Method and system for in-situ laser welding of aircraft titanium alloy frames based on mobile robots

Abstract

This invention discloses an in-situ laser welding scheme for aircraft titanium alloy frames based on a mobile robot. The method includes: a mobile robot interacting with a host computer through a machine vision module to determine the location and morphology of damage to the titanium alloy component; the host computer planning a welding path and welding process parameters based on the location, morphology, and material of the damaged area, as well as the current posture of the mobile robot's robotic arm; the mobile robot controlling a mechanical grinding module on the robotic arm to cut and clean the damaged area according to the welding path; and the mobile robot controlling the cladding head of a laser cladding module to weld the damaged area according to the welding path and welding process parameters. This method not only shortens the repair cycle and reduces the difficulty of repair implementation but is also applicable to the repair of damaged parts of various types of titanium alloys, demonstrating good versatility.

Description

Technical Field

[0001] This invention relates to the field of aircraft component welding technology, and in particular to an in-situ laser welding method and system for aircraft titanium alloy skeletons based on mobile robots. Background Technology

[0002] With the increasing demand for long-range advanced fighter jets, the requirement for a high thrust-to-weight ratio is becoming more stringent. Titanium alloys, due to their high specific strength, excellent corrosion resistance, and heat resistance, are widely used in aircraft, such as in load-bearing structures like frames, beams, and stringers. The main materials are TC4 and TA15 titanium alloys. When an aircraft is struck by a foreign object, hit by a warhead, or due to human error, in addition to damage to the skin, the densely packed internal frame is also easily damaged. Common damage patterns include holes and gaps. Traditional repair methods require removing the external skin and replacing the internal frame, a process that can take more than 48 hours, severely hindering the rapid recovery of aircraft functionality.

[0003] In actual repairs, to quickly restore the combat capability of an aircraft with a damaged frame, in-situ repair is essential, meaning rapid repairs must be carried out at the site of the damage. Currently, after cleaning the residual damage to the TC4 or TA15 titanium alloy frame, the main method for restoring the strength of the damaged area is through riveting reinforcement. However, this method is only suitable for TA15 titanium alloy frames with a thickness of less than 4mm, and it is difficult to implement, time-consuming, and results in poor strength recovery, thus hindering the repair of the aircraft's TA15 titanium alloy frame structure. Summary of the Invention

[0004] The purpose of this invention is to provide an in-situ laser welding method and system for aircraft titanium alloy skeletons based on mobile robots, which can solve at least one of the many problems existing in the prior art, such as high difficulty in aircraft component damage repair, long cycle, poor strength recovery, and strong application limitations.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] This invention provides an in-situ laser welding method for aircraft titanium alloy frames based on a mobile robot, applicable to an in-situ laser welding system for aircraft titanium alloy frames. The system includes a mobile robot integrating a laser cladding module and a machine vision module, and a host computer. The method includes:

[0007] The mobile robot interacts with the host computer through a machine vision module to determine the location and morphology of damage to the titanium alloy components.

[0008] The host computer plans the welding path and welding process parameters based on the location information of the damaged part, the damage morphology information, the material of the damaged part, and the current posture information of the mobile robot arm.

[0009] The mobile robot controls the mechanical grinding module on the robotic arm to cut and clean the damaged area according to the welding path.

[0010] The mobile robot controls the cladding head of the laser cladding module to weld the damaged area according to the welding path and the welding process parameters; wherein, the cladding head and the mechanical grinding module are both located at the front end of the robotic arm.

[0011] Optionally, the laser cladding module further includes: a fiber laser, a cooler, and a protective gas.

[0012] Optionally, the step of the mobile robot interacting with the host computer through a machine vision module to determine the damage location and damage morphology information of the titanium alloy component includes:

[0013] The machine vision module captures and acquires image information and sends the image information to the host computer; wherein, the machine vision module is disposed at the front end of the robotic arm;

[0014] The host computer locates the damaged area of ​​the aircraft's titanium alloy component based on the image information and feeds back the damaged area information to the mobile robot; wherein, the image information includes the morphological information of the damaged area;

[0015] The mobile robot invokes the machine vision module to scan the damaged area with a laser, obtains the first point cloud information of the damaged area, and sends the point cloud information to the host computer.

[0016] Optionally, the step of the host computer planning the welding path and welding process based on the location information of the damaged area, the damage morphology information, the material of the damaged area, and the current posture information of the mobile robot arm includes:

[0017] The host computer constructs a first three-dimensional model corresponding to the damaged area based on the first point cloud information.

[0018] The host computer generates welding planning information based on the first 3D model, the posture information of the mobile robot arm, and the material of the damaged part, and sends the welding planning information to the mobile robot. The welding planning information includes welding path and welding process parameters.

[0019] Optionally, the step of the host computer constructing a first three-dimensional model corresponding to the damaged area based on the first point cloud information includes:

[0020] The host computer preprocesses the first point cloud information to filter out invalid and outlier points, and obtains the second point cloud information.

[0021] The second point cloud information is filtered out using a voxel grid filtering algorithm to obtain the third point cloud information;

[0022] A preset point cloud reconstruction algorithm is used to perform point cloud 3D reconstruction on the third point cloud information to obtain the first 3D model.

[0023] Optionally, the step of the host computer preprocessing the first point cloud information to filter out invalid and outlier points to obtain the second point cloud information includes:

[0024] For each point in the first point cloud information, determine whether the coordinates of the point in the first dimension are within a preset first dimension value range;

[0025] If not, the point is considered invalid and filtered out.

[0026] Traverse each point in the first point cloud information and calculate the average distance between the point and each adjacent point; determine whether the average distance is within a preset distance range threshold.

[0027] If not, identify the point and each point adjacent to the point as outliers and filter out all outliers.

[0028] The first point cloud information after filtering out invalid and discrete points is determined as the second point cloud information.

[0029] Optionally, the step of using a preset point cloud reconstruction algorithm to perform point cloud 3D reconstruction on the third point cloud information to obtain the first 3D model includes:

[0030] Multiple preset point cloud reconstruction algorithms are used to perform point cloud 3D reconstruction on the third point cloud information to obtain multiple 3D models;

[0031] The model with the highest score among the multiple 3D models is selected as the first 3D model.

[0032] Optionally, the step of the host computer generating welding planning information based on the first 3D model, the posture information of the mobile robot arm, and the material of the damaged area includes:

[0033] The host computer performs damage site morphology recognition based on the first three-dimensional model to obtain the damage site morphology.

[0034] Determine the boundary dimensions of the morphology of the damaged area;

[0035] Based on the boundary dimensions and preset boundary correction rules, a second three-dimensional model is generated;

[0036] Based on the properties of the damaged area, welding process parameters and cutting process parameters are determined;

[0037] Based on the second 3D model and the posture information of the mobile robot arm, a welding path is generated.

[0038] This invention also provides an in-situ laser welding system for aircraft titanium alloy frames based on a mobile robot. The system includes a mobile robot and a host computer. The mobile robot includes a control cabinet, a machine vision module, and a laser cladding module.

[0039] The control cabinet is used to call the machine vision module to interact with the host computer to determine the damage location and damage morphology information of the titanium alloy component;

[0040] The host computer is used to plan the welding path and welding process parameters based on the location information of the damaged part, the damage morphology information, the material of the damaged part, and the current posture information of the mobile robot arm.

[0041] The control cabinet is also used to control the mechanical grinding module on the robotic arm to cut and clean the damaged area according to the welding path; and to control the cladding head of the laser cladding module to weld the damaged area according to the welding path and the welding process parameters; wherein the cladding head and the mechanical grinding module are both located at the front end of the robotic arm.

[0042] Optionally, the laser cladding module further includes: a fiber laser, a cooler, and a protective gas.

[0043] Optionally, the control cabinet is specifically used to call the machine vision module to capture and acquire image information, and send the image information to the host computer; wherein, the machine vision module is located at the front end of the robotic arm;

[0044] The host computer includes:

[0045] The positioning module is used to locate the damaged area of ​​the aircraft titanium alloy component based on the image information and feed back the damaged area information to the mobile robot; wherein, the image information includes the morphological information of the damaged area;

[0046] The control cabinet is also used to call the machine vision module to scan the damaged area with a laser, obtain the first point cloud information of the damaged area, and send the point cloud information to the host computer.

[0047] Optionally, the host computer includes:

[0048] The point cloud processing module is used to construct a first three-dimensional model corresponding to the damaged area based on the first point cloud information.

[0049] The welding planning module is used to generate welding planning information based on the first three-dimensional model, the posture information of the mobile robot arm, and the material of the damaged part, and send the welding planning information to the mobile robot. The welding planning information includes welding path and welding process parameters.

[0050] Optionally, the point cloud processing module is specifically used to preprocess the first point cloud information to filter out invalid points and outliers to obtain second point cloud information; to filter out the second point cloud information using a voxel grid filtering algorithm to obtain third point cloud information; and to perform point cloud 3D reconstruction on the third point cloud information using a preset point cloud reconstruction algorithm to obtain the first 3D model.

[0051] Optionally, when the point cloud processing module preprocesses the first point cloud information to filter out invalid and outlier points to obtain the second point cloud information, it is specifically used for:

[0052] For each point in the first point cloud information, determine whether the coordinates of the point in the first dimension are within a preset first dimension value range;

[0053] If not, the point is considered invalid and filtered out.

[0054] Traverse each point in the first point cloud information and calculate the average distance between the point and each adjacent point; determine whether the average distance is within a preset distance range threshold.

[0055] If not, identify the point and each point adjacent to the point as outliers and filter out all outliers.

[0056] The first point cloud information after filtering out invalid and discrete points is determined as the second point cloud information.

[0057] Optionally, the welding planning module is specifically used to identify the shape of the damaged part based on the first three-dimensional model to obtain the shape of the damaged part; determine the boundary size of the shape of the damaged part; generate a second three-dimensional model based on the boundary size and a preset boundary correction rule; determine welding process parameters and cutting process parameters based on the attributes of the damaged part; and generate a welding path based on the second three-dimensional model and the posture information of the mobile robot arm.

[0058] This application discloses a mobile robot-based in-situ laser welding solution for aircraft titanium alloy frames. The mobile robot interacts with a host computer via a machine vision module to determine the location and morphology of damage to the titanium alloy components. The host computer plans the welding path and welding process parameters based on the location, morphology, material, and current posture of the robot's arm. Following the welding path, the mobile robot controls a mechanical grinding module on its arm to cut and clean the damaged area. Finally, the mobile robot controls the cladding head of the laser cladding module to weld the damaged area according to the welding path and process parameters. This mobile robot-based in-situ laser welding solution for aircraft titanium alloy frames, by directly detecting, cutting, cleaning, and repairing damaged areas using a mobile robot, eliminates the need for a "teach-then-cut" approach, thus improving repair efficiency, shortening the repair cycle, and reducing the difficulty of repair. Secondly, the mobile robot has a long continuous working time and uses laser welding, making it applicable to repairing various types of damaged titanium alloy components, with strong strength recovery and good versatility. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the steps of an in-situ laser welding method for an aircraft titanium alloy frame based on a mobile robot, according to an embodiment of this application.

[0060] Figure 2 This is a schematic diagram illustrating the structure of an in-situ laser welding system for an aircraft titanium alloy frame according to an embodiment of this application;

[0061] Figure 3 This is a schematic diagram illustrating a mobile repair platform according to an embodiment of this application;

[0062] Figure 4 This is a schematic diagram illustrating the principle of surface point cloud slicing processing in an embodiment of this application;

[0063] Figure 5 This is a schematic diagram illustrating the voxel grid filtering principle of an embodiment of this application;

[0064] Figure 6 This is a schematic diagram illustrating the three-dimensional reconstruction effect of the surface damage point cloud of a 316L steel plate according to an embodiment of this application.

[0065] Figure 7 This is a schematic diagram illustrating the TC4-M titanium alloy skin hole repair reinforcement method according to an embodiment of this application;

[0066] Figure 8 This is a schematic diagram illustrating laser welding repair of a hole in the TC4-M titanium alloy skin according to an embodiment of this application;

[0067] Figure 9 This is a schematic diagram illustrating the mechanical connection repair of the TA1 5-M titanium alloy skeleton according to an embodiment of this application;

[0068] Figure 10 This is a macroscopic morphology diagram of the laser-welded weld seam of the TA15-M titanium alloy skeleton according to an embodiment of this application;

[0069] Figure 11 This is a schematic diagram showing the metallographic structure of the laser-welded weld seam of the TA1 5-M titanium alloy skeleton according to an embodiment of this application;

[0070] Figure 12 This is a structural block diagram illustrating an in-situ laser welding system for an aircraft titanium alloy frame based on a mobile robot, according to an embodiment of this application. Detailed Implementation

[0071] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0072] This application proposes an in-situ laser welding solution for aircraft titanium alloy frames based on a mobile robot. The solution involves interaction between a mobile robot integrating a laser cladding module and a machine vision module and a host computer to clean and repair damaged areas using laser welding. During operation, the mobile robot freely moves to the vicinity of the damaged aircraft titanium alloy component. A mechanical grinding module, driven by a robotic arm and utilizing the laser principle, cleans the damaged area. The robotic arm then drives a cladding head to perform laser welding on the damaged area, thus achieving in-situ repair of the damaged parts of the aircraft titanium alloy. This solution addresses numerous problems associated with aircraft component damage repair, such as high implementation difficulty, long cycle time, poor strength recovery, and strong application limitations. Furthermore, by incorporating machine vision technology, it overcomes the issues of complex operation and low repair efficiency associated with the "teach-then-cut" approach used in industrial robots.

[0073] The following description, in conjunction with the accompanying drawings, details the in-situ laser welding scheme for aircraft titanium alloy frames based on mobile robots provided in this application, through specific embodiments and application scenarios.

[0074] As attached Figure 1 As shown in the figure, the in-situ laser welding method for aircraft titanium alloy skeleton based on mobile robot according to the present application includes the following steps:

[0075] Step 101: The mobile robot interacts with the host computer through the machine vision module to determine the location and morphology of damage to the titanium alloy components.

[0076] The in-situ laser welding method for aircraft titanium alloy skeleton based on mobile robot provided in this embodiment is executed by an in-situ laser welding system for aircraft titanium alloy skeleton. The system includes a mobile robot integrating a laser cladding module and a machine vision module, and a host computer.

[0077] Figure 2 This is a schematic diagram illustrating the structure of an in-situ laser welding method system for an aircraft titanium alloy frame, according to an embodiment of this application. Figure 2 As shown, this system is based on "robot + machine vision" technology, which combines a robot and a laser cladding module (i.e., Figure 2 The mobile robot, as shown in the embodiment of this application, integrates welding repair modules, machine vision modules, and other components onto a mobile mechanism (such as a mobile repair platform). This mobile robot interacts with a host computer to perform in-situ damage cleaning and welding of aircraft titanium alloy parts. The robotic arm of the mobile robot is equipped with a laser cladding module and a machine vision module, which act as the human's eyes and hands. The host computer is equipped with a vision software system that can process images and point clouds, build 3D models, and plan welding paths. The control cabinet serves as the "brain" of the mobile robot, controlling the movement of the mobile repair platform, the robotic arm, and the operation of the laser cladding module and machine vision module.

[0078] The mobile robot provided in this application embodiment can be regarded as a highly mobile, multi-degree-of-freedom laser in-situ damage cleaning and welding platform with a mobile repair platform as its carrier and multiple modules working together. For example... Figure 3 As shown, the mobile repair platform can be a wheeled platform with a maximum moving speed of 10 km / h, has a certain obstacle-crossing ability, and can be moved by remote control to ensure flexible and rapid response to actual damage and repair requirements.

[0079] The mobile robot system consists of an ABB IRB4600 six-degree-of-freedom industrial robot, an IRC5 control system, and a FlexPendant teach pendant. Its working range is 2.05m, enabling it to move over a wide range of distances and meet the requirements of complex on-site repair environments. The effective arm load is 20Kg, ensuring the strength requirements of each module installed at the end of the robotic arm.

[0080] The machine vision module, based on the characteristics of vision sensors, combines structured light sensors and line laser sensors and mounts them on the front end of the robotic arm to construct an eye-in-hand system, enabling data scanning and acquisition. This module mainly includes: a structured light sensor, a line laser sensor, and a flange.

[0081] Structured light sensors project structured light onto the surface of a workpiece, and a camera acquires the image information to detect surface information. They feature a large scanning range and high scanning speed. By acquiring images of damaged areas using structured light sensors, rapid damage localization can be achieved. An exemplary model of structured light sensor is the SA-T1000, with a near-field FOV of 700×600mm and a far-field FOV of 4000×3000mm, enabling rapid damage localization over a wide viewing angle. It also communicates with a host computer via gigabit Ethernet to ensure real-time data transmission.

[0082] In practice, both the machine vision module and the laser cladding module can be installed at the front end of the robotic arm of the mobile robot; alternatively, the machine vision module can be installed at the front end of the robotic arm, and the laser cutting module can be installed at the bottom end of the robotic arm.

[0083] In one exemplary configuration, the laser cladding module further includes a fiber laser, a cooler, and a protective gas.

[0084] In one optional embodiment, the mobile robot interacts with the host computer through a machine vision module to determine the location and morphology of damage to the titanium alloy component, which may include the following sub-steps:

[0085] Sub-step 1: The mobile robot captures image information through the machine vision module and sends the image information to the host computer.

[0086] Sub-step 2: The host computer locates the damaged area of ​​the aircraft's titanium alloy component based on the image information and feeds back the damaged area information to the mobile robot.

[0087] The image information includes morphological information of the damaged area.

[0088] Sub-step 3: The mobile robot calls the machine vision module to scan the damaged area with a laser, obtain the first point cloud information of the damaged area, and send the point cloud information to the host computer.

[0089] Line laser sensors utilize the high directionality and brightness of laser light to measure the three-dimensional information of an object by transmitting a single line of laser light. Composed of a laser emitter and a camera, they feature simple structure, high precision, and easy image processing. Based on these characteristics, after the structured light sensor has located the damage site, this project uses a line laser sensor to scan the damaged area and capture real-time images of the line laser's state. The changes in the laser stripe information are then used to obtain the three-dimensional point cloud information of the damaged area.

[0090] Step 102: The host computer plans the welding path and welding process parameters based on the location information, damage morphology information, material of the damaged part, and the current posture information of the mobile robot arm.

[0091] In one alternative embodiment, planning the welding path and welding process parameters may include the following sub-steps:

[0092] Sub-step 1021: The host computer constructs the first three-dimensional model corresponding to the damaged area based on the first point cloud information.

[0093] The vision software system consists of a camera acquisition module, a system calibration module, a point cloud processing module, and auxiliary software. Vision algorithms enable automatic identification of hole damage, automatic planning of cutting paths, and automatic programming. The camera acquisition module acquires image information from the camera; the system calibration module unifies the camera's image coordinates to the robot's base coordinates; and the point cloud processing module analyzes the 3D point cloud information (the first point cloud information) acquired by the line laser sensor.

[0094] The vision software system runs on a host computer. During operation, the system first performs image capture, color setting, and image saving via the camera. Then, it completes system calibration through camera calibration, light plane calibration, and hand-eye calibration, unifying the camera's image coordinates to the robot's base coordinates. This is a prerequisite for the robot to accurately measure damaged objects. The point cloud processing module includes point cloud filtering, point cloud visualization, and point cloud size and color parameter functions. Common point cloud data processing methods include point cloud data preprocessing and 3D point cloud reconstruction. Based on the data processed by the point cloud processing module, mesh processing systems such as Meshlab are used to edit, render, and filter the 3D triangular mesh of the point cloud file. Simultaneously, 3D modeling of the damaged area is achieved using reverse engineering software, generating layered slices to lay the foundation for generating the cladding repair path (also known as the welding path). A schematic diagram of the curved surface point cloud slicing processing principle is shown below. Figure 4 As shown.

[0095] An optional method for a host computer to construct a first 3D model corresponding to the damaged area based on the first point cloud information may include the following sub-steps:

[0096] Sub-step 1: The host computer preprocesses the first point cloud information to filter out invalid and outlier points, and obtains the second point cloud information;

[0097] An example of how a host computer can preprocess the first point cloud information to filter out invalid and outlier points and obtain the second point cloud information is as follows:

[0098] For each point in the first point cloud information, determine whether the point's coordinates in the first dimension are within the preset first dimension value range; if not, the point is considered invalid; if so, it is considered valid and invalid points are filtered out. Traverse each point in the first point cloud information and calculate the average distance between the point and each of its neighboring points; determine whether the average distance is within a preset distance range threshold; if not, identify the point and each of its neighboring points as outliers and filter out all outliers; the first point cloud information after filtering out invalid points and discrete points is identified as the second point cloud information.

[0099] It should be noted that the specific values ​​of the preset first dimension value range and the preset distance range threshold can be flexibly set by those skilled in the art, and no specific restrictions are imposed on them in this embodiment.

[0100] Sub-step 1 involves point cloud data preprocessing: Two methods, pass-through filtering and statistical filtering, can be used to filter out irrelevant and outlier points from the point cloud library. The pass-through filtering algorithm removes irrelevant points by specifying a dimension (X, Y, Z) and its value range, traversing each point in the point cloud, and determining whether the point is within the value range, removing points outside the range. The statistical filtering algorithm traverses each point in the point cloud, calculates the average distance to all neighboring points, and deletes outliers by comparing the average distance to a standard range threshold, retaining the filtered point cloud data. Specifically, outlier removal is achieved using statistical filters from the point cloud library.

[0101] Sub-step 2: Use a voxel grid filtering algorithm to filter out the second point cloud information to obtain the third point cloud information;

[0102] Sub-step 2 involves further simplification of the preprocessed point cloud data. A large number of redundant data points increase algorithm runtime and thus affect 3D reconstruction efficiency; therefore, further simplification of the preprocessed point cloud data is necessary. For example, a voxel grid filtering algorithm can be used. This algorithm divides the point cloud data into fixed-size voxel grids, calculates the centroid of all points within each grid, deletes grids with no point cloud data, and approximates all points within each grid as centroids. The set of all voxel centroids represents the voxel grid filtered point cloud data. This method preserves the shape characteristics of the point cloud data and retains its original geometric structure. A schematic diagram of the voxel grid filtering principle is attached. Figure 5 As shown.

[0103] Sub-step 3: Use a preset point cloud reconstruction algorithm to perform point cloud 3D reconstruction on the third point cloud information to obtain the first 3D model.

[0104] An optional method for using a preset point cloud reconstruction algorithm to perform point cloud 3D reconstruction on the third point cloud information to obtain the first 3D model can be as follows:

[0105] Multiple preset point cloud reconstruction algorithms are used to perform point cloud 3D reconstruction on the third point cloud information to obtain multiple 3D models; the model with the highest score is selected from the multiple 3D models and determined as the first 3D model.

[0106] The preset point cloud reconstruction algorithms may include, but are not limited to: Delaunay, Possion, and rolling ball method.

[0107] Commonly used point cloud 3D reconstruction algorithms include Delaunay, Possion, and the rolling sphere method. Delaunay is a triangulation algorithm that, given a point set, inserts each point into a selected region containing that point set, searches and deletes neighboring triangles to form Delaunay cavities, and then connects each point to each vertex in the cavity to form a new triangular mesh. Possion is a reconstruction method based on Poisson meshes, which fits a surface indicator function to point clouds with normal data, extracts isosurfaces by setting a threshold, and achieves 3D reconstruction of the point cloud. The rolling sphere method is a local region growing 3D reconstruction algorithm. It first defines a sphere, which is defined within a seed triangle for rotation and rolling until it touches the next point. The edge of the sphere and the point form a triangle, and the surface triangle is reconstructed through continuous iteration. For point cloud data preprocessed and simplified due to structural damage, 3D reconstruction of the point cloud surface was performed using three point cloud reconstruction algorithms: Delaunay, Possion, and the rolling sphere method. The reconstruction effects were compared and analyzed to determine the optimal 3D point cloud reconstruction algorithm. Figure 6 This is a schematic diagram illustrating the three-dimensional reconstruction effect of the surface damage point cloud of a 316L steel plate according to an embodiment of this application. Figure 6 (a) is a schematic diagram of the reconstruction effect of the Delaunay reconstruction method. Figure 6 (b) is a schematic diagram of the reconstruction effect of the Possion reconstruction method. Figure 6 (c) is a schematic diagram of the reconstruction effect of the rolling ball method.

[0108] Step 1022: The host computer generates welding planning information based on the first 3D model, the posture information of the mobile robot arm, and the material of the damaged part, and sends the welding planning information to the mobile robot.

[0109] The cutting planning information includes the cutting planning path and cutting process parameters.

[0110] An optional method for generating welding planning information from the material of the damaged area using a host computer based on a first 3D model and the posture information of a mobile robot arm may include the following sub-steps:

[0111] Sub-step 1: The host computer performs damage site morphology recognition based on the first 3D model to obtain the damage site morphology;

[0112] The morphology of the damaged area may include, but is not limited to: groove type, planar type, curved surface type, and hole type.

[0113] Sub-step 2: Determine the boundary dimensions of the damaged area's morphology;

[0114] Sub-step 3: Generate a second 3D model based on the boundary dimensions and preset boundary correction rules;

[0115] The boundary correction rules can be flexibly set by those skilled in the art. For example, they can be set to indent the boundary by a preset size, such as 2cm, 1cm or 5mm. In this embodiment, no specific limitation is made.

[0116] Sub-step 4: Determine the cutting process parameters based on the material of the damaged area;

[0117] The attributes of the damaged area can be determined based on the type of workpiece in which the damaged area is located, the material of the damaged area, and the morphology of the damaged area. Different attributes correspond to different cutting and welding process parameters. The system has preset correspondences between different attributes and cutting and welding process parameters. After determining the attributes of the damaged area, the cutting and welding process parameters can be determined based on the preset correspondences.

[0118] For aircraft TC4-M titanium alloy materials with a thickness of 2mm to 5mm, the range of laser welding process parameters is shown in Table 1:

[0119] Table 1: Laser Welding Process Parameters for TC4-M Titanium Alloy

[0120]

[0121] For aircraft TA15-M titanium alloy materials with a thickness of 2mm to 5mm, the range of laser welding process parameters is shown in Table 2:

[0122] Table 2: Laser Welding Process Parameters for TA15-M Titanium Alloy

[0123]

[0124] In-situ laser welding of damaged areas in aircraft titanium alloy components primarily repairs punctures. This involves automatically identifying the puncture size and selecting an appropriate prefabricated patch. The puncture can be circular (e.g., 20mm, 30mm, 40mm, 50mm, and 60mm), oblong, or rectangular. A prefabricated patch of the same material, thickness, and shape as the damaged area is selected for in-situ laser welding to repair the damaged part of the aircraft titanium alloy component.

[0125] Sub-step 5: Generate the welding path based on the second 3D model and the posture information of the mobile robot arm.

[0126] Step 103: The mobile robot, following the welding path, controls the mechanical grinding module on the robotic arm to cut and clean the damaged area.

[0127] Step 104: The mobile robot controls the cladding head of the laser cladding module to weld the damaged area according to the welding path and welding process parameters.

[0128] The in-situ laser welding method for aircraft titanium alloy frames based on mobile robots provided in this application involves a mobile robot interacting with a host computer via a machine vision module to determine the location and morphology of damage to titanium alloy components. The host computer plans the welding path and welding process parameters based on the location, morphology, material, and current posture of the robot's robotic arm. Following the welding path, the mobile robot controls a mechanical grinding module on its robotic arm to cut and clean the damaged area. Finally, the mobile robot controls the cladding head of the laser cladding module to weld the damaged area according to the welding path and process parameters. This in-situ laser welding solution for aircraft titanium alloy frames based on mobile robots directly detects, cuts, cleans, and repairs damaged areas using laser welding. Firstly, it eliminates the need for a "teach-then-cut" approach, thus improving repair efficiency, shortening the repair cycle, and reducing the difficulty of repair. Secondly, the mobile robot has a long continuous working time and uses laser welding, making it applicable to repairing various types of damaged titanium alloy components, exhibiting strong strength recovery and good versatility.

[0129] The following is a specific example illustrating the in-situ welding method for aircraft titanium alloy components based on a mobile robot provided in this application.

[0130] This application uses in-situ laser welding of the TC4-M titanium alloy skin as an example for illustration.

[0131] The main load-bearing structural material of the damaged area is TC4-M titanium alloy, heat-treated in the M state (annealed state), with a thickness of 4 mm. The bore diameter of the welded specimen is 20 mm. The welding equipment used is MF SC-4000W, and the welding process parameters are shown in Table 3.

[0132] Table 3: Laser Welding Process Parameters for TC4-M Titanium Alloy Prefabricated Patches

[0133]

[0134] Table 4 summarizes the mechanical properties and cycle-related data of TC4-M titanium alloy after repairing the damaged (also known as the hole) area using the robot + laser welding repair solution provided in this application.

[0135] Table 4: Mechanical Properties and Repair Cycle of TC4 Titanium Alloy Skin for Pit Repair

[0136]

[0137] Figure 7 This is a schematic diagram showing the repair of TC4-M titanium alloy skin with riveting reinforcement. Figure 8 This diagram illustrates the laser welding repair of a puncture in the skin of a TC4-M titanium alloy. The repair results clearly show that laser welding is superior. Tensile performance tests were conducted on a 4mm thick, 80mm wide TC4-M titanium alloy tensile specimen. The base material could withstand a load of 328 kN; after a pre-fabricated 40mm diameter puncture, the remaining load it could withstand was 162.95 kN, with a remaining strength coefficient of 49.68%; after repair using traditional mechanical joining, the load it could withstand was 245.21 kN, with a strength recovery coefficient of 74.76%; after repair using the robotic + laser welding method provided in this application, the load it could withstand was 322.1 kN, with a strength recovery coefficient of 98.2%.

[0138] In terms of repair cycle, the traditional mechanical connection repair cycle is 5 hours; the robot + laser welding repair cycle proposed in this application is 0.5 hours.

[0139] The welding repair method for damaged aircraft titanium alloy skin provided in this specific example has several advantages. First, it can be performed on-site on the damaged aircraft, overcoming the problems of high difficulty, long cycle, and heavy spare parts supply associated with traditional replacement repairs. Second, it can replace mechanical repair methods for damaged aircraft titanium alloy components, thus solving the problems of high difficulty and long cycle associated with mechanical repair methods for damaged aircraft titanium alloy components. Third, this method has high welding efficiency; for a 50mm hole, the automatic program can complete the cutting in 2 minutes, greatly improving the cutting efficiency. Moreover, the impact on static strength after cutting is within 10%, which is within the acceptable range for practical applications.

[0140] The following specific example illustrates the in-situ laser welding method for aircraft titanium alloy skeletons based on mobile robots provided in this application.

[0141] In this embodiment, the in-situ laser welding of the damaged part of the TA1 5-M titanium alloy skeleton (also known as laser welding of TA 15-M titanium alloy prefabricated patch) is used as an example for illustration.

[0142] The main load-bearing structural material is TA15-M titanium alloy, heat-treated in the M state (annealed state), with a thickness of 4 mm. The bore diameter of the welded specimen is 40 mm. The welding equipment used is MFSC-4000W, and the welding process parameters are shown in Table 2.

[0143] Table 5: Laser Welding Process Parameters for TA 15-M Titanium Alloy Prefabricated Patches

[0144]

[0145] A schematic diagram of the TA1 5 titanium alloy skeleton mechanical connection (traditional) repair is shown below. Figure 9 As shown in the figure. The macroscopic morphology of the weld seam after laser welding of the TA 15-M titanium alloy skeleton using the in-situ laser welding method provided in this application is shown in the figure. Figure 10 As shown in the figure, the metallographic structure of the laser-welded weld seam of the TA15-M titanium alloy skeleton is as follows. Figure 11 As shown.

[0146] Welded joints exhibit two typical microstructures: weld zone microstructure and plate texture. A clear transition line exists between the two microstructures, with no obvious transition zone. The weld zone shows no significant defects (see...). Figure 4 The weld zone contains large columnar crystals inside and small equiaxed crystals on the outside. The weld has a small number of porosity defects, and the base metal has a typical texture.

[0147] The experimental results show that: tensile performance tests were conducted on TA1 5-M tensile specimens with a thickness of 4 mm and a width of 80 mm. The base material could withstand a load of 355 kN; after a pre-fabricated 40 mm diameter hole, the remaining load it could withstand was 177.22 kN, with a residual strength coefficient of 49.92%; after repair by traditional mechanical connection, the load it could withstand was 249.96 kN, with a strength recovery coefficient of 70.41%; after repair by robot + laser welding, the load it could withstand was 330.86 kN, with a strength recovery coefficient of 93.2%. Regarding the repair cycle, the traditional mechanical connection repair cycle was 6 hours; the robot + laser welding repair method provided in this application had a repair cycle of 0.7 hours.

[0148] Figure 12 The structural block diagram of an in-situ laser welding system for an aircraft titanium alloy frame based on a mobile robot, according to an embodiment of this application, is shown in this application.

[0149] The in-situ laser welding system for aircraft titanium alloy skeleton based on mobile robot provided in this application embodiment includes: mobile robot 201 and host computer 202. The mobile robot 201 includes: control cabinet 2011, machine vision module 2012 and laser cladding module 2013.

[0150] The control cabinet 2011 is used to call the machine vision module 2012 to interact with the host computer 201 to determine the damage location and damage morphology information of the titanium alloy component.

[0151] The host computer 202 is used to plan the welding path and welding process parameters based on the location information of the damaged part, the damage morphology information, the material of the damaged part, and the current posture information of the mobile robot arm.

[0152] The control cabinet 2011 is also used to control the mechanical grinding module on the robotic arm to cut and clean the damaged area according to the welding path; and to control the cladding head of the laser cladding module 2013 to weld the damaged area according to the welding path and the welding process parameters; wherein the cladding head and the mechanical grinding module are both located at the front end of the robotic arm.

[0153] Optionally, the laser cladding module further includes: a fiber laser, a cooler, and a protective gas.

[0154] Optionally, the control cabinet is specifically used to call the machine vision module to capture and acquire image information, and send the image information to the host computer; wherein, the machine vision module is located at the front end of the robotic arm;

[0155] The host computer includes:

[0156] The positioning module is used to locate the damaged area of ​​the aircraft titanium alloy component based on the image information and feed back the damaged area information to the mobile robot; wherein, the image information includes the morphological information of the damaged area;

[0157] The control cabinet is also used to call the machine vision module to scan the damaged area with a laser, obtain the first point cloud information of the damaged area, and send the point cloud information to the host computer.

[0158] Optionally, the host computer includes:

[0159] The point cloud processing module is used to construct a first three-dimensional model corresponding to the damaged area based on the first point cloud information.

[0160] The welding planning module is used to generate welding planning information based on the first three-dimensional model, the posture information of the mobile robot arm, and the material of the damaged part, and send the welding planning information to the mobile robot. The welding planning information includes welding path and welding process parameters.

[0161] Optionally, the point cloud processing module is specifically used to preprocess the first point cloud information to filter out invalid points and outliers to obtain second point cloud information; to filter out the second point cloud information using a voxel grid filtering algorithm to obtain third point cloud information; and to perform point cloud 3D reconstruction on the third point cloud information using a preset point cloud reconstruction algorithm to obtain the first 3D model.

[0162] Optionally, when the point cloud processing module preprocesses the first point cloud information to filter out invalid and outlier points to obtain the second point cloud information, it is specifically used for:

[0163] For each point in the first point cloud information, determine whether the coordinates of the point in the first dimension are within a preset first dimension value range;

[0164] If not, the point is considered invalid and filtered out.

[0165] Traverse each point in the first point cloud information and calculate the average distance between the point and each adjacent point; determine whether the average distance is within a preset distance range threshold.

[0166] If not, identify the point and each point adjacent to the point as outliers and filter out all outliers.

[0167] The first point cloud information after filtering out invalid and discrete points is determined as the second point cloud information.

[0168] Optionally, the welding planning module is specifically used to identify the shape of the damaged part based on the first three-dimensional model to obtain the shape of the damaged part; determine the boundary size of the shape of the damaged part; generate a second three-dimensional model based on the boundary size and a preset boundary correction rule; determine welding process parameters and cutting process parameters based on the attributes of the damaged part; and generate a welding path based on the second three-dimensional model and the posture information of the mobile robot arm.

[0169] In the in-situ laser welding system for aircraft titanium alloy frames based on a mobile robot provided in this application embodiment, the mobile robot interacts with the host computer through a machine vision module to determine the location and morphology of damage to the titanium alloy components. The host computer plans the welding path and welding process parameters based on the location, morphology, material, and current posture of the mobile robot's robotic arm. Following the welding path, the mobile robot controls the mechanical grinding module on its robotic arm to cut and clean the damaged area. Finally, the mobile robot controls the cladding head of the laser cladding module to weld the damaged area according to the welding path and welding process parameters. This in-situ laser welding system for aircraft titanium alloy frames based on a mobile robot directly detects, cuts, cleans, and repairs damaged areas using laser welding. Firstly, it eliminates the need for a "teach-then-cut" approach, thus improving repair efficiency and shortening the repair cycle while reducing the difficulty of repair. Secondly, the mobile robot has a long continuous working time and uses laser welding, making it suitable for repairing various types of damaged titanium alloy components, offering strong strength recovery and good versatility.

[0170] This invention also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0171] Memory, used to store computer programs;

[0172] When the processor executes the program stored in the memory, it implements each step of the in-situ laser welding method for aircraft titanium alloy skeleton based on mobile robot executed by the host computer in the above method embodiment.

[0173] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0174] The communication interface is used for communication between the aforementioned terminal and other devices.

[0175] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0176] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0177] In another embodiment of the present invention, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to implement the method steps executed by the host computer as described in any of the above embodiments.

[0178] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0179] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for in-situ laser welding of aircraft titanium alloy frames based on mobile robots, characterized in that, An in-situ laser welding system for aircraft titanium alloy frames, the system comprising a mobile robot integrating a laser cladding module and a machine vision module, and a host computer, the method comprising: The mobile robot interacts with the host computer through a machine vision module to determine the location and morphology of damage to the titanium alloy components. The host computer plans the welding path and welding process parameters based on the location information of the damaged part, the damage morphology information, the material of the damaged part, and the current posture information of the mobile robot arm. The mobile robot controls the mechanical grinding module on the robotic arm to cut and clean the damaged area according to the welding path. The mobile robot controls the cladding head of the laser cladding module to weld the damaged area according to the welding path and the welding process parameters; wherein, the cladding head and the mechanical grinding module are both located at the front end of the robotic arm.

2. The method according to claim 1, characterized in that, The laser cladding module also includes: a fiber laser, a cooling unit, and a protective gas.

3. The method according to claim 1, characterized in that, The steps for the mobile robot to interact with the host computer through a machine vision module to determine the location and morphology of damage to the titanium alloy component include: The machine vision module captures and acquires image information and sends the image information to the host computer; wherein, the machine vision module is disposed at the front end of the robotic arm; The host computer locates the damaged area of ​​the aircraft's titanium alloy component based on the image information and feeds back the damaged area information to the mobile robot; wherein, the image information includes the morphological information of the damaged area; The mobile robot invokes the machine vision module to scan the damaged area with a laser, obtains the first point cloud information of the damaged area, and sends the point cloud information to the host computer. Furthermore, the step of the host computer planning the welding path and welding process based on the location information of the damaged area, the damage morphology information, the material of the damaged area, and the current posture information of the mobile robot arm includes: The host computer constructs a first three-dimensional model corresponding to the damaged area based on the first point cloud information. The host computer generates welding planning information based on the first 3D model, the posture information of the mobile robot arm, and the material of the damaged part, and sends the welding planning information to the mobile robot. The welding planning information includes welding path and welding process parameters.

4. The method according to claim 3, characterized in that, The step of the host computer constructing a first three-dimensional model corresponding to the damaged area based on the first point cloud information includes: The host computer preprocesses the first point cloud information to filter out invalid and outlier points, and obtains the second point cloud information. The second point cloud information is filtered out using a voxel grid filtering algorithm to obtain the third point cloud information; The first three-dimensional model is obtained by performing point cloud 3D reconstruction on the third point cloud information using a preset point cloud reconstruction algorithm; further, the step of the host computer preprocessing the first point cloud information to filter out invalid points and outliers to obtain the second point cloud information includes: For each point in the first point cloud information, determine whether the coordinates of the point in the first dimension are within a preset first dimension value range; If not, the point is considered invalid and filtered out. Traverse each point in the first point cloud information and calculate the average distance between the point and each adjacent point; determine whether the average distance is within a preset distance range threshold. If not, identify the point and each point adjacent to the point as outliers and filter out all outliers. The first point cloud information after filtering out invalid and discrete points is determined as the second point cloud information.

5. The method according to claim 4, characterized in that, The step of using a preset point cloud reconstruction algorithm to perform point cloud 3D reconstruction on the third point cloud information to obtain the first 3D model includes: Multiple preset point cloud reconstruction algorithms are used to perform point cloud 3D reconstruction on the third point cloud information to obtain multiple 3D models; The model with the highest score from the plurality of 3D models is selected as the first 3D model; further, the step of the host computer generating welding planning information based on the first 3D model, the posture information of the mobile robot arm, and the material of the damaged area includes: The host computer performs damage site morphology recognition based on the first three-dimensional model to obtain the damage site morphology. Determine the boundary dimensions of the morphology of the damaged area; Based on the boundary dimensions and preset boundary correction rules, a second three-dimensional model is generated; Based on the properties of the damaged area, welding process parameters and cutting process parameters are determined; Based on the second 3D model and the posture information of the mobile robot arm, a welding path is generated.

6. A mobile robot-based in-situ laser welding system for aircraft titanium alloy frames, characterized in that, The system includes a mobile robot and a host computer. The mobile robot includes a control cabinet, a machine vision module, and a laser cladding module. The control cabinet is used to call the machine vision module to interact with the host computer to determine the damage location and damage morphology information of the titanium alloy component; The host computer is used to plan the welding path and welding process parameters based on the location information of the damaged part, the damage morphology information, the material of the damaged part, and the current posture information of the mobile robot arm. The control cabinet is also used to control the mechanical grinding module on the robotic arm to cut and clean the damaged area according to the welding path; and to control the cladding head of the laser cladding module to weld the damaged area according to the welding path and the welding process parameters; wherein the cladding head and the mechanical grinding module are both located at the front end of the robotic arm.

7. The system according to claim 6, characterized in that, The laser cladding module also includes: a fiber laser, a cooling unit, and a protective gas.

8. The system according to claim 6, characterized in that: The control cabinet is specifically used to call the machine vision module to capture and acquire image information, and send the image information to the host computer; wherein, the machine vision module is located at the front end of the robotic arm; The host computer includes: The positioning module is used to locate the damaged area of ​​the aircraft titanium alloy component based on the image information and feed back the damaged area information to the mobile robot; wherein, the image information includes the morphological information of the damaged area; The control cabinet is also used to call the machine vision module to scan the damaged area with a laser, obtain the first point cloud information of the damaged area, and send the point cloud information to the host computer. Furthermore, the host computer includes: The point cloud processing module is used to construct a first three-dimensional model corresponding to the damaged area based on the first point cloud information. The welding planning module is used to generate welding planning information based on the first three-dimensional model, the posture information of the mobile robot arm, and the material of the damaged part, and send the welding planning information to the mobile robot. The welding planning information includes welding path and welding process parameters.

9. The system according to claim 8, characterized in that, The point cloud processing module is specifically used to preprocess the first point cloud information to filter out invalid and outlier points, obtaining second point cloud information; to filter out the second point cloud information using a voxel grid filtering algorithm, obtaining third point cloud information; and to perform point cloud 3D reconstruction on the third point cloud information using a preset point cloud reconstruction algorithm, obtaining the first 3D model. Further, when the point cloud processing module preprocesses the first point cloud information to filter out invalid and outlier points and obtains the second point cloud information, it is specifically used for: For each point in the first point cloud information, determine whether the coordinates of the point in the first dimension are within a preset first dimension value range; If not, the point is considered invalid and filtered out. Traverse each point in the first point cloud information and calculate the average distance between the point and each adjacent point; determine whether the average distance is within a preset distance range threshold. If not, identify the point and each point adjacent to the point as outliers and filter out all outliers. The first point cloud information after filtering out invalid and discrete points is determined as the second point cloud information.

10. The system according to claim 8, characterized in that, The welding planning module is specifically used to identify the shape of the damaged area based on the first three-dimensional model to obtain the shape of the damaged area; determine the boundary dimensions of the damaged area shape; generate a second three-dimensional model based on the boundary dimensions and preset boundary correction rules; determine welding process parameters and cutting process parameters based on the attributes of the damaged area; and generate a welding path based on the second three-dimensional model and the posture information of the mobile robot arm.

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