A numerical simulation-based optimization method for fabric liquid forming process

Abstract

This invention belongs to the field of composite material molding technology, and relates to a method for optimizing fabric liquid molding processes based on numerical simulation. This invention obtains the optimal fiber main permeability through a process optimization method based on numerical simulation. K x* Auxiliary penetration rate K y* Injection pressure P i* The molding process and RTM injection scheme were optimized to effectively improve the molding quality of thick fabrics, reduce defects such as bubbles and dry spots, shorten injection time, reduce test costs, and improve production efficiency.

Description

Technical Field

[0001] This invention belongs to the field of composite material molding technology and relates to a method for optimizing fabric liquid molding process based on numerical simulation. Background Technology

[0002] Resin transfer molding (RTM) is an important method for producing composite material structural parts with high mechanical strength and good surface quality by injecting resin into fiber-reinforced preforms. It is widely used in aerospace, automotive, wind power generation and other fields.

[0003] The molding of thick fabrics presents significant challenges in current processes. The resin wettability and permeability of thick unidirectional and three-dimensional woven hybrid fabrics differ significantly, directly affecting the quality of composite parts. Literature (Causes and Control Methods of Dry Spots in Resin Transfer Molding Composite Airfoils [J]. Journal of Composite Materials, 2021, 38(3): 809-815.) indicates that differences in volume fraction and edge effects in different regions lead to variations in resin permeability, resulting in an envelope phenomenon and thus causing dry spot defects. During resin infusion, due to differences in resin permeability, defects such as bubbles and dry spots are easily generated in thick fabrics, reducing the performance and reliability of the parts.

[0004] Traditional process optimization methods mostly rely on manual experience and trial-and-error, such as the literature (Reverse Analysis of Manufacturing Defects in RTM Integral Molded Composite Structures [C]. Proceedings of the 7th National Conference on MTS Materials Testing (I), 2007: 91-94.). This not only consumes a lot of time and cost, but also makes it difficult to ensure the stability and consistency of the production process. With the increase in the size of composite materials and the complexity of molded parts, the optimization and control of liquid molding processes are becoming increasingly difficult, and the need for optimization of liquid molding processes for thick fabrics is becoming increasingly urgent. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the prior art and provide a method for optimizing fabric liquid forming process based on numerical simulation.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for optimizing fabric liquid molding process based on numerical simulation. The first step involves selecting the fabric preform and resin, and obtaining the optimal fiber main permeability K through numerical simulation. x* Optimal fiber-assisted permeability K y* Optimal injection pressure P i* At the same time, obtain the best mold filling method;

[0008] The second step involves designing multiple RTM infusion schemes (the elements involved are: the location and number of injection ports, the location and number of dispensing ports, the location of the injection channel, and the location of the dispensing channel), and conducting simulations based on the results of the first step, and selecting the optimal RTM infusion scheme based on the simulation results.

[0009] The third step involves adopting the optimal RTM perfusion protocol and using the P from the first step. i* And the best filling method for liquid molding of thick fabrics.

[0010] As a preferred technical solution:

[0011] As described above, in the first step of the fabric liquid forming process optimization method based on numerical simulation, K is obtained through numerical simulation. x* K y* The steps are as follows:

[0012] (i) Obtain the main fiber permeability and secondary fiber permeability when the fabric preform is infused with dimethyl silicone oil through experiments;

[0013] (ii) The injection time t1 when resin-injected fabric preform is obtained by simulation. The set values ​​of fiber main permeability and fiber auxiliary permeability during simulation are the same as those obtained in step (i).

[0014] (iii) The injection time t2 when resin-injected fabric preforms are obtained through experiments. The process parameters during the experiment are the same as those during the simulation in step (ii).

[0015] (iv) Determine if |t2-t1| / t2≤10%; 10% is chosen as the tolerance standard because it can just balance the efficiency of simulation calculation and the feasibility of process control: it can avoid a surge in costs by simplifying the model, and it can cover process variables such as resin viscosity fluctuations and fabric preform deviations. Moreover, it has been verified by engineering practice to ensure the consistency of composite material performance and maximize production yield. If the tolerance standard is too low, it will lead to excessive complexity of the simulation model and a surge in calculation costs. In actual processes, uncontrollable factors such as resin viscosity fluctuations and fabric preform thickness deviations are difficult to support such a strict error standard, which lacks engineering economy and feasibility. If the tolerance standard is too high, it will cover up process defects such as mold leakage and inconsistent flow fronts, resulting in insufficient curing degree of composite materials and decreased mechanical properties. It will also increase the cost of trial molding and reduce the yield, thus losing the accurate guidance significance of simulation for production.

[0016] The specific process of step (i) in the above-described method for optimizing fabric liquid forming process based on numerical simulation is as follows:

[0017] First, the experimental setup is constructed. A glass plate and a vacuum bag above it form a sealed cavity. A fabric preform is placed inside the sealed cavity, and a camera is fixed above the vacuum bag. The vacuum bag has an injection port directly above the fabric preform, which is connected to a dimethyl silicone oil storage tank via an injection pipe equipped with an injection valve. The vacuum bag also has an outlet located below the fabric preform, which is connected to a dimethyl silicone oil collection tank via an outlet pipe equipped with an outlet valve. The dimethyl silicone oil collection tank is connected to a vacuum pump via a vacuum pipe.

[0018] Then, close the glue injection valve, open the glue dispensing valve and vacuum pump, evacuate to a vacuum state, and maintain pressure.

[0019] Finally, the camera was activated and the injection valve was opened. Starting from a certain moment, the position r of the flow front of dimethyl silicone oil in the main permeability direction of the fiber was collected at fixed time intervals. x The position r of the flow front of dimethyl silicone oil in the direction of fiber-assisted permeability y T is obtained by calculation using the formula. x T y The formula is as follows:

[0020] ;

[0021] In the formula, i = x, y; Represents the radius of the injection port;

[0022] Draw T i Follow The curve of change was obtained, and the slope was obtained through linear fitting. And after further calculation, When i=x, Represents the main fiber permeability, when i=y, The fiber-assisted permeability is represented by the following formula:

[0023] ;

[0024] In the formula, This represents the resin viscosity, expressed in Pa·s. Represents porosity; This represents the pressure difference between the injection port inlet and the flow front, expressed in Pa.

[0025] The above The derivation of the calculation formula is as follows:

[0026] First, the basic formula for calculating penetration rate is: To simplify the calculation, let (in, It is The position parameters of the dimethyl silicone oil filling front at time t are obtained by processing the position ri of the flow front in the corresponding direction of the dimethyl silicone oil filling front. Substituting into the basic permeability calculation formula, we can obtain ;and Finally, by substituting the values, we obtain the above formula.

[0027] The specific process of step (ii) in the numerical simulation-based fabric liquid forming process optimization method described above is as follows:

[0028] Design an RTM injection scheme (the elements involved are: the location and number of injection ports, the location and number of outlet ports, the location of injection channels, and the location of outlet channels), and set the simulation parameters before starting the simulation. The simulation parameters include resin viscosity, fiber volume fraction, injection pressure, primary fiber permeability, secondary fiber permeability, layup direction, board thickness, and number of fabric preform layup layers.

[0029] In the first step of the numerical simulation-based optimization method for fabric liquid forming process described above, the optimal local permeability K is obtained through numerical simulation. l* Optimal edge penetration rate K e* And determine whether |log 10 K lx* -log 10 K x* |<1、|log 10 K ly* -log 10 K y* |<1、|log 10 K ex* -log 10 K x* |<1、|log 10 K ey* -log 10 K y* If |<1, proceed to the next step; otherwise, after reselecting the fabric preform, start the first step again. The reselected fabric preform is made of the same material as the original fabric preform, but the preforming process parameters are different. Edge penetration rate is the penetration rate caused by the edge effect.

[0030] |log 10 K lx* -log 10 K x* |<1、|log 10 K ly* -log 10 K y* |<1、|log 10 K ex*-log 10 K x* |<1、|log 10 K ey* -log 10 K y* |<1, i.e., the optimal local permeability K l* Optimal edge penetration rate K e* The order of magnitude, with the fiber main permeability K x* Fiber-assisted permeability K y* The absolute values ​​of the order-of-magnitude differences are all less than 1. When the order-of-magnitude differences between the optimal local permeability, optimal edge permeability and the main fiber permeability, and the auxiliary fiber permeability are greater than 1, the resin flow front advances faster in areas with high permeability values ​​and slower in areas with low permeability values. The difference in flow rate between the two can cause some areas of the fabric preform to not be fully filled by resin, which may lead to molding defects such as dry spots and bubbles. Therefore, it is necessary to ensure that the above order-of-magnitude differences are as small as possible and as close to 0 as possible to ensure the uniformity of resin filling.

[0031] As described above, in the first step of the fabric liquid forming process optimization method based on numerical simulation, K is obtained through numerical simulation. l* The steps are as follows:

[0032] (a) Use HyperMesh to create a rectangular model, with a pre-defined local rectangular region in the middle, and divide the rectangular model into multiple unit meshes;

[0033] (b) Import the rectangular model into PAM-RTM software, set the left side as the injection port and the right side as the outlet, set a main permeability A, a set of local permeability (i.e., the permeability of the local rectangular area), an injection pressure, a porosity, and a mold filling method, and then perform numerical simulation. The main permeability A is the permeability of the area in the rectangular model other than the local rectangular area.

[0034] (c) Analyze the numerical simulation results and take the local permeability where no envelope phenomenon appears in the numerical simulation results as K. l* Envelope phenomenon is the phenomenon of a fluid enveloping air.

[0035] As described above, in the first step of the fabric liquid forming process optimization method based on numerical simulation, K is obtained through numerical simulation. e* The steps are as follows:

[0036] a) Use HyperMesh to create a rectangular model, with a pre-defined local rectangular region in the middle, and divide the rectangular model into multiple unit meshes;

[0037] b) Import the rectangular model into PAM-RTM software, set the left side as the injection port and the right side as the outlet, set a main permeability B, a set of edge permeability, an injection pressure, and a porosity, set a mold filling method, and then perform numerical simulation. The main permeability B is the permeability of the area in the rectangular model other than the edge.

[0038] c) Analyze the numerical simulation results, and take the edge penetration rate with the weakest edge effect in the numerical simulation results as K. e* .

[0039] As described above, in the first step of the fabric liquid forming process optimization method based on numerical simulation, P is obtained through numerical simulation. i* The steps are as follows:

[0040] (A) Use HyperMesh to create a rectangular model, with a pre-defined local rectangular region in the middle, and divide the rectangular model into multiple unit meshes;

[0041] (B) Import the rectangular model into the PAM-RTM software, set the left side as the injection port and the right side as the outlet, set an overall permeability, a set of injection pressures, a porosity, and a mold filling method, and then perform numerical simulation.

[0042] (C) Analyze the numerical simulation results and take the injection pressure that does not exhibit an envelope phenomenon and has the shortest perfusion time under the given conditions as P. i* .

[0043] The first step of the fabric liquid molding process optimization method based on numerical simulation, as described above, to obtain the optimal mold filling method through numerical simulation, is as follows:

[0044] A) Use HyperMesh to create a rectangular model, with a pre-defined local rectangular region in the middle, and divide the rectangular model into multiple unit meshes;

[0045] B) Import the rectangular model into the PAM-RTM software, set the left side as the injection port and the right side as the outlet, set an overall permeability, an injection pressure, and a porosity, and set two filling methods. Then perform numerical simulation. The two filling methods are constant speed injection and constant pressure injection.

[0046] C) Analyze the numerical simulation results and select the filling method that does not exhibit an envelope phenomenon and has the shortest injection time under the given conditions as the optimal filling method.

[0047] The second step of the numerical simulation-based fabric liquid forming process optimization method described above is as follows:

[0048] First, a three-dimensional model of the composite material part was created using SolidWorks.

[0049] Then, the 3D model of the composite material part is imported into HyperMesh software for triangular mesh generation;

[0050] Next, the 3D model of the composite material part was imported into PAM-RTM software. Boundary conditions, simulation parameters, and filling method were set, and simulation was performed. The simulation parameters included resin viscosity, fiber volume fraction, injection speed, primary fiber permeability, secondary fiber permeability, and injection pressure. The primary fiber permeability was the same as K... x* Fiber-assisted permeability is the same as K y* Injection pressure is the same as P i* The filling method is the same as the optimal filling method in the first step.

[0051] Finally, the simulation results were analyzed, and the RTM perfusion scheme that did not show dry spots or bubble defects and had the shortest perfusion time under the conditions was selected as the optimal RTM perfusion scheme.

[0052] Beneficial effects:

[0053] (1) The present invention obtains the optimal fiber main permeability K through a process optimization method based on numerical simulation. x* Auxiliary penetration rate K y* Injection pressure P i* The molding process and RTM injection scheme have been optimized to effectively improve the molding quality of thick fabrics, reduce defects such as bubbles and dry spots, and shorten injection time to improve production efficiency.

[0054] (2) The process optimization method and system of the present invention have broad application prospects. They are not only applicable to the manufacturing of rail transit components, but can also be extended to composite material molding in aerospace, marine engineering and other fields.

[0055] (3) The process optimization method based on numerical simulation avoids the traditional trial and error method, reduces the experimental cost, improves the efficiency of process optimization, and ensures the uniformity of resin flow during the molding of thick fabrics.

[0056] (4) Through simulation and experimental verification, the feasibility of the optimized RTM injection scheme (including the position of the injection port / outlet, the flow channel layout, etc.) in actual production was ensured, which effectively improved the quality of liquid-molded parts of thick fabrics and reduced the scrap rate. Attached Figure Description

[0057] Figure 1A schematic diagram of the experimental apparatus for measuring fabric permeability; wherein, 1-glass plate, 2-vacuum bag, 3-fabric preform, 4-camera, 5-glue injection pipe, 6-dimethyl silicone oil storage tank, 7-glue discharge pipe, 8-dimethyl silicone oil collection tank, 9-vacuum pipe, 10-vacuum pump, 11-release cloth, 12-sealant.

[0058] Figure 2 In the middle 'a', T represents the direction of the primary and secondary permeability of the unidirectional fabric. i Over time The variation curves and linear fitting graphs, where b represents the main and secondary permeability directions of the three-dimensional woven fabric. i Over time The variation curve and linear fitting graph;

[0059] Figure 3 A schematic diagram of the finite element mesh and boundary grouping for RTM injection of fabric preforms;

[0060] Figure 4 A schematic diagram of the rectangular model mesh and region division for numerical simulation of local permeability;

[0061] Figures 5-8 Cloud maps showing the resin filling time distribution of the RTM process under different edge penetration rates; among them, Figure 5 Corresponding to 1×10 -12 m 2 , Figure 6 Corresponding to 1×10 -11 m 2 , Figure 7 Corresponding to 1×10 -10 m 2 , Figure 8 Corresponding to 1×10 -9 m 2 ;

[0062] Figure 9 A schematic diagram of the model mesh grouping for the infill simulation;

[0063] Figure 10 A comparison chart of historical pressure curves of pressure sensors under different filling methods;

[0064] Figure 11 A schematic diagram of the glue injection channel and outlet layout for the fourth RTM grouting scheme for the side beam of the bogie frame.

[0065] Figure 12The figure shows the simulation results of filling and pressure distribution for the optimal grouting scheme (Scheme 4) of the bogie frame side beam; where (a) is the simulation result of the filling degree of the front side beam model, (a') is the simulation result of the filling degree of the back side beam model; (b) is the simulation result of the filling time of the front side beam model, (b') is the simulation result of the filling time of the back side beam model; (c) is the simulation result of the injection pressure of the front side beam model, and (c') is the simulation result of the injection pressure of the back side beam model. Detailed Implementation

[0066] The present invention will be further described below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0067] A method for optimizing fabric liquid forming process based on numerical simulation, the specific steps of which are as follows:

[0068] S1. Select the fabric preform and resin, and obtain the optimal fiber main permeability K through numerical simulation. x* Optimal fiber-assisted permeability K y* Optimal injection pressure P i* Optimal local penetration rate K l* Optimal edge penetration rate K e* Simultaneously, obtain the optimal filling method and determine whether |log 10 K lx* -log 10 K x* |<1、|log 10 K ly* -log 10 K y* |<1、|log 10 K ex* -log 10 K x* |<1、|log 10 K ey* -log 10 K y* If |<1, proceed to the next step; otherwise, after reselecting the fabric preform, start the first step again. The reselected fabric preform is made of the same material as the original fabric preform, but the preforming process parameters are different.

[0069] K is obtained through numerical simulation. x* K y* The steps are as follows:

[0070] (i) The main fiber permeability and secondary fiber permeability of the fabric preform were obtained by experiment when dimethyl silicone oil (viscosity 0.05 Pa⋅s) was used to infuse the fabric preform.

[0071] First, such as Figure 1 As shown, an experimental setup is constructed. A glass plate 1 and a vacuum bag 2 (manufacturer: Shanghai Ligo Technology Co., Ltd., model LVF2308) above it form a sealed cavity. The glass plate 1 and vacuum bag 2 are sealed together with sealant 12. A fabric preform 3 is placed inside the sealed cavity, and a release cloth 11 is laid on top of the fabric preform 3. A camera 4 is fixed above the vacuum bag 2. The vacuum bag 2 has an injection port directly above the fabric preform 3, which is connected to a dimethyl silicone oil storage tank 6 via an injection pipe 5. The injection pipe 5 has an injection valve. The vacuum bag 2 has an outlet located below the fabric preform 3, which is connected to a dimethyl silicone oil collection tank 8 via an outlet pipe 7. The outlet pipe 7 has an outlet valve. The dimethyl silicone oil collection tank 8 is connected to a vacuum pump 10 via a vacuum pipe 9.

[0072] Then, close the glue injection valve, open the glue dispensing valve and vacuum pump, evacuate to a vacuum state, and maintain pressure.

[0073] Finally, the camera was activated and the injection valve was opened. Starting from a certain moment, the position r of the flow front of dimethyl silicone oil in the main permeability direction of the fiber was collected at fixed time intervals. x The position r of the flow front of dimethyl silicone oil in the direction of fiber-assisted permeability y T is obtained by calculation using the formula. x T y The formula is as follows:

[0074] ;

[0075] In the formula, i = x, y; Represents the radius of the injection port;

[0076] Draw T i Over time The curve of change was obtained and linearly fitted (the linear fitting coefficient is denoted as R). 2 i i = x, y, R 2 x correspond R 2 y correspond ) to obtain the slope And after further calculation, When i=x, Represents the main fiber permeability, when i=y, The fiber-assisted permeability is represented by the following formula:

[0077] ;

[0078] In the formula, This represents the resin viscosity, expressed in Pa·s. Represents porosity; This represents the pressure difference between the injection port inlet and the flow front, expressed in Pa.

[0079] In this step, the fabric preform is made of a low-thickness unidirectional fabric (unidirectional carbon fiber cloth, manufactured by Yixing Zhongtan Technology Co., Ltd., with a basis weight of 300g / m²). 2 (360mm in length, 200mm in width, and 0.30mm in thickness) and a thick three-dimensional woven fabric (1200g / m²). 2 The experiment was conducted with a thickness of 1.33 mm. The preparation method of the three-dimensional woven fabric is as follows: carbon fiber (Zhongfu Shenying Carbon Fiber Co., Ltd., SYT49S-12K) is used to weave the fabric using a 352-spindle circular braiding machine. The weaving structure is a three-dimensional angular interlocking structure with a weaving angle of 30°. The number of yarn rows is 4, with 88 spindles in each row. The four rows of spindles interweave with each other to form a three-dimensional woven fabric.

[0080] The position r of the flow front of dimethyl silicone oil in the main permeability direction of the fiber was collected. x The position r of the flow front of dimethyl silicone oil in the direction of fiber-assisted permeability y For unidirectional fabrics, data is collected every 5 seconds starting from 10 seconds, and for three-dimensional woven fabrics, data is collected every 10 seconds starting from 12 seconds.

[0081] The experimental data for the two fabric preforms are shown in Table 1:

[0082] Table 1

[0083]

[0084] The main and secondary permeability directions T of unidirectional and three-dimensional woven fabrics i Over time The variation curves and linear fitting are as follows: Figure 2 As shown;

[0085] (ii) such as Figure 3 As shown, an RTM injection scheme (resin injection channel injection) was designed, and simulation parameters were set before starting the simulation to obtain the injection time t1 when resin-injected fabric preforms were used. The simulation parameter settings are shown in Table 2.

[0086] Table 2

[0087] Unidirectional fabric 3D woven fabric Resin viscosity 0.2 Pa⋅s 0.2 Pa⋅s fiber volume fraction 54% 50% Injection pressure 0.4MPa 0.4MPa ply direction 0° 30° Plate thickness 4mm 4mm Number of layers in prefabricated fabric 16 3 Fiber main permeability <![CDATA[1.12×10 -10 m 2 ]]> <![CDATA[8.03×10 -11 m 2 ]]> Fiber-assisted permeability <![CDATA[4.70×10 -11 m 2 ]]> <![CDATA[5.54×10 -11 m 2 ]]>

[0088] The simulation result for unidirectional fabric is 176s, indicating fast edge filling speed and high pressure near the glue injection channel; the simulation result for 3D woven fabric is 1914s, showing a Z-axis penetration trend, with the resin flow fronts on the upper and lower surfaces basically synchronized.

[0089] (iii) The injection time t2 when resin-injected fabric preforms are obtained through experiments. The process parameters during the experiment are the same as those during the simulation in step (ii).

[0090] The experimental result for unidirectional fabrics was 185s, and the experimental result for three-dimensional woven fabrics was 1857s.

[0091] (iv) Determine whether |t2-t1| / t2≤10% is true. If so, use the fiber primary permeability and fiber secondary permeability obtained in step (i) as K respectively. x* K y* Otherwise, return to step (i).

[0092] Calculations show that both unidirectional and three-dimensional woven fabrics satisfy |t2-t1| / t2≤10%, with a result of 4.86% for unidirectional fabrics and 3.07% for three-dimensional woven fabrics.

[0093] P is obtained through numerical simulation. i* The steps are as follows:

[0094] (A) Use HyperMesh to create a rectangular model (360mm×200mm), with a preset local rectangular area (44mm×15mm) in the middle, and divide the rectangular model into 6452 unit meshes;

[0095] (B) Import the rectangular model into the PAM-RTM software, set the left side as the injection port and the right side as the dispensing port, and set an overall permeability (1×10⁻⁶). -10 m 2 A set of injection pressures (0.2MPa, 0.4MPa, 0.6MPa, 0.8MPa, 1.0MPa, 1.2MPa, 1.4MPa, 1.6MPa, 1.8MPa, 2.0MPa), a porosity (50%), and a molding method (constant pressure injection) were used for numerical simulation.

[0096] (C) Analyze the numerical simulation results and take the injection pressure that does not exhibit an envelope phenomenon and has the shortest perfusion time under the given conditions as P. i* ;

[0097] Different injection pressures (0.2~2.0 MPa) were set in PAM-RTM software to analyze the effects of injection pressure on injection time, bubble generation, and fiber preform deformation. When the injection pressure increased, the injection time shortened accordingly, but the decreasing trend of injection time became less pronounced after the injection pressure exceeded a certain value. Simultaneously, excessive injection pressure increased the risk of fiber preform erosion deformation and mold deformation. When the injection pressure was low, the injection time was correspondingly prolonged. Excessive injection time may cause the resin to solidify before complete injection, leading to a sharp increase in resin viscosity, difficulty in flow, and ultimately, mold filling failure. Specifically, when the injection pressure is 0.2 MPa (lower pressure), capillary effect dominates resin flow, resulting in faster resin flow velocity and a leading flow front between fiber bundles. During injection, the lateral flow of resin easily traps air, forming bubbles and causing mold filling defects. When the injection pressure is 1.0~2.0 MPa (higher pressure), pressure gradient dominates resin flow, causing faster resin flow velocity and a leading flow front between fiber bundles, which also leads to mold filling defects. Furthermore, excessive pressure further increases the risk of deformation of the fiber preform and the mold. Based on the above analysis, 0.4 MPa is selected as the optimal injection pressure P. i* ;

[0098] K is obtained through numerical simulation. l* The steps are as follows:

[0099] (a) such as Figure 4 As shown, a rectangular model (360mm×200mm) is created using HyperMesh, with a pre-defined local rectangular area (44mm×15mm) in the middle, dividing the rectangular model into 6452 unit meshes.

[0100] (b) Import the rectangular model into the PAM-RTM software, set the left side as the glue inlet and the right side as the glue outlet, and set a bulk permeability A (1×10⁻⁶). -10 m 2 ), a set of local permeability (1×10) -13 m 2 1×10 -12 m 2 1×10 -11 m 2 1×10 - 10 m 2 With an injection pressure (0.4 MPa) and a porosity (50%), and after setting a filling method (constant pressure injection), numerical simulation is performed. Here, the main permeability A is the permeability of the area outside the local rectangular region in the rectangular model.

[0101] (c) Analyze the numerical simulation results and take the local permeability where no envelope phenomenon appears in the numerical simulation results as K.l* Envelope phenomenon is the phenomenon of a fluid enveloping air;

[0102] Observing the changes in the resin flow front in the simulation results, the smaller the permeability of the internal rectangular local region, the more the resin flow front lags behind other regions when flowing through that region. When the local permeability is 1×10 -12 m 2 and 1×10 -13 m 2 Even smaller, a distinct envelope phenomenon appears in localized areas; when the penetration rate in a localized area is 1×10⁻⁶. -11 m 2 At that time, the resin flow front in this region only lagged behind the rest of the region and did not form an envelope;

[0103] Edge permeability is the permeability caused by the edge effect, and K is obtained through numerical simulation. e* The steps are as follows:

[0104] a) Use HyperMesh to create a rectangular model (360mm×200mm), with a preset local rectangular area (44mm×15mm) in the middle, and divide the rectangular model into 6452 unit meshes.

[0105] b) Import the rectangular model into the PAM-RTM software, set the left side as the glue inlet and the right side as the glue outlet, and set a bulk permeability B (1×10⁻⁶). -12 m 2 ), a set of edge penetration rates (1×10) -12 m 2 1×10 -11 m 2 1×10 -10 m 2 1×10 -9 m 2 With an injection pressure (0.4 MPa) and a porosity (50%), and after setting a filling method (constant pressure injection), numerical simulation is performed. The main permeability B is the permeability of the area outside the edge in the rectangular model.

[0106] c) Analyze the numerical simulation results, and take the edge penetration rate with the weakest edge effect in the numerical simulation results as K. e* ;

[0107] like Figures 5-8 As shown, the permeability of the area between the carbon fiber fabric and the upper edge of the mold is set to 1×10 in the PAM-RTM software. -12 m 2 1×10 -11 m 2 1×10 -10 m2 1×10 -9 m 2 The permeability of the remaining areas of the model is set to 1×10. -12 m 2 The flow of resin in the edge region and the rest of the region was observed through simulation: when the edge permeability was 1×10⁻⁶. -12 m 2 At the initial stage, the resin flow front in the edge region and the rest of the region are almost a straight line; as the edge permeability increases, the resin flow velocity in the edge region gradually accelerates, and the edge effect becomes more pronounced, which may cause mold filling defects such as bubbles and dry spots. Based on the above results, 1×10⁻⁶ is selected. -12 m 2 As the optimal edge penetration rate K e* ;

[0108] The steps to obtain the optimal mold filling method through numerical simulation are as follows:

[0109] A) such as Figure 9 As shown, a rectangular model (360mm×200mm) is created using HyperMesh, with a pre-defined local rectangular area (44mm×15mm) in the middle, dividing the rectangular model into 6452 unit meshes.

[0110] B) Import the rectangular model into the PAM-RTM software, set the left side as the glue inlet and the right side as the glue outlet, and set an overall permeability (1×10⁻⁶). −10 m 2 A numerical simulation was performed with an injection pressure (0.4 MPa) and a porosity (50%), and two filling methods were set, namely constant-speed injection and constant-pressure injection.

[0111] C) Analyze the numerical simulation results and select the filling method that does not exhibit an envelope phenomenon and has the shortest injection time under the given conditions as the optimal filling method.

[0112] like Figure 10As shown, the pressure response during the mold filling process was recorded using two pressure sensor points, and the injection time and pressure response were compared. The injection time results show that the injection time for constant-speed injection (corresponding to the two pressure sensor points, curves C and D) is shorter than that for constant-pressure injection (corresponding to the two pressure sensor points, curves A and B). This is likely because the pressure gradient for constant-speed injection is larger than that for constant-pressure injection. Furthermore, at the two pressure sensor points, the pressure increase trend of curves C and D (constant-speed injection) is higher than that of curves A and B (constant-pressure injection), respectively. Observing the intercept points on the time axis, the pressure at the same sensor point... The time difference between the constant pressure injection curve B and the constant speed injection curve D, as well as the time difference between the constant pressure injection curve A and the constant speed injection curve C corresponding to another sensor point, are all very small. This indicates that the resin flow front reaches the same sensor point position in approximately the same time under the two filling methods. Therefore, when preparing small structural parts or structural parts with short filling times, the constant pressure filling method can achieve the same results under lower pressure and reduce the risk of the fabric preform being blown apart. However, when preparing larger structural parts, the constant speed injection method is more efficient and can also avoid the problem of premature resin curing and filling failure caused by excessively long injection time.

[0113] S2. Design multiple RTM perfusion schemes and perform simulations based on the results of S1. Select the optimal RTM perfusion scheme based on the simulation results.

[0114] First, a three-dimensional model of the composite material part was created using SolidWorks.

[0115] Then, the 3D model of the composite material part is imported into HyperMesh software for triangular mesh generation (10mm triangular mesh, 22840 elements in total).

[0116] Next, the 3D model of the composite material part was imported into PAM-RTM software. Boundary conditions, simulation parameters, and filling method were set before simulation was performed. The simulation parameters included resin viscosity (0.2 Pa⋅s), fiber volume fraction (50%), and injection speed (3 g⋅s). −1 ), fiber primary permeability, fiber secondary permeability (composite material parts are prepared by combining unidirectional fabrics and three-dimensional braided fabrics, and the primary permeability and secondary permeability of the two fabrics need to be set separately to match the material combination of the actual part), injection pressure, fiber primary permeability is the same as K x* Fiber-assisted permeability is the same as K y* Injection pressure is the same as P i* The filling method is the same as the optimal filling method for S1.

[0117] Finally, the simulation results were analyzed, and the RTM perfusion scheme that did not show dry spots or bubble defects and had the shortest perfusion time under the conditions was selected as the best RTM perfusion scheme.

[0118] Taking the side beam of the bogie frame as an example, five RTM injection schemes are designed based on its structural characteristics and injection conditions; Scheme 1: The injection ports and outlets are evenly distributed along the mold, with two injection ports and two outlets in the variable cross-section area and the wider area, resulting in 14 injection ports on one side of the mold (7 on each side of the central axis) and 14 outlets on the other side; Scheme 2: The injection channel is located at the middle of the bottom of the mold along the length of the side beam, and the outlets are located on the upper surface of the mold, with one outlet in each of the two disc-like areas and the two variable cross-section areas, and two injection ports symmetrically located in the middle of the mold, for a total of 6 outlets; Scheme 3: The injection channel is located at the middle of the length of the mold facade, and the outlets are located at two disc-like areas, two variable cross-section areas, and the symmetrical middle position on the other facade; Scheme 4: ... Figure 11 As shown, the injection channels are opened in the mold area along the edge line on the corresponding side beam, and are opened along the length direction. The outlets are evenly distributed on both sides of the mold center axis along the diagonal edge line, with a total of 15 outlets. Option 5: Injection channels and outlet channels are opened in the mold positions on the four edges of the side beam. The areas corresponding to the front upper edge and the rear lower edge are set as injection channels, and the areas corresponding to the front lower edge and the rear upper edge are set as outlet channels. Each outlet channel has 5 outlets.

[0119] The simulation results of five injection schemes were analyzed. Scheme 1 had a dry zone and an injection time of 165696 s; Scheme 2 had no dry zone and an injection time of 7040 s; Scheme 3 had a dry zone and an injection time of 7593 s; Scheme 4 (the simulation results of filling and pressure distribution are as follows) Figure 12 Scheme 4 (shown) has no dry area and an injection time of 6891s; Scheme 5 has a dry area and an injection time of 2202s; Scheme 4 is determined to be the optimal injection scheme.

[0120] S3. Adopt the optimal RTM perfusion protocol and use the P of S1. i* And the optimal molding method for liquid molding of thick fabrics;

[0121] According to the selected side beam injection scheme four, conduct actual RTM tests; prepare side beam fabric preforms, including three-dimensional weaving and laying unidirectional carbon fiber fabric; prepare side beam RTM molds, and perform mold cleaning, application of release agent, and airtightness checks; debug the RTM injection machine and control the ratio of epoxy resin and curing agent.

[0122] Perform RTM injection on the side beam: First, set the back pressure, then maintain vacuum assistance throughout the injection process. After the glue outlet is evenly dispensing glue and no air bubbles overflow, close the glue outlet and perform a pressure holding operation. After the injection is completed, adjust the curing temperature of the curing oven for curing. After natural cooling, demold and perform post-processing on the side beam.

[0123] The actual pouring time of the side beam was recorded as 7309s, which is within a reasonable error range compared to the simulated pouring time (approximately 6.07%). Furthermore, the side beam showed virtually no obvious defects after curing. The shortcomings of the experiment were analyzed, such as the inability to observe the actual flow front position and the potential for shear deformation in the fabric preform. To address these shortcomings, the process parameters were optimized and adjusted to further improve the stability of the process and the quality of the manufactured parts.

Claims

1. A method for optimizing fabric liquid forming process based on numerical simulation, characterized in that, The first step is to select the fabric preform and resin, and obtain the optimal fiber main permeability K through numerical simulation. x* Optimal fiber-assisted permeability K y* Optimal injection pressure P i* At the same time, obtain the best mold filling method; The second step is to design multiple RTM perfusion schemes and conduct simulations based on the results of the first step, and select the optimal RTM perfusion scheme based on the simulation results. The third step involves adopting the optimal RTM perfusion protocol and using the P from the first step. i* And the best filling method for liquid molding of thick fabrics.

2. The method for optimizing fabric liquid forming process based on numerical simulation according to claim 1, characterized in that, In the first step, K is obtained through numerical simulation. x* K y* The steps are as follows: (i) Obtain the main fiber permeability and secondary fiber permeability when the fabric preform is infused with dimethyl silicone oil through experiments; (ii) The injection time t1 when resin-injected fabric preform is obtained by simulation. The set values ​​of fiber main permeability and fiber auxiliary permeability during simulation are the same as those obtained in step (i). (iii) The injection time t2 when resin-injected fabric preforms are obtained through experiments. The process parameters during the experiment are the same as those during the simulation in step (ii). (iv) Determine whether |t2-t1| / t2≤10% is true. If so, use the fiber primary permeability and fiber secondary permeability obtained in step (i) as K respectively. x* K y* Otherwise, return to step (i).

3. The method for optimizing fabric liquid forming process based on numerical simulation according to claim 2, characterized in that, The specific process of step (i) is as follows: First, the experimental setup is constructed. A glass plate and a vacuum bag above it form a sealed cavity. A fabric preform is placed inside the sealed cavity, and a camera is fixed above the vacuum bag. The vacuum bag has an injection port directly above the fabric preform, connected to a dimethyl silicone oil storage tank via an injection pipe equipped with an injection valve. The vacuum bag also has an outlet located below the fabric preform, connected to a dimethyl silicone oil collection tank via an outlet pipe equipped with an outlet valve. The dimethyl silicone oil collection tank is connected to a vacuum pump via a vacuum pipe. Then, close the glue injection valve, open the glue dispensing valve and vacuum pump, evacuate to a vacuum state, and maintain pressure. Finally, the camera was activated and the injection valve was opened. Starting from a certain moment, the position r of the flow front of dimethyl silicone oil in the main permeability direction of the fiber was collected at fixed time intervals. x The position r of the flow front of dimethyl silicone oil in the direction of fiber-assisted permeability y T is obtained by calculation using the formula. x T y The formula is as follows: ； In the formula, i = x, y; Represents the radius of the injection port; Draw T i Follow The curve of change was obtained, and the slope was obtained through linear fitting. And after further calculation, When i=x, Represents the main fiber permeability, when i=y, The fiber-assisted permeability is represented by the following formula: ； In the formula, This represents the resin viscosity, measured in Pa·s. Represents porosity; This represents the pressure difference between the injection port inlet and the flow front, expressed in Pa.

4. The method for optimizing fabric liquid forming process based on numerical simulation according to claim 2, characterized in that, The specific process of step (ii) is as follows: After designing the RTM infusion scheme and setting the simulation parameters, the simulation was started. The simulation parameters include resin viscosity, fiber volume fraction, injection pressure, primary fiber permeability, secondary fiber permeability, layup direction, board thickness, and number of fabric preform layup layers.

5. The method for optimizing fabric liquid forming process based on numerical simulation according to claim 1, characterized in that, In the first step, the optimal local permeability K was also obtained through numerical simulation. l* Optimal edge penetration rate K e* And determine whether |log 10 K lx* -log 10 K x* |<1、|log 10 K ly* -log 10 K y* |<1、|log 10 K ex* -log 10 K x* |<1、|log 10 K ey* -log 10 K y* If |<1, proceed to the next step; Conversely, after selecting a new fabric preform, the first step is restarted. The new fabric preform is made of the same material as the original fabric preform, but the preforming process parameters are different. Edge permeability is the permeability caused by the edge effect.

6. The method for optimizing fabric liquid forming process based on numerical simulation according to claim 5, characterized in that, In the first step, K is obtained through numerical simulation. l* The steps are as follows: (a) Use HyperMesh to create a rectangular model, with a pre-defined local rectangular region in the middle, and divide the rectangular model into multiple unit meshes; (b) Import the rectangular model into PAM-RTM software, set the left side as the injection port and the right side as the outlet, set a main permeability A, a set of local permeability, an injection pressure, a porosity, and a mold filling method, and then perform numerical simulation. The main permeability A is the permeability of the area outside the local rectangular area in the rectangular model. (c) Analyze the numerical simulation results and take the local permeability where no envelope phenomenon appears in the numerical simulation results as K. l* Envelope phenomenon is the phenomenon of a fluid enveloping air.

7. The method for optimizing fabric liquid forming process based on numerical simulation according to claim 5, characterized in that, In the first step, K is obtained through numerical simulation. e* The steps are as follows: a) Use HyperMesh to create a rectangular model, with a pre-defined local rectangular region in the middle, and divide the rectangular model into multiple unit meshes; b) Import the rectangular model into PAM-RTM software, set the left side as the injection port and the right side as the outlet, set a main permeability B, a set of edge permeability, an injection pressure, and a porosity, set a mold filling method, and then perform numerical simulation. The main permeability B is the permeability of the area in the rectangular model other than the edge. c) Analyze the numerical simulation results, and take the edge penetration rate with the weakest edge effect in the numerical simulation results as K. e* .

8. The method for optimizing fabric liquid forming process based on numerical simulation according to claim 1, characterized in that, In the first step, P is obtained through numerical simulation. i* The steps are as follows: (A) Use HyperMesh to create a rectangular model, with a pre-defined local rectangular region in the middle, and divide the rectangular model into multiple unit meshes; (B) Import the rectangular model into the PAM-RTM software, set the left side as the injection port and the right side as the outlet, set an overall permeability, a set of injection pressures, a porosity, and a mold filling method, and then perform numerical simulation. (C) Analyze the numerical simulation results and take the injection pressure that does not exhibit an envelope phenomenon and has the shortest perfusion time under the given conditions as P. i* .

9. The method for optimizing fabric liquid forming process based on numerical simulation according to claim 1, characterized in that, The first step, obtaining the optimal mold filling method through numerical simulation, involves the following steps: A) Use HyperMesh to create a rectangular model, with a pre-defined local rectangular region in the middle, and divide the rectangular model into multiple unit meshes; B) Import the rectangular model into the PAM-RTM software, set the left side as the injection port and the right side as the outlet, set an overall permeability, an injection pressure, and a porosity, and set two filling methods. Then perform numerical simulation. The two filling methods are constant speed injection and constant pressure injection. C) Analyze the numerical simulation results and select the filling method that does not exhibit an envelope phenomenon and has the shortest injection time under the given conditions as the optimal filling method.

10. The method for optimizing fabric liquid forming process based on numerical simulation according to claim 1, characterized in that, The specific process of the second step is as follows: First, a three-dimensional model of the composite material part was created using SolidWorks. Then, the 3D model of the composite material part is imported into HyperMesh software for triangular mesh generation; Next, the 3D model of the composite material part was imported into PAM-RTM software. Boundary conditions, simulation parameters, and filling method were set, and simulation was performed. The simulation parameters included resin viscosity, fiber volume fraction, injection speed, primary fiber permeability, secondary fiber permeability, and injection pressure. The primary fiber permeability was the same as K... x* Fiber-assisted permeability is the same as K y* Injection pressure is the same as P i* The filling method is the same as the optimal filling method in the first step. Finally, the simulation results were analyzed, and the RTM perfusion scheme that did not show dry spots or bubble defects and had the shortest perfusion time under the conditions was selected as the optimal RTM perfusion scheme.

Images