Large aircraft assembly damping test device and test method

Abstract

The application provides a large aircraft assembly damping test device and a test method, and belongs to the technical field of assembly damping test. Based on the test device, firstly, a parameterized model of a wallboard simulation piece with a damping device is established; and secondly, damping parameters considering deformation and stress distribution of key points are designed. The application provides a simulation piece for function test of a fuselage wallboard, which can adapt to the function test of fuselage wallboard equipment, such as a fuselage wallboard automatic docking and posture adjusting system. The parameterized model of the wallboard simulation piece with the damping device is established, and the damping parameters considering deformation and stress distribution of key points are designed, so that the simulation piece can adapt to the function test of the fuselage wallboard equipment. The simulation piece has a simple structure and is convenient to process, thereby reducing the cost and large equipment direct production risk for manufacturing units.

Description

Technical Field

[0001] This invention belongs to the field of assembly damping testing technology, specifically relating to a large aircraft assembly damping testing device and testing method for equipment function testing. Background Technology

[0002] In the civil aviation manufacturing industry, the performance of large-scale automated equipment directly determines the precision, efficiency, and safety of aircraft production. Before being put into formal production and use, this equipment must undergo rigorous functional testing. This is not only a crucial step in technology verification but also a core line of defense ensuring the quality of aircraft manufacturing. Functional testing aims to comprehensively verify the equipment's core functions, such as positioning accuracy, motion control, load-bearing capacity, and automated program execution, through systematic testing. This ensures stable and efficient operation in actual production scenarios, avoiding risks such as production accidents, cost overruns, and delivery delays caused by equipment performance defects, thus laying a solid and reliable foundation for aircraft manufacturing.

[0003] Functional testing of civil aircraft equipment typically employs fuselage simulator design schemes. This scheme, based on the principle of similarity, uses advanced computer-aided design and finite element analysis techniques to accurately replicate the key features of aircraft components, creating highly realistic simulators, taking into account the actual structure, dimensions, material properties, and mechanical characteristics of the target aircraft parts. These simulators are not simply shape replicas; rather, they maintain a high degree of consistency with real components in terms of material mechanical properties, connection structures, and load transfer paths, accurately simulating the stress states and working environments of aircraft components in actual production.

[0004] During the testing process, the simulated components act as "substitutes" for real aircraft parts, bearing and transmitting various test loads, such as clamping forces during assembly, impact forces from moving parts, and clamping forces from fixtures. High-precision sensors are placed on the simulated components to monitor key parameters such as stress, strain, and displacement in real time. Combined with an automated testing system, the equipment's motion trajectory, positioning accuracy, and operational procedures are dynamically monitored and data collected. Simultaneously, the simulated components can also be used to verify the compatibility of the equipment with aircraft components, test the responsiveness of automated programs to different operating conditions, and evaluate the reliability and stability of the equipment in complex environments. This simulation-based testing method effectively reduces testing costs and shortens the verification cycle, while also enabling comprehensive and detailed testing and optimization of equipment functions in a highly controllable environment, providing solid data support and technical assurance for the successful production of large-scale automated equipment. Summary of the Invention

[0005] The purpose of this invention is to provide a damping test device and test method for large aircraft assembly, so as to solve the technical problems such as production accidents and cost overruns caused by equipment performance defects mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A damping test device for large aircraft assembly includes multiple damping devices. Each damping device includes a positioner, a unidirectional magnetorheological variable damper, a ball seat, and a positioning ball head. The positioner is used to install the damping device on a simulation frame. The ball seat is a cylinder with a blind hole that moves with the positioning ball head.

[0008] Another objective of this invention is to provide a method for testing the damping of large aircraft assembly based on the above-mentioned testing device, comprising the following steps:

[0009] Step 101, Construct a digital 3D model of the simulation component: Process the real wall panel model to simplify it into a simulation component with a damping device, which includes a unidirectional magnetorheological variable damper, a ball seat, a positioning ball head, and a locator for installation.

[0010] Step 102: Based on the program automation interface, implement the programmatic definition of the model: the position of each positioner along the x, y, and z directions is expressed by three offset constraint values. By using the dimensions of the simulated part, determine the allowable position variation range of each positioner along the three axes, and represent the range with sketch constraint values.

[0011] Step 103, establish a damped parametric finite element model: mesh the simulated component, and apply boundary conditions at the corresponding positions of the finite element model according to the coordinate position of the locator; set gravity loads on the simulated component; define the contact relationships between components;

[0012] Step 104: Design damping parameters that take into account the deformation and stress distribution at key points.

[0013] Further, in step 102, the offset of the locator in the x direction is determined by the distance between the edges of the two bosses of the locator; the offset of the locator in the z direction is determined by the offset between the edge of the locator boss and the edge of the simulation frame; and the offset of the locator in the y direction is determined by the distance between the locating ball head and the bottom end plate of the locator.

[0014] Furthermore, in step 103, corresponding fixed boundary conditions are assigned to the bottom end faces of the eight locator ball seats in the finite element model; the damping of the damper is simulated using a node set, and the node set load is modified using Python code to generate a batch of simulation files.

[0015] Furthermore, in step 103, the positioner base plate and the simulation frame are in bonded contact, and the positioning ball head and the ball seat are in general hard contact.

[0016] Furthermore, the design considering the damping parameters for deformation and stress distribution at key points includes the following steps:

[0017] Step 201: Establish loss functions for deformation and stress distribution at key points of the real panel and the simulated component;

[0018] Step 202: Based on machine learning, train the mapping relationship between damping parameters and process optimization objective function;

[0019] Step 203: Optimize the agent model using the particle swarm optimization algorithm.

[0020] Furthermore, in step 201, the deformation loss function It is expressed by the root mean square of the difference between the coordinates of key points of the deformed simulated part and the real wall panel.

[0021] ,

[0022] In the formula, The x-coordinate value of the i-th deformation monitoring point on the simulated part after the positioning load causes an offset; The x-coordinate value of the i-th deformation monitoring point of the actual wall panel after the positioning load causes an offset; , , These represent the coordinates of the deformation monitoring points along the x, y, and z directions after offset, respectively; k is the number of deformation monitoring points.

[0023] Stress loss function Expressed as,

[0024] ,

[0025] In the formula, The number of stress monitoring units, to Mises stress for each monitoring unit;

[0026] In summary, the comprehensive loss function Expressed as,

[0027] .

[0028] Further, in step 202, firstly, multiple simulation models with different damping are generated, and then the different models are solved by finite element analysis software API, and their loss functions are calculated. The damping data and objective function values ​​are stored in CSV format. Secondly, the dataset is preprocessed, including data scaling and data augmentation. Finally, based on the processed sample data, the Gaussian process regression method is used, and the mapping relationship between damping parameters and objective function values ​​is trained through the squared exponential kernel function to obtain the surrogate model.

[0029] Furthermore, in step 203, after optimizing the surrogate model using the particle swarm optimization algorithm, the result is substituted back into the finite element model to calculate its objective function, and the optimization effect is verified to meet the requirements.

[0030] The present invention has the following beneficial effects:

[0031] This invention provides a simulated component for functional testing of fuselage panel equipment, suitable for functional testing of fuselage panel equipment, such as automatic docking and attitude adjustment systems for fuselage panels. A parametric model of the simulated component with a damping device is established, and damping parameters considering deformation and stress distribution at key points are designed to adapt it to functional testing of fuselage panel equipment. Its simple structure and ease of manufacturing reduce costs and the risk of direct production of large-scale equipment for manufacturing units. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating a parametric modeling method for a wall panel simulation component with a damping device, according to an embodiment of the present invention.

[0034] Figure 2 This is a lightweight digital model according to an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of the positioner damping device according to an embodiment of the present invention;

[0036] Figure 4 This is a flowchart illustrating the procedural definition of parameters in an embodiment of the present invention.

[0037] Figure 5 A flowchart illustrating the damping parameter design method for considering deformation and stress distribution at key points in this invention.

[0038] Figure 6 This is a flowchart of the particle swarm optimization algorithm according to an embodiment of the present invention.

[0039] In the attached diagram, the components represented by each label are as follows:

[0040] Positioner-1, Unidirectional magnetorheological variable damper-2, Ball seat-3, Positioning ball head-4, Simulation frame-5. Detailed Implementation

[0041] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] See Figure 2 , 3 As shown, a damping testing device for large aircraft assembly includes multiple damping devices. Each damping device includes a positioner 1, a unidirectional magnetorheological variable damper 2, a ball seat 3, and a positioning ball head 4. The positioner 1 is used to mount the damping device on a simulation frame 5. The ball seat 3 is a cylinder with a blind hole, and its size and position are based on the positioning ball head 4, enabling it to follow the movement of the positioning ball head 4. Figure 3 As shown.

[0043] Based on the above testing device, a damping test method for large aircraft assembly is proposed, including the following steps: First, establish a parametric model of the wall panel simulation component with damping device; second, design damping parameters that take into account the deformation and stress distribution at key points.

[0044] like Figure 1 The diagram shown is a flowchart of a parametric modeling method for a wall panel simulation component with a damping device according to an embodiment of the present invention, including the following steps:

[0045] Step 101: Construct a digital 3D model of the simulation component.

[0046] The detailed real-world wall panel model was processed, including member simplification, damping equivalence, and assembly relationship simplification. The wall panel model was simplified into a simulated component with damping devices. The overall digital model of the lightweight simulated component is as follows: Figure 2 As shown.

[0047] Step 102: Based on the CATIA automation interface, implement the programmatic definition of the model.

[0048] The position of each positioner along the x, y, and z directions is expressed by three offset constraint values. The offset in the x direction is determined by the distance between the edges of the two bosses of the positioner; the offset in the z direction is determined by the offset between the edge of the positioner boss and the edge of the simulated frame; and the offset in the y direction is determined by the distance between the positioning ball head and the bottom end plate of the positioner. These three offset values ​​are all expressed by the Offset feature defined under Sketch Features in the CATIA model tree.

[0049] An object-oriented approach is used, with class files written in C#, and the type library provided by the CATIA Automation API interface is added to the C# project's references. The process of obtaining the CATIA simulator and positioner models and modifying the corresponding sketch constraints needs to be done step-by-step according to the object hierarchy, such as... Figure 4 As shown.

[0050] The positions of the positioners along the three axes need to be within a reasonable range; otherwise, interference between positioners or overly concentrated positional distribution may occur. By simulating the dimensions of the component, the allowable positional variation range of each positioner along the three axes is determined, and this range is represented by sketch constraint values.

[0051] Step 103: Establish a parameterized finite element model with damping.

[0052] The lightweight model is meshed, and boundary conditions are applied at the corresponding locations in the finite element model based on the coordinates of the digital model locators. Fixed boundary conditions are then applied to the bottom faces of the eight locator spheres in the finite element model.

[0053] The simulated component is subject to its own weight during the positioning process, therefore a gravity load needs to be set, with the gravitational acceleration set to 9800 mm / s². 2 Meanwhile, a magnetorheological damper is installed above each ball-head positioner. To simulate its damping, the dampers are set as a node set, and the loads on the node set are modified using Python code to generate a batch of simulation files. Since there are welded relationships between the components, the contact relationships between the components need to be defined during finite element preprocessing. Specifically, the contact between the positioner base plate and the frame is a bonded contact, while the contact between the positioner ball head and the positioner ball seat is a general hard contact.

[0054] The model was meshed using C3D10 as the element type, with tetrahedral meshes. To improve computational convergence, the mesh at the spherical head was locally refined.

[0055] like Figure 5 The diagram shown is a flowchart of a damping parameter design method considering deformation and stress distribution at key points, as described in an embodiment of the present invention. The method includes the following steps:

[0056] Step 201: Establish loss functions for deformation and stress distribution at key points of the real panel and the simulated component.

[0057] A Python script can be used to automatically read the result file after each calculation, from which relevant information about the observation points and observation units can be extracted. The displacement of the observation points is obtained by reading the displacement field from the file; the stress of the observation units is obtained by reading the element MISES stress from the stress field in the file.

[0058] The observation points of the simulated component are distributed on the frame beams. After obtaining the displacement of each observation point, mathematical methods can be used to fit the displacement to obtain the "linear" deformation of the frame beams, and this deformation can be used to calculate the overall deformation of the wall panel after positioning. The deformation of the same points on the real wall panel can be calculated using the same method through finite element analysis.

[0059] A deformation loss function is established, which is expressed by the root mean square of the difference between the coordinates of key points of the deformed simulated part and the real wall panel.

[0060] (1)

[0061] In the formula, The x-coordinate value of the i-th deformation monitoring point on the simulated part after the positioning load causes an offset; The x-coordinate value of the i-th deformation monitoring point of the actual wall panel after the positioning load causes an offset; , , These represent the coordinates of the deformation monitoring points along the x, y, and z directions after offset, respectively; k is the number of deformation monitoring points.

[0062] Establish a stress loss function. In the script, a loop statement is set up to sequentially read the four Mises stresses of each element, average them, sum them, and then average them again using the number of elements to obtain the overall average Mises stress, which serves as the stress equilibrium index. The stress field results in the finite element analysis software Abaqus are stored in array form. For the j-th element, the four Mises stress values ​​of each tetrahedral element can be extracted using sfield.values[0+4*j].data. The stress loss function is expressed by equation (2).

[0063] (2)

[0064] In the formula, The number of stress monitoring units, to Mises stress for each monitoring unit.

[0065] In summary, the overall loss function can be expressed by equation (3).

[0066] (3)

[0067] Step 202: Based on machine learning, train the mapping relationship between damping parameters and process optimization objective function.

[0068] First, multiple simulation models with different damping are generated. Then, finite element methods are used to solve each model separately using the Abaqus API, and their loss functions are calculated. The damping data and objective function values ​​are stored in CSV format.

[0069] Preprocess the dataset, including data scaling and data augmentation.

[0070] The data scaling method is shown in equation (4), where A The scaling factor should be selected based on the required range of the objective function, but the factor should not be too large, otherwise the objective function may become negative, which is detrimental to the training of the regression model. y fit and y fitnew These are the objective function values ​​before and after scaling, respectively. y fitmax and y fitmin These are the maximum and minimum values ​​of the objective function before scaling, respectively.

[0071] (4)

[0072] During data augmentation, two sets of data with smaller objective functions were selected from the sample, and their input and output values ​​were randomly oscillated within a 10% range to generate new data. Based on the processed sample data, a Gaussian process regression method was used, and the mapping relationship between the damping parameter and the objective function value was trained using a squared exponential kernel function to obtain the surrogate model.

[0073] Step 203: Optimize the agent model using the particle swarm optimization algorithm.

[0074] The surrogate model was optimized using the particle swarm optimization algorithm. After optimization, the results were substituted back into the finite element model to calculate its objective function, verifying whether the optimization effect met the requirements. The overall process of process optimization is as follows: Figure 6 As shown.

[0075] The embodiments in this specification are described in a progressive manner. Similar or identical content between parts can be referred to each other. Each part focuses on explaining the differences from other parts.

[0076] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for testing damping during assembly of a large aircraft, characterized by comprising the following steps: Step 101, Construct a digital 3D model of the simulation component: Process the real wall panel model to simplify it into a simulation component with a damping device; Step 102: Based on the program automation interface, implement the programmatic definition of the model: the position of each positioner along the x, y, and z directions is expressed by three offset constraint values. By using the dimensions of the simulated part, determine the allowable position variation range of each positioner along the three axes, and represent the variation range with sketch constraint values. Step 103, establish a damped parametric finite element model: mesh the simulated component, and apply boundary conditions at the corresponding positions of the finite element model according to the coordinate position of the locator; set gravity loads on the simulated component; define the contact relationships between components; Step 104: Design damping parameters that take into account the deformation and stress distribution at key points; Includes the following steps: Step 201: Establish the loss function for deformation and stress distribution at key points of the real panel and the simulated component; deformation loss function. It is expressed by the root mean square of the difference between the coordinates of key points of the deformed simulated part and the real wall panel. ， In the formula, The x-coordinate value of the i-th deformation monitoring point on the simulated part after the positioning load causes an offset; The x-coordinate value of the i-th deformation monitoring point of the actual wall panel after the positioning load causes an offset; , , These represent the coordinates of the deformation monitoring points along the x, y, and z directions after offset, respectively; k is the number of deformation monitoring points. Stress loss function Expressed as, ， In the formula, The number of stress monitoring units, to Mises stress for each monitoring unit; In summary, the comprehensive loss function Expressed as, ； Step 202: Based on machine learning, train the mapping relationship between damping parameters and the process optimization objective function. First, generate multiple simulation models with different damping, then use the API of the finite element analysis software to solve the different models using finite element methods and calculate their loss functions. Store the damping data and objective function values ​​in CSV format. Second, preprocess the dataset, including data scaling and data augmentation. Finally, based on the processed sample data, use the Gaussian process regression method and train the mapping relationship between damping parameters and objective function values ​​using the squared exponential kernel function to obtain the surrogate model. Step 203: After optimizing the surrogate model using the particle swarm optimization algorithm, the result is substituted back into the finite element model to calculate its objective function, and the optimization effect is verified to meet the requirements.

2. The method for testing damping during assembly of a large aircraft according to claim 1, characterized in that, In step 102, the offset of the locator in the x direction is determined by the distance between the edges of the two bosses of the locator; the offset of the locator in the z direction is determined by the offset between the edge of the locator boss and the edge of the simulation frame; and the offset of the locator in the y direction is determined by the distance between the locating ball head and the bottom end plate of the locator.

3. The method for testing damping during assembly of a large aircraft according to claim 2, characterized in that, In step 103, corresponding fixed boundary conditions are assigned to the bottom end faces of the eight locator ball seats in the finite element model; the damping of the damper is simulated using a node set.

4. The method for testing damping during assembly of a large aircraft according to claim 3, characterized in that, In step 103, the positioner base plate and the simulation frame are in bonded contact, and the positioning ball head and the ball seat are in general hard contact.

5. A large aircraft assembly damping testing device, employing the large aircraft assembly damping testing method as described in claim 1, characterized in that, It includes multiple damping devices, including a positioner (1), a unidirectional magnetorheological variable damper (2), a ball seat (3) and a positioning ball head (4). The positioner (1) is used to install the damping device on the simulation frame (5). The ball seat (3) is a cylinder with a blind hole that moves with the positioning ball head (4).

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