CN101920558A - Process window optimization method of resin-base composite curvature laminate in autoclave molding - Google Patents
Process window optimization method of resin-base composite curvature laminate in autoclave molding Download PDFInfo
- Publication number
- CN101920558A CN101920558A CN2010101942774A CN201010194277A CN101920558A CN 101920558 A CN101920558 A CN 101920558A CN 2010101942774 A CN2010101942774 A CN 2010101942774A CN 201010194277 A CN201010194277 A CN 201010194277A CN 101920558 A CN101920558 A CN 101920558A
- Authority
- CN
- China
- Prior art keywords
- parameter
- resin
- laminate
- volume fraction
- curvature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 176
- 230000008569 process Effects 0.000 title claims abstract description 136
- 238000005457 optimization Methods 0.000 title claims abstract description 42
- 238000000465 moulding Methods 0.000 title claims abstract description 39
- 239000002131 composite material Substances 0.000 title claims abstract description 30
- 239000000835 fiber Substances 0.000 claims abstract description 76
- 230000007547 defect Effects 0.000 claims abstract description 29
- 229920005989 resin Polymers 0.000 claims description 66
- 239000011347 resin Substances 0.000 claims description 66
- 230000002950 deficient Effects 0.000 claims description 36
- 239000000463 material Substances 0.000 claims description 34
- 239000004744 fabric Substances 0.000 claims description 19
- 238000005516 engineering process Methods 0.000 claims description 16
- 229920000049 Carbon (fiber) Polymers 0.000 claims description 12
- 239000004917 carbon fiber Substances 0.000 claims description 12
- 239000003822 epoxy resin Substances 0.000 claims description 12
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 12
- 229920000647 polyepoxide Polymers 0.000 claims description 12
- 238000004088 simulation Methods 0.000 claims description 10
- 239000003292 glue Substances 0.000 claims description 8
- 238000004519 manufacturing process Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 6
- 238000010438 heat treatment Methods 0.000 claims description 5
- 238000007654 immersion Methods 0.000 claims description 4
- 238000006467 substitution reaction Methods 0.000 claims description 4
- XQUPVDVFXZDTLT-UHFFFAOYSA-N 1-[4-[[4-(2,5-dioxopyrrol-1-yl)phenyl]methyl]phenyl]pyrrole-2,5-dione Chemical compound O=C1C=CC(=O)N1C(C=C1)=CC=C1CC1=CC=C(N2C(C=CC2=O)=O)C=C1 XQUPVDVFXZDTLT-UHFFFAOYSA-N 0.000 claims description 3
- 229920002748 Basalt fiber Polymers 0.000 claims description 3
- 238000010521 absorption reaction Methods 0.000 claims description 3
- 229920006231 aramid fiber Polymers 0.000 claims description 3
- 239000000805 composite resin Substances 0.000 claims description 3
- 239000004643 cyanate ester Substances 0.000 claims description 3
- 239000003365 glass fiber Substances 0.000 claims description 3
- 239000005011 phenolic resin Substances 0.000 claims description 3
- 229920001568 phenolic resin Polymers 0.000 claims description 3
- 229920003192 poly(bis maleimide) Polymers 0.000 claims description 3
- 239000002356 single layer Substances 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000013386 optimize process Methods 0.000 abstract description 2
- 230000002708 enhancing effect Effects 0.000 abstract 1
- 239000000047 product Substances 0.000 description 32
- 239000010410 layer Substances 0.000 description 14
- 229920013657 polymer matrix composite Polymers 0.000 description 11
- 239000011160 polymer matrix composite Substances 0.000 description 11
- 239000000284 extract Substances 0.000 description 10
- 230000006870 function Effects 0.000 description 7
- 230000015572 biosynthetic process Effects 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 6
- 238000000605 extraction Methods 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000035699 permeability Effects 0.000 description 4
- 230000002265 prevention Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000006835 compression Effects 0.000 description 3
- 238000007906 compression Methods 0.000 description 3
- 238000006073 displacement reaction Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000009413 insulation Methods 0.000 description 2
- 238000012797 qualification Methods 0.000 description 2
- 229920001187 thermosetting polymer Polymers 0.000 description 2
- 201000004569 Blindness Diseases 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 238000007598 dipping method Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000004836 empirical method Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 238000012252 genetic analysis Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012856 packing Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010008 shearing Methods 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Landscapes
- Moulding By Coating Moulds (AREA)
- Casting Or Compression Moulding Of Plastics Or The Like (AREA)
Abstract
The invention discloses a process window optimization method applicable to a resin-base composite curvature laminate in autoclave molding, and the method is based on a resin-base composite hot-press molding analogue unit and a process parameter optimization goal setting unit, is combined with a defect property database unit and gives a hot-press molding process window of the curvature laminate, thus preparing the curvature laminate that meets the requirements of the average fiber volume fraction and the probability of defects of the laminate in the optimized process window, and an alarm is given out when a defect occurs in the process of the hot-press molding, thus guiding the resin-base composite curvature laminate to carry out industrial operation of the hot-press molding and enhancing the qualified rate of products.
Description
Technical field:
The present invention relates to a kind of hot press forming technology that is applicable to polymer matrix composites, specifically, be meant a kind of optimization method that is applicable to the process window of resin-base composite curvature laminate in the autoclave molding process.
Background technology:
Process window is meant and satisfies control of process parameters scope under the target call condition that process window is wide more to show that the manufacturability of material system is good more, then easy more realization set objective in the actual process.
In September, 2004 publish " a kind of composite autoclave forming process Fig. 8-62 is disclosed in the 233rd page of the polymer matrix composite handbook.The relation (or claiming pressure regime) that exists between the relation that exists between temperature-time as can be seen from the figure (or claiming temperature schedule) and pressure-time.
More be to formulate technological parameter at present in actual production by experience and a large amount of test, it is trial-and-error method, the applicability of the technological parameter that this method optimizes is very poor, only change the geometry of product even raw material are constant, also will grope new technological specification again, this has caused long, problem such as percent defective is high and reliability is low of lead time undoubtedly.Another kind is that expert system and sensor monitoring technology are combined, information by being arranged on machined materials such as the real-time collecting temperature of sensor in the curing system, pressure also feeds back to real-time monitoring system in the computer, described real-time monitoring system is according to certain principle technological parameter in time to be adjusted, thereby forming process is control effectively promptly online curing monitoring method.This method has been eliminated the blindness of empirical method to a certain extent, improved parts quality stability, but sensor can stay generally that thereby product is inner to influence its performance, and the costing an arm and a leg of many sensors and monitoring equipment, and makes this method be difficult to rapid popularization.In addition, the quality of expert system on-line monitoring depends on the degree of perfection of its principle, and for complicated situation, it is very difficult formulating rational principle.
In actual applications, the aircraft composite is of a great variety, the version complexity, and curvature laminate is a kind of typical structure form, promptly is made up of the two-stage structure of flat plate section and segmental arc, referring to three-dimensional structure schematic diagram shown in Figure 1.Curvature laminate is different with equal thick laminate, because segmental arc exists, the arc length difference of thickness direction diverse location, under external force except the distortion of thickness direction, can produce detrusion simultaneously, and the deformation extent difference of diverse location place laminate, resin bearing pressure difference, two dimension takes place and flows in resin, and promptly the interior direction of thickness direction and face flows; Simultaneously because the anisotropy of composite, the distortion of resin flows and laminate is very complicated in forming process, be easy to generate various defectives, as hole, rich resin, bridge formation etc., these cause being applicable to that the process window of equal thick laminate no longer satisfies the preparation requirement of curvature laminate, under the condition that breaks away from production line, set up process window optimization method, the evaluating material manufacturability, for reduction manufacturing cost, raising product percent of pass, and the hot-forming industrialization operation of band curvature structure is significant.
Summary of the invention:
The objective of the invention is to propose the process window optimization method of a kind of resin-base composite curvature laminate in autoclave molding, this method is the initial process parameter F with curvature laminate
7Be the input information source, the relevant treatment relation that provides in unit 2, characteristic material data library unit 3 and the defect characteristics Database Unit 4 is set by molding technique parameter, in conjunction with product configuration and mesh generation unit 1 and process parameter optimizing target unit 6 requirements are set then, in hot-forming analogue unit 5, handle at last, thereby the average fiber volume fraction of curvature laminate is satisfied in acquisition
Produce probability P with the hole defective
1The more optimal process window that requires, this more optimal process window is applied to resin-base composite curvature laminate in hot-forming industrialization operation, can improve the qualification rate of product.
The product initial setting up parameter F of product configuration and 1 output of mesh generation unit
1Include mould-type, suction glue mode, the prepreg overlay number of plies, mould arc part radian, paste die face arc radius, horizontal equal thick laminate length, vertical equal thick laminate length;
Molding technique parameter is provided with the moulding process parameter F of unit 2 outputs
2Include envionmental humidity, initial temperature, heating rate, temperature-time relationship, add normal pressure, pressure applying moment;
The material parameter F of characteristic material data library unit 3 outputs
3Include resin kind, kinds of fibers, fabric type, prepreg initial fiber volume fraction and the initial thickness in monolayer of prepreg; Described resin kind is epoxy resin, phenolic resins, cyanate ester resin, bismaleimide resin etc.; Described kinds of fibers is glass fibre, carbon fiber, aramid fiber, basalt fibre etc.; Described fabric type is unidirectional pre-immersion material, one-way fabric, plain cloth, twills, satin fabric etc.;
The defect characteristics parameter F of defect characteristics Database Unit 4 outputs
4Include various manufacturing defect such as hole that hot press forming technology produces, layering, rich resin, poor glue;
Hot-forming analogue unit 5 is at first according to product initial setting up parameter F
1, moulding process parameter F
2, material parameter F
3With defect characteristics parameter F
4Carry out the hot press forming technology process simulation, obtain mass parameter
Then according to fitness function
To the average fiber volume fraction
Target average fiber volume fraction
Standard deviation with target average fiber volume fraction
Compare, obtain the fitness parameter; Produce probability according to the hole defective then
To resin pressure P
rWith the resin minimum pressure P that stops air bubble growth
vCompare, obtain the hole defective and produce probability; Produce probability to initial process window parameter F according to fitness parameter and hole defective at last
7Be optimized processing, the process window after obtaining to optimize;
It is to be used to provide optimization aim parameter F that the process parameter optimizing target is provided with unit 6
6Give hot-forming analogue unit 5; Can be in hot-forming analogue unit 5 according to optimization aim parameter F
6Further the initial process window is provided with the initial process parameter F that unit (7) provides
7Be optimized, thereby embody the average fiber volume fraction of curvature laminate
Produce probability P with the hole defective
1The preferred process window;
The initial process window is provided with the initial process parameter F of unit 7 outputs
7Include the lower limit temperature T1 of impressed pressure, the first platform temperature T1
Min=70～130 ℃, the ceiling temperature T1 of the first platform temperature T1
Max=100～160 ℃, the minimum of a value PC of the PC that exerts pressure
MinThe maximum PC of≤0.1MPa and the PC that exerts pressure
Max〉=0.6MPa.
The present invention is optimized method to the process window of curvature laminate and has following advantage:
1. with the experimental parameter F in the initial process window 7
7Be the basis, utilize and optimize the processing simulation that mechanism automatic adjustment molding technique parameter (impressed pressure, pressure applying moment) carries out curvature laminate, reject the process conditions that porosity content is higher, the discontented foot-eye of average fiber volume fraction requires, process window can offer with reference to the person with data file and graphic form and use or be used for hot-forming industrialization operation after the optimization that simulation obtains.
2. the present invention utilizes computer technology to realize optimization to the curvature laminate process window, have simple to operate, simulation precision height, real-time characteristics.
3. the optimization process window that adopts the present invention to obtain can prepare and satisfy the low curvature laminate that requires of average fiber volume fraction, porosity content.
4. resin base composite material hot-pressed formation analogy method and defect characteristics database combination among the present invention can be estimated defective and produce probability, and report to the police when defective occurring in the hot-forming process.
Description of drawings:
Fig. 1 is a curvature laminate two-dimension plane structure schematic diagram.
Fig. 2 is the structural representation block diagram of the hot-forming polymer matrix composites curvature laminate of the present invention process window optimization system.
Fig. 3 is provided with the interface for the mesh generation to curvature laminate.
Fig. 4 is provided with the interface of unit for molding technique parameter of the present invention.
Fig. 4 A is the graph of a relation of temperature, pressure and time three in the existing autoclave forming process.
The interface that Fig. 5 is provided with for initial process window of the present invention.
The interface that Fig. 6 is provided with for optimization aim of the present invention.
Fig. 7 is the cross-sectional view of carbon fiber/epoxy resin curvature laminate.
Fig. 7 A is the network subdivision schematic diagram of carbon fiber/epoxy resin curvature laminate.
Fig. 7 B is the computer interface of carbon fiber/epoxy resin curvature laminate.
Fig. 7 C is the process window of carbon fiber/epoxy resin curvature laminate after optimizing.
The specific embodiment:
The present invention is described in further detail below in conjunction with drawings and Examples.
The present invention is a kind of hot-forming process of utilizing computer technology to come analog tree resin-based composite curvature laminate, is the initial process parameter F with curvature laminate
7Be the input information source, the relevant treatment relation that provides in unit 2, characteristic material data library unit 3 and the defect characteristics Database Unit 4 is set by molding technique parameter, in conjunction with product configuration and mesh generation unit 1 and process parameter optimizing target unit 6 requirements are set then, in hot-forming analogue unit 5, handle at last, thereby the average fiber volume fraction of curvature laminate is satisfied in acquisition
Produce probability P with the hole defective
1The more optimal process window that requires, this more optimal process window is applied to resin-base composite curvature laminate in hot-forming industrialization operation, can improve the qualification rate of product.
Referring to shown in Figure 2, a kind of resin-base composite curvature laminate, process window optimization method in autoclave molding of being applicable to of the present invention includes in this process window optimization process that product configuration and mesh generation unit 1, molding technique parameter are provided with unit 2, characteristic material data library unit 3, defect characteristics Database Unit 4, hot-forming analogue unit 5, unit 6 is set the process parameter optimizing target and the initial process window is provided with unit 7.
In the present invention, the product initial setting up parameter F of product configuration and 1 output of mesh generation unit
1Include mould-type, suction glue mode, the prepreg overlay number of plies, mould arc part radian, paste die face arc radius, horizontal equal thick laminate length, vertical equal thick laminate length, these parameters can carry out the desired parameters typing of simulation process by interface (referring to shown in Figure 3) mode, also can read by data file.
In the present invention, molding technique parameter is provided with the moulding process parameter F of unit 2 outputs
2Include envionmental humidity, initial temperature, heating rate, temperature-time relationship, add normal pressure, pressure applying moment, these parameters can carry out the parameter typing by interface (referring to shown in Figure 4) mode, be stored in the computer after also can resolving acquisition, extract corresponding file when waiting for utilization and get final product by Mathematical Modeling.In September, 2004 publish " a kind of composite autoclave forming process Fig. 8-62 (being shown in Fig. 4 A) is disclosed in the 233rd page of the polymer matrix composite handbook, temperature in insulation 30～60min section is called the first platform temperature T1, the temperature of insulation 120 ± 10min section is called the second platform temperature T2, as can be seen from the figure, the relation between temperature-time and pressure-time.Because the present invention is will be to the initial process parameter F of curvature laminate
7Be optimized, therefore the temperature that is provided with has difference.
In the present invention, the material parameter F of characteristic material data library unit 3 outputs
3Include resin kind, kinds of fibers, fabric type, prepreg initial fiber volume fraction and the initial thickness in monolayer of prepreg; Described resin kind is epoxy resin, phenolic resins, cyanate ester resin, bismaleimide resin etc.; Described kinds of fibers is glass fibre, carbon fiber, aramid fiber, basalt fibre etc.; Described fabric type is unidirectional pre-immersion material, one-way fabric, plain cloth, twills, satin fabric etc.; Resin kind, kinds of fibers, form of fabric difference, its thermal conductivity factor, specific heat capacity, density value and different with functional relationship of temperature, the testing arrangement that adopts the laboratory to design is voluntarily measured; The difference of resin kind relates to the value difference of parameter in its Viscosity Model and the model, can adopt rheometer test to obtain; Kinds of fibers, fabric type, ply sequence influence fibrage permeability, fibrolaminar compression property, and the testing arrangement that adopts the laboratory to design is voluntarily measured; The characteristic material data storehouse has comprised the parameter in the described Viscosity Model relevant with the resin kind, parameters such as described and kinds of fibers, fabric type, permeability, compression property Model parameter and thermal conductivity factor that ply sequence is relevant, specific heat capacity, density be BJ University of Aeronautics ﹠ Astronautics's Materials Academy through for many years to the polymer matrix composites system experimentize research, test, measure and obtain, have accuracy, reliability, once be used as one of industrial analysis data source by polymer matrix composite technology experiment chamber, Beijing.
In the present invention, the defect characteristics parameter F of defect characteristics Database Unit 4 outputs
4Include various manufacturing defect such as hole that hot press forming technology produces, layering, rich resin, poor glue, the present invention is for to (the initial process parameter F of curvature laminate of the process window in the moulding
7) be optimized, only analyze, and hole is the cavity that forms in the composite material forming process at the hole defective, generally characterize with porosity.Described porosity is meant that volume that exist in the resin between fibrage, the microcosmic aperture accounts for the ratio of total composite volume, represents with percentage, and be one of major defect of composite.
Referring to shown in Figure 5, in the present invention, the initial process window is provided with the initial process parameter F of unit 7 outputs
7Include the lower limit temperature T1 of impressed pressure, the first platform temperature T1
MinThe ceiling temperature T1 of (being set to 70～130 ℃), the first platform temperature T1
MaxThe minimum of a value PC of (being set to 100～160 ℃), the PC that exerts pressure
MinThe maximum PC of (smaller or equal to 0.1MPa) and the PC that exerts pressure
Max(more than or equal to 0.6MPa); The mathematical expression mode is initial process parameter F
7={ T1
Min, T1
Max, PC
Min, PC
Max.In the present invention, T1
MinAnd T1
MaxBe material parameter F according to characteristic material data library unit 3
3Choose; PC
MinAnd PC
MaxBe to decide with thermosetting resin base composite material process planning pressure basic demand according to aerospace structure.
In the present invention, unit 6 is set is to be used to provide optimization aim parameter F to the process parameter optimizing target
6Give hot-forming analogue unit 5; Can be in hot-forming analogue unit 5 according to optimization aim parameter F
6Further the initial process window is provided with the initial process parameter F that unit 7 provides
7Be optimized, thereby embody the average fiber volume fraction of curvature laminate
Produce probability P with the hole defective
1The preferred process window.Referring to shown in Figure 6, the process parameter optimizing target is provided with the optimization aim parameter F in the unit 6
6Include target average fiber volume fraction
The standard deviation of target average fiber volume fraction
Produce probability P with the hole defective
1Target average fiber volume fraction
The standard deviation of target average fiber volume fraction
Be in using with the thermosetting resin based composites according to aerospace structure the fiber volume fraction basic demand to be decided, the hole defective produces probability P
1Characterize the possibility that hole produces, probability is 0 o'clock, and the expression porosity is lower than and equals 1%, and probability is 1 o'clock, and the expression porosity is higher than 1%, and the structural composite material application requirements has been satisfied in the definition that among the present invention the hole defective is produced probability.Process window after the optimization is on initial process window basis, by in hot-forming analogue unit 5, handling, acquisition satisfy optimization aim parameter F
6Process window after the optimization that requires, the process window after the optimization has increased the regulation to impressed pressure and pressure applying moment span on the basis of initial process window.
Produce reason in hot-forming process mesopore and mainly be that air entrainment in the fiber lay down layer is not got rid of thoroughly or volatile matters such as the solvent that contains in the resin, aqueous vapor are not dissolved in the resin fully, exist and finally be fixed up with the form of bubble along with the curing of resin, the formation of hole defective and temperature, resin pressure are relevant, adopt the resin minimum pressure P that stops air bubble growth among the present invention
rAs hole defect characteristics characterization parameter, by the mass parameter F that obtains with hot-forming analogue unit 5
5Middle resin pressure P
rRelatively, obtain the hole defective and produce probability P
1, and with it as one of foundation of process window optimization.Described mass parameter F
5Comprise resin pressure P
rWith average fiber volume fraction
Its mathematical expression mode is a mass parameter
In the present invention, hot-forming analogue unit 5 is at first according to product initial setting up parameter F
1, moulding process parameter F
2, material parameter F
3With defect characteristics parameter F
4Carry out the hot press forming technology process simulation, obtain mass parameter
Then according to fitness function
To the average fiber volume fraction
Target average fiber volume fraction
Standard deviation with target average fiber volume fraction
Compare, obtain the fitness parameter; Produce probability according to the hole defective then
Resin minimum pressure P to resin pressure P and prevention air bubble growth compares, and obtains the hole defective and produces probability; Produce probability to initial process window parameter F according to fitness parameter and hole defective at last
7Be optimized processing, the process window after obtaining to optimize.
In the present invention, the minimum requirements of known computer is more than the CPU PIV 1.8G, more than the internal memory 512M, more than the hard disk 40G.Use computer intrinsic computation performance, hot-forming polymer matrix composites product mesopore genetic analysis method is easy to operate, analysis result is reliable.Use this method to instruct resin base composite material hot-pressed formation industrialization operation, reduce manufacturing cost and improve product percent of pass.
In the present invention, the implementation step of polymer matrix composites curvature laminate hot press forming technology window has:
The first step: in described product configuration and mesh generation unit 1, extract product initial setting up parameter F
1In the present invention, according to product initial setting up parameter F
1Mean curvature laminate original dimension, adopt computer technology that curvature laminate is carried out three-dimensional configuration, and the three-dimensional laminate figure of setting up acquisition is carried out mesh generation handle, acquisition has the laminate model (referring to shown in Figure 3) of node, and described node layer template die type is saved as a text formatting file (* .TXT); The extraction of simulation desired parameters after described text formatting file can make things convenient for.
Second step: extraction moulding experimental parameter F in the unit 2 is set at described molding technique parameter
2
The 3rd step: in described characteristic material data library unit 3, extract material parameter F
3
The 4th step: in described defect characteristics Database Unit 4, extract defect characteristics parameter F
4
The 5th step: the target parameter F that extraction process is optimized in the unit 6 is set in described process optimization target
6
The 6th step: in described initial process windows units 7, extract initial process window F7={T1
Min, T1
Max, PC
Min, PC
Max;
The 7th step: at first set T1=T1
Min, Pc=Pc
Min, to pressure applying moment t
CjiaBe optimized;
The 8th the step: in described hot-forming analogue unit 5 with initial setting up parameter F
1, moulding process parameter F
2, material parameter F
3At resin flows and laminate deformation relationship formula W
1The middle processing resolved and obtained product pressure inside the resin P
rWith product average fiber volume fraction
Then with the average fiber volume fraction
Substitution fitness function F
iThe middle parsing obtains the fitness parameter; Then with product pressure inside the resin P
rWith defect characteristics parameter F
4In the resin minimum pressure P of prevention air bubble growth
vRelatively, obtain the hole defective and produce probability P
1In the present invention, the fitness parameter of acquisition and hole defective produce probability P
1Size can be used for estimating hot-forming process, the reasonability of the current set technological parameter of curvature laminate.
The 9th step: adopt and optimize process window condition F
0The 8th step was carried out iteration handle, and will pick up result's (curvature laminate process window after the optimization) and be stored in the hot-forming analogue unit 5.In the present invention, the result who is obtained can export to and carry out actual making curvature laminate usefulness in the autoclave; The analysis that also can be used as the polymer matrix composites process is used.
Described in the present invention resin flows and laminate deformation relationship formula W
1For:
In the formula, x is illustrated in layer 0 ° of direction in shop, edge in the local coordinate system, and z is illustrated in vertical thickness direction in the local coordinate system, σ
XxX direction stress under the expression local coordinate system, τ
XzExpression local coordinate system down cut stress, P
rExpression resin bearing pressure, ε
vThe expression body strain, S
XxX directional permeability under the expression local coordinate system, S
ZzZ directional permeability under the expression local coordinate system, μ represents resin viscosity, t represents simulated time.
Stress and strain satisfies relational expression W in the formula (1)
2For:
In the formula, σ
XxX direction stress under the expression local coordinate system, τ
XzExpression local coordinate system down cut stress, ε
XxX direction strain under the expression local coordinate system, σ
ZzZ direction stress under the expression local coordinate system, ε
ZzZ direction strain under the expression local coordinate system, γ
XzThe strain of expression local coordinate system down cut, G
XzExpression material modulus of shearing, E represents the bending modulus of fiber, β represents fibrage compression property coefficient, V
0Expression prepreg initial fiber volume fraction, V
fThe expression fiber volume fraction, V
aExpression dense packing fiber volume fraction.
Relational expression W is satisfied in strain and displacement in the formula (2)
3For:
In the formula, ε
XxX direction strain under the expression local coordinate system, ε
ZzZ direction strain under the expression local coordinate system, γ
XzThe strain of expression local coordinate system down cut, u represents x direction displacement under the local coordinate system, v represents z direction displacement under the local coordinate system.
The mean curvature laminate diverse location fiber volume fraction V of place of the present invention
fWith z direction strain stress under the local coordinate system
ZzThe pass be:
In the formula, V
0Expression prepreg initial fiber volume fraction, ε
ZzZ direction strain under the expression local coordinate system; Through type (4) is resolved and is obtained the fiber volume fraction V of curvature laminate diverse location place
f
The average fiber volume fraction of curvature laminate of the present invention
With fiber volume fraction V
fThe pass is:
In the formula, N represents the prepreg overlay number of plies, and i represents the i fibrage,
Represent i fibrage fiber volume fraction,
Expression average fiber volume fraction; Through type (8) obtains laminate average fiber volume fraction
The resin minimum pressure of described in the present invention prevention air bubble growth
T is illustrated in the hot-forming temperature in effective process time in the formula; (RH)
0Relative humidity when reaching balance for resin water absorption before hot-forming.
Described in the present invention fitness function
Expression target average fiber volume fraction,
The standard deviation of expression target average fiber volume fraction.
Described in the present invention optimization process window condition
In, the k increase step number of representing to exert pressure, value is an integer, scope is
M represents that pressure applying moment increases step number, and value is an integer, and scope is
t
C3The concluding time of representing for the 3rd segment process time; t
C1The time started of representing for the second segment process time; t
CjiaExpression pressurization is (claiming pressure applying moment again) constantly, with zero point of process time be benchmark.Shown in Fig. 4 A, abscissa is meant process time (min of unit), 0～t
C1Represented for the first segment process time, t
C1～t
C2Represented for the second segment process time, t
C2～t
C3Represented for the 3rd segment process time; Left side ordinate is meant the hot-forming temperature in effective process time; The right ordinate is meant hot press molding pressure (being read by Pressure gauge) in effective process time.
Described optimization process window condition F
0Implementation step be:
The first step: T1=T1
Min, making k=0, m=0 then has Pc=Pc
Min, t
Cjia=t
C1
Second step: according to T1, Pc, t
CjiaValue, in hot-forming analogue unit 5, resolve and obtain product pressure inside the resin P
rWith product average fiber volume fraction
Then with the average fiber volume fraction
Substitution fitness function F
iThe middle parsing obtains the fitness parameter; Then with product pressure inside the resin P
rWith the resin minimum pressure P that stops air bubble growth
vRelatively, obtain the hole defective and produce probability P
1
The 3rd step: if P
1=1, then pressure applying moment is not optimized, make k=k+1, increase the Pc that exerts pressure, repeated for second step;
The 4th step: if P
1=0, fitness parameter＜0 is then no longer to pressure applying moment t
CjiaBe optimized;
The 5th step: if P
1=0, second step was then repeated for, from t in fitness parameter＞0
Cjia=t
C1Begin pressure applying moment is optimized, record is also preserved and is satisfied P
1=0 and the pressure applying moment t of fitness parameter=0
Cjia
The 6th the step: repeat second and third, four, five steps, up to Pc=Pc
Max, record is also preserved and is satisfied fitness parameter=0 and P
1=0 exert pressure and corresponding pressure applying moment;
The 7th step: make T1=T1, repeat second and third, four, five, six steps, record is also preserved and is satisfied fitness parameter=0 and P
1=0 exert pressure and corresponding pressure applying moment.
Embodiment 1: the carbon fiber/epoxy resin curvature laminate that unidirectional shop is 30 layers
The shop layer structure of embodiment 1 expression has A layer fiber 101, A layer prepreg 101, B layer prepreg 102 referring to shown in Figure 7 in order discharging from bottom to up ... N layer prepreg 130 inhaled gummed paper 31.This is the shop coating systems of a kind of common " the prepreg overlay number of plies ", and prepreg has been meant the dipping that is used to make composite a kind of intermediate materials of carbon fiber of epoxy resin is a kind of commercial goods.
The first step: in described product configuration and mesh generation unit 1, pick up product initial setting up parameter F
1, described product initial setting up parameter F
1Middle mould-type is a former, and inhaling the glue mode is that single face is inhaled glue, and prepreg initial fiber volume fraction is 55%, 30 layers of the prepreg overlay numbers of plies, laminate original depth h
0Be 4.3mm, female mould forming, 90 ° of mould arc part radians, pasting the die face arc radius is 4mm, horizontal and vertical equal thick laminate length is 25mm.In Patran software, create out laminate profile graphics (referring to shown in Figure 1), then described three-dimensional laminate figure is carried out 30 * 100 mesh generations and handle (shown in Fig. 7 A), acquisition has the laminate model (having the color three dimension picture on the display screen shows) of node, and described node layer template die type saved as text formatting file, i.e. a CurveL.TXT;
Second step: extraction process parameter F in the unit 2 is set at described molding technique parameter
2, temperature-time relationship is for being raised to 130 ℃ with the heating rate of 1.5 ℃/min from 25 ℃ of room temperatures, is raised to 180 ℃ and constant temperature 1 hour with the heating rate of 1.5 ℃/min from 130 ℃ behind 130 ℃ of following constant temperature 20min, cools off naturally then., on computers by resolving rise time and temperature data it is kept in the temperatedata.txt file according to temperature-time relationship, adds normal pressure P
aBe 0.3MPa, vacuum is 0.1MPa, and pressure applying moment is 4600s.At the computer visualization interface (referring to shown in Figure 4) simulation is set is 1200s, time step Δ t=10s total time.
The 3rd step: in described characteristic material data library unit 3, extract material parameter F
3, the resin kind: epoxy resin, kinds of fibers: carbon fiber, fabric type: unidirectional pre-immersion material, ply sequence: unidirectional shop layer, fiber is along 0 ° of direction lay.According to described material parameter, can from the characteristic material data storehouse, extract and calculate required and fiber and resin associated arguments and comprise parameter in Viscosity Model, penetration rate model, the compact model.
The 4th step: in described defect characteristics Database Unit 4, extract defect characteristics parameter F
4, described defect characteristics parameter F
4For stoping the resin minimum pressure P of air bubble growth
v, promptly
Relative humidity (RH) when the resin water absorption reaches balance before hot-forming
0Be 50%; Hot-forming temperature T is provided with the experimental parameter F that unit 2 extracts by molding technique parameter
2Middle temperature and time relation curve obtains.
The 5th step: the target parameter F that extraction process is optimized in the unit 6 is set in described process optimization target
6Target average fiber volume fraction
Be 63%, the standard deviation of target average fiber volume fraction
Be 5%, the hole defective produces probability P
1Be 0.
The 6th step: in described initial process windows units 7, extract initial process window F
7={ T1
Min, T1
Max, Pc
Min, Pc
Max, T1
MinBe 120 ℃, T1
MaxBe 130 ℃, Pc
MinBe 0.1MPa, Pc
MaxBe 0.6MPa.
The 7th step: at first set T1=120 ℃, Pc=0.1MPa is to pressure applying moment t
CjiaBe optimized;
The 8th the step: in described resin base composite material hot-pressed formation analogue unit 5 with initial setting up parameter F
1, moulding process parameter F
2, material parameter F
3At resin flows and laminate deformation relationship formula W
1The middle processing resolved and obtained product pressure inside the resin P
r, fiber volume fraction V
fAnd product average fiber volume fraction
Then with the average fiber volume fraction
Substitution fitness function F
iThe middle parsing obtains the fitness parameter; Then with product pressure inside the resin P
rWith defect characteristics parameter F
4In the resin minimum pressure P of prevention air bubble growth
vRelatively, obtain the hole defective and produce probability P
1
The 9th step: pick up and optimize algorithmic function F
0, and repeated for the 8th step, produce probability P up to fitness parameter and hole defective
1Satisfy stopping rule simultaneously, calculate to finish, export the result to autoclave or store in the computer (interface during simulation such as Fig. 7 B); Process window after the carbon fiber/epoxy resin curvature laminate optimization that the unidirectional shop of parsing acquisition is 30 layers, referring to Fig. 7 C, a point, b point are illustrated under certain temperature and pressure condition among the figure, satisfy target average fiber volume fraction
Be 63 ± 5%, the hole defective produces probability P
1Be 0 the pressure applying moment value lower limit and the upper limit.
Simultaneously, select process conditions in the process window after optimization, promptly first platform temperature is 130 ℃, exert pressure and be 0.5MPa, pressure applying moment is 3800s, has prepared the carbon fiber/epoxy resin curvature laminate of 30 layers of unidirectional shops, and making laminate average fiber content is 64%, porosity is 0.8%, has satisfied the optimization aim requirement.
The present invention is a kind of polymer matrix composites curvature laminate that is applicable to, process window optimization method in hot-forming process, this method is with the resin base composite material hot-pressed formation analogue unit, the process parameter optimizing target is provided with the unit and is the basis, binding deficient performance data library unit, provide curvature laminate hot press forming technology window, in the process window of optimizing, can prepare the curvature laminate that satisfies laminate average fiber volume fraction and defective generation probability demands, report to the police when in hot-forming process, defective occurring, instruct the hot-forming industrialization operation of polymer matrix composites curvature laminate, improve product percent of pass.
Claims (6)
1. the process window optimization method of a resin-base composite curvature laminate in autoclave molding, this optimization method utilizes computer technology to come the hot-forming process of analog tree resin-based composite curvature laminate, is the initial process parameter F with curvature laminate
7Be the input information source, the relevant treatment relation that provides in unit (2), characteristic material data library unit (3) and the defect characteristics Database Unit (4) is set by molding technique parameter, in conjunction with product configuration and mesh generation unit (1) and process parameter optimizing target unit (6) requirement is set then, in hot-forming analogue unit (5), handle at last, thereby the average fiber volume fraction of curvature laminate is satisfied in acquisition
Produce probability P with the hole defective
1The more optimal process window that requires is characterized in that:
The product initial setting up parameter F of product configuration and mesh generation unit (1) output
1Include mould-type, suction glue mode, the prepreg overlay number of plies, mould arc part radian, paste die face arc radius, horizontal equal thick laminate length, vertical equal thick laminate length;
Molding technique parameter is provided with the moulding process parameter F of unit (2) output
2Include envionmental humidity, initial temperature, heating rate, temperature-time relationship, add normal pressure, pressure applying moment;
The material parameter F of characteristic material data library unit (3) output
3Include resin kind, kinds of fibers, fabric type, prepreg initial fiber volume fraction and the initial thickness in monolayer of prepreg; Described resin kind is epoxy resin, phenolic resins, cyanate ester resin, bismaleimide resin etc.; Described kinds of fibers is glass fibre, carbon fiber, aramid fiber, basalt fibre etc.; Described fabric type is unidirectional pre-immersion material, one-way fabric, plain cloth, twills, satin fabric etc.;
The defect characteristics parameter F of defect characteristics Database Unit (4) output
4Include various manufacturing defect such as hole that hot press forming technology produces, layering, rich resin, poor glue;
Hot-forming analogue unit (5) is at first according to product initial setting up parameter F
1, moulding process parameter F
2, material parameter F
3With defect characteristics parameter F
4Carry out the hot press forming technology process simulation, obtain mass parameter
Then according to fitness function
To the average fiber volume fraction
Target average fiber volume fraction
Standard deviation with target average fiber volume fraction
Compare, obtain the fitness parameter; Produce probability according to the hole defective then
To resin pressure P
rWith the resin minimum pressure P that stops air bubble growth
vCompare, obtain the hole defective and produce probability; Produce probability to initial process window parameter F according to fitness parameter and hole defective at last
7Be optimized processing, the process window after obtaining to optimize;
It is to be used to provide optimization aim parameter F that the process parameter optimizing target is provided with unit (6)
6Give hot-forming analogue unit (5); Can be in hot-forming analogue unit (5) according to optimization aim parameter F
6Further the initial process window is provided with the initial process parameter F that unit (7) provides
7Be optimized, thereby embody the average fiber volume fraction of curvature laminate
Produce probability P with the hole defective
1The preferred process window;
The initial process window is provided with the initial process parameter F of unit (7) output
7Include the lower limit temperature T1 of impressed pressure, the first platform temperature T1
Min=70～130 ℃, the ceiling temperature T1 of the first platform temperature T1
Max=100～160 ℃, the minimum of a value PC of the PC that exerts pressure
MinThe maximum PC of≤0.1MPa and the PC that exerts pressure
Max〉=0.6MPa.
2. the process window optimization method of resin-base composite curvature laminate according to claim 1 in autoclave molding is characterized in that: the fiber volume fraction V of curvature laminate diverse location place
fWith z direction strain stress under the local coordinate system
ZzThe pass be
V
0Expression prepreg initial fiber volume fraction, ε
ZzZ direction strain under the expression local coordinate system.
3. the process window optimization method of resin-base composite curvature laminate according to claim 1 in autoclave molding is characterized in that: the average fiber volume fraction of curvature laminate
With fiber volume fraction V
fThe pass is
N represents the prepreg overlay number of plies, and i represents the i fibrage,
Represent i fibrage fiber volume fraction,
Expression average fiber volume fraction.
4. the process window optimization method of resin-base composite curvature laminate according to claim 1 in autoclave molding is characterized in that: the resin minimum pressure that stops air bubble growth
T is illustrated in the hot-forming temperature in effective process time in the formula; (RH)
0Relative humidity when reaching balance for resin water absorption before hot-forming.
5. the process window optimization method of resin-base composite curvature laminate according to claim 1 in autoclave molding is characterized in that: optimize the process window condition
The k increase step number of representing to exert pressure, value is an integer, scope is
M represents that pressure applying moment increases step number, and value is an integer, and scope is
t
C3The concluding time of representing for the 3rd segment process time; t
C1The time started of representing for the second segment process time; t
CjiaExpression pressurization constantly, with zero point of process time be benchmark.
6. the process window optimization method of resin-base composite curvature laminate in autoclave molding according to claim 1 or 5 is characterized in that optimizing process window condition F
0Implementation step be:
The first step: T1=T1
Min, making k=0, m=0 then has Pc=Pc
Min, t
Cjia=t
C1
Second step: according to T1, Pc, t
CjiaValue, in hot-forming analogue unit 5, resolve and obtain product pressure inside the resin P
rWith product average fiber volume fraction
Then with the average fiber volume fraction
Substitution fitness function F
iThe middle parsing obtains the fitness parameter; Then with product pressure inside the resin P
rWith the resin minimum pressure P that stops air bubble growth
vRelatively, obtain the hole defective and produce probability P
1
The 3rd step: if P
1=1, then pressure applying moment is not optimized, make k=k+1, increase the Pc that exerts pressure, repeated for second step;
The 4th step: if P
1=0, fitness parameter＜O is then no longer to pressure applying moment t
CjiaBe optimized;
The 5th step: if P
1=0, fitness parameter＞O then repeated for second step, from t
Cjia=t
ClBegin pressure applying moment is optimized, record is also preserved and is satisfied P
1=0 and the pressure applying moment t of fitness parameter=O
Cjia
The 6th the step: repeat second and third, four, five steps, up to Pc=Pc
Max, record is also preserved and is satisfied fitness parameter=O and P
1=0 exert pressure and corresponding pressure applying moment;
The 7th step: make T1=T1
Max, repeat second and third, four, five, six steps, record is also preserved and is satisfied fitness parameter=O and P
1=0 exert pressure and corresponding pressure applying moment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010194277 CN101920558B (en) | 2010-05-28 | 2010-05-28 | Process window optimization method of resin-base composite curvature laminate in autoclave molding |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010194277 CN101920558B (en) | 2010-05-28 | 2010-05-28 | Process window optimization method of resin-base composite curvature laminate in autoclave molding |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101920558A true CN101920558A (en) | 2010-12-22 |
| CN101920558B CN101920558B (en) | 2013-03-27 |
Family
ID=43335889
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201010194277 Active CN101920558B (en) | 2010-05-28 | 2010-05-28 | Process window optimization method of resin-base composite curvature laminate in autoclave molding |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101920558B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102693454A (en) * | 2011-03-23 | 2012-09-26 | 东北林业大学 | System for managing hot-pressing parameters of medium-density fiberboards |
| CN106777563A (en) * | 2016-11-29 | 2017-05-31 | 南京航空航天大学 | A kind of assay method of inorganic non-metallic fiber random strength and shortage probability |
| CN109117512A (en) * | 2018-07-18 | 2019-01-01 | 北京玻钢院复合材料有限公司 | For simulating the analogue system and emulation mode of wind-powered blade mold manufacturing process |
| CN109352963A (en) * | 2018-10-17 | 2019-02-19 | 江西洪都航空工业集团有限责任公司 | A kind of Aircraft Canopy Transparencies refractive power improvement and manufacturing process |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4773021A (en) * | 1987-07-31 | 1988-09-20 | General Electric Company | Adaptive model-based pressure control and method of resin cure |
| JPH09308995A (en) * | 1996-05-17 | 1997-12-02 | Meiki Co Ltd | Actual data output method of hot press |
| CN101089754A (en) * | 2007-06-22 | 2007-12-19 | 北京航空航天大学 | Optimization method for process pressure of resin base composite equal thick laminate hot-press technology |
-
2010
- 2010-05-28 CN CN 201010194277 patent/CN101920558B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4773021A (en) * | 1987-07-31 | 1988-09-20 | General Electric Company | Adaptive model-based pressure control and method of resin cure |
| JPH09308995A (en) * | 1996-05-17 | 1997-12-02 | Meiki Co Ltd | Actual data output method of hot press |
| CN101089754A (en) * | 2007-06-22 | 2007-12-19 | 北京航空航天大学 | Optimization method for process pressure of resin base composite equal thick laminate hot-press technology |
Non-Patent Citations (2)
| Title |
|---|
| 李艳霞等: "L 形复合材料层板热压工艺密实变形过程的数值模拟", 《复合材料学报》 * |
| 杨秉宪: "考虑层间应力的复合材料层板优化设计", 《北京航空航天大学学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102693454A (en) * | 2011-03-23 | 2012-09-26 | 东北林业大学 | System for managing hot-pressing parameters of medium-density fiberboards |
| CN106777563A (en) * | 2016-11-29 | 2017-05-31 | 南京航空航天大学 | A kind of assay method of inorganic non-metallic fiber random strength and shortage probability |
| CN106777563B (en) * | 2016-11-29 | 2020-03-13 | 南京航空航天大学 | Method for measuring random strength and defect probability of inorganic nonmetallic fibers |
| CN109117512A (en) * | 2018-07-18 | 2019-01-01 | 北京玻钢院复合材料有限公司 | For simulating the analogue system and emulation mode of wind-powered blade mold manufacturing process |
| CN109117512B (en) * | 2018-07-18 | 2019-10-18 | 北京玻钢院复合材料有限公司 | For simulating the analogue system and emulation mode of wind-powered blade mold manufacturing process |
| CN109352963A (en) * | 2018-10-17 | 2019-02-19 | 江西洪都航空工业集团有限责任公司 | A kind of Aircraft Canopy Transparencies refractive power improvement and manufacturing process |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101920558B (en) | 2013-03-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN100461192C (en) | Simulating method for resin flow and limanite deformed in resin-base composite L-shaped laminate forming | |
| CN101920558B (en) | Process window optimization method of resin-base composite curvature laminate in autoclave molding | |
| US10828870B2 (en) | Process for manufacturing apron board of high-speed rail equipment cabin using composite material | |
| Yang et al. | Compaction behavior and part thickness variation in vacuum infusion molding process | |
| CN105488623A (en) | Aircraft quality feature data processing method | |
| CN100456176C (en) | Optimization method for process pressure of resin base composite equal thick laminate hot-press technology | |
| Minakuchi et al. | Effect of inter-laminar toughened layers on process-induced strain and deformation of L-shaped composites | |
| Hubert et al. | Tool interface pressure during the forming of model composite corners | |
| Li et al. | Numerical and experimental study of the bleeder flow in autoclave process | |
| Park et al. | Analytical modeling of composite molding by resin infusion with flexible tooling: VARI and RFI processes | |
| Kumar et al. | Investigations on the multistability of series-connected unsymmetric laminates | |
| CN101089862A (en) | Simulating method for resin flow and fibre dense in resin-base composite equal thick laminate forming | |
| CRUZ et al. | Effect of thermal residual stresses on buckling and post-buckling properties of laminated composites perimetrally reinforced | |
| Murri et al. | Fatigue and damage tolerance analysis of a hybrid composite tapered flexbeam | |
| Gajjar et al. | Experimental investigations on process parameters for manufacturing of antenna reflectors using vacuum infusion process | |
| Bere et al. | Design and analysis of carbon/epoxy composite tubular parts | |
| Haris et al. | Vacuum Infusion Simulation for Radome Manufacturing Using Woven Flax Fibre and Glass Fibre | |
| Nicassio et al. | Time-dependent shape of bistable unsymmetric carbon fibers-epoxy thin laminates | |
| Yokozeki et al. | Investigation into property control of VaRTM composites by resin infusion process | |
| Li et al. | Numerical analysis of parametric effects on consolidation of angle‐bended composite laminates | |
| Puzyretskii et al. | Modeling and Optimization of Manufacturing Processes of the Hybrid Composite Propeller Blade | |
| Chen et al. | Numerical simulations and optimization of foam filled free-curved surface composite part | |
| CN108804790B (en) | Fiber metal laminated plate curing deformation numerical simulation method | |
| Nayak et al. | Sahu, and Siba Sankar Mahapatra | |
| Bi et al. | Sensitivity Analysis of Influencing Factors on Mould Surface Deformation of Resin Matrix Composites by Autoclave Moulding |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230523 Address after: No. 2010, 20th Floor, Building 2, No. 88 Jinfeng Road, Damian Street, Longquanyi District, Chengdu City, Sichuan Province, 610100 Patentee after: Chengdu Newgate Technology Innovation Service Co.,Ltd. Address before: 100191 No. 37, Haidian District, Beijing, Xueyuan Road Patentee before: BEIHANG University |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231215 Address after: 213000 building 11, No. 90, gangang Middle Road, Xinbei District, Changzhou City, Jiangsu Province (in Binjiang intelligent equipment enterprise port) Patentee after: Jiangsu keluwei New Material Technology Co.,Ltd. Address before: No. 2010, 20th Floor, Building 2, No. 88 Jinfeng Road, Damian Street, Longquanyi District, Chengdu City, Sichuan Province, 610100 Patentee before: Chengdu Newgate Technology Innovation Service Co.,Ltd. |