A method for optimizing process parameters of a robot edge milling of an aeronautical thin-walled composite component

Abstract

The application discloses an aviation thin-walled composite component robot milling edge process parameter optimization method, which comprises the following steps: constructing a milling force prediction model, predicting the milling force based on machining equipment parameters and machining process parameters; constructing a robot milling edge machining system, taking the predicted milling force as input, obtaining robot end tool vibration response and workpiece vibration response, and detecting workpiece surface roughness; and based on different machining equipment parameters and / or machining process parameters, obtaining a milling force-workpiece surface roughness database; optimizing the milling force-workpiece surface roughness database with the minimum milling force and the optimal milling quality as the target, and outputting the optimal combination; the aviation thin-walled composite component robot milling edge machining quality is significantly improved, and the problem of large milling vibration and poor machining quality caused by the difficulty in adjusting and controlling the robot process parameters of the aviation large weak rigid component is solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology in aerospace, specifically to a method for optimizing the process parameters of robotic milling for thin-walled composite components in aerospace. Background Technology

[0002] Carbon fiber composites possess advantages such as high specific strength, high specific stiffness, fatigue resistance, corrosion resistance, and customizable properties. Compared with traditional metal materials (such as aluminum alloys), composites can significantly reduce weight while maintaining the same strength requirements. Currently, composites are frequently used in aerospace thin-walled composite components, such as wing skins and aero-engine blades, because these parts play a crucial role in the aerodynamic performance and overall structural integrity of aircraft, typically requiring high dimensional precision. Robotic processing undoubtedly brings irreplaceable benefits to composite material processing. Robots are highly automated and flexible devices that, compared to dedicated processing and manufacturing equipment, offer advantages such as strong versatility, low cost, and stable performance indicators. The same robot can process multiple parts, significantly improving the production efficiency of composite materials and facilitating low-cost manufacturing. Compared to traditional processing methods, robotic processing of composite materials can substantially reduce production costs.

[0003] Large composite material components suffer from poor molding precision. During the molding process, a margin is usually left at the edges as a sacrificial layer, which is removed by milling after curing. After processing, robots are typically used to mill the edges along the outer contour to meet high-precision assembly requirements. Although using robotic processing systems makes the milling process more intelligent and flexible, the weak stiffness of robots causes chatter and milling force fluctuations, which easily lead to chatter during processing. This inevitably reduces the quality of the milled edges and exacerbates CFRP delamination and fiber bundle debonding. Therefore, there is an urgent need for a method to optimize the process parameters of robotic milling for aerospace thin-walled composite components to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications, which can effectively solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications, comprising the following steps:

[0006] A milling force prediction model is constructed to predict milling forces based on machining equipment parameters and machining process parameters;

[0007] A robotic milling system is constructed, using the predicted milling force as input, to obtain the vibration response of the robot's end tool and the workpiece, and to detect the surface roughness of the workpiece.

[0008] Based on different machining equipment parameters and / or machining process parameters, obtain a milling force-workpiece surface roughness database;

[0009] The optimization aims to minimize milling force and optimize milling quality by filtering the milling force-workpiece surface roughness database and outputting the optimal combination.

[0010] Preferably, the equipment processing parameters include robot milling pose, tool material and workpiece material, and the processing parameters include milling speed, spindle speed, milling depth, milling width, composite material thickness and tool diameter;

[0011] Based on the single-factor experimental method, data on the impact of each parameter change on the milling force were obtained;

[0012] Based on orthogonal experimental design, milling test data with different parameter combinations were obtained, and a milling force-workpiece surface roughness database was constructed.

[0013] Preferably, the robot milling 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 cutting tool. The thin-walled component subsystem model includes a tooling and a workpiece.

[0014] The AGV, robot body, end effector, and tooling are considered as rigid body elements in spatial vibration, the cutting tool is considered as an elastic body element, and the workpiece is considered as a planar plate element in spatial vibration. The elements are connected by spatial elastic hinges.

[0015] Preferably, the overall transfer equation of the robot milling system is:

[0016] ;

[0017] in, It is the overall transfer matrix of the robot milling system. It is the set of state vectors of the robot milling system.

[0018] Preferably, the AGV, the four links of the robot, and the end effector are regarded as spatial vibration rigid body elements, numbered 2, 4, 6, 8, 10, and 12 respectively, and their transfer matrices are denoted as follows: , , , , , Treating the cutting tool as an elastic element, numbered 14, its transfer matrix is ​​denoted as... The components are connected by spatial elastic hinges and are numbered 1, 3, 5, 7, 9, 11, and 13, respectively. Their transfer matrices are denoted as follows: , , , , , , ;

[0019] The overall transfer equation of the mobile robot subsystem is:

[0020] ;

[0021] in, It is the state vector at the input end of the mobile robot subsystem. Number 1 is the spatial elastic hinge connecting the wheel and the ground, and number 0 is the boundary. It is the state vector at the output end of the mobile robot subsystem, number 14 is the robot end tool, and number 0 is the boundary.

[0022] Preferably, the workpiece is considered as a spatially vibrating planar plate element, numbered 15, and its transfer matrix is ​​denoted as... The tooling is considered as a spatially vibrating rigid body element, numbered 17, and its transfer matrix is ​​denoted as... The components are connected by spatial elastic hinges and are numbered 16 and 18 respectively. Their transfer matrices are denoted as follows: and ;

[0023] The overall transfer equation for the thin-walled component subsystem is:

[0024] ;

[0025] in, It is the state vector at the input end of the thin-walled component subsystem. Number 18 is the spatial elastic hinge connecting the tooling to the ground, and number 0 is the boundary. It is the state vector at the output end of the thin-walled component subsystem. Number 15 is the thin-walled component, and number 0 is the boundary.

[0026] Preferably, the dynamic equation of the robot milling system is:

[0027] ;

[0028] in, Here, K is the system mass matrix, K is the system stiffness matrix, and C is the system damping matrix. For the system's displacement coordinate array, and They are The first and second derivatives relative to time, This is a column matrix representing external forces and torques, containing the dynamic interaction forces between the two subsystem models, with the force being the milling force. .

[0029] 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. The vibration monitoring module uses an accelerometer and is arranged in the vibration-sensitive area at the edge of the workpiece to collect vibration signals during the machining process.

[0030] Preferably, optimization is performed with the goal of minimizing milling force and optimizing milling quality, specifically as follows:

[0031] ;

[0032] Where Ra is the surface roughness of the workpiece. Let v be the angle of n joints of the robot. s n is the milling speed, d is the milling depth, w is the milling width, h is the composite material thickness, and D is the tool diameter.

[0033] A milling force prediction model is constructed to predict milling forces based on machining equipment parameters and machining process parameters;

[0034] A robotic milling system is constructed, using the predicted milling force as input, to obtain the vibration response of the robot's end tool and the workpiece, and to detect the surface roughness of the workpiece.

[0035] Based on different machining equipment parameters and / or machining process parameters, obtain a milling force-workpiece surface roughness database;

[0036] The optimization aims to minimize milling force and optimize milling quality by filtering the milling force-workpiece surface roughness database and outputting the optimal combination.

[0037] Beneficial effects: This invention integrates a milling force prediction model and a robotic milling system, comprehensively considering the influence of process parameters on milling force under different robot milling postures and workpiece material characteristics. This allows for the acquisition of dynamic performance parameters such as robot vibration response and milling quality under different process parameters. Furthermore, by combining optimization, an optimization design method for robotic milling process parameters of aerospace composite thin-walled components is established with the goal of minimizing milling force and optimizing milling quality. Ultimately, the optimal combination of process parameters is obtained, resulting in a significant improvement in the machining quality of robotic milling of aerospace thin-walled composite components. This solves the problem of large milling vibration and poor machining quality caused by the difficulty in controlling the process parameters of large aerospace weakly rigid components. Attached Figure Description

[0038] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0039] In the attached diagram:

[0040] Figure 1 This is a flowchart of the method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications according to the present invention;

[0041] Figure 2 This is a schematic diagram of the robot milling system of the present invention;

[0042] Figure 3 This is a schematic diagram of the structure of the robot milling test platform of the present invention. Detailed Implementation

[0043] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe a method for optimizing the process parameters of robotic milling of aerospace thin-walled composite components, or several specific implementation methods, and does not strictly limit the scope of protection specifically claimed by this invention.

[0044] Example: Figure 1 As shown, a method for optimizing the process parameters of robotic milling for thin-walled aerospace composite components includes the following steps:

[0045] Set the robot's machining parameters and machining process parameters. The robot's machining parameters include the robot's milling pose, tool material, and workpiece material. The machining process parameters include the milling speed v. s Spindle speed n, depth of cut d, width of cut w, composite material thickness h, tool diameter D;

[0046] By using single-factor experimental methods, the influence of each parameter within a reasonable range on the milling force was analyzed, and the influence data of each parameter change on the milling force was obtained. Orthogonal experimental design method was used to formulate a robot milling test plan for aerospace thin-walled composite components, and milling test data of different parameter combinations were obtained to construct a milling force-workpiece surface roughness database.

[0047] A robotic milling system is constructed, which 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 cutting tool. The thin-walled component subsystem model includes a tooling and a workpiece. The AGV, robot body, end effector, and tooling are regarded as spatially vibrating rigid body elements, the cutting tool is regarded as an elastic body element, and the workpiece is regarded as a spatially vibrating planar plate element. The elements are connected by spatial elastic hinges.

[0048] The overall transfer equation for the robot milling system is:

[0049] ;

[0050] in, It is the overall transfer matrix of the robot milling system. It is the set of state vectors of the robot milling system.

[0051] A milling force prediction model is constructed to predict milling forces based on machining equipment parameters and machining process parameters;

[0052] Using the predicted milling force as input, the robot end-tool vibration response and workpiece vibration response are obtained, and the workpiece surface roughness is detected; and based on different machining equipment parameters and / or machining process parameters, a milling force-workpiece surface roughness database is obtained.

[0053] Among them, the milling force prediction model is based on the BP neural network to establish a milling force evaluation model. It takes 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 is selected with three layers, namely input layer, hidden layer and output layer. The input layer includes six process parameters, the output layer is the milling force, and the hidden layer is a single layer with 10 nodes. The evaluation model is trained based on experimental data under different combinations of process parameters.

[0054] The optimization aims to minimize milling force and optimize milling quality by filtering the milling force-workpiece surface roughness database and outputting the optimal combination.

[0055] refer to Figures 2-3 As shown, applying the multibody system transfer matrix method, based on the natural properties of each component, the AGV, the four links of the robot, and the end effector are respectively regarded as spatial vibrating rigid body elements, numbered 2, 4, 6, 8, 10, and 12, and their transfer matrices are denoted as follows: , , , , , Treating the cutting tool as an elastic element, numbered 14, its transfer matrix is ​​denoted as... The workpiece is considered as a spatially vibrating planar plate element, numbered 15, and its transfer matrix is ​​denoted as... The tooling is considered as a spatially vibrating rigid body element, numbered 17, and its transfer matrix is ​​denoted as... The components are connected by spatial elastic hinges and are numbered 1, 3, 5, 7, 9, 11, 13, 16, and 18, respectively. Their transfer matrices are denoted as follows: , , , , , , , , The mobile robot machining system is considered as a multi-rigid-flexible coupled dynamic model consisting of AGV, robot, end effector, tooling, components, and fixtures under the action of ground support, milling force, and control force.

[0056] The overall transfer equation for the mobile robot subsystem is:

[0057] ;

[0058] in, It is the state vector at the input end of the mobile robot subsystem. Number 1 is the spatial elastic hinge connecting the wheel and the ground, and number 0 is the boundary. It is the state vector at the output end of the mobile robot subsystem, number 14 is the robot end tool, and number 0 is the boundary;

[0059] The overall transfer equation for the thin-walled component subsystem is:

[0060] ;

[0061] in, For the workpiece, For tooling, and It is a spatial elastic hinge; It is the state vector at the input end of the thin-walled component subsystem. Number 18 is the spatial elastic hinge connecting the tooling to the ground, and number 0 is the boundary. It is the state vector at the output end of the thin-walled component subsystem. Number 15 is the thin-walled component, and number 0 is the boundary.

[0062] Solving for the total transfer matrix of the two subsystems above, we can obtain their natural frequencies and configurations. Further combining this with the system's dynamic equations, the dynamic equations of the robot milling system are:

[0063] ;

[0064] in, Here, K is the system mass matrix, K is the system stiffness matrix, and C is the system damping matrix. For the system's displacement coordinate array, and They are The first and second derivatives relative to time, This is a column matrix representing external forces and torques, containing the dynamic interaction forces between the two subsystem models, with the force being the milling force. .

[0065] like Figure 3As shown, to address the problems of easy deformation, large vibration, and difficulty in ensuring machining accuracy during the milling process of thin-walled workpieces, this embodiment includes a vacuum adsorption module, a force detection module, and a vibration monitoring module in the tooling 20. The vacuum adsorption module includes a support column array 23 for adsorbing the workpiece; the layout of the suction cup is optimized and the vacuum degree is controlled, and the thin-walled workpiece 19 is supported by the vacuum suction cup to achieve uniform support and reliable fixation of the thin-walled workpiece 19, effectively reducing clamping deformation; the force detection module consists of a force sensor 21 and a dedicated signal conditioning circuit. By machining mounting holes at preset positions on the workpiece, the thin-walled workpiece is opened and fixed to the force sensor 21. The force sensor is placed below the thin-walled workpiece to monitor the dynamic cutting force during the milling process in real time; the vibration monitoring module uses an accelerometer 22, which is arranged in the vibration-sensitive area of ​​the edge of the thin-walled workpiece to collect vibration signals during the machining process; during the experiment, force sensors, accelerometers, and other detection equipment are used to accurately measure and quantitatively analyze indicators such as milling force signals and vibration signals to ensure the reliability and scientific nature of the research results.

[0066] During the experiment, a multi-channel data acquisition system was used to simultaneously acquire the output signals of the force sensor and accelerometer. Real-time monitoring, storage, and analysis of the milling force and vibration signals were achieved using relevant software. Based on different combinations of process parameters, the feed rate, machine speed, and milling posture were given in the robot system. The components of the milling force F in the x, y, and z directions could be derived from the force sensor software. The surface roughness of the milled test piece was then measured. By comparing the measurement results of milling force and milling quality under different process parameters, a quantitative relationship between milling parameters, machining quality, and milling force was established, providing a reliable basis for optimizing the machining process.

[0067] Based on the experimental data, 70% of the obtained data was selected to train the neural network model. Before training, the data needed to be normalized, and the maximum number of training iterations and expected error were set. After the prediction model was trained, it was validated using experimental data under multiple working conditions, and the model's performance was evaluated. Based on this, an optimization was established with the objectives of minimizing milling force and optimizing milling quality, specifically:

[0068] ;

[0069] Where Ra is the surface roughness of the workpiece. Let v be the angle of n joints of the robot. s n is the milling speed, d is the milling depth, w is the milling width, h is the composite material thickness, and D is the tool diameter.

[0070] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. For those skilled in the art, after learning the contents described in the present invention, several equivalent changes and substitutions can be made without departing from the principle of the present invention. These equivalent changes and substitutions should also be considered to fall within the protection scope of the present invention.

Claims

1. A method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications, characterized in that, Includes the following steps: A milling force prediction model is constructed to predict milling forces based on machining equipment parameters and machining process parameters; A robotic milling system is constructed, using the predicted milling force as input, to obtain the vibration response of the robot's end tool and the workpiece, and to 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; The optimization aims to minimize milling force and optimize milling quality by filtering the milling force-workpiece surface roughness database and outputting the optimal combination.

2. The method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications according to claim 1, characterized in that: The equipment processing parameters include robot milling pose, tool material and workpiece material, and the processing parameters include milling speed, spindle speed, milling depth, milling width, composite material thickness and tool diameter; Based on the single-factor experimental method, data on the impact of each parameter change on the milling force were obtained; Based on orthogonal experimental design, milling test data with different parameter combinations were obtained, and a milling force-workpiece surface roughness database was constructed.

3. The method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications according to claim 2, characterized in that: The robotic milling 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 cutting tool. The thin-walled component subsystem model includes a tooling and a workpiece. The AGV, robot body, end effector, and tooling are considered as rigid body elements in spatial vibration, the cutting tool is considered as an elastic body element, and the workpiece is considered as a planar plate element in spatial vibration. The elements are connected by spatial elastic hinges.

4. The method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications according to claim 3, characterized in that: The overall transfer equation for the robot milling system is: ； in, It is the overall transfer matrix of the robot milling system. It is the set of state vectors of the robot milling system.

5. The method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications according to claim 4, characterized in that: Consider the AGV, the four links of the robot, and the end effector as spatial vibrating rigid body elements, numbered 2, 4, 6, 8, 10, and 12 respectively, and denote their transfer matrices as follows: , , , , , Treating the cutting tool as an elastic element, numbered 14, its transfer matrix is ​​denoted as... The components are connected by spatial elastic hinges and are numbered 1, 3, 5, 7, 9, 11, and 13, respectively. Their transfer matrices are denoted as follows: , , , , , , ; The overall transfer equation of the mobile robot subsystem is: ； in, It is the state vector at the input end of the mobile robot subsystem. Number 1 is the spatial elastic hinge connecting the wheel and the ground, and number 0 is the boundary. It is the state vector at the output end of the mobile robot subsystem, number 14 is the robot end tool, and number 0 is the boundary.

6. The method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications according to claim 4, characterized in that: The workpiece is considered as a spatially vibrating planar plate element, numbered 15, and its transfer matrix is ​​denoted as... The tooling is considered as a spatially vibrating rigid body element, numbered 17, and its transfer matrix is ​​denoted as... The components are connected by spatial elastic hinges and are numbered 16 and 18 respectively. Their transfer matrices are denoted as follows: and ; The overall transfer equation for the thin-walled component subsystem is: ； in, It is the state vector at the input end of the thin-walled component subsystem. Number 18 is the spatial elastic hinge connecting the tooling to the ground, and number 0 is the boundary. It is the state vector at the output end of the thin-walled component subsystem. Number 15 is the thin-walled component, and number 0 is the boundary.

7. The method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications according to any one of claims 4-6, characterized in that: The dynamic equations of the robot milling system are: ； in, Here, K is the system mass matrix, K is the system stiffness matrix, and C is the system damping matrix. For the system's displacement coordinate array, and They are The first and second derivatives relative to time, This is a column matrix representing external forces and torques, containing the dynamic interaction forces between the two subsystem models, with the force being the milling force. .

8. The method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications according to claim 3, characterized in that: 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. The vibration monitoring module uses an accelerometer and is arranged in the vibration-sensitive area at the edge of the workpiece to collect vibration signals during the machining process.

9. The method for optimizing the process parameters of robotic milling of thin-walled composite components for aerospace applications according to claim 7, characterized in that: The optimization aims to minimize milling force and achieve optimal milling quality, specifically as follows: ； Where Ra is the surface roughness of the workpiece. Let v be the angle of n joints of the robot. s n is the milling speed, d is the milling depth, w is the milling width, h is the composite material thickness, and D is the tool diameter.

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