CN112632813A - Optimization method of curing system of large-thickness resin-based composite material - Google Patents
Optimization method of curing system of large-thickness resin-based composite material Download PDFInfo
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Abstract
The invention discloses an optimization method of a curing system of a large-thickness resin-based composite material. According to the method, the curing kinetic equation of the prepreg is obtained by using the dynamic differential scanning calorimetry experimental data of the prepreg; then, establishing a temperature distribution model of the composite material in the curing process by using numerical analysis software and finite element simulation software, and realizing numerical simulation calculation of the temperature distribution and the curing degree in the manufacturing process of the composite material; based on a Support Vector Machine (SVM) neural network algorithm, a proxy model is established to solve an optimization problem, and further optimization of process parameters is achieved. The curing system optimized by the method can ensure that the central curing temperature rise of the composite material is respectively reduced by 54 percent and 71 percent at the peak values of the two heat-preservation steps, the curing degree of the composite material at the central low temperature is obviously reduced, the large-range long chain crosslinking curing of resin molecules is ensured, the mechanical property of the composite material molding is further ensured, and the method can be widely applied to the manufacturing of large-thickness resin-based composite material parts such as wind power, ships, fan blades and the like.
Description
Technical Field
The invention belongs to the field of composite material molding and manufacturing, and relates to an optimization method of a curing system of a large-thickness resin-based composite material.
Background
The composite material structure with variable cross section and super large thickness is widely used in the fields of composite material fan blades of large bypass ratio aviation turbofan engines, wind power blades, ships and the like. Because of the poor thermal conductivity of the composite material during the curing process and the accompanying exothermic curing phenomenon, the curing regime different from that of the thin-wall composite material is adopted for the large-thickness composite material in order to ensure the curing uniformity and control the residual stress.
The large-thickness curing system recommended by prepreg suppliers is only suitable for a certain thickness range, and whether the large-thickness curing system is suitable for parts with the thickness of more than 70mm, such as tenons of fan blades of dual-channel engines, is a problem which needs to be researched urgently. The solidification reaction speed of the inside, reduction center of high temperature heat preservation step messenger's solidification temperature more evenly transmits the combined material in the accessible increases, makes the inside solidification exothermic can distribute out more in time, avoids too high solidification temperature rise.
However, studies have shown that excessive increase of the curing degree of the composite material at a temperature lower than the ideal curing temperature causes the molecular crosslinking structure of the resin matrix to be a dispersed local short-chain crosslinking structure rather than a large-scale long-chain crosslinking structure, thereby seriously affecting the mechanical properties of the molding. Therefore, the selection of the curing parameters of the ultra-thick composite material needs to find a balance between the control of the curing temperature rise of the center and the guarantee of the large-range long-chain crosslinking curing of resin molecules so as to guarantee the mechanical property of the composite material molding.
In order to solve the problems, the invention provides an optimization method of a curing system of a large-thickness resin-based composite material, which is used for establishing a temperature distribution model of the curing process of the composite material by combining numerical analysis and finite element simulation from the curing dynamics and rheological properties of the prepreg, accurately and efficiently optimizing the curing system, and is particularly suitable for prepregs without a curing dynamics model and a curing system of the large-thickness composite material.
Disclosure of Invention
In order to solve the technical problem, the invention provides an optimization method capable of effectively reducing the central curing temperature rise of the large-thickness composite material.
The technical scheme adopted by the invention is as follows:
a method for optimizing a curing system of a resin-based composite material with large thickness is used for optimizing parameters of low-temperature heat-preservation steps in a two-step curing system of prepreg, and comprises the following steps:
s1: performing data fitting on an autocatalytic reaction kinetic model by using dynamic differential scanning calorimetry experimental data of the target prepreg to obtain a curing kinetic model of the prepreg;
s2: acquiring a viscosity temperature curve of the prepreg, and determining a temperature range with the viscosity lower than a threshold value according to the viscosity temperature curve;
s3: based on a curing kinetic model and a viscosity-temperature curve, adopting numerical analysis software to analyze the curing behavior of the prepreg under different isothermal conditions in the temperature range, taking the influence of a local short-chain cross-linked structure on the mechanical performance as a judgment standard, and selecting the temperature of a low-temperature heat-preservation step in the temperature range;
s4: under the condition of keeping the temperature of the high-temperature heat-preservation step and the time length of the high-temperature heat-preservation step in the original prepreg curing system unchanged, establishing the temperature of the prepreg-made composite material curing process by using Abaqus finite element simulation softwareThe degree distribution model is used for realizing numerical simulation calculation of temperature distribution and curing degree in the composite material manufacturing process so as to obtain a plurality of groups of simulation data; wherein each group of analog data comprises the temperature rise rate v and the low-temperature heat preservation step time length t1The peak temperature T of the low-temperature heat-preservation step at the central point in the composite material is obtained by lower simulationmax1High temperature insulation step peak temperature Tmax2And a degree of cure;
s5: training the SVM neural network by using the simulation data as training data under the condition of keeping the temperature of the high-temperature heat-preservation step and the time length of the high-temperature heat-preservation step in the original prepreg curing system unchanged to obtain an agent model, wherein the agent model is used for training the SVM neural network at the temperature-rise rate v and the low-temperature heat-preservation step time length t1Outputting the peak temperature T of the low-temperature heat-preservation step of the composite material for inputmax1High temperature insulation step peak temperature Tmax2And the degree of cure at the end of the low temperature insulating step;
s6: solving an optimization problem aiming at the agent model through a multi-objective optimization algorithm to obtain an optimal solution of two input values in the agent model which enables an objective function to be minimum, wherein the objective function is a linear weighted sum of three output values of the agent model; using the optimal solution of the temperature and the heating rate v of the low-temperature heat-preservation step and the time length t of the low-temperature heat-preservation step1The optimal solution of (2) is used as the low-temperature heat-preservation step temperature, the heating rate and the low-temperature heat-preservation step duration in the parameters of the optimal curing system of the target prepreg, so that the optimization of the curing system of the large-thickness resin-based composite material is realized.
As a further improvement of the method of the present invention, the optimization method is applicable to prepregs with a high-thickness composite cure system or prepregs without a cure kinetic model and without a high-thickness composite cure system.
As a further improvement of the method of the invention, the kinetic model of the autocatalytic reaction of the prepreg in S1 is shown in formula 1:
In the formula: alpha is the degree of cure of the material, d alpha/dt is the cure reaction rate, k1And k2Respectively a non-catalytic polymerization reaction rate constant and an autocatalytic polymerization reaction rate constant, m and n are reaction series, A is a pre-factor, R is a universal gas constant, T is an absolute temperature, and E is reaction activation energy of a curing reaction.
As a further improvement of the method, in the dynamic differential scanning calorimetry experiment in S1, the selected temperature rise rate is any temperature between 1-20 ℃/min, preferably 3 ℃/min, 5 ℃/min, 10 ℃/min, 15 ℃/min or 20 ℃/min; the dynamic differential scanning calorimetry experimental data should include temperature, heat flow rate, and rate of temperature rise.
As a further improvement of the method of the present invention, the threshold value in S2 is 10 to 50pa · S.
As a further improvement of the method of the present invention, in the judgment criteria described in S3, the highest temperature at which the increase in the degree of solidification within 60 minutes of isothermal heating does not exceed 5% is preferably used as the low temperature holding step temperature.
As a further improvement of the method of the present invention, the temperature distribution model of the composite material curing process described in S4 employs the following heat transfer model:
in the formula: ρ and CpRespectively, material density and material specific heat capacity, kxx、kyyAnd kzzRespectively the thermal conductivity of the material in three directions,
the heat release rate of the curing is calculated by the following formula:
in the formula vfIs the fiber volume fraction, HrIs the total heat release per mass of resin cured.
As a further improvement of the method, in S6, the time cost and the influence of the central curing temperature rise on the performance are comprehensively considered, and according to the difference of the resin system and the thickness, the curing temperature rise rate below 15mm is preferably 1.5-3 ℃, and the curing temperature rise rate above 15mm is preferably 0.5-2 ℃.
As a further improvement of the method of the present invention, the simulation data in S5 is sampled by Latin Hypercube Sampling (LHS), and the sampling points are 100 sets, wherein 90 sets are used for building the proxy model, and 10 sets are used for verifying the proxy model.
As a further improvement of the method of the present invention, the SVM neural network described in S5 can be solved by the following formula:
in the formula, K (x, x)i) Is a function, or positive definite kernel, meaning that there is a mapping, x, from the input space to the feature spaceiIs the i-th feature vector, yiIs a class mark, αiAs lagrange multiplier, b*For the optimal solution, N is the total number of categories.
By means of the scheme, the invention at least has the following advantages: on one hand, the steps of the method are not only suitable for the prepreg of the existing curing kinetic model, but also suitable for the prepreg without the curing kinetic model and the curing system of the large-thickness composite material, and the application range is wide; on the other hand, the method achieves a balance between the control of the local short chain cross-linking structure and the central curing temperature rise, provides an optimization method aiming at the curing system of the resin-based composite material with large thickness through finite element simulation and SVM neural network algorithm, and is accurate and efficient.
The above description is only an outline of the technical solution of the present invention, and in order to clearly understand the technical means of the present invention and to implement the method according to the content of the description, the following detailed description will be given to the method for optimizing the curing system of the resin-based composite material with large thickness, which is provided by the present invention, with reference to the accompanying drawings and the embodiments.
Drawings
FIG. 1 is a dynamic DSC curve of a prepreg provided by the present invention at different temperature rise rates;
FIG. 2 is a viscosity-temperature curve for a prepreg provided by a supplier;
FIG. 3 is a verification diagram of an actual curing process of a prepreg curing kinetic model provided by the present invention;
FIG. 4 is a graph of isothermal curing of prepregs based on numerical analysis, as provided by the present invention;
FIG. 5 is a finite element analysis component model provided by the present invention;
FIG. 6 is a graph of temperature at the center of a large thickness composite provided by the present invention that was not optimized and optimized by the method of the present invention versus degree of cure.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In the present invention, the autocatalytic reaction kinetic model, the Kissinger method, the immobilizer method, the SVM neural network algorithm, and the like are proper terms known to those skilled in the art, and are not limited to any specific terms.
Generally, a supplier of prepregs will provide a corresponding cure regime (MRCC) for the prepreg, which includes five parameters: temperature T of low-temperature heat-preservation stepmax1High temperature insulating step temperature Tmax2Time t of low-temperature heat-preservation step1Time t of high-temperature heat-preservation step2And a temperature rise rate v. However, as mentioned above, the large thickness curing system recommended by the prepreg supplier is only applicable to a certain thickness range, and the recommended curing system parameters are not necessarily applicable when the thickness exceeds a certain range, and it is necessary to optimize the thickness. Among the five parameters, the high-temperature heat-preservation step parameter is sensitive to the influence of the final curing effect, and the recommended parameter of a supplier needs to be adopted preferentially, but the low-temperature heat-preservation step parameter can be optimized to a certain extent so as toThe use under the scene of its big thickness of satisfying is convenient.
The invention provides an optimization method of a curing system of a resin-based composite material with large thickness, which is used for optimizing low-temperature heat-preservation step parameters in a two-step curing system of prepreg and comprises the following steps:
s1: and (3) performing data fitting on the autocatalytic reaction kinetic model by using the dynamic differential scanning calorimetry experimental data of the target prepreg to obtain a curing kinetic model of the prepreg.
In this step, the autocatalytic kinetic model of the prepreg is shown in the following two formulas:
In the formula: alpha is the degree of cure of the material, d alpha/dt is the cure reaction rate, k1And k2Respectively a non-catalytic polymerization reaction rate constant and an autocatalytic polymerization reaction rate constant, m and n are reaction series, A is a pre-factor, R is a universal gas constant, T is an absolute temperature, and E is reaction activation energy of a curing reaction.
In the two formulas, the type of prepreg needs to be determined by specifically selecting a model of which formula is used.
The specific method of the dynamic differential scanning calorimetry experiment is the prior art, and is not repeated, and the temperature rise rate selected in the dynamic differential scanning calorimetry experiment is any temperature between 1 and 20 ℃/min, preferably 3 ℃/min, 5 ℃/min, 10 ℃/min, 15 ℃/min or 20 ℃/min. In addition, dynamic differential scanning calorimetry experimental data should include temperature, heat flow rate, and ramp rate.
After the curing kinetic model is obtained, the curing kinetic model of the prepreg can be subjected to actual process verification to ensure that the model is feasible, and if the accuracy of the model is poor, correction is required. The process verification can be carried out by a solidification stopping method, a plurality of sampling points are selected at the heat preservation step for verification and comparison of the measured value and the model predicted value, and preferably, one sampling point is arranged at intervals of 20 or 30 min.
S2: and acquiring a viscosity temperature curve of the prepreg by using a rotational rheology method or a supplier data manual, and determining a temperature range with the viscosity lower than a threshold value according to the viscosity temperature curve. The threshold value here may be determined to be an optimum value according to the type of prepreg, and is generally 10 to 50pa · s.
S3: and (3) analyzing the curing behavior of the prepreg under different isothermal conditions in the temperature range by adopting numerical analysis software based on a curing kinetic model and a viscosity-temperature curve, taking the influence of a local short-chain cross-linking structure on the mechanical performance as a judgment standard, and selecting the temperature of the low-temperature heat-preservation step in the temperature range.
In the step, the influence of resin fluidity and low-temperature curing on performance needs to be comprehensively considered, a temperature range with the viscosity lower than a threshold value (such as the range lower than 10-50 pa · s) in a viscosity-temperature curve is selected, curing degree-time curves at different isothermal temperatures (with 10 ℃ as an interval) are fitted according to a curing kinetic equation, and the time is selected to be 300min, so that the curing behavior of the prepreg is analyzed. The numerical analysis software should have the capability of solving nonlinear differential equations, and matlab commercial mathematical software is preferred. And reasonably setting the number of the heat preservation steps and the heat preservation time based on the result obtained by the analysis, and preferably selecting 1 or 2 heat preservation steps. In order to reduce the influence of the local short-chain cross-linked structure on the mechanical performance, the above judgment standard is preferably set as the highest temperature with the solidification degree not exceeding 5% within isothermal 60min is selected as the temperature of the low-temperature heat-preservation step; the heat preservation time is preferably 30-180 min according to different thicknesses, 30-120 min below 15mm is recommended, 60-180 min above 15mm is recommended, and the specific time is determined by optimizing the subsequent steps.
S4: under the condition of keeping the high-temperature heat preservation step temperature and the high-temperature heat preservation step duration in the original prepreg curing system unchanged (namely, the high-temperature heat preservation step temperature and the high-temperature heat preservation step duration in the subsequent simulation process are both fixed to the original recommended values in the curing system provided by a prepreg supplier), establishing a composite prepreg by utilizing Abaqus finite element simulation softwareCombining the temperature distribution model of the material curing process, and realizing the numerical simulation calculation of the temperature distribution and the curing degree in the composite material manufacturing process, thereby obtaining a plurality of groups of simulation data. Wherein each group of analog data comprises the temperature rise rate v and the low-temperature heat preservation step time length t1The peak temperature T of the low-temperature heat-preservation step at the central point in the composite material is obtained by lower simulationmax1High temperature insulation step peak temperature Tmax2And a degree of cure. Heating rate v and low-temperature heat-preservation step duration t in each group of simulation data1All are different.
In this step, the following heat transfer model is used as the temperature distribution model of the composite material curing process:
in the formula: ρ and CpRespectively, material density and material specific heat capacity, kxx、kyyAnd kzzRespectively the thermal conductivity of the material in three directions,
the heat release rate of the curing is calculated by the following formula:
in the formula vfIs the fiber volume fraction, HrIs the total heat release per mass of resin cured.
The simulation data may be sampled by a latin hypercube sampling method (LHS) based on a numerical simulation of the temperature distribution model. For example, in one embodiment, 100 sets of sampling points are used, and a total of 100 sets of simulation data are obtained, wherein 90 sets can be used for building the proxy model and 10 sets are used for verifying the proxy model.
S5: similarly, under the condition of keeping the temperature of the high-temperature heat-preservation step and the time length of the high-temperature heat-preservation step in the original curing system of the prepreg unchanged, the prepreg is obtained by the methodThe simulation data is used as training data to train the SVM neural network to obtain a proxy model, and the proxy model is used for carrying out heat preservation at a heating rate v and a low-temperature heat preservation step duration t1Outputting the peak temperature T of the low-temperature heat-preservation step of the composite material for inputmax1High temperature insulation step peak temperature Tmax2And the degree of cure at the end of the low temperature insulating step.
The specific structure of the SVM neural network belongs to the prior art, and the SVM algorithm can be solved by the following formula:
in the formula, K (x, x)i) Is a function, or positive definite kernel, meaning that there is a mapping, x, from the input space to the feature spaceiIs the i-th feature vector, yiIs a class mark, αiAs lagrange multiplier, b*For the optimal solution, N is the total number of categories.
S6: solving an optimization problem aiming at the agent model through a multi-objective optimization algorithm to obtain an optimal solution of two input values in the agent model which enables an objective function to be minimum, wherein the objective function is a linear weighted sum of three output values of the agent model; using the optimal solution of the temperature and the heating rate v of the low-temperature heat-preservation step and the time length t of the low-temperature heat-preservation step1The optimal solution of (2) is used as the low-temperature heat-preservation step temperature, the heating rate and the low-temperature heat-preservation step duration in the parameters of the optimal curing system of the target prepreg, so that the optimization of the curing system of the large-thickness resin-based composite material is realized.
That is, after the SVM neural network is introduced into the multi-objective optimization algorithm, the optimization objectives are the solidification degree at the end of the low-temperature heat preservation step and the peak temperature T of the two heat preservation stepsmax1、Tmax2The optimization variable is the low-temperature heat-preservation step duration t1And a temperature rise rate v. Because of having a plurality of optimization targets, the multi-target optimization algorithm is a uniform target method, a linear weighting method is adopted to convert the multi-target optimization problem into a target function, and the solidification degree and the peak temperature of two heat preservation steps can be normalized and then are subjected to linear weighting to be used as a target functionAnd (4) counting.
In the step, in the optimization process of the curing system, the time cost and the influence of the central curing temperature rise on the performance need to be comprehensively considered, and the optimal curing temperature rise rate is determined according to the difference between the resin system and the thickness, wherein the curing temperature rise rate below 15mm is preferably 1.5-3 ℃, and the curing temperature rise rate above 15mm is 0.5-2 ℃.
The optimization method is not only suitable for the prepreg with a large-thickness composite material curing system, but also suitable for the prepreg without a curing kinetic model and the large-thickness composite material curing system.
In order to further enable those skilled in the art to better understand the specific implementation process and technical effects of the optimization method, the method is applied to a specific embodiment.
Example (b):
a method for optimizing a curing system of a large-thickness composite material comprises the following steps:
(1) 10-15 mg of prepreg sample is placed in an aluminum crucible, the nitrogen flow is 50mL/min, five heating rates of 3, 5, 10, 15 and 20 ℃/min are selected in the example, and dynamic DSC curves under different heating rates are obtained, as shown in figure 1.
(2) Determination of exothermic peak temperatures T of dynamic DSC curves at different ramp rates by means of FIG. 1pThe curing reaction activation energy E was determined by the Kissinger method.
(3) Using the autocatalytic cure reaction kinetic model, a cure kinetic model of the prepreg was constructed by nonlinear fitting software (the nonlinear fitting software of this example is preferably an origin function mapping tool) in the form shown below:
wherein alpha is the degree of curing of the material, d alpha/dt is the rate of curing reaction, k1And k2Is a non-catalytic polymerization reaction rate constant and an autocatalytic polymerization reaction rate constant, m and n are reaction orders, A is a pre-index factor, and R is a common gasAnd D, fitting the obtained curing kinetic model, wherein T is absolute temperature, E is reaction activation energy of a curing reaction, and the obtained curing kinetic model is shown as the following formula:
(4) the viscosity-temperature curve provided by the supplier is shown in FIG. 2, and the viscosity of the prepreg is determined to be in a lower range of 100-140 ℃.
(5) And (3) carrying out actual process verification on the curing kinetic model of the prepreg by using a curing stopping method. Five sampling points are selected on the 180 ℃ heat preservation step, one sampling point is arranged at intervals of 30min, the curing degree test value of the sample is compared with the predicted value of the curing kinetic model, and the verification result is shown in figure 3, which shows that the curing kinetic model is feasible.
(6) Based on a curing kinetic model and a viscosity temperature curve, the curing behavior of the prepreg under different isothermal conditions of 100 ℃, 110 ℃, 120 ℃, 130 ℃ and 140 ℃ in a range with lower viscosity is analyzed by adopting an ode function in numerical analysis software matlab, and the result is shown in fig. 4, in order to reduce the influence of a local short-chain cross-linking structure on the mechanical property, a first heat preservation step is selected to be arranged at 110 ℃ according to the judgment standard, namely the temperature of the low-temperature heat preservation step is set to be 110 ℃.
(7) Selecting a composite material component made of the prepreg shown in fig. 5 as a finite element analysis model, wherein the effective size is 150 multiplied by 100 multiplied by 36.8mm, the layering is unidirectional 0-degree layering along the length direction of the component, and the temperature and the curing degree of the internal central point A of the component are calculated in total of 200 layers. Based on an LHS sampling method, 100 groups of sampling points are selected, and a subroutine is written to establish a temperature distribution model of the composite material curing process by using finite element simulation software Abaqus, so that numerical simulation calculation of temperature distribution and curing degree in the manufacturing process of 100 groups of sampling points is realized. The temperature profile of the composite material during curing is obtained by a heat transfer model shown below:
where ρ and CpRespectively, material density and material specific heat capacity, kxx、kyyAnd kzzRespectively the thermal conductivity of the material in three directions,
for curing heat release rate, obtained by the following formula:
in the formula vfIs the fiber volume fraction, HrIs the total heat release per mass of resin cured.
Thus, 100 sets of sample points obtain 100 sets of simulation data, of which 90 sets can be used for building the proxy model and 10 sets are used for verifying the proxy model. Each group of simulation data comprises the temperature rise rate v and the low-temperature heat preservation step time length t1The peak temperature Tmax of the low-temperature heat preservation step at the central point in the composite material is obtained by lower simulation1And the peak temperature Tmax of the high-temperature heat preservation step2And a degree of cure. Heating rate v and low-temperature heat-preservation step duration t in each group of simulation data1All are different.
(8) In order to achieve balance between controlling the curing temperature rise of the center and ensuring the large-range long chain crosslinking curing of resin molecules so as to ensure the mechanical property of the composite material molding, a proxy model is established by utilizing an SVM neural network algorithm based on 100 sets of simulation data in the step (7), and the proxy model is subjected to temperature rise rate v and low-temperature heat preservation step duration t1Outputting the peak temperature T of the low-temperature heat-preservation step of the composite material for inputmax1High temperature insulation step peak temperature Tmax2And the degree of cure at the end of the low temperature insulating step. After training with the SVM neural network for 90 sets of simulated data, the validation was passed in the remaining 10 sets of simulated data. It should be noted that when training the SVM neural network, the temperature of the high-temperature heat-preservation step and the duration of the high-temperature heat-preservation step are kept consistent with the two parameter values in the original prepreg curing system.
Then theAnd importing the trained agent model into an optimization algorithm based on a unified objective method, and solving an optimization problem to obtain an optimal solution of two input values in the agent model with the minimum objective function. The target function is the linear weighted sum of three output values of the proxy model, and the three output values are weighted after being normalized to be used as the target function. In the objective function of this embodiment, the peak temperature T of the low temperature holding stepmax1High temperature insulation step peak temperature Tmax2And the weight of the degree of curing at the end of the low-temperature heat-preservation step is 0.3, 0.5 and 0.2 respectively.
Thereby, the optimal solution of the temperature of the low-temperature heat-preservation step obtained in the step (6), the temperature-rise rate v obtained in the step (8) and the low-temperature heat-preservation step time length t can be obtained1The optimal solution of the method is to replace the low-temperature heat preservation step temperature, the heating rate and the low-temperature heat preservation step duration in the original curing system parameters of the prepreg, and to keep the high-temperature heat preservation step temperature, the heating rate and the high-temperature heat preservation step duration in the original curing system parameters, so that the optimized optimal curing system parameters are obtained, and the optimization of the curing system of the large-thickness resin-based composite material is realized.
In this example, the round post-cure system was optimized as: firstly, the temperature is increased from room temperature to 110 ℃ at the speed of 1.3 ℃/min, the temperature is kept for 35min at 110 ℃, then the temperature is increased to 180 ℃ at the speed of 1.3 ℃/min, the temperature is kept for 120min, and finally the temperature is reduced to the room temperature.
(9) In order to verify the effect of the invention, the temperature and curing degree curve of the point A after Optimization in the step (8) is extracted, and compared with the temperature and curing degree curve of the point A under a curing system (MRCC) provided by a supplier, the central curing temperature rise of the composite material is respectively reduced by 54% and 71% at the peak values of two heat preservation steps, the curing degree under the central low temperature is obviously reduced, and the large-range long chain crosslinking curing of resin molecules is ensured so as to ensure the mechanical property of the composite material molding.
The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention. Various changes and modifications may be made by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present invention. Therefore, the technical scheme obtained by adopting the mode of equivalent replacement or equivalent transformation is within the protection scope of the invention.
Claims (10)
1. An optimization method of a curing system of a resin-based composite material with large thickness is used for optimizing parameters of low-temperature heat-preservation steps in a two-step curing system of prepreg, and is characterized in that: the method comprises the following steps:
s1: performing data fitting on an autocatalytic reaction kinetic model by using dynamic differential scanning calorimetry experimental data of the target prepreg to obtain a curing kinetic model of the prepreg;
s2: acquiring a viscosity temperature curve of the prepreg, and determining a temperature range with the viscosity lower than a threshold value according to the viscosity temperature curve;
s3: based on a curing kinetic model and a viscosity-temperature curve, adopting numerical analysis software to analyze the curing behavior of the prepreg under different isothermal conditions in the temperature range, taking the influence of a local short-chain cross-linked structure on the mechanical performance as a judgment standard, and selecting the temperature of a low-temperature heat-preservation step in the temperature range;
s4: under the condition of keeping the temperature of a high-temperature heat-preservation step and the time length of the high-temperature heat-preservation step in an original prepreg curing system unchanged, establishing a temperature distribution model of a composite material curing process made of prepreg by using Abaqus finite element simulation software, and realizing numerical simulation calculation of temperature distribution and curing degree in the composite material manufacturing process so as to obtain a plurality of groups of simulation data; wherein each group of analog data comprises the temperature rise rate v and the low-temperature heat preservation step time length t1The peak temperature T of the low-temperature heat-preservation step at the central point in the composite material is obtained by lower simulationmax1High temperature insulation step peak temperature Tmax2And a degree of cure;
s5: training the SVM neural network by using the simulation data as training data under the condition of keeping the temperature of the high-temperature heat-preservation step and the time length of the high-temperature heat-preservation step in the original prepreg curing system unchanged to obtain an agent model, wherein the agent model is used for training the SVM neural network at the temperature-rise rate v and the low-temperature heat-preservation step time length t1For inputting and outputting low temperature of the composite materialPeak temperature T of heat-insulating stepmax1High temperature insulation step peak temperature Tmax2And the degree of cure at the end of the low temperature insulating step;
s6: solving an optimization problem aiming at the agent model through a multi-objective optimization algorithm to obtain an optimal solution of two input values in the agent model which enables an objective function to be minimum, wherein the objective function is a linear weighted sum of three output values of the agent model; using the optimal solution of the temperature and the heating rate v of the low-temperature heat-preservation step and the time length t of the low-temperature heat-preservation step1The optimal solution of (2) is used as the low-temperature heat-preservation step temperature, the heating rate and the low-temperature heat-preservation step duration in the parameters of the optimal curing system of the target prepreg, so that the optimization of the curing system of the large-thickness resin-based composite material is realized.
2. The method of optimizing a cure system for a high build resin-based composite material of claim 1, wherein the method is applied to prepregs with a high build composite cure system or prepregs without a cure kinetic model and without a high build composite cure system.
3. The method for optimizing the curing system of the large-thickness resin-based composite material as claimed in claim 1, wherein the dynamic model of the autocatalytic reaction of the prepreg in S1 is represented by the following formula:
In the formula: alpha is the degree of cure of the material, d alpha/dt is the cure reaction rate, k1And k2Respectively a non-catalytic polymerization reaction rate constant and an autocatalytic polymerization reaction rate constant, m and n are reaction series, A is a pre-factor, R is a universal gas constant, T is an absolute temperature, and E is reaction activation energy of a curing reaction.
4. The method for optimizing the curing system of the large-thickness resin-based composite material as claimed in claim 1, wherein the dynamic differential scanning calorimetry experiment selects any temperature with a heating rate of 1-20 ℃/min, preferably 3 ℃/min, 5 ℃/min, 10 ℃/min, 15 ℃/min or 20 ℃/min; the dynamic differential scanning calorimetry experimental data should include temperature, heat flow rate, and rate of temperature rise.
5. The method for optimizing the curing system of the large-thickness resin-based composite material as claimed in claim 1, wherein the threshold value in S2 is 10-50 pa-S.
6. The method for optimizing the curing system of the large-thickness resin-based composite material as claimed in claim 1, wherein the highest temperature, at which the curing degree increases by no more than 5% within 60min of isothermal temperature, is preferably selected as the low-temperature holding step temperature in the judgment standard of S3.
7. The method for optimizing the curing system of the large-thickness resin-based composite material as claimed in claim 1, wherein the temperature distribution model of the curing process of the composite material as described in S4 adopts the following heat transfer model:
in the formula: ρ and CpRespectively, material density and material specific heat capacity, kxx、kyyAnd kzzRespectively the thermal conductivity of the material in three directions,
the heat release rate of the curing is calculated by the following formula:
in the formula vfIs the fiber volume fraction, HrIs the total heat release per mass of resin cured.
8. The method for optimizing the curing system of the large-thickness resin-based composite material according to claim 1, wherein the influence of time cost and central curing temperature rise on the performance is comprehensively considered in S6, and according to the difference between the resin system and the thickness, the curing temperature rise rate below 15mm is preferably 1.5-3 ℃, and the curing temperature rise rate above 15mm is preferably 0.5-2 ℃.
9. The method for optimizing the curing system of the large-thickness resin-based composite material as claimed in claim 1, wherein the simulation data in S5 is sampled by Latin Hypercube Sampling (LHS), and the sampling points are 100 groups, wherein 90 groups are used for establishing the proxy model, and 10 groups are used for verifying the proxy model.
10. The method for optimizing the curing system of the resin-based composite material with large thickness as claimed in claim 1, wherein the SVM neural network described in S5 is solved by the following formula:
in the formula, K (x, x)i) Is a function, or positive definite kernel, meaning that there is a mapping, x, from the input space to the feature spaceiIs the i-th feature vector, yiIs a class mark, αiAs lagrange multiplier, b*For the optimal solution, N is the total number of categories.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113378427A (en) * | 2021-05-11 | 2021-09-10 | 三峡大学 | Calculation method for evaluating wind load fracture resistance of branches and trunks of arbor |
| CN113607772A (en) * | 2021-08-04 | 2021-11-05 | 西北工业大学 | Method and system for determining damage of toughened composite material during curing molding |
| CN114202262A (en) * | 2022-02-21 | 2022-03-18 | 德州联合拓普复合材料科技有限公司 | Prepreg process improvement method and system based on neural network and storage medium |
| CN116631549A (en) * | 2023-07-25 | 2023-08-22 | 北京理工大学 | Optimization method, device, equipment and medium for composite material curing system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101523394A (en) * | 2006-10-06 | 2009-09-02 | 日立化成工业株式会社 | Method for analyzing fluidity of resin material including particles and fluidity analysis system |
| CN102490370A (en) * | 2011-11-15 | 2012-06-13 | 中国人民解放军国防科学技术大学 | Liquid model molding technology for preparing polymer matrix composite material |
| US20150111141A1 (en) * | 2013-10-22 | 2015-04-23 | Xerox Corporation | Bio-Based Toner Resin with Increased Fusing Performance |
| WO2015072040A1 (en) * | 2013-11-18 | 2015-05-21 | 株式会社日立製作所 | Resin flow behavior calculation method, and resin flow behavior calculation program |
| CN110197009A (en) * | 2019-05-09 | 2019-09-03 | 西北工业大学 | A kind of prediction technique of polymer matrix composites curing reaction |
-
2020
- 2020-12-03 CN CN202011410760.1A patent/CN112632813B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101523394A (en) * | 2006-10-06 | 2009-09-02 | 日立化成工业株式会社 | Method for analyzing fluidity of resin material including particles and fluidity analysis system |
| CN102490370A (en) * | 2011-11-15 | 2012-06-13 | 中国人民解放军国防科学技术大学 | Liquid model molding technology for preparing polymer matrix composite material |
| US20150111141A1 (en) * | 2013-10-22 | 2015-04-23 | Xerox Corporation | Bio-Based Toner Resin with Increased Fusing Performance |
| WO2015072040A1 (en) * | 2013-11-18 | 2015-05-21 | 株式会社日立製作所 | Resin flow behavior calculation method, and resin flow behavior calculation program |
| CN110197009A (en) * | 2019-05-09 | 2019-09-03 | 西北工业大学 | A kind of prediction technique of polymer matrix composites curing reaction |
Non-Patent Citations (2)
| Title |
|---|
| LI-GONG YANG 等: "Improvement of electrical characteristics of fluorinated perylene diimide thin-film transistors by gate dielectric surface treatment", 《2007 ASIA OPTICAL FIBER COMMUNICATION AND OPTOELECTRONICS CONFERENCE》 * |
| 郭启微 等: "基于DSC法的RTM工艺用6421双马树脂固化反应分析", 《宇航材料工艺》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113378427A (en) * | 2021-05-11 | 2021-09-10 | 三峡大学 | Calculation method for evaluating wind load fracture resistance of branches and trunks of arbor |
| CN113378427B (en) * | 2021-05-11 | 2023-03-10 | 三峡大学 | Calculation method for evaluating wind load fracture resistance of branches and trunks of arbor |
| CN113607772A (en) * | 2021-08-04 | 2021-11-05 | 西北工业大学 | Method and system for determining damage of toughened composite material during curing molding |
| CN114202262A (en) * | 2022-02-21 | 2022-03-18 | 德州联合拓普复合材料科技有限公司 | Prepreg process improvement method and system based on neural network and storage medium |
| CN116631549A (en) * | 2023-07-25 | 2023-08-22 | 北京理工大学 | Optimization method, device, equipment and medium for composite material curing system |
| CN116631549B (en) * | 2023-07-25 | 2023-09-22 | 北京理工大学 | Optimization method, device, equipment and medium for composite material curing system |
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