonlinear characteristics of chaotic sequences make the search process more flexible, enabling better exploration of the solution space and improving the effectiveness and convergence speed of the optimization algorithm.

[0076] Generate a random number and determine if the random number is less than the adaptive transformation probability; if so, proceed to the global search phase; otherwise, proceed to the local search phase. Optionally, the adaptive transition probability is specifically: ; in, P represents the adaptive transition probability of the i-th butterfly individual in the t-th iteration. min P represents the minimum transition probability. max f represents the maximum transition probability. max This represents the fitness value of the globally optimal butterfly individual. Let represent the fitness value of the i-th butterfly individual, and log represent the logarithmic function.

[0077] In this invention, the use of adaptive transition probabilities to determine whether to enter the global search or local search phase dynamically adjusts the search strategy to balance exploration and exploitation capabilities. As iterations proceed, the fitness value of the globally optimal butterfly individual continuously optimizes, with individuals having lower fitness being more likely to enter the global search phase, thus accelerating the search for the global optimum. Conversely, individuals with higher fitness are more likely to enter the local search phase, refining the current solution and optimizing local regions. Through this adaptive mechanism, the algorithm can more effectively find the globally optimal solution in the solution space while avoiding premature entrapment in local optima, thereby improving search efficiency and result quality.

[0078] During the global search phase, based on the fragrance concentration value, the butterfly individual is guided to approach the globally optimal butterfly individual, and its position is updated accordingly. ; in, ω represents the position of the i-th butterfly in the (t+1)-th iteration. t This represents the nonlinear weighting factor at the t-th iteration. This represents the position of the i-th butterfly in the t-th iteration, where rand represents a random number between 0 and 1, and x best f represents the position of the globally optimal butterfly individual. i represents the fragrance concentration value of the i-th butterfly individual, and Cauchy represents the Cauchy variation factor.

[0079] In this invention, during the global search phase, a strategy of guiding individual butterflies closer to the globally optimal butterfly and updating their positions effectively accelerates the search for the global optimum. By using fragrance concentration values ​​and the Cauchy variation factor, this method combines randomness and guidance, enhancing the diversity and flexibility of the search.

[0080] Optionally, the nonlinear weighting factor is specifically: ; Where t represents the current iteration number, T m ω represents the maximum number of iterations. max ω represents the maximum weighting factor. min This represents the minimum weight factor.

[0081] In this invention, the weight factor is gradually decreased as the number of iterations increases, thereby achieving a balanced exploration and development during the search process. In the initial stage (lower number of iterations), a larger weight factor helps enhance the breadth of the global search, encouraging the algorithm to explore the entire solution space and avoiding premature convergence. As iterations progress, the weight factor gradually decreases, allowing the algorithm to focus more on local optimization and more precisely search for the optimal solution in the solution space. Through this gradual convergence strategy, the algorithm can effectively avoid getting trapped in local optima while improving the efficiency and accuracy of searching for the global optimum.

[0082] Optionally, the aroma concentration value is specifically: ; Among them, f i This represents the fragrance concentration value of the i-th individual butterfly. Let α represent the sensory factor coefficient of the i-th butterfly individual at the t-th iteration, where I represents the stimulus intensity and α represents the power exponent.

[0083] In this invention, by adjusting the search behavior of individual butterflies using fragrance concentration values, the flexibility and adaptability of the butterflies during the search process can be effectively enhanced. The product of the sensory factor coefficient and the stimulus intensity, adjusted through a power exponent, allows the fragrance concentration value of an individual to reflect its current fitness and the level of motivation for the search. Higher fragrance concentration values ​​guide butterflies closer to the global optimum, enhancing global search capabilities, while lower fragrance concentration values ​​allow for more detailed local searches, finely optimizing certain regions in the solution space. This dynamic adjustment mechanism based on fitness and stimulus intensity helps improve the convergence speed of the algorithm and the efficiency of finding the global optimum.

[0084] Optionally, the update method for the perceived factor coefficients is as follows: ; in, represents the sensory factor coefficient of the i-th butterfly individual at the t-th iteration, b represents the fragrance concentration update control parameter, typically taken as 0.025, and T m This indicates the maximum number of iterations.

[0085] In this invention, by updating the sensory factor coefficients, the search behavior of individual butterflies can be dynamically adjusted to better adapt to changes during the iteration process. As the number of iterations increases, the sensory factor coefficients are gradually adjusted, allowing the individual's fragrance concentration value to more accurately reflect its search state. A larger update step size initially guides the individual to conduct a larger search, while as iterations progress, the update step size gradually decreases, encouraging the individual to perform a more refined local search when approaching the global optimum. This adaptive update mechanism helps balance exploration and exploitation, improves the stability and efficiency of the search, and avoids premature convergence or getting trapped in local optima.

[0086] During the local search phase, based on the fragrance concentration value, two butterfly individuals are randomly selected to move closer together, and their positions are updated accordingly. ; in, This represents the position of the j-th butterfly in the t-th iteration. This represents the position of the k-th butterfly individual at the t-th iteration.

[0087] In this invention, during the local search phase, a strategy of randomly selecting two butterfly individuals to approach each other based on fragrance concentration values ​​and updating their positions enhances the flexibility and diversity of the local search. Individuals explore local areas with higher precision, particularly exchanging information between butterfly individuals with similar fitness, thereby avoiding getting trapped in local optima.

[0088] Update the fitness values ​​of each individual butterfly and the global best individual.

[0089] Determine if the current iteration count has reached the maximum iteration count; if so, output the set of process parameters represented by the butterfly individual with the highest fitness; otherwise, return to continue iterating.

[0090] This invention combines finite element simulation with the butterfly optimization algorithm to achieve comprehensive optimization of production efficiency, material utilization, and product quality by minimizing key indicators (filling time, unfilled area volume, weld line length, and resin waste). This not only reduces defects such as bubbles and improves product mechanical properties but also lowers material waste and production costs, demonstrating extremely high industrial application value and economic benefits.

[0091] In this invention, finite element analysis technology is used to simulate the resin processing and molding process under various process parameters. The goal is to minimize filling time, unfilled area volume, weld line length, and resin waste. The optimal process parameters are determined by the butterfly optimization algorithm. This can reduce filling time and improve production efficiency, reduce unfilled area volume and weld line length, thereby reducing the generation of bubbles in the weld collision area, increasing the local density of the resin product, improving the strength and rigidity of the resin product, and also reducing resin waste and lowering material costs.

[0092] In one possible implementation, the resin processing optimization method further includes: S10: Through a deep reinforcement learning-based intelligent agent, the resin processing is controlled to maintain optimal process parameters.

[0093] In this invention, by introducing an intelligent agent based on deep reinforcement learning to control process parameters, real-time optimization, intelligent adjustment and efficient automation of the resin processing process are achieved. This not only improves production efficiency and product quality, but also significantly reduces costs and reliance on manual labor, providing an advanced solution for resin processing in complex process scenarios.

[0094] Reference manual attached Figure 4 The diagram shows a structural schematic of a resin processing optimization system provided by the present invention.

[0095] The present invention also provides a resin processing optimization system 20, comprising: Processor 201; The memory 202 stores computer-readable instructions, which, when executed by the processor 201, implement the resin processing optimization method as described in the method embodiment.

[0096] The resin processing optimization system 20 provided by the present invention can execute the above-mentioned resin processing optimization method and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate further.

[0097] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing resin processing technology, characterized in that, include: S1: Construct a finite element model of the resin transfer molding process; S2: Divide the finite element model into elements; S3: Set the boundary conditions for the finite element model; S4: Set the descriptive equations for the flow process of the resin fluid and the curing process in the finite element model; S5: The flow front tracking algorithm is used to track the flow front in the finite element model; S6: Initialize the process parameters for the resin transfer molding process; S7: Load the process parameters into the finite element model to simulate the resin processing and molding process under various process parameters; S8: Based on the simulation results of the resin processing and molding process, determine the filling time, unfilled area volume, weld line length, and resin waste under various process parameters; S9: With the goal of minimizing filling time, unfilled area volume, weld line length, and resin waste, the optimal process parameters are determined using the butterfly optimization algorithm.

2. The resin processing optimization method according to claim 1, characterized in that, S3 specifically includes: S301: Set the pressure at the mold injection port to equal the resin injection pressure; S302: Set the pressure at the vent and resin flow interface to be equal to atmospheric pressure; S303: Set the normal mass flux of resin fluid at the mold boundary to zero.

3. The resin processing optimization method according to claim 1, characterized in that, S4 specifically includes: S401: Darcy's law is used to describe the flow process of resin fluid in porous media; S402: The flow mass balance equation is used to describe the mass balance of the resin fluid during the flow process; S403: The transient energy balance equation is used to describe the heat transfer process of resin fluid curing in the mold; S404: The curing kinetic equation is used to describe the chemical transformation process of the resin fluid in the mold during curing.

4. The resin processing optimization method according to claim 1, characterized in that, The flow front tracking algorithm is specifically the level set algorithm.

5. The resin processing optimization method according to claim 1, characterized in that, The flow front tracking algorithm is specifically a flow analysis network algorithm; S5 specifically includes: S501: Employs a flow analysis network algorithm to calculate the flow volume between adjacent nodes. ； Among them, I i Let P represent the flow volume at the i-th node, K represent the permeability, and P represent the flow volume at the i-th node. in P represents the internal pressure of the unit. out Indicates the pressure of adjacent units; S502: Update the fill fraction for each node based on the flow volume: ； in, This represents the fill score of the i-th node at the (n+1)-th time step. V represents the fill score of the i-th node at the n-th time step. i This represents the control volume of the i-th node. This represents the adaptive step size at the nth time step; S503: Tracks the flow front based on the fill fraction and adaptive step size.

6. The resin processing optimization method according to claim 5, characterized in that, The method for determining the adaptive step size is as follows: Calculate the optimal time step for each node: ； in, This represents the optimal time step for the i-th node; The minimum of the optimal time steps for each node is used as the adaptive step size for the next iteration.

7. The resin processing optimization method according to claim 1, characterized in that, S9 specifically includes: S901: Construct a process optimization objective function with the goal of minimizing filling time, unfilled area volume, weld line length, and resin waste. S902: With the goal of minimizing the process optimization objective function, the optimal process parameters are determined using the butterfly optimization algorithm.

8. The resin processing optimization method according to claim 1, characterized in that, The specific process parameters include: injection pressure, injection speed, injection time, number and location of injection ports, number and location of venting ports, mold temperature, resin material, and curing time.

9. The resin processing optimization method according to claim 1, characterized in that, Also includes: S10: By using an agent based on deep reinforcement learning, the resin processing is controlled to maintain the optimal process parameters.

10. A resin processing optimization system, characterized in that, include: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the resin processing optimization method as described in any one of claims 1 to 9.

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