CN116663332B - Parameter optimization method, device, equipment and medium for composite material forming process - Google Patents
Parameter optimization method, device, equipment and medium for composite material forming process Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 113
- 239000002131 composite material Substances 0.000 title claims abstract description 80
- 230000008569 process Effects 0.000 title claims abstract description 74
- 238000005457 optimization Methods 0.000 title abstract description 14
- 238000005470 impregnation Methods 0.000 claims abstract description 149
- 238000009826 distribution Methods 0.000 claims abstract description 82
- 238000009413 insulation Methods 0.000 claims description 33
- 229920005989 resin Polymers 0.000 claims description 29
- 239000011347 resin Substances 0.000 claims description 29
- 238000005070 sampling Methods 0.000 claims description 19
- 239000004744 fabric Substances 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 12
- 239000000835 fiber Substances 0.000 claims description 10
- 229920006231 aramid fiber Polymers 0.000 claims description 4
- 239000003822 epoxy resin Substances 0.000 claims description 4
- 230000035699 permeability Effects 0.000 claims description 4
- 229920000647 polyepoxide Polymers 0.000 claims description 4
- 238000000465 moulding Methods 0.000 description 16
- 239000011148 porous material Substances 0.000 description 6
- 238000004088 simulation Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000002474 experimental method Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 230000008595 infiltration Effects 0.000 description 2
- 238000001764 infiltration Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 239000011800 void material Substances 0.000 description 2
- 229920000049 Carbon (fiber) Polymers 0.000 description 1
- 239000004760 aramid Substances 0.000 description 1
- 229920003235 aromatic polyamide Polymers 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000004917 carbon fiber Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007598 dipping method Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/26—Composites
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- G—PHYSICS
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Abstract
The application relates to the technical field of composite materials, in particular to a parameter optimization method, device, equipment and medium for a composite material forming process. The method comprises the following steps: constructing a target impregnation model of the composite material by using Hypermesh software; the target impregnation model is imported into PAM-RTM software, and a target porosity model is built in the PAM-RTM software; inputting a plurality of groups of input parameter sets of a composite material forming process into PAM-RTM software to obtain the porosity distribution and impregnation rate distribution of a target impregnation model; wherein the porosity distribution comprises total porosity corresponding to each set of input parameters, and the impregnation rate distribution comprises impregnation rates corresponding to each set of input parameters; based on the porosity distribution and the impregnation rate distribution, parameters of the composite material forming process are optimized. The technical scheme provided by the application can rapidly determine the porosity distribution of the composite material, thereby effectively guiding manufacturers to improve the parameters of the forming process.
Description
Technical Field
The application relates to the technical field of composite materials, in particular to a parameter optimization method, device, equipment and medium for a composite material forming process.
Background
The fiber surface is inert, and the infiltration performance with the resin matrix is poor in the process of forming the composite material by using a vacuum impregnation process.
In the related art, the fiber fabric or the resin matrix is modified to improve the infiltration performance of the fiber fabric and the resin matrix, so that the generation of void defects is reduced. However, the above solution requires an actual impregnation experiment to obtain the porosity distribution of the impregnated composite material, and thus this method cannot quickly determine the porosity distribution of the composite material, and thus cannot effectively guide manufacturers to improve the parameters of the molding process.
Therefore, there is a need to provide a method, apparatus, device and medium for optimizing parameters of a composite material molding process to solve the above technical problems.
Disclosure of Invention
In order to solve the problem that the porosity distribution of the composite material cannot be determined quickly in the prior art, the embodiment of the application provides a parameter optimization method, device, equipment and medium for a composite material forming process.
In a first aspect, an embodiment of the present application provides a method for optimizing parameters of a composite material molding process, including:
constructing a target impregnation model of the composite material by using Hypermesh software;
the target impregnation model is imported into PAM-RTM software, and a target porosity model is built in the PAM-RTM software;
inputting a plurality of groups of input parameter sets of a composite material forming process into PAM-RTM software to obtain the porosity distribution and impregnation rate distribution of a target impregnation model; wherein the porosity distribution comprises total porosity corresponding to each set of input parameters, and the impregnation rate distribution comprises impregnation rates corresponding to each set of input parameters;
based on the porosity distribution and the impregnation rate distribution, parameters of the composite material forming process are optimized.
In a second aspect, an embodiment of the present application further provides a parameter optimization apparatus for a composite material molding process, including:
the building module is used for building a target impregnation model of the composite material by utilizing Hypermesh software;
the importing module is used for importing the target impregnation model into the PAM-RTM software and constructing a target porosity model in the PAM-RTM software;
the input module is used for inputting a plurality of groups of input parameter sets of the composite material forming process into PAM-RTM software to obtain the porosity distribution and the impregnation rate distribution of the target impregnation model; wherein the porosity distribution comprises total porosity corresponding to each set of input parameters, and the impregnation rate distribution comprises impregnation rates corresponding to each set of input parameters;
and the determining module is used for optimizing parameters of the composite material forming process based on the porosity distribution and the impregnation rate distribution.
In a third aspect, an embodiment of the present application further provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor implements the method of any embodiment of the present application when executing the computer program.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method of any of the embodiments of the present application.
The parameter optimization method, the device, the equipment and the medium for the composite material molding process provided by the embodiment of the application are characterized in that firstly, a target impregnation model of a composite material is constructed by utilizing Hypermesh software, then the target impregnation model is imported into PAM-RTM software, a target porosity model is constructed in the PAM-RTM software, then a plurality of groups of input parameter sets of the composite material molding process are input into the PAM-RTM software to obtain the porosity distribution and the impregnation rate distribution of the target impregnation model, and finally, the parameters of the composite material molding process are optimized based on the porosity distribution and the impregnation rate distribution. Therefore, the above technical scheme discusses the reason for forming the pores of the composite material by using a numerical simulation analysis mode, so that the parameters of the composite material forming process can be rapidly optimized, and the problem that the porosity distribution of the composite material cannot be rapidly determined in the prior art is solved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for optimizing parameters of a composite material forming process according to an embodiment of the present application;
FIG. 2 is a hardware architecture diagram of an electronic device according to an embodiment of the present application;
FIG. 3 is a block diagram of a parameter optimization apparatus for a composite material molding process according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a simulation of a target impregnation model according to an embodiment of the present application;
FIG. 5 is a schematic illustration of a simulation of a target porosity model according to an embodiment of the present application;
fig. 6 is a schematic diagram illustrating a simulation of a porosity distribution of a target impregnation model according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present application are within the scope of protection of the present application.
Referring to fig. 1, an embodiment of the present application provides a method for optimizing parameters of a composite material molding process, including:
step 100, constructing a target impregnation model of the composite material by using Hypermesh software;
step 102, importing a target impregnation model into PAM-RTM software, and constructing a target porosity model in the PAM-RTM software;
104, inputting a plurality of groups of input parameter sets of a composite material forming process into PAM-RTM software to obtain the porosity distribution and the impregnation rate distribution of a target impregnation model; wherein the porosity distribution comprises total porosity corresponding to each set of input parameters, and the impregnation rate distribution comprises impregnation rates corresponding to each set of input parameters;
and 106, optimizing parameters of the composite material forming process based on the porosity distribution and the impregnation rate distribution.
In this embodiment, firstly, a Hypermesh software is utilized to construct a target impregnation model of the composite material, then the target impregnation model is imported into PAM-RTM software, a target porosity model is constructed in the PAM-RTM software, then a plurality of groups of input parameter sets of a composite material forming process are input into the PAM-RTM software to obtain a porosity distribution and an impregnation rate distribution of the target impregnation model, and finally parameters of the composite material forming process are optimized based on the porosity distribution and the impregnation rate distribution. Therefore, the above technical scheme discusses the reason for forming the pores of the composite material by using a numerical simulation analysis mode, so that the parameters of the composite material forming process can be rapidly optimized, and the problem that the porosity distribution of the composite material cannot be rapidly determined in the prior art is solved.
The manner in which the individual steps shown in fig. 1 are performed is described below.
For step 100:
in one embodiment of the present application, the insulation pull rod model is selected as the objective impregnation model (see fig. 4), wherein the fiber fabric forming the composite material may be, for example, an aramid fiber fabric, and the carbon fiber fabric may be selected, and the resin forming the composite material may be, for example, an epoxy resin. The shape of the target impregnation model is not particularly limited, and for example, the shape of the target impregnation model can be columnar, tubular or other shapes, and the shape can be adjusted according to actual application scenes.
For step 102:
in one embodiment of the present application, the target impregnation model is imported into the PAM-RTM software, and a target porosity model is built in the PAM-RTM software (see fig. 5) to provide for subsequent acquisition of the porosity profile and impregnation rate profile of the target impregnation model.
In one embodiment of the application, the target porosity model is:
in the method, in the process of the application,V s is the total porosity;athe first preset coefficient;ba second preset coefficient;νis the flow rate of the resin;ca third preset coefficient;γis the surface tension of the resin;θis the contact angle of the resin.
In this embodiment, a function of total porosity related to impregnation rate, resin surface tension, viscosity and contact angle may be obtained by constructing a target porosity model, so that the porosity distribution and impregnation rate distribution of the target impregnation model are obtained by using the input parameter set later.
For step 104:
in this step, the porosity distribution (see fig. 6) and impregnation rate distribution of the target impregnation model are obtained by inputting a plurality of sets of input parameter sets of the composite material molding process into PAM-RTM software; wherein the porosity distribution comprises total porosity corresponding to each set of input parameters, and the impregnation rate distribution comprises impregnation rates corresponding to each set of input parameters; the porosity distribution and impregnation rate distribution of the multiple sets of input parameter sets and the target impregnation model are shown in table 1; it should be noted that, the data in table 1 are only a part of the input parameter sets and the total porosity and the impregnation rate corresponding to the input parameter sets in the embodiments of the present application, and the data in the table are only used to illustrate the technical solutions of the embodiments of the present application, but not limit the embodiments.
TABLE 1 partial input parameter sets and the total porosity and impregnation rate corresponding thereto
In the table of the present application,Tis the dipping temperature,PTo impregnate the vacuum degree，γIs the surface tension of the resin and,μin order to achieve the viscosity of the resin,V s in order to achieve a total porosity of the porous body,vis the impregnation rate.
In one embodiment of the application, the input parameter sets include an impregnation vacuum degree, a permeability of the fiber fabric, a surface tension, a viscosity and a contact angle of the resin, and each input parameter set corresponds to a respective impregnation temperature.
The input parameter sets are experimentally measured or provided by manufacturers, and each group of input parameter sets corresponds to one impregnation temperature; each set of input parameters includes impregnation vacuum, permeability of the fiber fabric, surface tension, viscosity and contact angle of the resin; and inputting each group of input parameter set to obtain the corresponding porosity distribution and impregnation rate distribution of the target impregnation model so as to optimize the parameters of the composite material forming process based on the porosity distribution and the impregnation rate distribution.
In one embodiment of the present application, after step 104, the method further comprises:
determining a target area with total porosity greater than a first preset threshold in the target impregnation model based on the porosity distribution of the target impregnation model;
acquiring the porosity distribution of an actual insulation pull rod in a target area;
and adjusting a preset coefficient of the target porosity model based on the porosity distribution of the target impregnation model and the actual insulation pull rod in the target area until the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than a second preset threshold value.
In this embodiment, voids are inevitably present during the molding process of the composite material, but the porosity can be reduced to facilitate the improvement of the performance of the composite material. In order to provide more effective guidance for the improvement of the parameters of the composite material molding process in actual production, it is necessary to ensure that the porosity distribution of the target impregnation model and the porosity distribution of the actual insulating tie rod are within a preset error range (i.e., less than a second preset threshold); therefore, a region with more obvious pore distribution (i.e., a target region of the target impregnation model) needs to be selected and compared with the pore distribution of a region corresponding to the actual insulation pull rod (i.e., a target region of the actual insulation pull rod), and the preset coefficient of the target pore model is adjusted until the pore distribution of the target impregnation model and the actual insulation pull rod in the target region is within a preset error range (i.e., smaller than a second preset threshold value).
It should be noted that the porosity distribution of the actual insulation pull rod in the target area can be obtained through a metallographic experiment or a micro CT test; the inventor finds that the number of void defects in different positions of the insulating pull rod composite material prepared by adopting the optimized parameters of the forming process is obviously reduced, and the porosity is reduced. Simulated porosity before optimizationV s 6.41% (total porosity in the target impregnation model), while experiments verify porosityVt5.59% (total porosity of the actual insulating pull rod), and the prediction accuracy error is 14.67%; simulated porosity after optimizationV s 3.52% (total porosity in the target impregnation model), while experiments verify porosityV t The prediction accuracy error was 2.82% (total porosity of the actual insulated tie rod) and 19.88%. It can be seen that the simulation and experimental results have good agreement and the porosity after improvement is reduced from 5.59% to 2.82%. The micro CT result shows that the porosity of the aramid fiber insulation pull rod is obviously reduced after the process condition is optimized.
In one embodiment of the present application, the step of adjusting the preset coefficient of the target porosity model based on the porosity distribution of the target impregnation model and the actual insulation pull rod in the target area until the difference between the average total porosities of the target impregnation model and the actual insulation pull rod in the target area is smaller than the second preset threshold value may specifically include:
when the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is not less than a second preset threshold value, performing:
step S1, keeping a first preset coefficient and a third preset coefficient unchanged, and gradually reducing the second preset coefficient;
step S2, judging whether the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than a second preset threshold value or not when the current sampling point is the same as the current sampling point for each sampling point in the process that the second preset coefficient is reduced to a third preset threshold value; if yes, finishing the adjustment of the preset coefficient of the target porosity model; if not, executing the step S3;
step S3, keeping the first preset coefficient unchanged, determining the second preset coefficient as a third preset threshold value, and gradually reducing the third preset coefficient;
step S4, judging whether the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than a second preset threshold value or not at the current sampling point for each sampling point in the process that the third preset coefficient is reduced to a fourth preset threshold value; if yes, finishing the adjustment of the preset coefficient of the target porosity model; if not, executing step S5;
step S5, determining the second preset coefficient as a third preset threshold value and determining the third preset coefficient as a fourth preset threshold value, and gradually reducing the first preset coefficient;
and S6, judging whether the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than a second preset threshold value or not at the current sampling point according to each sampling point in the first preset coefficient reducing process until the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than the second preset threshold value.
In this embodiment, when the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is not smaller than the second preset threshold, the target porosity model is optimized by adjusting the preset coefficient until the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is finally obtained and smaller than the second preset threshold, so as to better guide manufacturers to improve parameters of the molding process.
It should be noted that, from the target porosity model, the importance of the weight on the total porosity is as follows from high to low: the second preset coefficient, the third preset coefficient and the first preset coefficient. Therefore, in order to reduce the value of the total porosity by adjusting the preset coefficient as soon as possible, the inventors creatively found that the second preset coefficient, the third preset coefficient and the first preset coefficient can be sequentially reduced according to the adjustment logic of the preset coefficient until the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than the second preset threshold value.
For step 106:
in this step, the total porosity with the lowest value can be found based on the total porosity distribution of the target impregnation model in table 1, and according to the total porosity with the lowest value, the impregnation rate, the impregnation temperature and the impregnation vacuum corresponding to the total porosity can be determined as the optimal parameters of the composite material forming process, and the optimal parameters are provided for manufacturers to effectively guide the manufacturers to improve the parameters of the composite material forming process in actual production.
As shown in fig. 2 and 3, an embodiment of the present application provides a parameter optimizing apparatus for a composite material molding process. The apparatus embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software. In terms of hardware, as shown in fig. 2, a hardware architecture diagram of an electronic device where a parameter optimization apparatus for a composite material forming process provided in an embodiment of the present application is located, in addition to a processor, a memory, a network interface, and a nonvolatile memory shown in fig. 2, the electronic device where the embodiment is located may generally include other hardware, such as a forwarding chip responsible for processing a packet, and so on. Taking a software implementation as an example, as shown in fig. 3, the device in a logic sense is formed by reading a corresponding computer program in a nonvolatile memory into a memory by a CPU of an electronic device where the device is located and running the computer program.
As shown in fig. 3, the parameter optimizing apparatus for a composite material forming process provided in this embodiment includes:
a building module 300 for building a target impregnation model of the composite material using Hypermesh software;
an importing module 302, configured to import the target impregnation model into PAM-RTM software, and build a target porosity model in the PAM-RTM software;
the input module 304 inputs a plurality of groups of input parameter sets of the composite material forming process into PAM-RTM software to obtain the porosity distribution and the impregnation rate distribution of the target impregnation model; wherein the porosity distribution comprises total porosity corresponding to each set of input parameters, and the impregnation rate distribution comprises impregnation rates corresponding to each set of input parameters;
the determination module 306 optimizes parameters of the composite formation process based on the porosity profile and the impregnation rate profile.
In an embodiment of the present application, the building module 300 may be configured to perform the step 100 in the above method embodiment, the importing module 302 may be configured to perform the step 102 in the above method embodiment, the inputting module 304 may be configured to perform the step 104 in the above method embodiment, and the determining module 306 may be configured to perform the step 106 in the above method embodiment.
In one embodiment of the present application, the input parameter sets include an impregnation vacuum degree, a permeability of the fiber fabric, a surface tension, a viscosity and a contact angle of the resin, and each of the input parameter sets corresponds to a respective impregnation temperature.
In one embodiment of the application, the fibrous fabric forming the composite material comprises an aramid fibrous fabric, the resin forming the composite material comprises an epoxy resin, and the target impregnation pattern comprises an insulating tie pattern.
In one embodiment of the application, the target porosity model is:
in the method, in the process of the application,V s is the total porosity;athe first preset coefficient;ba second preset coefficient;νis the flow rate of the resin;ca third preset coefficient;γis the surface tension of the resin;θis the contact angle of the resin;μis the viscosity of the resin.
In one embodiment of the present application, the determining module 306 is configured to perform the following operations:
the impregnation rate, impregnation temperature and impregnation vacuum corresponding to the lowest value of the total porosity are determined as optimal parameters for the composite forming process.
In one embodiment of the present application, further comprising:
the verification module is used for executing the following operations:
determining a target area in the target impregnation model, wherein the total porosity of the target area is greater than a first preset threshold value, based on the porosity distribution of the target impregnation model;
acquiring the porosity distribution of the actual insulating pull rod in the target area;
and adjusting a preset coefficient of the target porosity model based on the target impregnation model and the porosity distribution of the actual insulating pull rod in the target area until the difference between the average total porosity of the target impregnation model and the average total porosity of the actual insulating pull rod in the target area is smaller than a second preset threshold.
In one embodiment of the present application, the verification module is configured to, when executing the porosity distribution in the target area based on the target impregnation model and the actual insulation pull rod, adjust a preset coefficient of the target porosity model until a difference between average total porosities in the target area of the target impregnation model and the actual insulation pull rod is smaller than a second preset threshold, execute the following operations:
when the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is not smaller than the second preset threshold value, executing:
step S1, keeping the first preset coefficient and the third preset coefficient unchanged, and gradually reducing the second preset coefficient;
step S2, judging whether the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than a second preset threshold value or not at the current sampling point for each sampling point in the process that the second preset coefficient is reduced to a third preset threshold value; if yes, finishing the adjustment of the preset coefficient of the target porosity model; if not, executing the step S3;
step S3, keeping the first preset coefficient unchanged, determining the second preset coefficient as a third preset threshold value, and gradually reducing the third preset coefficient;
step S4, judging whether the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than the second preset threshold value or not at the current sampling point for each sampling point in the process that the third preset coefficient is reduced to the fourth preset threshold value; if yes, finishing the adjustment of the preset coefficient of the target porosity model; if not, executing step S5;
step S5, determining the second preset coefficient as a third preset threshold value and determining the third preset coefficient as a fourth preset threshold value, and gradually reducing the first preset coefficient;
step S6, judging whether the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than the second preset threshold value or not at the current sampling point according to each sampling point in the first preset coefficient reducing process until the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than the second preset threshold value.
It should be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on a parameter optimizing apparatus for a composite material molding process. In other embodiments of the application, a parameter optimization device for a composite material forming process may include more or fewer components than shown, or certain components may be combined, certain components may be split, or different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
The content of information interaction and execution process between the modules in the device is based on the same conception as the embodiment of the method of the present application, and specific content can be referred to the description in the embodiment of the method of the present application, which is not repeated here.
The embodiment of the application also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the parameter optimization method of the composite material molding process in any embodiment of the application is realized.
The embodiment of the application also provides a computer readable storage medium, and the computer readable storage medium is stored with a computer program, when the computer program is executed by a processor, the processor is caused to execute the parameter optimization method of the composite material forming process in any embodiment of the application.
Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present application.
Examples of the storage medium for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer by a communication network.
Further, it should be apparent that the functions of any of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
It is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: various media in which program code may be stored, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (8)
1. A method for optimizing parameters of a composite material forming process, comprising:
constructing a target impregnation model of the composite material by using Hypermesh software;
the target impregnation model is imported into PAM-RTM software, and a target porosity model is built in the PAM-RTM software;
inputting a plurality of groups of input parameter sets of a composite material forming process into the PAM-RTM software to obtain the porosity distribution and the impregnation rate distribution of the target impregnation model; wherein the porosity profile comprises total porosity corresponding to each set of input parameters, and the impregnation rate profile comprises impregnation rates corresponding to each set of input parameters;
optimizing parameters of the composite material forming process based on the porosity distribution and the impregnation rate distribution;
the fiber fabric forming the composite material comprises an aramid fiber fabric, the resin forming the composite material comprises epoxy resin, and the target impregnation model comprises an insulating pull rod model;
the target porosity model is:
in the method, in the process of the application,V s is the total porosity;the first preset coefficient; b is a second preset coefficient; v is the flow rate of the resin;ca third preset coefficient;γis the surface tension of the resin;θis the contact angle of the resin;μis the viscosity of the resin.
2. The method of claim 1, wherein the input parameter sets comprise an impregnation vacuum, a permeability of the fiber fabric, a surface tension, a viscosity, and a contact angle of the resin, and each of the input parameter sets corresponds to a respective impregnation temperature.
3. The method of claim 2, wherein optimizing parameters of the composite forming process based on the porosity profile and the impregnation rate profile comprises:
the impregnation rate, impregnation temperature and impregnation vacuum corresponding to the lowest value of the total porosity are determined as optimal parameters for the composite forming process.
4. The method of claim 1, further comprising, after said obtaining the porosity profile and impregnation rate profile of the target impregnation model:
determining a target area in the target impregnation model, wherein the total porosity of the target area is greater than a first preset threshold value, based on the porosity distribution of the target impregnation model;
acquiring the porosity distribution of the actual insulating pull rod in the target area;
and adjusting a preset coefficient of the target porosity model based on the target impregnation model and the porosity distribution of the actual insulating pull rod in the target area until the difference between the average total porosity of the target impregnation model and the average total porosity of the actual insulating pull rod in the target area is smaller than a second preset threshold.
5. The method of claim 4, wherein adjusting the preset coefficient of the target porosity model based on the porosity distribution of the target impregnation model and the actual insulation tie in the target area until the difference between the average total porosity of the target impregnation model and the actual insulation tie in the target area is less than a second preset threshold comprises:
when the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is not smaller than the second preset threshold value, executing:
step S1, keeping the first preset coefficient and the third preset coefficient unchanged, and gradually reducing the second preset coefficient;
step S2, judging whether the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than a second preset threshold value or not at the current sampling point for each sampling point in the process that the second preset coefficient is reduced to a third preset threshold value; if yes, finishing the adjustment of the preset coefficient of the target porosity model; if not, executing the step S3;
step S3, keeping the first preset coefficient unchanged, determining the second preset coefficient as a third preset threshold value, and gradually reducing the third preset coefficient;
step S4, judging whether the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than the second preset threshold value or not at the current sampling point for each sampling point in the process that the third preset coefficient is reduced to the fourth preset threshold value; if yes, finishing the adjustment of the preset coefficient of the target porosity model; if not, executing step S5;
step S5, determining the second preset coefficient as a third preset threshold value and determining the third preset coefficient as a fourth preset threshold value, and gradually reducing the first preset coefficient;
step S6, judging whether the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than the second preset threshold value or not at the current sampling point according to each sampling point in the first preset coefficient reducing process until the difference between the average total porosity of the target impregnation model and the actual insulation pull rod in the target area is smaller than the second preset threshold value.
6. A parameter optimizing apparatus for a composite material forming process, comprising:
the building module is used for building a target impregnation model of the composite material by utilizing Hypermesh software;
the importing module is used for importing the target impregnation model into PAM-RTM software and constructing a target porosity model in the PAM-RTM software;
the input module is used for inputting a plurality of groups of input parameter sets of a composite material forming process into the PAM-RTM software to obtain the porosity distribution and the impregnation rate distribution of the target impregnation model; wherein the porosity profile comprises total porosity corresponding to each set of input parameters, and the impregnation rate profile comprises impregnation rates corresponding to each set of input parameters;
a determining module for optimizing parameters of the composite material forming process based on the porosity distribution and the impregnation rate distribution;
the fiber fabric forming the composite material comprises an aramid fiber fabric, the resin forming the composite material comprises epoxy resin, and the target impregnation model comprises an insulating pull rod model;
the target porosity model is:
in the method, in the process of the application,V s is the total porosity;the first preset coefficient; b is a second preset coefficient; v is the flow rate of the resin;ca third preset coefficient;γis the surface tension of the resin;θis the contact angle of the resin;μis the viscosity of the resin.
7. An electronic device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the method of any of claims 1-5 when the computer program is executed.
8. A computer readable storage medium, having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method of any of claims 1-5.
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| KR20110135065A (en) * | 2010-06-10 | 2011-12-16 | 에스케이이노베이션 주식회사 | Micro-porous polyolefin film with thermally stable hybrid-composite layers |
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