CN117077295A - Pre-connection number optimization method based on pre-connection digital twin model - Google Patents
Pre-connection number optimization method based on pre-connection digital twin model Download PDFInfo
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Abstract
The invention discloses a pre-connection number optimization method based on a pre-connection digital twin model, which comprises the following steps of: s1: dividing the wallboard into a plurality of wallboard typical units with similar geometric constraint conditions according to the positions of the inner-shaped clamping plates, wherein the wallboard typical units are used as physical entities corresponding to the pre-connected digital twin model; s2: measuring the assembly gap between the skin and the stringer connecting holes on the typical units of the wall plates, and modeling the assembly gap between the skin and the stringer by using a cubic spline interpolation curve; s3: establishing a finite element model with the same size as the typical units of the wall plates as a corresponding pre-connected digital twin model; and steps S4-S5. The preferred method is based on a pre-connection digital twin model to optimize the number of pre-connection pieces, and is more scientific and effective than relying on manual experience.
Description
Technical Field
The invention relates to the field of intelligent manufacturing, in particular to a pre-connection number optimization method based on a pre-connection digital twin model.
Background
In the assembly process of the typical aviation wallboard with the weak-rigidity thin-wall structure, due to the existence of multiple error sources such as part manufacturing errors, positioning errors, part deformation and the like, a certain assembly gap exists between the skin and the framework after each part is positioned on the wallboard assembly type frame. Excessive assembly clearance not only can lead to burrs entering interlayer scratch products during hole making, but also can lead to bulging phenomenon and the like of wallboard components after hole making connection, thereby influencing the fatigue life and aerodynamic shape accuracy of assembled aviation wallboard. In order to solve the problem, it is proposed to suppress the assembly gap through a pre-connection process, but the pre-connection process at present mainly depends on the experience of workers, lacks scientific basis, and the number of pre-connection pieces is an important parameter in the pre-connection process parameters, so that the pre-assembly period and the assembly gap suppression effect are determined to a great extent. It is generally accepted in engineering that the number of pre-connectors does not exceed 30% of the number of connection holes, but this experience has not led to the distinction between different panel types and therefore cannot accommodate the connection of different panel types.
Disclosure of Invention
The invention aims to provide a pre-connection number optimization method based on a pre-connection digital twin model, which can meet the connection of different wallboard types.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a pre-connectorized digital twin model based method of optimizing the number of pre-connectorized components comprising the steps of, in order:
s1: dividing the wallboard into a plurality of wallboard typical units with similar geometric constraint conditions according to the positions of the inner-shaped clamping plates, wherein the wallboard typical units are used as physical entities corresponding to the pre-connected digital twin model;
s2: measuring the assembly gap between the skin and the stringer connecting holes on the typical units of the wall plates, and modeling the assembly gap between the skin and the stringer by using a cubic spline interpolation curve;
s3: establishing a finite element model with the same size as the typical units of the wall plates as a corresponding pre-connected digital twin model;
s4: writing Python script by adopting homographySuper-cubic sampling of Latin to generateA step S3 of completing the pre-connection process simulation of each pre-connection layout sample by calling the finite element model of the pre-connection layout sample to obtain a residual gap at the position of the connection hole which is not pre-connected, wherein the assumption is->The average residual gap in the pre-connection layout is defined by the following first formula:
；
wherein,for the number of connection holes which are not pre-connected, < >>Representing the residual gap at the connection hole location where no pre-connection is made;
will beThe corresponding +.>The average residual gaps are ordered from large to small if +.>For the odd number, taking one average residual gap in the middle of the sequence as the median of the average residual gap; if->If the number is even, taking the average value of two average residual gaps in the middle of the sequence as the average residual gap median; average residual gap medianIs composed of the following componentsThe second formula defines:
；
s5: setting the initial value of the number of the pre-connectors to be 1, and continuously increasing the number of the pre-connectors ifThe number of pre-connectors currently described is taken as the preferred result.
Preferably, in step S3, the skin is modeled according to the assembly gap cubic spline interpolation curve obtained in step S2, and the rest of the parts including the stringers are modeled using their nominal geometric model; the skin and the inner-shaped clamping plate are completely attached under the compression effect of the bandage, in the aspect of constraint, the freedom degrees of the second skin positioning surface in the X, Y, Z directions are limited, the freedom degrees of the first skin positioning surface in the Y, Z directions are limited, the stringers are positioned based on the skin, then the stringers are clamped by means of the clamping blocks and the screw rods, in the aspect of constraint, the freedom degrees of the stringer positioning surface in the Y direction are limited, and the freedom degrees of the stringer positioning points in the X and Z directions are limited; bolts as pre-connectors are simulated by the beam units, and rigid beam constraints are set by the MPC constraints, with nuts simulating the bolts exerting compression on the annular area in contact with the skin and stringers.
Preferably, the pre-connected layout sample in step S4 is optimized through genetic algorithm and response surface training.
By adopting the design scheme, the invention has the beneficial effects that: the optimized method of the number of the pre-connecting pieces can establish the aviation wallboard pre-connecting digital twin model capable of accurately describing initial assembly gaps, realize the optimized of the number of the pre-connecting pieces in the technology, provide theoretical and technical support for the optimized of the aviation wallboard pre-connecting assembly process in the intelligent manufacture of aviation industry, meet the connection of different wallboard types, optimize the number of the pre-connecting pieces based on the pre-connecting digital twin model, and be more scientific and effective compared with the method depending on manual experience.
Drawings
FIG. 1 is a schematic view of a typical unit of a wall panel according to the present invention;
FIG. 2 is a schematic representation of the assembly gap characterization based on a cubic spline interpolation curve;
FIG. 3 is a schematic illustration of a digital twin model of an aircraft panel typical unit pre-connection;
in the figure: 1. screw rod 2, clamping block 3, skin 4, internal clamping plate 5, stringer,
6. the first skin locating surface, 7, the stringer locating surface, 8, the stringer locating point, 9 the second skin locating surface.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A pre-connectorized digital twin model based method of optimizing the number of pre-connectorized components comprising the steps of, in order:
s1: as shown in fig. 1, because the aviation wallboard is large in size and has a large number of connecting pieces, in order to ensure that the pre-connected digital twin model has higher calculation efficiency, the wallboard is divided into a plurality of wallboard typical units with similar geometric constraint conditions according to the position of the inner-shaped clamping plate 4 based on the san-valan principle, and the wallboard typical units are used as physical entities corresponding to the pre-connected digital twin model;
s2: measuring the assembly gap at the connecting hole of the skin 3 and the stringer 5 on a typical unit of the wallboard by adopting conventional measuring tools such as a feeler gauge and the like, and modeling the assembly gap between the skin 3 and the stringer 5 by utilizing a cubic spline interpolation curve; as shown in fig. 2, wherein,and->The position coordinates of the two end points of the skin 3 and the stringer 5 correspond to the assembly gap of +>；/>Is +.>The position coordinates of the connecting holes corresponding to the fitting clearances ∈>From the measurements.
In this embodiment, the stringer 5 is idealized to its nominal geometry by considering that the skin 3, which is typically less stiff when pre-attached, is attached to the stringer 5, and the assembly gap is believed to be caused by deformation and errors of the skin 3. In addition, the assembly gap between the skin 3 and the stringer 5 is considered to be zero under a fixed clamping action.
The nominal geometric model herein is an idealized stringer 5 geometric model that does not take into account errors.
S3: as shown in fig. 3, a finite element model of the same size as a typical unit of the wall panel is built as a corresponding pre-connection digital twin model for simulating the assembly gap variation under the pre-connection constraint.
Wherein, the skin 3 is modeled according to the assembly clearance cubic spline interpolation curve obtained in the step S2, and the rest parts including the stringers are modeled by adopting a nominal geometric model thereof; the skin and the inner-shaped clamping plate are completely attached under the compression effect of the bandage, and in the aspect of constraint, the degrees of freedom of the second skin locating surface 9 in the three directions X, Y, Z are limited, and the degrees of freedom of the first skin locating surface 6 in the two directions Y, Z are limited. Positioning the stringer based on the skin, and clamping by means of the clamping block 2 and the screw 1, wherein in the aspect of constraint, the freedom degree of the stringer positioning surface 7 in the Y direction is limited, and the freedom degrees of the stringer positioning points 8 in the X and Z directions are limited; bolts as pre-connectors are simulated by the beam units and rigid beam constraints are set by the MPC constraints, the nuts of the simulated bolts exerting a compression action on the annular region in contact with the skin 3 and stringers 5.
S4: python script is written, and uniform Latin hypercube sampling capable of representing overall probability distribution based on less sample size is adopted, so that the Python script can be used as a pre-connection holeExtracting +.>Are used for setting the pre-connection, generating +.>The pre-connection layout samples are respectively connected, and the finite element model in the step S3 is called to complete the pre-connection process simulation of each pre-connection layout sample, so as to obtain the non-pre-connected +.>Residual gap at the position of the connection hole, supposing +.>In the case of the individual pre-connection layout, the +.>The residual gap at the position of the connecting hole is +.>The average residual gap is defined by the following first formula:
；
wherein,for the number of connection holes which are not pre-connected, < >>Representing the residual gap at the connection hole location where no pre-connection is made;
the uniform Latin hypercube sampling in this embodiment also enables the generation of a suitable uniform distribution of samples in the case of discrete variables, the algorithm being particularly suited to generating an initial dataset for optimization by genetic algorithm and Response Surface Method (RSM) training, whereas classical Latin hypercube algorithms create the correct distribution only in the case of continuous input variables.
Considering that the average residual gap of the pre-connected rear wall panel is generally smaller thanWill->The corresponding +.>The average residual gaps are ordered from large to small if +.>For the odd number, taking one average residual gap in the middle of the sequence as the median of the average residual gap; if->If the number is even, taking the average value of two average residual gaps in the middle of the sequence as the average residual gap median; average residual gap median>Defined by the following second formula:
；
s5: number of pre-connectorsThe initial value of (1) is set to be 1, the number of the pre-connection pieces is continuously increased, ifAt least one of the number of pre-connectorsWhen the half pre-connection layout sample meets the overall gap suppression requirement, the current number of pre-connection pieces is used as a preferred result, and the preferred result is used as a basis for the follow-up pre-connection layout, the pre-connection sequence and the pre-tightening force optimization.
In summary, the method can establish the aviation wallboard pre-connection digital twin model capable of accurately describing the initial assembly gap, realizes the optimization of the number of the pre-connection pieces in the technology, can provide theoretical and technical support for the optimization of the aviation wallboard pre-connection assembly process in the intelligent manufacture of aviation industry, and the optimization method of the number of the pre-connection pieces can meet the connection of different wallboard types.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. A pre-connectorized quantity optimization method based on a pre-connectorized digital twin model, characterized by: the method comprises the following steps of:
s1: dividing the wallboard into a plurality of wallboard typical units with similar geometric constraint conditions according to the positions of the inner-shaped clamping plates, wherein the wallboard typical units are used as physical entities corresponding to the pre-connected digital twin model;
s2: measuring the assembly gap between the skin and the stringer connecting holes on the typical units of the wall plates, and modeling the assembly gap between the skin and the stringer by using a cubic spline interpolation curve;
s3: establishing a finite element model with the same size as the typical units of the wall plates, and taking the finite element model as a corresponding pre-connected digital twin model;
s4: writing Python script, and generating by uniformly Latin hypercube samplingA step S3 of completing the pre-connection process simulation of each pre-connection layout sample by calling the finite element model of the pre-connection layout sample to obtain a residual gap at the position of the connection hole which is not pre-connected, wherein the assumption is->The average residual gap in the pre-connection layout is defined by the following first formula:
；
wherein,for the number of connection holes which are not pre-connected, < >>Representing the residual gap at the connection hole location where no pre-connection is made;
will beThe corresponding +.>The average residual gaps are ordered from large to small if +.>For the odd number, taking one average residual gap in the middle of the sequence as the median of the average residual gap; if->If the number is even, taking the average value of two average residual gaps in the middle of the sequence as the average residual gap median; average residual gap medianDefined by the following second formula:
；
s5: setting the initial value of the number of the pre-connectors to be 1, and continuously increasing the number of the pre-connectors ifThe number of pre-connectors currently described is taken as the preferred result.
2. A pre-connectorized digital twinning model based pre-connectorized quantity optimization method as defined in claim 1, wherein: in step S3, modeling the skin according to the assembly clearance cubic spline interpolation curve obtained in step S2, wherein the rest parts including the stringers are modeled by adopting a nominal geometric model; the skin and the inner-shaped clamping plate are completely attached under the compression effect of the bandage, in the aspect of constraint, the degrees of freedom of the second skin locating surface in the three directions are limited, the degrees of freedom of the first skin locating surface in the two directions are limited, the stringers are located based on the skin, then the clamping blocks and the screw are used for clamping, the degrees of freedom of the stringer locating surface in the Y direction are limited, and the degrees of freedom of the stringer locating points in the X and Z directions are limited; bolts as pre-connectors are simulated by the beam units, and rigid beam constraints are set by the MPC constraints, with nuts simulating the bolts exerting compression on the annular area in contact with the skin and stringers.
3. A pre-connectorized digital twinning model based pre-connectorized quantity optimization method as defined in claim 1, wherein: the pre-connected layout sample in the step S4 is optimized through genetic algorithm and response surface method training.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190384870A1 (en) * | 2018-06-13 | 2019-12-19 | Toyota Jidosha Kabushiki Kaisha | Digital twin for vehicle risk evaluation |
| CN112977876A (en) * | 2021-04-08 | 2021-06-18 | 泉州装备制造研究所 | Wing box assembling gap control device and method |
| CN113111436A (en) * | 2021-04-15 | 2021-07-13 | 泉州装备制造研究所 | Airplane large component pre-connection layout and multi-constraint action sequence optimization method |
| CN115229117A (en) * | 2022-07-29 | 2022-10-25 | 东北大学 | Wallboard riveting deformation control method based on digital twinning |
| EP4181009A1 (en) * | 2021-11-11 | 2023-05-17 | Airbus Defence and Space, S.A.U. | Computer-implemented method for determining gaps and overlaps between two or more aircraft real parts with a defined kinematic beetween them |
| CN116579209A (en) * | 2023-05-11 | 2023-08-11 | 南京航空航天大学 | Wing structure deformation real-time prediction method based on digital twin and neural network |
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- 2023-10-16 CN CN202311335471.3A patent/CN117077295B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190384870A1 (en) * | 2018-06-13 | 2019-12-19 | Toyota Jidosha Kabushiki Kaisha | Digital twin for vehicle risk evaluation |
| CN112977876A (en) * | 2021-04-08 | 2021-06-18 | 泉州装备制造研究所 | Wing box assembling gap control device and method |
| CN113111436A (en) * | 2021-04-15 | 2021-07-13 | 泉州装备制造研究所 | Airplane large component pre-connection layout and multi-constraint action sequence optimization method |
| EP4181009A1 (en) * | 2021-11-11 | 2023-05-17 | Airbus Defence and Space, S.A.U. | Computer-implemented method for determining gaps and overlaps between two or more aircraft real parts with a defined kinematic beetween them |
| CN115229117A (en) * | 2022-07-29 | 2022-10-25 | 东北大学 | Wallboard riveting deformation control method based on digital twinning |
| CN116579209A (en) * | 2023-05-11 | 2023-08-11 | 南京航空航天大学 | Wing structure deformation real-time prediction method based on digital twin and neural network |
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