CN101089754A - Optimizing method of pressure system in hot-pressing process of resin-based composites equal-thick laminates - Google Patents

Abstract

A method for optimizing pressure system of hot-pressing process for preparing resin based composite material and equal thick laminated board includes applying genetic algorithm and utilizing relationship G1 of resin flow to fiber tightness and relationship G2 of fiber born pressure to fiber volume fraction in resin based composite material hot-press forming analog unit to carry out optimization on pressure size and pressurizing opportunity based on temperature system for obtaining satisfied volume friction and distribution of object fiber.

Description

Optimizing method of pressure system in hot-pressing process of resin-based composites equal-thick laminates

Technical field:

The present invention relates to an optimization method of hot pressing process, specifically, it refers to an optimization method suitable for the pressure system of thick laminated board hot pressing process such as resin-based composite materials, which is achieved by computer, resin-based composite material hot pressing The forming simulation unit, process parameter optimization target and method unit are realized. The method of the present invention can quickly determine the optimized pressure system that meets the target requirements under the condition of being separated from the actual production line, and the optimized process system can be combined with the hot-press forming numerical control system. Guide the industrial operation of hot-press forming of thick laminates such as resin-based composite materials.

Background technique:

Thermocompression molding has always been the most important process for the production of advanced resin matrix composite parts with main load-bearing structures in the aerospace field. However, the high manufacturing cost seriously restricts its development and application. At present, the main reasons for the high manufacturing cost of advanced resin matrix composite components are as follows: (1) The development mode is backward, the development cycle is long, and the efficiency is low. At present, the development of composite material structures is based on experimentation. First, the test of the sample, and then the scale test. After repeated several times to determine the manufacturing process, the development cycle is long; the optimization of the process parameters of the composite material structure is based on experience and experiments. High cost and poor science. (2) Manufacturing specifications are not universal. A relatively reasonable manufacturing process specification for a certain composite material component formed from a large number of experiments is only applicable to this specific situation. Once the structural form of the composite material component is changed, a large number of new tests are required, which is costly and time-consuming. (3) The controllability of the quality of the parts is poor, resulting in scattered performance of the composite material, low allowable value of the material, and low qualification rate of the component. The main method to solve the high manufacturing cost of advanced resin-based composite materials: carry out numerical simulation and optimization of the manufacturing process, and develop new high-efficiency and low-cost manufacturing technologies. Its core is to realize the transformation of the development mode and the simulation and optimization of the manufacturing process on the basis of clarifying the thermochemical and thermophysical changes in the manufacturing process.

In thermocompression molding, resin-based composites undergo a series of physical, chemical and chemical-physical coupling changes under the action of heat and pressure, including heat conduction, curing exotherm, resin flow, fiber compaction, etc. On the one hand, they determine The performance of the final part reflects the rationality of the process parameters; on the other hand, it is affected by the characteristics of the raw material and the shape of the part. The typical process system of thermocompression molding of resin-based composite materials (see Figure 1C) includes the relationship between temperature and time (also known as the temperature system), the relationship between pressure and time (also known as the pressure system, that is, the size of the pressure and the timing of pressurization) . The molding pressure determines the driving force of the resin flow process. At the same time, the pressure and the timing of pressurization are the main factors affecting the fiber content, distribution uniformity and thickness of the final laminate. The mathematical model of the molding process and the optimization method of the process parameters are established. Computer and numerical technology can predict the molding quality of parts, evaluate the molding process, optimize process parameters, and quickly formulate the best process system to meet the target requirements when they are out of the production line. one of the scientific approaches.

Invention content:

The purpose of the present invention is to propose a method for optimizing the pressure system of the hot-pressing process of equal-thickness laminates, which is a process optimization method suitable for improving the quality of hot-pressing forming of resin-based composite material parts. This method combines the relationship between resin flow and fiber compaction G ₁ and the relationship between fiber bearing pressure and fiber volume fraction G ₂ in the thermal compression molding simulation unit of resin matrix composites through genetic algorithm. Based on the temperature system, the pressure and The timing of pressurization is optimized to obtain a pressure system that meets the target fiber volume fraction and its uniform distribution, the highest efficiency and engineering feasibility. Combining the optimized process parameters obtained by the present invention with the numerical control system in hot press forming can guide the industrial operation of hot press forming of thick laminates such as resin-based composite materials, shorten the development cycle, reduce manufacturing costs, and improve product quality.

The present invention is a digital method for optimizing the pressure regime of the hot-pressing process of resin-based composite materials and other thick laminates by using computer simulation. The PCO (Pressure Cycle Optimization) method includes a workpiece configuration and grid division unit 1, Molding process parameter setting unit 2, material property database unit 3, resin-based composite material thermoforming simulation unit 4, product quality prediction unit 5, process parameter optimization target and method unit 6, and process parameter optimization unit 7. The laminate quality prediction unit 5, under the condition that the workpiece configuration and the initial setting parameter _F1 are known, picks up the process parameter F2 in the molding process parameter setting unit ₂ and the material characteristic database The material parameter F ₂ in unit 3, and the initial setting parameter F ₁ , process parameter F ₂ and material parameter F ₃ are processed in the resin-based composite material thermocompression molding simulation unit 4 to obtain the parameters that determine the quality of the laminate, Stored in the workpiece quality prediction unit 5. The optimization process parameter unit 7 picks up the initial setting parameter F ₁ of the part in the part configuration and meshing unit 1, picks up the process parameter F 2 in the molding process parameter setting unit ₂ and the The material parameter F ₃ in the material characteristic database unit 3 is processed in the resin-based composite material thermoforming simulation unit 4 to obtain the parameters that determine the quality of the laminate, and is combined with the process parameter optimization target and method in the unit 6 The optimized target parameter _F4 is compared, and the molding process parameters are adjusted in combination with the corresponding process parameter optimization method to obtain the process system parameters that meet the optimized target parameter _F4 , output and store in the optimized process parameter unit 7. Using the PCO method of the present invention, on the basis of the temperature system, the pressure and timing of pressurization can be optimized to obtain a pressure system that satisfies the target fiber volume fraction and uniform distribution, has the highest efficiency and is engineering feasible.

The pressure system optimization (PCO) method of the hot-pressing process of thick laminates such as resin-based composite materials of the present invention has the following advantages:

1. Part configuration and mesh division unit 1. Molding process parameter setting unit 2 sets parameters through a visual interface, and introduces it into the simulation unit 4 of resin-based composite material thermocompression molding. Its operation is simple and the simulation efficiency is high. High, strong real-time performance;

2. Using a relatively mature material property database 3, the accuracy of the parameters related to resin and fiber properties required for simulation is guaranteed, so the simulation accuracy is high;

3. Resin matrix composite material thermoforming simulation unit 4 is based on the combination of mathematical models and computer technology. It can simulate the process of resin flow and fiber compaction in the thermocompression molding of resin matrix composite materials with equal thickness laminates, and can predict the thickness and density of laminates. Fiber volume fraction, to avoid molding problems such as uneven distribution of fibers in the laminate in industrial production;

4. Process parameter optimization target and method unit 6, resin-based composite material thermoforming simulation unit 4 combined with part quality prediction unit 5, the genetic algorithm can be used to optimize the pressure system when it is out of the actual production line state, optimize the process parameters 7 and The combination of thermoforming numerical control system can guide the industrialized operation of thermocompression forming of thick laminates such as resin-based composite materials, shorten the development cycle, reduce manufacturing costs, and improve product quality.

Description of drawings:

Figure 1 is a schematic block diagram of the simulation system for thermocompression molding of resin-based composite materials.

Fig. 1A is a schematic diagram of the setting interface of the unit for setting the initial conditions of the equal-thickness laminate of the present invention.

FIG. 1B is a schematic diagram of the setting interface of the molding process parameter setting unit of the present invention.

Fig. 1C is a schematic diagram of a typical process system for thermocompression molding of the resin-based composite material of the present invention.

FIG. 1D is a schematic diagram of an interface for setting optimization goals in the present invention.

Fig. 2 is a structural schematic diagram of the laminate model of the present invention.

Figure 3 is a structural schematic diagram of the T700SC carbon fiber/epoxy 5228 resin laminate.

Fig. 3A is a schematic diagram of the network division of the T700SC carbon fiber/epoxy 5228 resin laminate.

Figure 3B is the computer interface of the T700SC carbon fiber/epoxy 5228 resin laminate.

Figure 4 shows the fiber volume fraction distribution in the thickness direction of the laminate at t=3000s.

Figure 5 shows the distribution of fiber bearing pressure in the thickness direction of the laminate at t=3000s.

Detailed ways:

The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

The present invention is a digital method for optimizing the pressure system of the hot-pressing process of resin-based composite materials and other thick laminates by using computer simulation. The pressure system optimization method is referred to as the PCO (Pressure Cycle Optimization) method for short. Shape and grid subdivision unit 1, molding process parameter setting unit 2, material property database unit 3, resin matrix composite material thermoforming simulation unit 4, part quality prediction unit 5, process parameter optimization target and method unit 6, optimization Process parameter unit 7 (see Figure 1).

The laminate quality prediction unit 5, under the condition that the workpiece configuration and the initial setting parameter _F1 are known, picks up the process parameter F2 in the molding process parameter setting unit ₂ and the material characteristic database The material parameter F ₂ in unit 3, and the initial setting parameter F ₁ , process parameter F ₂ and material parameter F ₃ are processed in the resin-based composite material thermocompression molding simulation unit 4 to obtain the parameters that determine the quality of the laminate, Stored in the workpiece quality prediction unit 5.

The optimization process parameter unit 7 picks up the initial setting parameter F ₁ of the part in the part configuration and meshing unit 1, picks up the process parameter F 2 in the molding process parameter setting unit ₂ and the The material parameter F ₃ in the material characteristic database unit 3 is processed in the resin-based composite material thermoforming simulation unit 4 to obtain the parameters that determine the quality of the laminate, and is combined with the process parameter optimization target and method in the unit 6 The optimized target parameter _F4 is compared, and the molding process parameters are adjusted in combination with the corresponding process parameter optimization method to obtain the process system parameters that meet the optimized target parameter _F4 , output and store in the optimized process parameter unit 7.

As shown in Fig. 1A, in the present invention, the initial configuration parameters _F1 of the part in the part configuration and grid unit 1 include the glue absorption method, the initial fiber volume fraction of the prepreg, the number of plies of the prepreg, The initial thickness of the laminate, these parameters can be entered through the interface.

Referring to Fig. 1B, in the present invention, the process parameter _F2 in the molding process parameter setting unit includes temperature-time relationship, external positive pressure, vacuum degree, and pressurization timing, and these parameters can be entered through the interface. It can also be obtained through mathematical model analysis and stored in the computer, and the corresponding files can be extracted when waiting for use.

Referring to FIG. 1C , a schematic diagram of a typical process system for thermocompression molding of resin-based composite materials, including the relationship between temperature and time (also known as temperature system), and the relationship between pressure and time (also known as pressure system). In the figure, the abscissa t _c refers to the process time (unit: s), 0 ~ t _c1 represents the first stage of process time, t _c1 ~ t _c2 represents the second stage of process time, t _c2 ~ t _c3 represents the third stage of process Time, t _c3 ~ t _c4 represent the fourth process time; the left ordinate T refers to the hot pressing forming temperature within the effective process time, T ₁ represents the initial temperature in the first process time, T ₁ takes a value of 18 ~ 33°C; T ₂ means the temperature in the second stage of process time, and the value of T ₂ is 70-160°C; T ₃ means the temperature in the fourth stage of process time, and the value of T ₃ is 100-210°C. Naturally cool to room temperature after the fourth process time. The abscissa _tcjia represents the moment of pressurization (also known as the timing of pressurization), based on the zero point of the process time; the ordinate on the right refers to the hot pressing pressure within the effective process time, P ₁ represents the degree of vacuum, and the value of P ₁ It is 0.0～0.1MPa; P ₂ represents the ambient atmospheric pressure of the laminate before t _cjia , usually the value of P ₂ is 0.1MPa; P ₂ represents the external force (referring to the gauge pressure) applied after t _cjia , and the value of P ₃ is 0.1 ~2.0 MPa.

In the present invention, the material parameter F ₃ in the material characteristic database unit 3 includes resin type, fiber type, fabric type, layup mode; Described resin type is epoxy resin, phenolic resin, cyanate ester resin, double horse Resin, etc.; the fiber type is glass fiber, carbon fiber, aramid fiber, basalt fiber, etc.; the fabric type is unidirectional prepreg, unidirectional fabric, plain weave, twill weave, satin weave, etc.; Layer methods include unidirectional layup, orthogonal layup, quasi-isotropic layup, etc.; different resin types, the viscosity model and the value of the parameters involved in the model are different, which can be obtained by rheometer test; fiber type, The fabric type and layering method affect the permeability of the fiber layer and the compression characteristics of the fiber layer, which are measured by a test device designed by the laboratory; the material property database includes the viscosity data associated with the resin type, the fiber type , fabric type, and layering method related permeability and compression characteristics data are obtained by the School of Materials, Beihang University after years of experimental research, testing, and measurement of resin-based composite material systems. They are accurate and reliable. It is used as one of the data sources for process analysis by Beijing Polymer Matrix Composite Materials Technology Laboratory.

In the present invention, the optimization of the pressure regime is to optimize the process pressure P _c and pressurization timing respectively under the condition of setting the temperature regime in the hot press forming. The pressure system includes the process pressure P _c and pressurization timing, the temperature system is related to the resin, the process pressure P _c refers to the sum of the applied pressure P _a and the degree of vacuum P _b , and the applied positive pressure P _a refers to the heat Pressure tank pressure or hot press mechanical pressure, the vacuum degree _Pb refers to the true degree in the prepreg packaging system, and the optimization of the process pressure _Pc is carried out by genetic algorithm under the conditions of temperature system and pressurization timing Get the process pressure value required to meet the target. The optimization of the pressurization timing is to obtain the pressurization timing required to meet the target through the genetic algorithm under the condition that the temperature regime and the process pressure value are determined.

In the present invention, the process parameter optimization target and method unit 6 is optimized by genetic algorithm; referring to FIG. 1D, the optimization target parameter _F4 in the process parameter optimization target and method unit 6 is the target average fiber volume fraction.

In the present invention, the minimum requirement of the known computer is more than 1.8G of CPU PIV, more than 256M of memory, and more than 20G of hard disk. Borrowing the inherent computing characteristics of the computer, the digital simulation method of the hot pressing molding process of resin-based composite materials is easy to operate and the simulation results are accurate. Computer simulation can effectively shorten the development cycle, reduce development costs and improve the quality of parts.

In the present invention, under the condition that the temperature regime and pressurization timing are determined, the optimization steps of the process pressure _Pc include:

Step 1: Under the condition of process pressure P _c (0.1<P _c <2.0MPa), pick the fitness function Fi;

表示平均纤维体积分数，通过树脂流动与纤维密实关系式G₁和纤维承载压力与纤维体积分数关系式G₂获得，

表示设定的目标平均纤维体积分数；The fitness function $\mathrm{Fi}=\min (\stackrel{\&OverBar;}{V_f}-\stackrel{\&OverBar;}{V_{\mathrm{fg}}}),$ In the formula,

Indicates the average fiber volume fraction, which is obtained by the relationship between resin flow and fiber compaction G ₁ and the relationship between fiber bearing pressure and fiber volume fraction G ₂ ,

Indicates the set target average fiber volume fraction;

The second step: within the determined process pressure P _c range, pick the feasible solution group;

Step 3: Analyzing the average fiber volume fraction of laminates corresponding to each feasible solution in the feasible solution group in the resin-based composite material thermocompression molding simulation unit 4

The analysis of the average fiber volume fraction of the laminate in the resin-based composite material thermocompression molding simulation unit 4 The steps are:

Step 1: Extract the initial setting parameter _F1 of the part in the part configuration and meshing unit 1 (see Fig. 1A); in the present invention, according to the initial setting parameter _F1 of the part, the middle plate For the initial thickness, computer technology is used to carry out a three-dimensional model of equal-thickness laminates (see Figure 2), and the established three-dimensional laminate graphics are meshed to obtain a laminate model with nodes (see Figure 3A). shown), and save the node laminate model as a text format file (*.TXT); the text format file can facilitate the extraction of parameters required for future simulations. In Fig. 2, the x-axis represents the length direction of the laminate model, and the z-axis represents the thickness direction of the laminate model.

Step 2: Extract the process parameter F ₂ in the molding process parameter setting unit 2, wherein the value of the process pressure P _c is obtained from the group of feasible solutions in the second step;

Step 3: Extract material parameter F ₃ in the material property database unit 3;

Step 4: Obtain the relational expression G ₁ between resin flow and fiber compaction and the relational expression G ₂ between fiber bearing pressure and fiber volume fraction in the resin-based composite material thermocompression molding simulation unit 4, and use finite difference and Newton iterative methods to analyze Obtain the fiber bearing pressure P _f and fiber volume fraction V _f in the laminate, and analyze the average fiber volume fraction of the laminate according to the fiber volume fraction at different positions in the laminate

The relationship between resin flow and fiber compaction

${\mathrm{<figure-callout\; id="1"\; label="G"\; filenames="A20071011773000191.png,A20071011773000211.png"\; state="\{\{state\}\}">G</figure-callout>}}_1\&DoubleRightArrow;\frac{\&PartialD;P_f}{\&PartialD;t}=\frac{V_f^2}{{\mathrm{<figure-callout\; id="0"\; label="V"\; filenames="A20071011773000201.png"\; state="\{\{state\}\}">V</figure-callout>}}_0^2}\&Center\; Dot;\frac{d{P}_f}{d{V}_f}\&Center\; Dot;\frac{\&PartialD;}{\&PartialD;z}\left(\frac{V_f\&CenterDot;S_{\mathrm{zz}}}{\mathrm{\&mu;}}\frac{\&PartialD;}{\&PartialD;z}(P_f-P_c)\right),$ In the formula, P _f represents the fiber bearing pressure, P _c represents the process pressure, t represents the simulation time, V _f represents the fiber volume fraction, V ₀ represents the initial fiber volume fraction of the prepreg, z represents the thickness direction of the laminate, and S _zz represents the ply Permeability in the thickness direction of the plate, μ represents the viscosity of the resin;

The fiber bearing pressure and fiber volume fraction relational expression

${\mathrm{<figure-callout\; id="2"\; label="G"\; filenames="A20071011773000191.png,A20071011773000221.png"\; state="\{\{state\}\}">G</figure-callout>}}_2\&DoubleRightArrow;P_f=\frac{3\mathrm{\&pi;E}}{{\mathrm{\&beta;}}^4}\&CenterDot;\frac{\sqrt{V_f/V_0}-1}{{\left(\sqrt{V_a/V_f}\right)}^4},$ In the formula, P _f represents the fiber bearing pressure, E represents the flexural modulus of the fiber, β represents the compressive characteristic coefficient of the fiber layer, V _f represents the fiber volume fraction, V ₀ represents the initial fiber volume fraction of the prepreg, and V _a represents the densely packed fibers Volume fraction.

将目标平均纤维体积分数

和第三步解析得到的平均纤维体积分数 代入适应度函数Fi，解析适应度值，按照适应度值的大小来评价可行解的好坏；以优胜劣汰的机制，将适应度差的染色体淘汰掉，对幸存的染色体根据其适应度值的好坏，按概率随机选择，进行繁殖，形成新的群体；通过杂交和变异的操作，产生子代，杂交是随机选择两条染色体(双亲)，将某一点或多点的基因互换而产生两个新个体；变异是基因中的某一点或多点发生突变；Step 4: Extract the target average fiber volume fraction of process optimization in the process optimization target and method unit 6

target mean fiber volume fraction

and the average fiber volume fraction obtained from the analysis in the third step Substitute into the fitness function Fi, analyze the fitness value, and evaluate the quality of the feasible solution according to the size of the fitness value; use the mechanism of survival of the fittest to eliminate the chromosomes with poor fitness, and the surviving chromosomes according to their fitness value Bad, randomly select according to the probability, carry out reproduction, and form a new group; through the operation of hybridization and mutation, produce offspring, hybridization is to randomly select two chromosomes (parents), exchange genes at one or more points to produce two a new individual; mutation is a mutation at one or more points in the gene;

Step 5: Repeat the operations of Step 3 and Step 4 on the progeny population to carry out a new round of genetic evolution until the fitness value satisfies the stopping rule, and the iteration stops, that is, the optimal solution is found.

In the present invention, under the condition that the temperature regime and process pressure are determined, the optimization steps of pressurization timing include:

Step 1: Under the condition of pressurization timing t _cjia (0<t _cjia <120min), pick the fitness function Ei;

表示平均纤维体积分数，通过树脂流动与纤维密实关系式G₁和纤维承载压力与纤维体积分数关系式G₂获得，

表示设定的目标平均纤维体积分数；The fitness function $\mathrm{Ei}=\min (\stackrel{\&OverBar;}{V_f}-\stackrel{\&OverBar;}{V_{\mathrm{fg}}}),$ In the formula,

Indicates the average fiber volume fraction, which is obtained by the relationship between resin flow and fiber compaction G ₁ and the relationship between fiber bearing pressure and fiber volume fraction G ₂ ,

Indicates the set target average fiber volume fraction;

Step 2: Pick the group of feasible solutions within the determined scope and pressurization time;

The analysis methods of the third step, the fourth step and the fifth step are the same as the optimization method of the process pressure. In the present invention, the initial thickness of the laminate in the initial setting parameter _F1 of the workpiece $h_{}$${}_0=\frac{{\mathrm{Na}}_f}{{\mathrm{\&rho;}}_{\mathrm{<figure-callout\; id="0"\; label="f\; V"\; filenames="A20071011773000201.png"\; state="\{\{state\}\}">f</figure-callout>}}{V}_{}{}_0},$ In the formula, N represents the number of prepreg plies, _af represents the surface density of prepreg fibers, _ρf represents the bulk density of fibers, and _V0 represents the initial fiber volume fraction of prepregs.

In the present invention, the analysis in the simulation unit 4 of resin-based composite material thermocompression molding is as follows:

(1) The relationship between resin flow and fiber compaction G ₁ :

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="G"\; filenames="A20071011773000191.png,A20071011773000211.png"\; state="\{\{state\}\}">G</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&DoubleRightArrow;</span>\frac{<span\; class="notranslate">\; \&PartialD;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}{<span\; class="notranslate">\; \&PartialD;</span>\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="0"\; label="V"\; filenames="A20071011773000201.png"\; state="\{\{state\}\}">V</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; \&Center\; Dot;</span>\frac{\mathrm{<span\; class="notranslate">\; d</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}{\mathrm{<span\; class="notranslate">\; d</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}<span\; class="notranslate">\; \&CenterDot;</span>\frac{<span\; class="notranslate">\; \&PartialD;</span>}{<span\; class="notranslate">\; \&PartialD;</span>\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; zz</span>}}}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}\frac{<span\; class="notranslate">\; \&PartialD;</span>}{<span\; class="notranslate">\; \&PartialD;</span>\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

In the formula, P _f represents the fiber bearing pressure, P _c represents the process pressure, t represents the simulation time, V _f represents the fiber volume fraction, V ₀ represents the initial fiber volume fraction of the prepreg, z represents the thickness direction of the laminate, and S _zz represents the ply Permeability in the thickness direction of the plate, μ represents the viscosity of the resin;

In formula (1), the initial condition of the fiber bearing pressure is: P _f =0 at any position in the laminate.

The boundary conditions set in formula (1) are:

On the border where the suction material is placed:

P _f =P _c (2)

In the formula, P _c represents the process pressure, and it satisfies P _c = _Pa + P _b , that is, the process pressure is the sum of the applied positive pressure P _a and the degree of vacuum P _b ;

On the boundary where no adhesive material is laid or the boundary of the center plane of the upper and lower surface symmetrical adhesive layer structure:

$\frac{\&PartialD;V_f}{\&PartialD;z}=0,$ $\frac{\&PartialD;P_f}{\&PartialD;z}=0,$ $\frac{{\&PartialD;}^2{P}_f}{\&PartialD;{\mathrm{<figure-callout\; id="2"\; label="z"\; filenames="A20071011773000191.png,A20071011773000221.png"\; state="\{\{state\}\}">z</figure-callout>}}^2}\&NotEqual;0$ (3)

In the formula, P _f represents the fiber bearing pressure, V _f represents the fiber volume fraction, and z represents the thickness direction of the laminate. According to formula (1) and the initial and boundary conditions, the finite difference method is used to analyze the fiber bearing pressure P _f .

(2) Fiber bearing pressure and fiber volume fraction satisfy the relation G ₂ :

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="2"\; label="G"\; filenames="A20071011773000191.png,A20071011773000221.png"\; state="\{\{state\}\}">G</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&DoubleRightArrow;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 3</span>}\mathrm{<span\; class="notranslate">\; \&pi;E</span>}}{{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}^{\mathrm{<span\; class="notranslate">\; 4</span>}}}<span\; class="notranslate">\; \&Center\; Dot;</span>\frac{\sqrt{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{{<span\; class="notranslate">\; (</span>\sqrt{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 4</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

In the formula, P _f represents the fiber bearing pressure, V _f represents the fiber volume fraction, E represents the flexural modulus of the fiber, and β represents the characteristic coefficient of the deformation ability of the fiber layer under pressure during the thermocompression forming process, referred to as the fiber layer Compression characteristic coefficient, V ₀ represents the initial fiber volume fraction of the prepreg, and V _a represents the fiber volume fraction under the condition of fiber hexagonal dense packing, which is a constant, referred to as densely packed fiber volume fraction.

(3) In order to efficiently and quickly calculate the fiber volume fraction V _f from the fiber bearing pressure P _f , firstly, the number of iterations is 0, that is $V_f^{\left(0\right)}={\mathrm{<figure-callout\; id="0"\; label="V"\; filenames="A20071011773000201.png"\; state="\{\{state\}\}">V</figure-callout>}}_0$ start, then the next step in the iterative calculation $V_f^{(m+1)}=\frac{1}{2}(V_f^{\left(m\right)}+V_a)$ until $P_f\left(V_f^{(m+1)}\right)>P$ End; then, use the Newton iteration method to analyze the fiber volume fraction V _f , and the Newton iteration model G ₃ is as follows:

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="3"\; label="G"\; filenames="A20071011773000191.png"\; state="\{\{state\}\}">G</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; \&DoubleRightArrow;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; P</span>}}{\frac{\mathrm{<span\; class="notranslate">\; d</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}{\mathrm{<span\; class="notranslate">\; d</span>}{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}{<span\; class="notranslate">\; |</span>}_{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

In the formula, m represents the number of iterations, V _f ^m represents the fiber volume fraction of iteration m times, P _f represents the fiber bearing pressure, and P represents the fiber bearing pressure at the current moment;

(4) in the present invention, the relationship between resin load-bearing pressure _Pf and fiber load-bearing pressure _Pf is:

P _r =P _c -P _f (6)

In the formula, P _r represents the bearing pressure of the resin, P _f represents the bearing pressure of the fiber, and P _c represents the process pressure; the resin bearing pressure P _r is obtained by analyzing the formula (6).

(5) the relationship between the middle laminate thickness h of the present invention and the fiber volume fraction V _f is:

$\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="0"\; label="N\; V"\; filenames="A20071011773000201.png"\; state="\{\{state\}\}">N</figure-callout></span><figure-callout\; id="0"\; label="N\; V"\; filenames="A20071011773000201.png"\; state="\{\{state\}\}">\; </figure-callout>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\frac{{\mathrm{<span\; class="notranslate">\; </span>}}_{}}{}\frac{{\mathrm{<span\; class="notranslate">V</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}}{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

In the formula, h represents the thickness of the laminate, V ₀ represents the initial fiber volume fraction of the prepreg, D _z represents the initial thickness of the prepreg sheet, N represents the number of prepreg layup layers, i represents the i-th fiber layer, V _f ⁱ Indicates the fiber volume fraction of the i-th fiber layer; through the analysis of formula (7), the thickness h of the laminate can be obtained.

(6) in the present invention, laminate average fiber volume fraction The relationship with fiber volume fraction V _f is:

$\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

表示平均纤维体积分数；通过式(8)得到层板平均纤维体积分数 In the formula, N represents the number of prepreg plies, i represents the i-th fiber layer, V _f ⁱ represents the fiber volume fraction of the i-th fiber layer,

Indicates the average fiber volume fraction; the average fiber volume fraction of the laminate is obtained by formula (8)

Example 1: The optimization of the process pressure of 30-layer T700SC carbon fiber/epoxy 5228 resin and other thick laminates in one direction.

The structure of Example 1 is shown in Figure 3. In the mold 1, there are bottom-absorbing paper 31, A-layer fiber 101, A-layer resin 201, B-layer fiber 102, B-layer resin 202, and C-layer fiber in order from bottom to top in the mold 1. 103 , C layer resin 203 , D layer fiber 104 , D layer resin 204 , F layer fiber 130 , F layer resin 230 , and blotter paper 32 . This is a common layup system of "prepreg layup layers" (see Figure 1A), but the T700SC carbon fiber/epoxy 5228 resin material itself is a combination of fibers and resins. A schematic diagram of the structure is shown.

表示平均纤维体积分数，根据树脂流动与纤维密实关系式G₁和纤维承载压力与纤维体积分数关系式G₂获得，

表示设定的目标平均纤维体积分数；Step 1: Under the condition of process pressure P _c (0.1<P _c <2.0MPa), pick the fitness function $\mathrm{Fi}=\min (\stackrel{\&OverBar;}{V_f}-\stackrel{\&OverBar;}{V_{\mathrm{fg}}}),$ In the formula,

Indicates the average fiber volume fraction, obtained from the relationship between resin flow and fiber compaction G ₁ and the relationship between fiber bearing pressure and fiber volume fraction G ₂ ,

Indicates the set target average fiber volume fraction;

The second step: within the determined process pressure P _c range, select 10 process pressure values to form a feasible solution group;

Step 3: Analyzing the average fiber volume fraction of laminates corresponding to each feasible solution in the feasible solution group in the resin-based composite material thermocompression molding simulation unit 4

的步骤有：The analysis of the average fiber volume fraction of the laminate in the resin-based composite material thermocompression molding simulation unit 4

The steps are:

Step 1: Pick up the initial setting parameter F ₁ of the part in the part configuration (see Figure 3) and mesh division unit 1. The initial fiber volume fraction V ₀ of the material is 55%, the number of prepreg layers is 30, and the initial thickness h ₀ of the laminate is 4.26mm. Create a three-dimensional laminate graphic in the Patran software, then carry out 30 × 10 grid subdivision processing on the three-dimensional laminate graphic (see Figure 3A), and obtain a laminate model with nodes (there will be Color three-dimensional picture shows), and described node laminate model is saved as a text format file, namely MLCB.TXT;

Step 2: Extract the process parameter F2 in the molding process parameter setting unit _2. The temperature-time relationship is to rise from room temperature 30°C to 130°C at a heating rate of 2°C/min, and keep the temperature at 130°C for 25 minutes. The heating rate was 2°C/min from 130°C to 180°C, and finally kept at 180°C for 60 minutes. According to the temperature-time relationship, the time and temperature data are generated by analysis on the computer and saved in the data.txt file; the vacuum degree is 0.0MPa, the pressurization time is 75min, and the process pressure is determined from the feasible solution group determined in the third step get. On the computer visualization interface (see FIG. 3B ), set the total simulation time to 3000s, and the time step Δt=10s.

Step 3: Extract material parameter F ₃ from the material property database unit, resin type: epoxy 5228, fiber type: T700SC carbon fiber, fabric type: unidirectional prepreg, layup method: unidirectional layup. According to the material parameters, the parameters related to the fiber and resin required for calculation can be extracted from the material property database, including parameters in the viscosity model, fiber permeability model, and fiber compression model.

Step 4: In the resin-based composite material thermocompression molding simulation unit 4, the relationship between resin flow and fiber compaction G ₁ and the relationship between fiber bearing pressure and fiber volume fraction G ₂ are obtained, and the finite difference and Newton iterative methods can be used. The fiber bearing pressure P _f and fiber volume fraction V _f at different positions in the laminate are obtained by analysis, and the average fiber volume fraction of the laminate is obtained by analysis according to the fiber volume fraction V _f at different positions in the laminate

分别为：According to the value of the process pressure in the feasible solution group, the average fiber volume fraction of the laminate is obtained analytically through the APCPS unit

They are:

和第三步解析得到的平均纤维体积分数

代入适应度函数Fi，解析适应度值，按适应度值大小来评价可行解好坏；Step 4: Extract the target average fiber volume fraction for process optimization in Process Optimization Objectives and Methods Unit 6 $\stackrel{\&OverBar;}{V_{\mathrm{fg}}}=62\%;$ and target mean fiber volume fraction

and the average fiber volume fraction obtained from the analysis in the third step

Substitute into the fitness function Fi, analyze the fitness value, and evaluate the quality of the feasible solution according to the fitness value;

According to the process pressure value in the feasible solution group, the fitness values obtained by analysis are:

With the mechanism of survival of the fittest, the chromosomes with poor fitness are eliminated, and the surviving chromosomes are randomly selected according to their fitness value according to the probability, and reproduced to form a new group; through hybridization and mutation operations, offspring are produced. Hybridization is the random selection of two chromosomes (parents), and the exchange of genes at one or more points to produce two new individuals; mutation is the mutation of one or more points in the gene;

Step 5: Repeat the operations of Step 3 and Step 4 on the progeny population to carry out a new round of genetic evolution until the fitness value is the smallest, stop the iteration, and output the optimal solution.

Through the optimization method of the process pressure, it can be analyzed and obtained to meet the target fiber volume fraction $\stackrel{\&OverBar;}{V_{\mathrm{fg}}}=62\%$ The process pressure is 0.415MPa. Under the initial conditions of the laminate and the temperature regime, the target fiber volume fraction of the laminate is met $\stackrel{\&OverBar;}{V_{\mathrm{fg}}}=62\%$ The optimal pressure system of the pressurization timing is 75min, and the process pressure is 0.415MPa.

为62.0％，优化得到的压力制度可以满足目标要求。Through the above simulation calculations, it is analyzed that under the condition of the optimized pressure system, the simulation time t=3000s, the fiber volume fraction distribution of the laminate thickness is shown in Figure 4. In the figure, the fiber distribution in the thickness direction is uneven, and the closer to the upper glue paper 32 and the position of 31 of the lower blotting paper, the larger the fiber volume fraction, the smaller the closer to the center of the laminate; the simulation time t=3000s, the fiber bearing pressure distribution of the thickness of the laminate is shown in Figure 5, close to the blotting paper The fiber bearing pressure at the position is the largest, and the closer to the center of the laminate, the smaller the fiber bearing pressure; at t=3000s, the thickness h of the laminate is 3.85mm, and the average fiber volume fraction

It is 62.0%, and the optimized pressure system can meet the target requirements.

The present invention is a digital method for optimizing the pressure system of the hot-pressing process of resin-based composite materials and other thick laminates by using computer simulation. To deal with problems such as poor controllability of parts quality and low pass rate of parts, the genetic algorithm and the fitness function Fi under the condition of pressure P _c are determined, and the relationship between resin flow and fiber density G ₁ and the relationship between fiber bearing pressure and fiber volume fraction are used. Formula G ₂ obtains the parameters of the pressure system that meet the target requirements, and realizes the optimization of the pressure system for the hot-pressing process of equal-thickness laminates under the condition of breaking away from the actual production line. The optimized process system can be combined with the resin-based composite material thermoforming numerical control system to guide the industrial production of thermocompression forming, ensure the molding quality of composite material parts, and improve production efficiency.

In the present invention, the physical significance represented by reference symbols is shown in the following table:

Claims (5)

1, a kind of digitizing solution that utilizes computer optimization polymer matrix composites equal thick laminate heat pressing process pressure process system, this method includes characteristic material data library unit (3), it is characterized in that also including product configuration and mesh generation unit (1), molding technique parameter unit (2), resin base composite material hot-pressed formation analogue unit (4), parts quality predicting unit (5), process parameter optimizing target and method unit (6), optimization technological parameter unit (7) are set;

Described laminate prediction of quality unit (5) is at known described product configuration and initial setting up parameter F ₁Condition under, by picking up described molding technique parameter experimental parameter F in the unit (2) is set ₂With the material parameter F in the described characteristic material data library unit (3) ₃, and with initial setting up parameter F ₁, experimental parameter F ₂With material parameter F ₃In described resin base composite material hot-pressed formation analogue unit (4), handle, obtain the parameter of decision laminate quality, and be stored in the parts quality predicting unit (5).

Described optimization technological parameter unit (7) is at known described product configuration and initial setting up parameter F ₁Condition under, by picking up described molding technique parameter experimental parameter F in the unit (2) is set ₂With the material parameter F in the described characteristic material data library unit (3) ₃, in described resin base composite material hot-pressed formation analogue unit (4), handle, obtain the parameter of decision laminate quality, and with described process parameter optimizing target and method unit (6) in optimization aim parameter F ₄Compare, the combined process parameter optimization method is adjusted molding technique parameter automatically, is met optimization aim parameter F ₄The process system parameter, export and be stored in and optimize in the technological parameter unit (7).

2, the digitized simulation method of equal thick laminate heat pressing process pressure process system according to claim 1 is characterized in that: described molding technique parameter is provided with operation pressure P under the condition that temperature schedule and pressure applying moment are determined in the unit (2) _cThe implementation step of optimizing has:

The first step: at operation pressure P _c(0.1＜P _c＜2.0MPa) under the condition, pick up fitness function Fi;

Described fitness function $\mathrm{Fi}=\min (\stackrel{\&OverBar;}{V_f}-\stackrel{\&OverBar;}{V_{\mathrm{fg}}}),$ In the formula,

Expression average fiber volume fraction is by resin flows and fibre dense relational expression G ₁With fiber bearing pressure and fiber volume fraction relational expression G ₂Obtain,

The target average fiber volume fraction that expression is set;

Second step: the operation pressure P that is determining _cIn the scope, pick up feasible solution colony;

The 3rd step: in described resin base composite material hot-pressed formation analogue unit (4), resolve the pairing laminate average fiber of each feasible solution volume fraction in the feasible solution colony

The 4th step: the target average fiber volume fraction that extraction process is optimized in described process optimization target and method unit (6) And with target average fiber volume fraction

Resolve the average fiber volume fraction that obtains with the 4th step Substitution fitness function Fi resolves fitness value, estimates the quality of this feasible solution according to the size of fitness value; With the mechanism of the survival of the fittest, the chromosome of fitness difference is eliminated, to the chromosome of surviving quality, select at random by probability according to its fitness value, breed, form new colony; Operation by hybridization and variation produces filial generation, and hybridization is to select two chromosomes (parents) at random, with certain one or more gene exchange and produce two new individual; Variation is that in the gene certain is undergone mutation;

The 5th the step: to filial generation colony repeat the 3rd the step, the 4th the step operation, carry out new round genetic evolution process, satisfy stopping rule up to fitness value, iteration stopping has promptly found optimum solution.

3, the digitized simulation method of equal thick laminate heat pressing process pressure process system according to claim 2 is characterized in that: the 3rd step was resolved the pairing laminate average fiber of feasible solution volume fraction in described resin base composite material hot-pressed formation analogue unit (4)

Its analyzing step is:

The 1st step: in described product configuration and mesh generation unit (1), extract product initial setting up parameter F ₁

The 2nd step: extraction process parameter F in the unit (2) is set at described molding technique parameter ₂, operation pressure P wherein _cValue obtains from feasible solution colony;

The 3rd step: in described characteristic material data library unit (3), extract material parameter F ₃

The 4th step: in described resin base composite material hot-pressed formation analogue unit (4), obtain resin flows and fibre dense relational expression G ₁With fiber bearing pressure and fiber volume fraction relational expression G ₂

Described resin flows and fibre dense relational expression $G_1\&DoubleRightArrow;\frac{\&PartialD;P_f}{\&PartialD;t}=\frac{V_f^2}{V_0^2}\&CenterDot;\frac{{\mathrm{dP}}_f}{{\mathrm{dV}}_f}\&CenterDot;\frac{\&PartialD;}{\&PartialD;z}\left(\frac{V_f\&CenterDot;S_{\mathrm{zz}}}{\mathrm{\&mu;}}\frac{\&PartialD;}{\&PartialD;z}(P_f-P_c)\right)\text{,}$ In the formula, P _fExpression fiber bearing pressure, P _cThe expression operation pressure, t represents simulated time, V _fThe expression fiber volume fraction, V ₀Expression prepreg initial fiber volume fraction, z presentation layer plate thickness direction, S _ZzThe permeability of presentation layer plate thickness direction, μ represents resin viscosity; Described operation pressure P _cSatisfy P _c=P _a+ P _b, P _aExpression adds normal pressure, P _bExpression vacuum tightness;

Described fiber bearing pressure and fiber volume fraction satisfy relational expression $G_2\&DoubleRightArrow;P_f=\frac{3\mathrm{\&pi;E}}{{\mathrm{\&beta;}}^4}\&CenterDot;\frac{\sqrt{V_f/V_0}-1}{{(\sqrt{V_a/V_f}-1)}^4},$ In the formula, P _fExpression fiber bearing pressure, E represents the bending modulus of fiber, β represents fibrage compression property coefficient, V _fThe expression fiber volume fraction, V ₀Expression prepreg initial fiber volume fraction, V _aExpression dense packing fiber volume fraction.

4, the digitized simulation method of equal thick laminate heat pressing process pressure process system according to claim 3 is characterized in that: the product configuration initial setting up parameter F in the 1st step ₁In the laminate original depth $h_0=\frac{{\mathrm{Na}}_f}{{\mathrm{\&rho;}}_f{V}_0},$ In the formula, N represents the prepreg overlay number of plies, a _fExpression prepreg fiber areal densities, ρ _fThe volume density of expression fiber, V ₀Expression prepreg initial fiber volume fraction.

5, the digitized simulation method of equal thick laminate heat pressing process pressure process system according to claim 3 is characterized in that: the fiber volume fraction V in the 4th step _fAdopt Newton iteration model G ₃Obtain; Described Newton iteration model $G_3\&DoubleRightArrow;V_f^{(m+1)}=V_f^{\left(m\right)}-\frac{P_f\left(V_f^{\left(m\right)}\right)-P}{\frac{{\mathrm{dP}}_f}{{\mathrm{dV}}_f}{|}_{V_f^{\left(m\right)}}},$ In the formula, m represents iterations, V _f ^mThe fiber volume fraction that the expression iteration is m time, P _fExpression fiber bearing pressure, P represents current time fiber bearing pressure.

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