Aircraft wing body butt joint method

Abstract

The invention provides an aircraft wing body butt joint method, which adopts an improved particle swarm optimization algorithm to carry out optimization analysis on an attitude adjustment process of aircraft wing body butt joint. By defining the position and tolerance of each assembly point in detail, the relative positions of the key points of the wing body in the three-dimensional space are evaluated and optimized, and accurate butt joint is achieved. In the process, various deformation and displacement possibly encountered by the assembly in an actual environment need to be comprehensively considered, and the deviation between a current assembly posture monitoring point location and an ideal state is calculated to evaluate the assembly quality. Detailed measurement and evaluation are carried out on displacement of important monitoring points based on information provided by suppliers and deformation analysis. An RRT * algorithm is adopted to plan a path of a progressive stage in an aircraft wing body docking process, namely path planning from an initial point to a target point. The core of the algorithm is that a search tree is efficiently generated and expanded, the tree is gradually expanded to a target state from an initial state, and an initial path can be quickly found through the incremental sampling algorithm.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing, and in particular to a method for connecting an aircraft wing to a body. Background Art

[0002] The aircraft wing-body docking process directly affects the structural integrity and production efficiency of the aircraft. The traditional wing-body docking method relies on a staged assembly process, including a progressive phase and an attitude adjustment phase. In the progressive phase, the wing is gradually moved closer to the fuselage. The main task of this phase is to ensure the rough positioning of the wing in space, but the movement process from the initial position to the end point still needs to be precisely controlled, because any initial position error will affect the assembly efficiency of the subsequent attitude adjustment phase. After the progressive phase is completed, the wing will be in a close position to the fuselage, but it has not yet been finally locked.

[0003] The next step is the attitude adjustment phase, in which the wing is precisely inserted or docked into the specified interface of the fuselage through fine adjustment. This includes fine-tuning the angle, position and height of the wing until all preset docking points meet the designed tolerance range. The attitude adjustment phase is the most critical part of the entire assembly process because it directly determines the final quality of the docking. In traditional methods, the attitude adjustment phase usually relies on high-precision measuring equipment and experienced operators to manually adjust the docking points.

[0004] According to the above process analysis, it can be seen that the traditional wing-body docking method is not only time-consuming, but may also lead to unqualified assembly due to insufficient precision. With the rapid development of aviation technology, the requirements for assembly speed and accuracy are also increasing, which urgently requires a more efficient and accurate docking technology to meet the needs of modern aviation manufacturing.

[0005] To address this problem, an embodiment of the present invention proposes a reverse path planning method based on the fusion of an improved particle swarm algorithm (PSO) and a fast random tree star algorithm (RRT*). By utilizing historical assembly data, this algorithm can reversely deduce the optimal path from the initial state to the final successful attitude adjustment state. This reverse planning method can not only significantly improve the efficiency of assembly, but also enhance the repeatability and accuracy of the process. By implementing this technology, a new solution can be provided for the aviation manufacturing industry to cope with increasingly complex assembly challenges. Summary of the invention

[0006] In view of the above problems in the prior art, the present application proposes an aircraft wing-body docking method, which defines the position and tolerance of each assembly point in detail, evaluates the relative position of the key points of the wing-body in three-dimensional space, comprehensively considers various deformations and displacements of the aircraft wing-body components in the actual environment, and realizes the intelligent reverse planning of the aircraft wing-body docking path. The specific steps include:

[0007] Firstly, the attitude adjustment optimization of wing-body docking is performed based on the improved particle swarm algorithm. The positions and speeds of all particles of the aircraft wing-body are randomly initialized, the speed of each particle is updated, and the position of each particle is updated based on the updated speed. It is judged whether the new position exceeds the maximum allowable deviation. If it exceeds the maximum allowable deviation, the position based on the deviation is updated and the fitness value of each particle is calculated. Then, it is judged whether the termination condition is met. If it does not exceed the maximum allowable deviation, it is directly judged whether the termination condition is met. If the termination condition is met, the optimization result is output. If the termination condition is not met, it returns to updating the speed of each particle.

[0008] Then, the aircraft wing-body docking process is optimized. A set of random offset positions of the aircraft wing is defined as the initial state using random configuration and target configuration to simulate the uncertain starting conditions in actual situations. Random points are generated in space. The node closest to the newly generated point in the initial state is found as the nearest node. The nearest node is turned to the random point to create a new node. The cost of reaching the new node from the starting point to the nearest node is calculated. The new node is added to the tree and the parent node of the new node is set to the nearest node. If the new node is within the threshold distance from the target, a path from the start to the target is constructed; if the new node is not within the threshold distance from the target, it returns to turning from the nearest node to the random point to create the new node.

[0009] Preferably, the maximum allowable deviation range takes into account the displacement of key points. Assume that for any key point i, its ideal position coordinate is (X i ,Y i ,Z i ), define D i is the design position of point i, V i is the standard deviation data provided by the supplier, Δ i is the actual measured deformation at point i, T i is the total tolerance of point i after considering the supplier data and deformation data; tolerance T i It can be expressed by the following formula:

[0010] T i =V i +α△ i (1)

[0011] Among them, α is a scaling factor used to adjust the weight of the deformation data in the total tolerance.

[0012] Preferably, for each key point, there is a single axial distance difference between the actual position and the ideal position, a symmetry tolerance, and a distance difference between specific paired points, and the tolerance T is i The calculation is broken down into five types of deviations:

[0013]

[0014] Where ΔX i,j is the cross-axis deviation, ΔY i,j is the uniaxial deviation, and is the desired distance between points i and j along the X and Y axes, ΔZ i is the absolute position deviation, z expected,i is its expected position, is the symmetry deviation, X L , Z L and X R , Z R are the coordinates of the symmetrical points on the left and right components, Δcomposite is the composite deviation, and δ is used to consider additional tolerance or correction.

[0015] Preferably, the posture evaluation index is designed as a complex fitness function "evaluate_assembly", which is used as the loss function of the subsequent improved particle swarm algorithm to optimize the posture adjustment process; a series of candidate solutions are initialized by the particle swarm algorithm, in which each particle represents a possible set of spatial coordinates of the wing, and the position and velocity of the particle are gradually iteratively updated through simulation to seek to minimize the fitness function; in this process, the motion trajectory of each particle is based on its current position, combined with the information of the individual historical best position and the group historical best position, so as to effectively guide the particle to move to an area with better fitness.

[0016] Preferably, the steps in optimizing the aircraft wing-body docking process include:

[0017] Define the initial state, which consists of a set of randomly offset positions of the aircraft wing to simulate the uncertain starting conditions in the actual situation; in each iteration, the algorithm first randomly generates a point; in order to improve the convergence rate of the algorithm, one point is generated around the target every ten iterations, and in a wider area the rest of the time;

[0018] Find the node closest to the newly generated point in the current tree as the nearest node; use the "steer" function to generate a new node from the nearest node toward the new point. This function ensures that the new node moves along a certain step length in the direction from the nearest node to the new point, thereby gradually approaching the target;

[0019] For each new node, the total cost from the starting point to that point is calculated and the new node is connected to the tree; if the new node is within the preset threshold, the goal is considered to have been reached and the search ends.

[0020] The above technical features can be combined in various suitable ways or replaced by equivalent technical features as long as the purpose of the present invention can be achieved.

[0021] The intelligent reverse planning method for aircraft wing-body docking path provided by the present invention has at least the following beneficial effects compared with the prior art:

[0022] The algorithm not only significantly improves the efficiency and accuracy of the docking process, but also ensures the feasibility and repeatability of the solution. The resulting three-dimensional path visualization clearly demonstrates the performance of the algorithm in dynamic space, verifying its potential in practical industrial applications. In addition, the results of this study not only provide a new algorithmic framework to deal with similar engineering problems, but also provide valuable references for future research in the fields of automation and intelligent manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be described in more detail below based on embodiments and with reference to the accompanying drawings, wherein:

[0024] Figure 1 This is a schematic diagram of key monitoring points in the wing-fuselage docking area;

[0025] Figure 2 It is a schematic diagram of the improved PSO algorithm flow applied to the posture adjustment stage;

[0026] Figure 3 It is a schematic diagram of the RRT* algorithm flow applied to the progressive stage;

[0027] Figure 4 is the normalized loss curve in the pose adjustment stage;

[0028] Figure 5 The expansion steps and node connection relationship of the RRT* algorithm;

[0029] Figure 6 This is the final path obtained by the RRT* algorithm by backtracking from the search tree. DETAILED DESCRIPTION

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] The technical problem to be solved by the present invention is to address the deficiencies and needs of the above-mentioned prior art, and to mainly achieve the following contents by establishing an optimization and planning method in stages:

[0032] (1) Wing-body docking attitude optimization based on improved particle swarm optimization (PSO)

[0033] (2) Progressive path planning for wing-body docking based on RRT*

[0034] The contents of these two parts consider the wing-body docking process in reverse, that is, the second stage is considered first, and the optimization result of the second stage is used as the moving target of the first stage, thus realizing the reverse planning of the path.

[0035] First, an improved particle swarm optimization (PSO) algorithm is used to optimize the attitude adjustment process of the aircraft wing-body docking. By defining the position and tolerance of each assembly point in detail, the relative position of the key points of the wing-body in three-dimensional space is evaluated and optimized to achieve precise docking. This process requires comprehensive consideration of various deformations and displacements that components may encounter in the actual environment, and the deviation between the current assembly attitude monitoring point and the ideal state is calculated to evaluate the quality of the assembly. Based on the information provided by the supplier and deformation analysis, the displacement of important monitoring points is measured and evaluated in detail.

[0036] Then, the RRT* algorithm is used to plan the path of the progressive stage of the aircraft wing-body docking process, that is, the path planning from the initial point to the target point. The core of the algorithm is to efficiently generate and expand a search tree, which starts from the initial state and gradually expands to the target state. This incremental sampling algorithm can quickly find the initial path.

[0037] In one embodiment, the displacement of key points is considered based on the supplier data as part of the tolerance range and reflected in the fitness function. Figure 1 As shown in Figure 1, it is a schematic diagram of the key monitoring points and deformation of the wing-body docking area. Assume that for any key point i, its ideal position coordinate is (X i ,Y i ,

[0038] Z i ), define D i is the design position of point i, V i is the standard deviation data provided by the supplier, Δ i is the actual measured deformation at point i, T i is the total tolerance of point i after taking into account the supplier data and deformation data. Tolerance T i It can be expressed by the following formula:

[0039] T i =V i +α△ i (1)

[0040] Among them, α is a scaling factor used to adjust the weight of the deformation data in the total tolerance.

[0041] For each key point, there is a single axial distance difference between the actual position and the ideal position, a symmetry tolerance, and a distance difference between specific paired points. Therefore, the tolerance T i The calculation is broken down into five types of deviations:

[0042]

[0043] Where ΔXi,j is the cross-axis deviation, ΔY i,j is the uniaxial deviation, and is the desired distance between points i and j along the X and Y axes, ΔZ i is the absolute position deviation, z expected,i is its expected position, is the symmetry deviation, X L , Z L and X R , Z R are the coordinates of the symmetrical points on the left and right components, Δcomposite is the composite deviation, and δ is used to consider additional tolerance or correction.

[0044] Integrating the above formula, the posture evaluation index is designed as the fitness function "evaluate_assembly", which is used as the loss function for the subsequent improved particle swarm algorithm to optimize the posture adjustment process. A series of candidate solutions are initialized with the particle swarm algorithm, where each particle represents a possible set of spatial coordinates of the wing, and the position and velocity of the particle are gradually updated iteratively through simulation to minimize the fitness function. In this process, the trajectory of each particle is adjusted based on its current position, combined with the information of the individual historical best position and the group historical best position, so as to effectively guide the particle to move to an area with better fitness.

[0045] However, the traditional particle swarm algorithm has some limitations when facing the posture adjustment scenario of aircraft wing-body docking. First, the traditional PSO converges quickly in the early stage of iteration. Although this can quickly reduce the error, it may prematurely lock in the local optimal solution rather than the global optimal solution, causing the wing to dock in an undesirable position; secondly, in the wing posture adjustment task, there is a lack of clear guidance on how to set the particle's speed, weighting factor and other parameters. It is usually necessary to continuously adjust through experiments according to the specific task. The uncertainty of this method may lead to the failure of the docking task; finally, the aircraft wing-body docking involves the precise control of multiple spatial coordinates, which belongs to a high-dimensional optimization problem. The traditional particle swarm algorithm is less efficient in dealing with high-dimensional problems, is easily restricted by dimensions, and is difficult to effectively explore all areas of the solution space. In view of the above limitations, the embodiment of the present invention is improved for the scenario of wing posture adjustment, including the mechanism of position update and speed adjustment, and the maximum allowable deviation of limiting the particle from the initial position, and the behavior of particles is implemented in the Particle class to ensure the effectiveness and practicality of the search.

[0046] Next, the mechanism of algorithm improvement is explained in detail, such as Figure 2 As shown in FIG. 1 , it is a schematic diagram of the improved PSO algorithm flow applied to the posture adjustment stage.

[0047] Step 201, dynamically adjust particle speed and position:

[0048] In particle swarm optimization, the position update of each particle depends on the combined influence of its historical best position, global best position and random factors. In the embodiment of the invention, the speed and position update formula of the standard particle swarm algorithm are improved by introducing an adaptive inertia weight w, as well as individual and social learning factors c1 and c2. Such a setting not only enhances the search ability of the algorithm, but also improves the convergence speed. The speed and position update formula of the particle swarm algorithm are as follows:

[0049]

[0050] in, is the velocity of particle i at time t; w is the inertia weight, which is used to control the continuity of particle velocity; c1 and c2 are learning factors, representing individual cognitive components and social cognitive components respectively; r1 and r2 are random numbers in the interval [0,1]; is the optimal position found by particle i so far, g best is the global optimal position; is the position of particle i at time t.

[0051] Step 202, improving the update formula of the dynamic inertia weight and the adaptive learning factor:

[0052]

[0053] Among them, w max and w min is the maximum and minimum value of the inertia weight, I i is the current iteration number, I max is the maximum number of iterations. and are the starting and ending values ​​of the learning factor c1, and and are the starting and ending values ​​of the learning factor c2. Based on this approach, the inertia weight w can be reduced as the number of iterations increases, c1 decreases linearly from a higher value, and c2 increases linearly from a lower value, thereby increasing the global search capability in the early stages of the search process and increasing the local search capability in the later stages of the iteration.

[0054] Step 203, limit the maximum offset:

[0055] In order to prevent invalid solutions during the particle search process, a mechanism is added to the particle class to limit the maximum distance that the particle deviates from the reference position. When the particle offset exceeds the preset maximum value, it will automatically adjust back to the maximum offset limit. First, calculate the particle offset according to the following formula:

[0056]

[0057] Step 204, compare whether the offset meets the maximum allowable offset threshold. If the offset is greater than the maximum allowable value, adjust the particle position so that its offset does not exceed the threshold range:

[0058]

[0059] Among them, μ represents the offset, p base Indicates the reference point, ∥μ∥ indicates the modulus length of the offset, μ max Represents the offset threshold. This measure ensures the effectiveness of the search and prevents the algorithm from blindly drifting in high-dimensional space.

[0060] Step 205, customized design of fitness function:

[0061] According to the specific requirements and tolerance range of aircraft wing-body docking, a custom fitness function was designed. This function not only takes into account the absolute position difference between components, but also includes the evaluation of structural symmetry and specific geometric constraints. This enables the algorithm to optimize more accurately for actual assembly quality issues.

[0062] Through these improvements, the particle swarm optimization algorithm proposed in the embodiment of the present invention can more effectively solve the attitude adjustment problem in the aircraft wing-body docking, and achieve a leap from theory to practical application.

[0063] Next, the RRT* algorithm is used to optimize the progressive phase of the aircraft wing-body docking process. The algorithm flow chart is shown in the figure below: Figure 3 shown.

[0064] Step 301, define the initial state, which consists of a set of randomly offset positions of the aircraft wing to simulate the uncertain starting conditions in the actual situation. In each iteration, the algorithm first randomly generates a point. In order to increase the convergence rate of the algorithm, one point is generated around the target every ten iterations, and the rest of the time it is generated in a wider area.

[0065] Step 302, find the node closest to the new generated point in the current tree as the nearest node. Use the "steer" function to generate a new node from the nearest node toward the new point, which ensures that the new node moves along a certain step length in the direction from the nearest node to the new point, thereby gradually approaching the target.

[0066] Step 303, calculate the total cost from the starting point to each new node and connect the new node to the tree. If the new node is close enough to the target state (within a preset threshold), the target is considered to have been reached and the search ends. Once the target node is found, through this method, the RRT* algorithm can not only effectively handle path planning problems in high-dimensional space, but also adapt to the uncertainty of the initial state, which is an ideal choice for solving the path planning of the progressive stage in the aircraft wing-body docking.

[0067] Next, we will implement the algorithm.

[0068] The pseudo code of the aircraft wing-body docking path inverse planning algorithm based on the improved PSO-RRT* is shown in Appendix 1.

[0069] Table 1

[0070]

[0071]

[0072]

[0073] Step 401, set 30 particles to maintain computational efficiency and perform 150 iterations. For the maximum offset setting, the historical measurement data of the qualified docking state is used as the predefined target configuration, and the particle position is randomly initialized in the range of [-1,1] on this basis, and the speed is initialized in a smaller range of [-0.1,0.1] to promote initial exploration behavior. The initial value of the inertia weight is 0.6, the individual learning factor c1 is reduced from the initial 2.5 to 0.5, and the social learning factor c2 is increased from the initial 0.5 to 2.5.

[0074] Step 402, output loss, the normalized loss curve of the posture adjustment stage is as shown in the attached Figure 4 shown.

[0075] Step 403, obtaining the key point information representing the spatial structure of the left wing by using the improved PSO algorithm, as shown in Table 2.

[0076] Table 2

[0077]

[0078] Step 404, taking the optimization result as the target endpoint of the path planning in the progressive stage, and setting the key parameters of the RRT* algorithm. In order to ensure the adequacy and efficiency of the search process, 2000 iterations are set. When generating new nodes, the step length is set to 200 units, which not only ensures the accuracy of the search, but also avoids the generation of overly dense nodes due to too small a step length, thereby accelerating the convergence of the algorithm. In order to determine whether the target has been reached, a threshold of 100 units is set. When the average distance between the new node and the target point is less than this value, the target is considered to have been successfully reached. Every 10 iterations of the algorithm, a Gaussian distributed random point with a perturbation of 2000 units is generated around the target point to increase the search coverage of the target area and improve the convergence speed. At other times, the random points are evenly distributed in the range of [-10000, 10000] to ensure that the entire search space is fully explored. The optimization of the RRT* algorithm designed in this study makes the search process more efficient, and the generated path shows good smoothness and feasibility while meeting spatial and mechanical constraints. The expansion steps of the algorithm and the node connection relationship are shown in the attached figure. Figure 5 As shown in the figure, the final path obtained by backtracking from the search tree is shown in the attached Figure 6 shown.

[0079] So far, we have successfully developed and demonstrated an aircraft wing-body docking path inverse planning algorithm based on the fusion of improved PSO and RRT* algorithms. Through the particle swarm algorithm, the attitude adjustment process is optimized, so that it can quickly find the optimal solution in complex spatial constraints. Furthermore, through the RRT* algorithm, accurate path planning from the initial state to the final attitude adjustment state is achieved, demonstrating its effectiveness in dealing with highly challenging spatial planning problems.

[0080] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. A method for connecting an aircraft wing to a body, characterized in that: By defining the position and tolerance of each assembly point in detail, evaluating the relative position of the key points of the wing-body in three-dimensional space, and comprehensively considering the various deformations and displacements of the aircraft wing-body components in the actual environment, the intelligent reverse planning of the aircraft wing-body docking path is realized. The specific steps include: Firstly, the attitude adjustment optimization of wing-body docking is performed based on the improved particle swarm algorithm. The positions and speeds of all particles of the aircraft wing-body are randomly initialized, the speed of each particle is updated, and the position of each particle is updated based on the updated speed. It is judged whether the new position exceeds the maximum allowable deviation. If it exceeds the maximum allowable deviation, the position based on the deviation is updated and the fitness value of each particle is calculated. Then, it is judged whether the termination condition is met. If it does not exceed the maximum allowable deviation, it is directly judged whether the termination condition is met. If the termination condition is met, the optimization result is output. If the termination condition is not met, it returns to updating the speed of each particle. Then, the aircraft wing-body docking process is optimized. A set of random offset positions of the aircraft wing is defined as the initial state using random configuration and target configuration to simulate the uncertain starting conditions in actual situations. Random points are generated in space. The node closest to the newly generated point in the initial state is found as the nearest node. The nearest node is turned to the random point to create a new node. The cost of reaching the new node from the starting point to the nearest node is calculated. The new node is added to the tree and the parent node of the new node is set to the nearest node. If the new node is within the threshold distance from the target, a path from the start to the target is constructed; if the new node is not within the threshold distance from the target, it returns to turning from the nearest node to the random point to create the new node.

2. The aircraft wing-body docking method according to claim 1, characterized in that: The maximum allowable deviation range takes into account the displacement of key points. Assume that for any key point i, its ideal position coordinates are (X i ,Y i ,Z i ), define D i is the design position of point i, V i is the standard deviation data provided by the supplier, Δ i is the actual measured deformation at point i, T i is the total tolerance of point i after considering the supplier data and deformation data; tolerance T i Expressed as follows: T i =V i +α△ i (1) Among them, α is a scaling factor used to adjust the weight of the deformation data in the total tolerance.

3. The aircraft wing-body docking method according to claim 2, characterized in that: For each key point, there is a single axial distance difference between the actual position and the ideal position, a symmetry tolerance, and a distance difference between specific paired points. The tolerance T i The calculation is broken down into five types of deviations: Where ΔX i,j is the cross-axis deviation, ΔY i,j is the uniaxial deviation, and is the desired distance between points i and j along the X and Y axes, ΔZ i is the absolute position deviation, z expected,i is its expected position, is the symmetry deviation, X L , Z L and X R , Z R are the coordinates of the symmetrical points on the left and right components, Δcomposite is the composite deviation, and δ is used to consider additional tolerance or correction.

4. The aircraft wing-body docking method according to claim 3, characterized in that: The posture evaluation index is designed as a complex fitness function "evaluate_assembly", which is used as the loss function of the improved particle swarm algorithm to optimize the posture adjustment process. A series of candidate solutions are initialized using the particle swarm algorithm, where each particle represents a possible set of spatial coordinates of the wing. The position and velocity of the particle are updated step by step through simulation to minimize the fitness function. In this process, the motion trajectory of each particle is adjusted based on its current position, combined with the information of the individual historical best position and the group historical best position, so as to effectively guide the particle to move to the area with better fitness.

5. The aircraft wing-body docking method according to claim 4, characterized in that: The steps in optimizing the aircraft wing-body docking process include: Define the initial state, which consists of a set of randomly offset positions of the aircraft wing to simulate the uncertain starting conditions in the actual situation; in each iteration, the algorithm first randomly generates a point; in order to improve the convergence rate of the algorithm, one point is generated around the target every ten iterations, and in a wider area the rest of the time; Find the node closest to the newly generated point in the current tree as the nearest node; use the "steer" function to generate a new node from the nearest node toward the new point. This function ensures that the new node moves along a certain step length in the direction from the nearest node to the new point, thereby gradually approaching the target; For each new node, the total cost from the starting point to the new node is calculated and the new node is connected to the tree; if the new node is within the preset threshold, the goal is considered to have been reached and the search ends.

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