Aviation thin-wall composite component robot edge milling process parameter optimization method

Abstract

The invention discloses an aviation thin-wall composite component robot edge milling process parameter optimization method, which comprises the following steps: constructing a milling force prediction model, and predicting a milling force based on machining equipment parameters and machining process parameters; a robot edge milling machining system is constructed, the predicted milling force serves as input, the robot tail end cutter vibration response and the workpiece vibration response are obtained, and the surface roughness of the workpiece is detected; based on different machining equipment parameters and / or machining process parameters, a milling force-workpiece surface roughness database is obtained; optimizing by taking the minimum milling force and the optimal milling quality as targets, screening a milling force-workpiece surface roughness database, and outputting an optimal combination; the edge milling machining quality of the aviation thin-wall composite component robot is remarkably improved, and the problems of large milling vibration and poor machining quality caused by the fact that technological parameters of the aviation large weak-rigidity component robot are difficult to regulate and control are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of aviation intelligent manufacturing, and specifically to an optimization method for the robot milling edge process parameters of an aviation thin-walled composite component. Background Technique

[0002] Carbon fiber composite materials have the advantages of high specific strength, high specific stiffness, fatigue resistance, corrosion resistance, and designable properties. Compared with traditional metal materials (such as aluminum alloys), under the same strength requirements, the weight of composite materials can be greatly reduced; currently, composite materials are often applied to aviation thin-walled composite components, such as some wing skins, blades of aviation engines, etc., because these parts play a key role in the aerodynamic performance and overall structural integrity of the aircraft, and they usually have high-precision dimensional requirements; robot processing undoubtedly brings irreplaceable benefits to composite material processing. A robot is a highly automated flexible device. Compared with special processing and manufacturing equipment, it has the advantages of strong versatility, low cost, and stable performance indicators. The same robot can be used to process multiple parts, greatly improving the production efficiency of composite materials and facilitating the realization of low-cost composite material manufacturing; compared with traditional processing methods, the production cost of robot processing composite materials can be significantly reduced.

[0003] The forming accuracy of large composite components is poor. During the forming process, a margin is usually left at the edge as a sacrificial layer, which is removed by milling after curing. After processing, a robot is usually used to mill the edge along its outer contour to meet the high-precision assembly requirements; although using a robot processing system will make the milling edge process more intelligent and flexible, due to the problems of machining chatter and milling force fluctuation caused by the weak stiffness characteristics of the robot, chatter phenomena are extremely likely to occur during processing, inevitably reducing the milling edge quality and exacerbating CFRP delamination and fiber bundle debonding; therefore, there is an urgent need for an optimization method for the robot milling edge process parameters of an aviation thin-walled composite component to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide an optimization method for the robot milling edge process parameters of an aviation thin-walled composite component, which can effectively solve the problems mentioned in the above background technique.

[0005] To solve the above technical problems, the present invention adopts the following technical solutions: An optimization method for the robot milling edge process parameters of an aviation thin-walled composite component, including the following steps: Construct a milling force prediction model, and predict the milling force based on the processing equipment parameters and processing technology parameters. Construct a robot milling edge processing system, use the predicted milling force as the input, obtain the tool vibration response and workpiece vibration response at the end of the robot, and detect the surface roughness of the workpiece. Obtain a milling force - workpiece surface roughness database based on different machining equipment parameters and / or machining process parameters; Optimize with the goal of minimizing milling force and optimizing milling quality, screen the milling force - workpiece surface roughness database, and output the optimal combination.

[0006] Preferably, the equipment machining parameters include the robotic milling pose, tool material, and workpiece material, and the machining process parameters include milling speed, spindle speed, milling depth, milling width, composite material thickness, and tool diameter; Based on the single - factor test method, obtain the data on the influence of the change of each parameter on the milling force; Based on the orthogonal test design method, obtain the milling test data of different parameter combinations, and construct a milling force - workpiece surface roughness database.

[0007] Preferably, the robotic edge - milling processing system includes a mobile robot subsystem model and a thin - walled component subsystem model. The mobile robot subsystem model includes an AGV, a robot body, an end - effector, and a tool, and the thin - walled component subsystem model includes a fixture and a workpiece; Regard the AGV, robot body, end - effector, and fixture as spatial vibration rigid - body elements, regard the tool as an elastic - body element, and regard the workpiece as a spatial vibration planar - plate element. The elements are connected by spatial elastic hinges.

[0008] Preferably, the total transfer equation of the robotic edge - milling processing system is: ; Wherein, is the total transfer matrix of the robotic edge - milling processing system, is the set of state vectors of the robotic edge - milling processing system.

[0009] Preferably, regard the AGV, four links of the robot, and the end - effector as spatial vibration rigid - body elements, numbered 2, 4, 6, 8, 10, 12 respectively, and their transfer matrices are respectively denoted as , , , , , ; Regard the tool as an elastic - body element, numbered 14, and its transfer matrix is denoted as ; The elements are connected by spatial elastic hinges, numbered 1, 3, 5, 7, 9, 11, 13 respectively, and their transfer matrices are respectively denoted as , , , , , , ; The total transfer equation of the mobile robot subsystem is as follows: ; Wherein, is the state vector at the input end of the mobile robot subsystem. The number 1 is the spatial elastic hinge connecting the wheel and the ground, and the number 0 is the boundary; is the state vector at the output end of the mobile robot subsystem. The number 14 is the end tool of the robot, and the number 0 is the boundary.

[0010] Preferably, the workpiece is regarded as a spatial vibration plane plate element with the number 15, and its transfer matrix is denoted as ; The tooling is regarded as a spatial vibration rigid body element with the number 17, and its transfer matrix is denoted as . The components are connected by spatial elastic hinges, numbered 16 and 18 respectively, and their transfer matrices are denoted as and ; The total transfer equation of the thin-walled component subsystem is as follows: ; Wherein, is the state vector at the input end of the thin-walled component subsystem. The number 18 is the spatial elastic hinge connecting the tooling and the ground, and the number 0 is the boundary; is the state vector at the output end of the mobile robot subsystem. The number 15 is the thin-walled component, and the number 0 is the boundary.

[0011] Preferably, the dynamic equation of the robot edge milling processing system is as follows: ; Wherein, is the system mass matrix, K is the system stiffness matrix, C is the system damping matrix, is the column matrix of the displacement coordinates of the system, and are respectively the first and second derivatives with respect to time, is the column matrix of external forces and external torques, including the dynamic interaction force between the two subsystem models, which is the milling force .

[0012] Preferably, the tooling includes a vacuum adsorption module, a force detection module, and a vibration monitoring module. The vacuum adsorption module includes an array of support columns for adsorbing the workpiece; the force detection module is used to monitor the dynamic cutting force during the milling process in real time, and the vibration monitoring module uses an acceleration sensor and is arranged in the vibration-sensitive area at the edge of the workpiece to collect vibration signals during the processing.

[0013] Preferably, optimization is carried out with the goal of minimizing the milling force and optimizing the milling quality, specifically as follows: ; where Ra is the surface roughness of the workpiece, are the n joint angles of the robot, v s is the milling speed, n is the spindle speed, d is the milling depth, w is the milling width, h is the composite material thickness, and D is the tool diameter.

[0014] Construct a milling force prediction model to predict the milling force based on the processing equipment parameters and processing technology parameters; Construct a robot milling edge processing system, take the predicted milling force as the input, obtain the tool vibration response and workpiece vibration response at the end of the robot, and detect the surface roughness of the workpiece; Based on different processing equipment parameters and / or processing technology parameters, obtain a milling force-workpiece surface roughness database; Optimize with the goal of minimizing the milling force and optimizing the milling quality, screen the milling force-workpiece surface roughness database, and output the optimal combination.

[0015] Beneficial effects: The present invention combines a milling force prediction model and a robot milling edge processing system, comprehensively considers the influence of process parameters on the milling force under different milling postures of the robot and workpiece material characteristics, so as to obtain the dynamic performance such as the robot vibration response and milling quality under different process parameters; further combined with optimization, an optimization design method for the robot milling edge process parameters of aerospace composite thin-walled components with the goal of minimizing the milling force and optimizing the milling quality is established, and finally the best process parameter combination is obtained, realizing a significant improvement in the machining quality of the robot milling edge of aerospace thin-walled composite components, and solving the problem of difficult adjustment of robot process parameters for large aerospace weak-rigid components resulting in large milling vibrations and poor machining quality. Brief Description of the Drawings

[0016] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention.

[0017] In the drawings: Figure 1 is the flow chart of the optimization method for the robot milling edge process parameters of the aerospace thin-walled composite component of the present invention; Figure 2 is the structural schematic diagram of the robot milling edge processing system of the present invention; Figure 3 is the structural schematic diagram of the robot milling test platform of the present invention. Detailed Embodiments

[0018] In order to make the objectives and advantages of the present invention more clear and understandable, the present invention will be specifically described below in conjunction with embodiments. It should be understood that the following text is only used to describe a method for optimizing the robotic milling edge process parameters of an aviation thin-walled composite component of the present invention or several specific implementation manners, and does not strictly limit the specific scope of protection claimed by the present invention.

[0019] Embodiment: As Figure 1 shown, a method for optimizing the robotic milling edge process parameters of an aviation thin-walled composite component includes the following steps: Set the processing parameters and machining process parameters of the robotic equipment. The processing parameters of the robotic equipment include the robotic milling pose, tool material, and workpiece material. The machining process parameters include the milling speed v s , spindle speed n, milling depth d, milling width w, composite material thickness h, and tool diameter D; Through the single-factor test method, analyze the influence law of the change of each parameter within a reasonable value range on the milling force, and obtain the influence data of the change of each parameter on the milling force; adopt the orthogonal test design method to formulate the robotic milling edge test plan for the aviation thin-walled composite component, obtain the milling test data of different parameter combinations, and construct a milling force-workpiece surface roughness database; Construct a robotic milling edge processing system. The robotic milling edge processing system includes a mobile robot subsystem model and a thin-walled component subsystem model. The mobile robot subsystem model includes an AGV, a robot body, an end effector, and a tool. The thin-walled component subsystem model includes a fixture and a workpiece; regard the AGV, robot body, end effector, and fixture as spatial vibration rigid body elements, regard the tool as an elastic body element, and regard the workpiece as a spatial vibration flat plate element. The elements are connected by spatial elastic hinges; The total transfer equation of the robotic milling edge processing system is: ; Among them, is the total transfer matrix of the robotic milling edge processing system, is the state vector set of the robotic milling edge processing system.

[0020] Construct a milling force prediction model, and predict the milling force based on the processing equipment parameters and machining process parameters; Use the predicted milling force as the input to obtain the vibration response of the end effector tool of the robot and the vibration response of the workpiece, and detect the surface roughness of the workpiece; and based on different processing equipment parameters and / or machining process parameters, obtain a milling force-workpiece surface roughness database; Among them, the milling force prediction model establishes a milling force evaluation model based on the BP neural network, taking six process parameters, namely milling speed, spindle speed, milling depth, milling width, composite material thickness and tool diameter, as input, and milling force as output. The BP neural network selects three layers, namely input layer, hidden layer and output layer. The input layer includes six process parameters, the output layer is the milling force, the hidden layer is a single layer, and 10 nodes are used. The evaluation model is obtained by training based on the test data under different process parameter combinations. Optimization is performed with the goal of minimizing milling force and optimizing milling quality, screening the milling force-workpiece surface roughness database, and outputting the optimal combination.

[0021] refer to Figures 2 - 3 As shown in the figure, the multi-body system transfer matrix method is applied. According to the natural properties of each component, the AGV, the four connecting rods of the robot, and the end are regarded as spatial vibration rigid body elements, numbered 2, 4, 6, 8, 10, and 12 respectively, and their transfer matrices are recorded as , , , , , ; Consider the tool as an elastic element, numbered 14, and its transfer matrix is ​​recorded as ; The workpiece is regarded as a spatial vibration plane plate element, numbered 15, and its transfer matrix is ​​recorded as ; The tooling is regarded as a spatial vibration rigid body element, numbered 17, and its transfer matrix is ​​recorded as ; Each element is connected by a spatial elastic hinge, numbered 1, 3, 5, 7, 9, 11, 13, 16, 18, and its transfer matrix is ​​recorded as , , , , , , , , The mobile robot processing system is regarded as a multi-rigid-flexible coupling dynamic model composed of AGV-robot-end-tool-component-tool under the action of ground support, milling force, control force, etc. Among them, the total transfer equation of the mobile robot subsystem is: ; in, is the state vector of the input end of the mobile robot subsystem, number 1 is the spatial elastic joint connecting the wheel and the ground, number 0 is the boundary; is the state vector of the output end of the mobile robot subsystem, number 14 is the robot end tool, number 0 is the boundary; The overall transfer equation for the thin-walled component subsystem is: ; Among them, is the workpiece, is the tooling, and are spatial elastic hinges; is the state vector at the input end of the thin-walled component subsystem. The spatial elastic hinge numbered 18 is the connection between the tooling and the ground, and the boundary is numbered 0; is the state vector at the output end of the mobile robot subsystem. The thin-walled component is numbered 15, and the boundary is numbered 0.

[0022] By solving the total transfer matrix of the above two subsystems, the natural frequencies and vibration modes can be obtained. Further combining with the dynamic equation of the system, the dynamic equation of the robot edge milling processing system is: The dynamic equation of the robot edge milling processing system is: ; Among them, is the system mass matrix, K is the system stiffness matrix, C is the system damping matrix, is the column matrix of the displacement coordinates of the system, and are respectively the first and second derivatives of with respect to time, is the column matrix of external forces and external torques, including the dynamic interaction force between the two subsystem models, which is the milling force

[0023] As Figure 3 shown, aiming at the problems such as easy deformation, large vibration, and difficult to guarantee machining accuracy during the edge milling process of thin-walled workpieces. In this embodiment, the tooling 20 includes a vacuum adsorption module, a force detection module, and a vibration monitoring module. The vacuum adsorption module includes a support column array 23 for adsorbing the workpiece; optimizing the layout of the suction cups and controlling the vacuum degree, using vacuum suction cups to support the thin-walled workpiece 19 to achieve uniform support and reliable fixation of the thin-walled workpiece 19, effectively reducing the clamping deformation; the force detection module is composed of a force sensor 21 and a dedicated signal conditioning circuit. By machining and installing holes at the preset positions of the workpiece, opening holes in the thin-walled workpiece, fixedly connecting the workpiece with the force sensor 21, and placing the force sensor under the thin-walled workpiece to monitor the dynamic cutting force during the milling process in real time; the vibration monitoring module uses an acceleration sensor 22, which is arranged in the vibration-sensitive area at the edge of the thin-walled workpiece to collect vibration signals during the machining process; during the experiment, detection equipment such as a force sensor and an acceleration sensor is used to accurately measure and quantitatively analyze indexes such as milling force signals and vibration signals to ensure the reliability and scientificity of the research results.

[0024] During the experiment, a multi-channel data acquisition system was used to synchronously collect the output signals of the force sensor and the acceleration sensor. Real-time monitoring, storage, and analysis of the milling force signal and the vibration signal were achieved through relevant software. According to different combinations of process parameters, the feed speed, machine tool speed, and milling attitude were given in the robot system. The components of the milling force F in the x, y, and z directions could be exported in the relevant software of the force sensor. The roughness of the milled surface of the test piece after milling was detected. By comparing the milling force and the measurement results of the milling quality under different process parameters, a quantitative relationship between the milling parameters, the machining quality, and the milling force was established, providing a reliable basis for optimizing the machining process.

[0025] A method for optimizing the robot milling edge process parameters of an aviation thin-walled composite component according to claim 3, wherein: the tooling includes a vacuum adsorption module, a force detection module, and a vibration monitoring module. The vacuum adsorption module includes an array of support columns for adsorbing the workpiece; the force detection module is used to monitor the dynamic cutting force during the milling process in real time, and the vibration monitoring module uses an acceleration sensor and is arranged in the vibration-sensitive area at the edge of the workpiece to collect the vibration signal during the machining process.

[0026] Among them, according to the test data, 70% of the obtained data is selected to train the neural network model. Before training, the data needs to be normalized. Set the maximum number of training times and the expected error. After training to obtain the prediction model, it is verified through the test data under multiple working conditions, and the performance of the model is evaluated; on this basis, an optimization is established with the minimum milling force and the best milling quality as the goals, specifically: ; Among them, Ra is the surface roughness of the workpiece, are the angles of n joints of the robot, v s is the milling speed, n is the spindle speed, d is the milling depth, w is the milling width, h is the composite material thickness, and D is the tool diameter.

[0027] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the above embodiments. For those of ordinary skill in the art in this technical field, after learning the content recorded in the present invention, without departing from the principle of the present invention, several equivalent transformations and substitutions can still be made, and these equivalent transformations and substitutions should also be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for optimizing the robotic milling edge process parameters of an aviation thin-walled composite component, characterized in that, It includes the following steps: Construct a milling force prediction model to predict the milling force based on machining equipment parameters and machining process parameters; Construct a robot edge-milling processing system, take the predicted milling force as the input, obtain the tool vibration response and workpiece vibration response at the end of the robot, and detect the surface roughness of the workpiece; Based on different machining equipment parameters and / or machining process parameters, obtain a milling force-workpiece surface roughness database; Optimize with the goal of minimizing the milling force and optimizing the milling quality, screen the milling force-workpiece surface roughness database, and output the optimal combination.

2. The method for optimizing the robotic milling edge process parameters of an aerospace thin-walled composite component according to claim 1, wherein: The equipment machining parameters include the robot milling pose, tool material, and workpiece material, and the machining process parameters include milling speed, spindle speed, milling depth, milling width, composite material thickness, and tool diameter; Based on the single-factor test method, obtain the influence data of the change of each parameter on the milling force; Based on the orthogonal test design method, obtain the milling test data of different parameter combinations, and construct a milling force-workpiece surface roughness database.

3. The optimization method of the robotic milling edge process parameters for an aviation thin-walled composite component according to claim 2, characterized in that: The robot edge-milling processing system includes a mobile robot subsystem model and a thin-walled component subsystem model. The mobile robot subsystem model includes an AGV, a robot body, an end effector, and a tool. The thin-walled component subsystem model includes a fixture and a workpiece; Regard the AGV, robot body, end effector, and fixture as spatial vibration rigid body elements, regard the tool as an elastic body element, and regard the workpiece as a spatial vibration flat plate element. The elements are connected by spatial elastic hinges.

4. The optimization method of the robot milling edge process parameters for an aviation thin-walled composite component according to claim 3, characterized in that: The total transfer equation of the robot edge-milling processing system is: ； Among them, is the total transfer matrix of the robotic milling-edge processing system, is the set of state vectors of the robotic milling-edge processing system.

5. The optimization method of the robotic milling edge process parameters for an aviation thin-walled composite component according to claim 4, characterized in that: Regard the AGV, the four links of the robot, and the end as spatial vibrating rigid body elements, numbered 2, 4, 6, 8, 10, 12 respectively, and their transfer matrices are denoted as , , , , , ; Regard the tool as an elastic body element, numbered 14, and its transfer matrix is denoted as ; The elements are connected by spatial elastic hinges, numbered 1, 3, 5, 7, 9, 11, 13 respectively, and their transfer matrices are denoted as , , , , , , ; The total transfer equation of the mobile robot subsystem is: ； Among them, is the state vector at the input end of the mobile robot subsystem. The number 1 represents the spatial elastic hinge connecting the wheel to the ground, and the number 0 represents the boundary; is the state vector at the output end of the mobile robot subsystem. The number 14 represents the end tool of the robot, and the number 0 represents the boundary.

6. The optimization method of the robotic milling edge process parameters for an aerospace thin-walled composite component according to claim 4, wherein: The workpiece is regarded as a spatial vibration plane plate element numbered 15, and its transfer matrix is denoted as ; the tooling is regarded as a spatial vibration rigid body element numbered 17, and its transfer matrix is denoted as , and each element is connected by a spatial elastic hinge, numbered 16 and 18 respectively, and their transfer matrices are denoted as and ; The total transfer equation of the thin-walled component subsystem is: ； Among them, is the state vector at the input end of the thin-walled component subsystem. The serial number 18 is the spatial elastic hinge for connecting the tooling to the ground, and the serial number 0 is the boundary; is the state vector at the output end of the mobile robot subsystem. The serial number 15 is the thin-walled component, and the serial number 0 is the boundary.

7. An optimization method for robotic milling edge process parameters of an aerospace thin-walled composite component according to any one of claims 4-6, characterized in that: The dynamic equation of the robot edge-milling processing system is: ； Among them, is the system mass matrix, K is the system stiffness matrix, C is the system damping matrix, is the column matrix of the displacement coordinates of the system, and are respectively the first and second derivatives with respect to time, is the column matrix of external forces and external torques, including the dynamic interaction force between the two subsystem models, which is the milling force .

8. The method for optimizing the robotic milling edge process parameters of an aerospace thin-walled composite component according to claim 3, characterized in that: The fixture includes a vacuum adsorption module, a force detection module, and a vibration monitoring module. The vacuum adsorption module includes an array of support columns for adsorbing the workpiece; the force detection module is used to monitor the dynamic cutting force during the milling process in real time, and the vibration monitoring module uses an acceleration sensor and is arranged in the vibration-sensitive area at the edge of the workpiece to collect vibration signals during the machining process.

9. The optimization method of robotic milling edge process parameters for an aerospace thin-walled composite component according to claim 7, wherein: Optimize with the goal of minimizing the milling force and optimizing the milling quality, specifically: ； where Ra is the surface roughness of the workpiece, are the n joint angles of the robot, v s is the milling speed, n is the spindle speed, d is the milling depth, w is the milling width, h is the composite thickness, and D is the tool diameter.

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