Antenna pedestal assembly simulation method and system based on digital twinning

Abstract

The invention belongs to the field of digital assembly, and discloses an antenna pedestal assembly simulation method and system based on digital twinning. An antenna pedestal digital twin model is constructed based on an antenna pedestal geometric model, an antenna pedestal meshing mechanism model and an antenna pedestal assembly rule model, the assembly process is simulated through the constructed antenna pedestal digital twin model, quality evaluation indexes are pre-judged before antenna pedestal entity assembly, and the repair amount is fed back. And assembling is performed after the quality evaluation meets the design requirement, so that the assembling efficiency is improved. The problems that in the prior art, the quality evaluation index needs to be guaranteed through repeated preassembling, measuring, base plate repairing and assembling and reassembling, the assembling means is outdated, the assembling efficiency is low, and the labor intensity is large in the assembling process of the antenna pedestal are solved.

Description

Technical Field

[0001] The present invention belongs to the field of digital assembly, and in particular relates to an antenna base assembly simulation method and system based on digital twins. Background Art

[0002] The antenna seat is a key component of the radar product servo drive system, a carrier platform for radar antenna and feeder system components, and a moving component for the antenna array to perform azimuth and elevation mechanical scanning. It is the core foundation for achieving target detection and spatial position feedback. The main quality evaluation indicators of the antenna seat include the parallelism of the gear pair axis, the center distance of the gear pair, the gear meshing spot area, etc. Due to reasons such as component processing errors, in the actual assembly process, the quality evaluation indicators are generally repeatedly guaranteed by pre-installation, measurement, repair of pads, and re-assembly. The assembly means are backward, the assembly efficiency is low, and the labor intensity is high.

[0003] As a link between the physical world and the digital world, digital twin technology uses mechanism models, real-time sensor data, etc. to build a multi-scale coupled "follow-up" twin model, which can simulate and map the real physical body's form and performance in virtual space.

[0004] The existing digital twin technology is limited to the simulation of geometric models, and all of them are assembly simulations of simple systems such as barrel sections and thin-walled parts. There is a lack of research on the mechanism model of large gear pair systems such as antenna bases. Summary of the invention

[0005] In order to solve the problems of the prior art and improve assembly efficiency, the present application provides an antenna base assembly simulation method and system based on digital twin.

[0006] In the first aspect, a method for simulating antenna base assembly based on digital twin is provided, and the technical solutions adopted are as follows:

[0007] Construct the antenna base geometry model;

[0008] The parametric rapid modeling method is used to build the antenna base geometric model to feedback the macroscopic characteristics of the antenna base entity;

[0009] Construct a model of the antenna base engagement mechanism;

[0010] The gear dynamic meshing theory and finite element analysis scheme are used to construct the antenna base meshing mechanism model to feedback the microscopic characteristics of the antenna base entity;

[0011] Construct antenna base assembly rule model;

[0012] Based on the antenna base assembly process, the assembly sequence and assembly constraints are defined, and an antenna base assembly rule model is constructed to improve the simulation confidence.

[0013] Build a digital twin model of the antenna base and obtain quality assessment index data through simulation;

[0014] Based on the antenna base geometry model, antenna base meshing mechanism model and antenna base assembly rule model, a digital twin model of the antenna base is constructed. The assembly process is simulated through the digital twin model of the antenna base to obtain quality evaluation index data.

[0015] Furthermore, the macroscopic characteristics of the antenna seat entity include size, weight, gear pair center distance, and gear pair axis parallelism.

[0016] Furthermore, the microscopic features of the antenna base entity include contact loads and engagement spots.

[0017] Furthermore, the quality assessment index data include the gear pair center distance, the gear pair axis parallelism, and the meshing spot area.

[0018] The constructed antenna base digital twin model is further reconstructed using the following method:

[0019] Build an antenna pedestal physical assembly measurement platform, scan components, obtain component point cloud data, pre-process the component point cloud data, extract the component feature data, and import it into the antenna pedestal digital twin model.

[0020] Furthermore, multi-view alignment and noise removal methods are used to pre-process the point cloud data of the components.

[0021] Furthermore, the pad is repaired based on the improved particle swarm optimization algorithm.

[0022] Furthermore, after the physical assembly of the antenna base is completed, the quality assessment index data is rechecked. If there is any deviation, a correction coefficient is formed and fed back to the digital twin model of the antenna base to complete the update and iteration of the model itself.

[0023] In a second aspect, an antenna base assembly simulation system based on digital twins is provided, wherein the system is used to implement the antenna base assembly simulation method based on digital twins as described in the first aspect, and the system comprises the following parts:

[0024] Antenna base geometric model building module, used to build the antenna base geometric model and feedback the macro characteristics of the antenna base entity;

[0025] Antenna base meshing mechanism model building module, used to build the antenna base meshing mechanism model and feedback the microscopic characteristics of the antenna base entity;

[0026] Antenna base assembly rule model building module, used to build antenna base assembly rule model to improve simulation confidence;

[0027] The antenna base digital twin model construction module obtains quality assessment index data through simulation.

[0028] The beneficial effects of the present invention are as follows:

[0029] In the prior art, the antenna base is assembled by repeatedly pre-assembling, measuring, repairing the pad, and reassembling to ensure the quality evaluation index. The assembly method is backward, the assembly efficiency is low, and the labor intensity is high.

[0030] The present invention simulates the assembly process through the constructed digital twin model of the antenna base, predicts the quality assessment indicators before the actual assembly of the antenna base, simulates and provides feedback on the repair amount of the pad during the assembly process, and performs assembly only after the quality assessment meets the design requirements, thereby improving assembly efficiency.

[0031] By importing the characteristic data of the key components of the antenna base into the digital twin model of the antenna base for reconstruction, the accuracy of the antenna base geometric model and the antenna base meshing mechanism model can be further improved, thereby improving the accuracy of the quality assessment index prediction of the digital twin model of the antenna base and further improving the assembly efficiency.

[0032] Based on the improved particle swarm optimization algorithm, the pad repair plan is optimized, which can quickly and accurately feedback the pad repair quantity, thereby ensuring the quality evaluation indicators and further improving the assembly efficiency.

[0033] After the physical assembly of the antenna base is completed, the relevant quality assessment parameters are rechecked. If there are deviations, correction coefficients are generated and fed back to the digital twin model of the antenna base to complete the model's own update and iteration, thereby improving the accuracy of the antenna base digital twin model's predicted quality assessment indicators and further improving assembly efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of an antenna base assembly simulation method based on digital twin according to the first embodiment of the present invention;

[0035] Figure 2 is a schematic diagram of an antenna base assembly simulation method based on digital twin according to the second embodiment of the present invention;

[0036] Figure 3 1 is a schematic diagram of an antenna base physical assembly detection platform in the second embodiment of the present invention, wherein 1 is a laser scanner; 2 is a mechanical arm; 3 is an antenna base system; 31 is a reducer; 32 is a pad; 33 is a pinion; 34 is a bearing; and 4 is a support platform;

[0037] Figure 4 This is a flowchart of optimizing the pad repair solution based on the improved particle swarm optimization algorithm in the third embodiment of the present invention. DETAILED DESCRIPTION

[0038] The present invention is further described below.

[0039] The present invention provides an antenna pedestal assembly simulation method based on digital twin. Based on the constructed antenna pedestal geometry model, antenna pedestal meshing mechanism model and antenna pedestal assembly rule model, a digital twin model of the antenna pedestal is constructed, and the assembly process is simulated through the digital twin model of the antenna pedestal.

[0040] Embodiment 1:

[0041] When simulating the assembly of the antenna base based on digital twin, it is necessary to construct the antenna base geometry model, the antenna base meshing mechanism model, the antenna base assembly rule model and the antenna base digital twin model. There is no order requirement for constructing the antenna base geometry model, the antenna base meshing mechanism model and the antenna base assembly rule model, as long as they are completed before constructing the antenna base digital twin model. This embodiment is carried out in the order of constructing the antenna base geometry model, constructing the antenna base meshing mechanism model, constructing the antenna base assembly rule model and constructing the antenna base digital twin model, such as Figure 1 shown.

[0042] Step 1: Construct the antenna base geometry model;

[0043] The geometric model of the antenna base is constructed using parametric rapid modeling related methods to feedback the macroscopic features of the antenna base entity, including size, weight, gear pair center distance, gear pair axis parallelism, etc. The key assembly features of the antenna base are extracted, such as the interface features between the reducer and the bearing base, the module, number of teeth, tooth shape of the pinion, the interface features with the reducer, the module, number of teeth, tooth shape of the bearing, the interface features with the bearing base, and the thickness of the pad, etc., and the geometric model of the antenna base is constructed in the CAD software. The commonly used CAD software is CREO, UG, etc.

[0044] Step 2: Construct the antenna base meshing mechanism model;

[0045] The gear dynamic meshing theory and finite element analysis scheme are used to construct the antenna base meshing mechanism model, which is mainly the gear bending deformation analysis model and the contact area analysis model, which is used to feedback the microscopic characteristics of the antenna base entity, including contact load, meshing spots, etc. The commonly used CAE software is ANSYS, KISSSOTF, etc.

[0046] Step 3: Construct antenna base assembly rule model;

[0047] Based on the antenna base assembly process, the assembly sequence and assembly constraints are defined, including the assembly relationship and assembly sequence of the reducer, pinion, bearing and bearing base, and the antenna base assembly rule model is constructed to improve the simulation confidence. The assembly relationship and assembly sequence are constrained in the CAD software. The commonly used CAD software is CREO, UG, etc.

[0048] Step 4: Build a digital twin model of the antenna base and obtain quality assessment index data through simulation;

[0049] Based on the antenna base geometry model, antenna base meshing mechanism model and antenna base assembly rule model, a digital twin model of the antenna base is constructed. The assembly process is simulated through the digital twin model of the antenna base to obtain quality assessment index data. The quality assessment index data includes the center distance of the gear pair, the parallelism of the gear pair axis, the meshing spot area, etc. When constructing the digital twin model of the antenna base, based on the CAD / CAE secondary development technology, the above-mentioned geometric model, mechanism model and rule model are integrated to carry out antenna base assembly simulation and guide the subsequent repair of the pad.

[0050] The assembly process is simulated by constructing a digital twin model of the antenna base, and the quality assessment indicators are predicted before the physical assembly of the antenna base. The repair amount of the pad during the assembly process is simulated and feedback is provided. Assembly is carried out only after the quality assessment meets the design requirements, thereby improving assembly efficiency.

[0051] Embodiment 2:

[0052] Based on the first embodiment, the constructed digital twin model of the antenna base is reconstructed, and the quality assessment index data is obtained through simulation. The technical solution is as follows: Figure 2 As shown, the specific method is as follows:

[0053] Build a physical assembly measurement platform for the antenna base, such as Figure 3As shown. The key components (including pinion 33, bearing 34, reducer 31, etc.) in the antenna base system 3 are placed on the support platform 4, fixed by the corresponding tooling, and the mechanical arm 2 clamps the laser scanner 1 to scan the appearance of each key component, obtain the point cloud data of each key component, and upload it to the industrial control network to achieve the uploading and storage of the point cloud data of each key component. The point cloud data of each key component is preprocessed by multi-view alignment and noise removal methods, and the relevant data of the key component features described in step 1 are extracted and imported into the digital twin model of the antenna base to complete the reconstruction of the digital twin model of the antenna base. The model reconstruction deviation should be less than 0.03mm. By importing the feature data of the key components of the antenna base into the digital twin model of the antenna base, the antenna base assembly data is integrated with the digital twin model of the antenna base, and the real physical body shape and performance of the antenna base are simulated and mapped with the digital twin model of the antenna base in the virtual space. The assembly process is simulated based on the reconstructed digital twin model of the antenna base, and the repair amount of the pad 32 during the assembly process is simulated and fed back to obtain quality assessment index data and determine whether the quality assessment index data meets the design requirements; the quality assessment index data includes the center distance of the gear pair, the parallelism of the gear pair axis, the meshing spot area, etc.

[0054] By importing the characteristic data of the key components of the antenna base into the digital twin model of the antenna base for reconstruction, the accuracy of the antenna base geometric model and the antenna base meshing mechanism model can be further improved, thereby improving the accuracy of the quality assessment index prediction of the digital twin model of the antenna base and further improving the assembly efficiency.

[0055] Embodiment three:

[0056] Based on the second embodiment, the pad repair solution is optimized based on the improved particle swarm optimization algorithm. The process is as follows: Figure 4 As shown. First, initialize each parameter, including determining the number and initial value of the particle swarm, and obtain a set of repair quantities based on the improved particle swarm optimization algorithm. Then, import the repair quantity into the digital twin model of the antenna base to predict the quality evaluation index, and then import it into the algorithm as the function fitness value of the next iteration until the number of iterations reaches the maximum value to obtain the final optimization result. Each time the optimization result is imported into the digital twin model of the antenna base, the driving model and the quality evaluation index change synchronously, and the value of the quality evaluation index is passed to the optimization algorithm as the function fitness. When the simulation result meets the requirements or the number of cycles is exhausted, the optimization is stopped, and the optimization result is transmitted to the assembly site through the industrial control network. Before the physical assembly of the antenna base system, complete the repair of the pad, and adjust the position of the reducer by repairing the pad to ensure the quality evaluation index. The specific method is as follows:

[0057] The particle velocity and position iteration formula of the improved particle swarm optimization algorithm is:

[0058] (1)

[0059] (2)

[0060] in, for time The position of a particle; for Moment The speed of a particle; For the next iteration The speed of a particle; is the particle dimension; is the random inertia weight; , is the asynchronous learning factor; , is a random factor, usually between 0 and 1; For the The particle is currently optimal; is the current global optimum.

[0061] Random weights The calculation method is as follows:

[0062] (3)

[0063] In the formula, , are the minimum and maximum values ​​of the random inertia weight respectively; It is a random number uniformly distributed between 0 and 1; is a normally distributed random number; is the standard deviation.

[0064] Asynchronous Variation Learning Factor

[0065] (4)

[0066] (5)

[0067] In the formula, Learning Factor The starting value of Learning Factor The end value of Learning Factor The starting value of Learning Factor The end value of Indicates the current iteration number; Indicates the maximum number of iterations.

[0068] In this embodiment, the dimension of the particle Represents the number of repair areas. Two pads need to be repaired during the repair process. The repair area of ​​a single pad is the upper, middle and lower areas of the pad. Therefore, the number of repair areas required is 6, that is, the particle dimension =6; Since the amount of pad repair does not exceed 0.5mm, the particle position matrix is ​​set ,in In the matrix time The position of the jth element in the first column of the particle. In order to prevent the particle position from increasing too quickly and causing the search ability to decrease, the position matrix optimization direction and compensation are set. ,in In the matrix time The velocity of the jth element in the first column of a particle; and Set to 0.5, and Set to 2.5, and Asynchronous increase and decrease.

[0069] Based on the improved particle swarm optimization algorithm, the pad repair plan is optimized, which can quickly and accurately feedback the pad repair quantity, thereby ensuring the quality evaluation indicators and further improving the assembly efficiency.

[0070] Embodiment 4:

[0071] After the physical assembly of the antenna pedestal is completed, the relevant quality assessment parameters are rechecked. If there is a deviation, a correction coefficient is formed and fed back to the digital twin model of the antenna pedestal to complete the model's own update and iteration. The accuracy of the antenna pedestal digital twin model's predicted quality assessment indicators is improved, further improving assembly efficiency.

[0072] The present invention provides an antenna base assembly simulation system based on digital twin, which is used to implement an antenna base assembly simulation method based on digital twin. The system includes the following parts:

[0073] The antenna base geometric model building module builds the antenna base geometric model through parametric rapid modeling related methods, which is used to feedback the macro characteristics of the antenna base entity, including size, weight, gear pair center distance, gear pair axis parallelism, etc.

[0074] The antenna base meshing mechanism model construction module adopts the gear dynamic meshing theory and finite element analysis scheme to construct the antenna base meshing mechanism model, which is used to feedback the microscopic characteristics of the antenna base entity, including contact load, meshing spots, etc.

[0075] The antenna pedestal assembly rule model construction module defines the assembly sequence and assembly constraints based on the typical assembly process of the antenna pedestal, and constructs the antenna pedestal assembly rule model to improve the simulation confidence.

[0076] The antenna base digital twin model construction module builds a digital twin model of the antenna base based on the antenna base geometric model constructed by the antenna base geometric model construction module, the antenna base meshing mechanism model constructed by the antenna base meshing mechanism model construction module, and the antenna base assembly rule model constructed by the antenna base assembly rule model construction module. The assembly process is simulated through the antenna base digital twin model to obtain quality evaluation index data. The quality evaluation index data includes the center distance of the gear pair, the parallelism of the gear pair axis, the meshing spot area, etc. The quality evaluation index data is obtained through simulation.

[0077] Although the present invention has been disclosed as above with preferred embodiments, the embodiments are not intended to limit the present invention. Any equivalent changes or modifications made without departing from the spirit and scope of the present invention also belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the content defined by the claims of this application.

Claims

1. An antenna base assembly simulation method based on digital twin, characterized in that: The steps include: Construct the antenna base geometry model; The parametric rapid modeling method is used to build the antenna base geometric model to feedback the macroscopic characteristics of the antenna base entity; Construct a model of the antenna base engagement mechanism; The gear dynamic meshing theory and finite element analysis scheme are used to construct the antenna base meshing mechanism model to feedback the microscopic characteristics of the antenna base entity; Construct antenna base assembly rule model; Based on the antenna base assembly process, the assembly sequence and assembly constraints are defined, and the antenna base assembly rule model is constructed to improve the simulation confidence; Build a digital twin model of the antenna base and obtain quality assessment index data through simulation; Based on the antenna base geometry model, antenna base meshing mechanism model and antenna base assembly rule model, a digital twin model of the antenna base is constructed. The assembly process is simulated through the digital twin model of the antenna base to obtain quality evaluation index data.

2. The antenna base assembly simulation method based on digital twin according to claim 1 is characterized in that: The macroscopic characteristics of the antenna base entity include size, weight, gear pair center distance, and gear pair axis parallelism.

3. The antenna base assembly simulation method based on digital twin according to claim 1 is characterized in that: The microscopic features of the antenna base entity include contact loads and meshing spots.

4. The antenna base assembly simulation method based on digital twin according to claim 1 is characterized in that: The quality evaluation index data include the gear pair center distance, gear pair axis parallelism, and meshing spot area.

5. The antenna base assembly simulation method based on digital twin according to claim 1 is characterized in that: The constructed digital twin model of the antenna base is reconstructed using the following method: Build an antenna pedestal physical assembly measurement platform, scan components, obtain component point cloud data, pre-process the component point cloud data, extract the component feature data, and import it into the antenna pedestal digital twin model.

6. The antenna base assembly simulation method based on digital twin according to claim 5 is characterized in that: Multi-view alignment and noise removal methods are used to preprocess the point cloud data of components.

7. The antenna base assembly simulation method based on digital twin according to claim 6 is characterized in that: Based on the improved particle swarm optimization algorithm, the pad is repaired.

8. The antenna base assembly simulation method based on digital twin according to claim 7 is characterized in that: After the physical assembly of the antenna base is completed, the quality assessment index data is rechecked. If there is any deviation, a correction coefficient is formed and fed back to the digital twin model of the antenna base to complete the model's own update and iteration.

9. An antenna base assembly simulation system based on digital twin, characterized in that: The system is used to implement the antenna base assembly simulation method based on digital twins as claimed in claim 1, and the system includes the following parts: Antenna base geometric model building module, used to build the antenna base geometric model and feedback the macro characteristics of the antenna base entity; Antenna base meshing mechanism model building module, used to build the antenna base meshing mechanism model and feedback the microscopic characteristics of the antenna base entity; Antenna base assembly rule model building module, used to build antenna base assembly rule model to improve simulation confidence; The antenna base digital twin model construction module obtains quality assessment index data through simulation.

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