Pose feature-based robot machining stability prediction method and device

Abstract

The application discloses a kind of robot processing stability prediction methods based on pose characteristics.The method comprises: the dynamic model of robot milling system is established;Dynamic performance analysis is carried out on the dynamic model of robot milling system, and the tool tip frequency response function under different robot each accessible machining pose is obtained;Solving the joint angle of each accessible redundancy angle of robot, robot body mass matrix and robot body stiffness matrix;Based on the tool tip frequency response function under each accessible machining pose, modal mass, modal damping and modal stiffness under each accessible redundancy angle are obtained;According to modal mass, modal damping and modal stiffness under each accessible redundancy angle, the stability prediction diagram of redundancy angle and limit cutting depth is obtained.The stability prediction diagram can provide machining process parameter selection guidance when robot milling operation in different poses, so as to effectively avoid the generation of robot milling chatter, and provide support for the improvement of robot milling quality and efficiency.

Description

Technical Field

[0001] This invention relates to the field of robotic machining, and in particular to a method, apparatus, electronic device, and computer-readable storage medium for predicting the stability of robotic machining based on pose characteristics. Background Technology

[0002] Large and complex structural components, such as spacecraft hulls, large aircraft skins, wind turbine blades, and ship propellers, are widely used in aerospace, aviation, energy, and defense industries. Robots, as representatives of intelligent manufacturing, are increasingly demonstrating their advantages in modern manufacturing. Due to their unique advantages such as large operating space, high flexibility, low cost, and high efficiency, robots are being increasingly widely used in the processing of large and complex structural components.

[0003] However, the weak stiffness characteristic of the open-chain serial structure of the robot makes it prone to chattering during milling, which seriously affects the surface quality of the milled parts and aggravates tool wear. This is one of the main problems restricting its application in high-precision milling of large structural parts.

[0004] To address chatter issues in milling, it's necessary to rationally configure process parameters through stability prediction. However, the pose-dependent nature of robot dynamics leads to significant differences in stability across different machining poses. Existing robot machining stability prediction methods typically require modal testing to obtain the dynamic parameters of the robot's tool tip under different poses, thus predicting stability for a given pose. This approach is inefficient and makes it difficult to effectively control milling chatter when the robot changes machining poses.

[0005] How to construct a stability prediction method related to robot machining pose, so that the stability prediction results can be applied to any reachable machining pose, has become an urgent problem to be solved. Summary of the Invention

[0006] In view of the above problems, the present invention is proposed to provide a method, apparatus, electronic device, and computer-readable storage medium for predicting robot machining stability based on pose characteristics to overcome or at least partially solve the above problems.

[0007] One embodiment of the present invention provides a method for predicting robot machining stability based on pose characteristics, the method comprising:

[0008] Establish a dynamic model of the robot milling system;

[0009] Dynamic performance analysis was performed on the dynamic model of the robot milling system to obtain the tool tip frequency response function under different robot reachable machining poses;

[0010] Based on inverse kinematics, the joint angles, robot mass matrix, and robot stiffness matrix of the robot under each reachable redundancy angle are solved. Different reachable redundancy angles correspond to different postures under different reachable machining positions.

[0011] Based on the tool tip frequency response function under each achievable machining pose, the modal mass, modal damping, and modal stiffness under each achievable redundancy angle are obtained according to the robot's joint angle, robot body mass matrix, and robot body stiffness matrix under each achievable redundancy angle.

[0012] Based on the regenerative flutter prediction model, the limiting cutting depth corresponding to each achievable redundancy angle is obtained according to the modal mass, modal damping and modal stiffness under each achievable redundancy angle, and a stability prediction diagram of the redundancy angle and the limiting cutting depth is obtained.

[0013] Optionally, establishing the dynamic model of the robot milling system includes:

[0014] A modified DH method is used to establish the kinematic model of the robot body;

[0015] Establish a dynamic model of the spindle system and a stiffness model of the spindle-tool holder-tool interface;

[0016] The dynamic model of the robot body, the dynamic model of the spindle system, and the stiffness model of the spindle-tool holder-tool interface are integrated to establish the dynamic model of the robot milling system.

[0017] Optionally, the spindle-tool holder-tool mating surface stiffness model considers the normal stiffness, tangential stiffness, and torsional contact stiffness of the mating surface.

[0018] Optionally, the dynamic performance analysis of the dynamic model of the robot milling system includes:

[0019] Dynamic performance analysis of the dynamic model of the robot milling system is performed based on the finite element method.

[0020] Another embodiment of the present invention provides a robot machining stability prediction device based on pose characteristics, comprising:

[0021] The dynamic model building unit is used to build the dynamic model of the robot milling system.

[0022] The tool tip frequency response function acquisition unit is used to perform dynamic performance analysis on the dynamic model of the robot milling system and obtain the tool tip frequency response function under different robot reachable machining poses.

[0023] The inverse kinematics solving unit is used to solve the robot's joint angles, robot body mass matrix, and robot body stiffness matrix based on inverse kinematics for each reachable redundancy angle. Different reachable redundancy angles correspond to different postures at different reachable machining positions.

[0024] The tool tip modal parameter acquisition unit is used to obtain the modal mass, modal damping, and modal stiffness under each achievable machining pose based on the tool tip frequency response function under each achievable machining pose, and according to the robot's joint angle, robot body mass matrix, and robot body stiffness matrix under each achievable redundancy angle.

[0025] The stability prediction unit is used to obtain the limit cutting depth corresponding to each reachable redundancy angle based on the modal mass, modal damping and modal stiffness under each reachable redundancy angle, based on the regenerative flutter prediction model, and to obtain the stability prediction diagram of the redundancy angle and the limit cutting depth.

[0026] Optionally, the dynamic model building unit is further used for:

[0027] A modified DH method is used to establish the kinematic model of the robot body;

[0028] Establish a dynamic model of the spindle system and a stiffness model of the spindle-tool holder-tool interface;

[0029] The dynamic model of the robot body, the dynamic model of the spindle system, and the stiffness model of the spindle-tool holder-tool interface are integrated to establish the dynamic model of the robot milling system.

[0030] Optionally, the spindle-tool holder-tool mating surface stiffness model considers the normal stiffness, tangential stiffness, and torsional contact stiffness of the mating surface.

[0031] Optionally, the blade tip frequency response function acquisition unit is further configured to:

[0032] Dynamic performance analysis of the dynamic model of the robot milling system is performed based on the finite element method.

[0033] Another embodiment of the present invention provides an electronic device, wherein the electronic device includes:

[0034] Processor; and,

[0035] A memory is configured to store computer-executable instructions, which, when executed, cause the processor to perform the methods described above.

[0036] Another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs that, when executed by a processor, implement the method described above.

[0037] The beneficial effect of this invention is that the stable prediction map obtained by this invention can provide guidance for the selection of machining process parameters when the robot is milling in different poses, thereby effectively avoiding the generation of chatter in robot milling and providing strong support for improving the quality and efficiency of robot milling. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating a stability prediction method related to robot machining pose according to an embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram of a stability prediction method related to robot machining pose according to an embodiment of the present invention.

[0040] Figure 3 This is a stability prediction diagram of redundancy angle and limiting cutting depth according to an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of a stability prediction device related to robot machining pose according to an embodiment of the present invention;

[0042] Figure 5 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown;

[0043] Figure 6 A schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present invention is shown. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0045] Figure 1 This is a flowchart illustrating a stability prediction method related to robot machining pose according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0046] S11: Establish the dynamic model of the robot milling system;

[0047] It is understood that the dynamic model of the robot milling system in this embodiment of the invention includes the robot body kinematic model, the spindle system dynamic model, and the spindle-tool holder-tool mating surface stiffness model.

[0048] S12: Perform dynamic performance analysis on the dynamic model of the robot milling system to obtain the tool tip frequency response function under different robot reachable machining poses;

[0049] Understandably, the reachable machining pose of a robot refers to the position and corresponding posture that the robot can reach during milling.

[0050] It should be noted that the dynamic performance analysis in this embodiment of the invention includes modal analysis and harmonic response analysis. A mode, as the result of decoupling dynamic characteristics in physical space, is the inherent vibration characteristic of a structure or system. Each mode has different natural frequencies and mode shapes. Harmonic response analysis is a technique used to analyze the periodic response generated by a continuous periodic load on a structural system, and to determine the stable response of a linear structure subjected to a load that varies sinusoidally with time. The purpose of the analysis is to calculate the response of the structure at the excitation force frequency, i.e., the response displacement and response stress, and to obtain the curve of the system's dynamic response versus the system's vibration frequency, called the amplitude-frequency curve.

[0051] In practical applications, modal analysis of the established dynamic model of the robot milling system can be performed through the linear perturbation frequency analysis step in Abaqus, and the modal analysis can be solved by the subspace method to obtain the natural frequencies and mode shapes of the model.

[0052] S13: Solve the joint angles, robot mass matrix and robot stiffness matrix of the robot under each reachable redundancy angle based on inverse kinematics, where different reachable redundancy angles correspond to different postures under different reachable machining positions;

[0053] Understandably, inverse kinematics solves for the position variables of each joint based on the position and orientation of the end effector.

[0054] It should be noted that rotating the robot around the tool axis coordinate system by any angle along the tool axis direction can achieve machining in different postures without changing the machining position and tool axis direction. This rotation angle is defined as the redundancy angle.

[0055] Machining posture has a significant impact on tool tip dynamics, therefore optimizing the machining posture is one of the methods to avoid regenerative chatter.

[0056] Understandably, regenerative chatter refers to the change in chip thickness and cutting force during machining due to a phase difference between the wavy surface left by the previous cutting tooth and the wavy surface generated by the current cutting tooth. Robotic machining systems have the advantage of motion redundancy, enabling different robot postures for the same machining position and tool axis direction. By optimizing the redundancy angles, the machining posture can be optimized to avoid regenerative chatter.

[0057] S14: Based on the tool tip frequency response function under each achievable machining pose, obtain the modal mass, modal damping, and modal stiffness under each achievable redundancy angle according to the robot's joint angle, robot body mass matrix, and robot body stiffness matrix under each achievable redundancy angle;

[0058] S15: Based on the regenerative flutter prediction model, the limiting cutting depth corresponding to each achievable redundancy angle is obtained according to the modal mass, modal damping and modal stiffness under each achievable redundancy angle, and a stability prediction diagram of the redundancy angle and the limiting cutting depth is obtained.

[0059] Understandably, the embodiments of the present invention are based on a regenerative chatter prediction model. Given a fixed spindle speed, feed rate, and radial depth of cut, the limiting depth of cut corresponding to each achievable redundant angle is determined, and a stability prediction map is generated based on the limiting depth of cut corresponding to each achievable redundant angle.

[0060] The stability prediction map obtained by the stability prediction method related to robot machining pose in the embodiments of the present invention can provide guidance for the selection of machining process parameters when the robot is milling in different poses, thereby effectively avoiding the generation of robot milling chatter and providing strong support for improving the quality and efficiency of robot milling.

[0061] In an optional embodiment of the present invention, establishing the dynamic model of the robot milling system includes:

[0062] A modified Denavit-Hartenberg method was used to establish the robot's kinematic model.

[0063] Establish a dynamic model of the spindle system and a stiffness model of the spindle-tool holder-tool interface;

[0064] The dynamic model of the robot body, the dynamic model of the spindle system, and the stiffness model of the spindle-tool holder-tool interface are integrated to establish the dynamic model of the robot milling system.

[0065] The robot body kinematic model established by the modified DH method in this embodiment of the invention is the MDH model.

[0066] In practical applications, the dynamic model of the robot body, the dynamic model of the spindle system, and the stiffness model of the spindle-tool holder-tool interface can be integrated based on finite element analysis software to establish the dynamic model of the robot milling system.

[0067] Specifically, the spindle-tool holder-tool mating surface stiffness model considers the normal stiffness, tangential stiffness, and torsional contact stiffness of the mating surface.

[0068] Understandably, the quality of the connections between several mating surfaces in the spindle-tool holder, tool holder-spring clip, and spring clip-tool system is a key factor affecting its dynamic characteristics. For the spindle-tool holder mating surface, treating it as rigid will cause discrepancies between the natural frequencies and frequency response functions of the spindle-tool holder-tool system's dynamic model and reality. Therefore, the accuracy of the mating surface stiffness model is crucial for establishing an accurate dynamic model of the spindle-tool holder-tool system.

[0069] To improve the accuracy of the spindle-tool holder-tool mating surface stiffness model, the spindle-tool holder-tool mating surface stiffness model of this embodiment of the invention simultaneously considers three factors: the normal stiffness of the mating surface, the tangential stiffness of the mating surface, and the torsional contact stiffness of the mating surface.

[0070] Specifically, the dynamic performance analysis of the dynamic model of the robot milling system includes:

[0071] Dynamic performance analysis of the dynamic model of the robot milling system is performed based on the finite element method.

[0072] Understandably, in order to improve prediction efficiency, embodiments of the present invention can use finite element analysis software to perform dynamic performance analysis based on the finite element analysis method.

[0073] In practical applications, the regenerative flutter prediction model is a two-degree-of-freedom flutter prediction model that considers the regenerative effect, and it can be solved using the fully discrete method.

[0074] Specifically, the two-degree-of-freedom flutter prediction model considering the regeneration effect is shown in Equation (1):

[0075] (1)

[0076] Where m, c, and k are modal mass, modal damping, and modal stiffness, respectively. This refers to the axial depth of cut. , , , As shown in formula (2):

[0077] (2)

[0078] in, and These are the tangential and radial cutting force coefficients, respectively; Let be the angular position of the j-th tooth of the milling cutter, which can be expressed as formula (3):

[0079] (3)

[0080] in, Where N is the rotational speed and N is the number of cutting teeth;

[0081] Window function The formula (4) is used to determine whether the j-th tooth of the milling cutter is in a cutting state:

[0082] (4)

[0083] in, and Let the entry angle and exit angle of the j-th cutting tooth be respectively:

[0084] (5)

[0085] in, D is the radial depth of cut, and D is the tool diameter.

[0086] definition Converting formula (1) into a state equation yields:

[0087] (6)

[0088] in, It is a constant matrix, representing the time-invariant properties of the system. The periodic coefficient matrix is ​​represented as follows; For state items, This is a time-delay term.

[0089]

[0090] Figure 2 This is a schematic diagram illustrating the principle of a stability prediction method related to robot machining pose according to an embodiment of the present invention. Figure 2 As shown, the robot machining pose-related stability prediction method of this embodiment includes:

[0091] (1) Input the robot's MDH model, machining parameters, tool parameters, and cutting force coefficients, etc.;

[0092] Machining parameters include spindle speed, radial depth of cut, and feed rate; tool parameters include tool diameter, number of teeth, and helix angle.

[0093] (2) Determine the machining position and the corresponding tool axis coordinate system, and determine the redundancy angle. The range.

[0094] (3) Scan redundancy angle .

[0095] (4) Calculate the tool coordinate system pose;

[0096] In this embodiment of the invention, the tool coordinate system pose is used to represent the robot's machining pose.

[0097] (5) Solve for the joint angles and the M and K matrices of the robot body under each reachable redundancy angle;

[0098] Where M is the robot's mass matrix and K is the robot's stiffness matrix.

[0099] (6) Obtain the tool tip frequency response function and tool tip modal parameters under each reachable machining pose of the robot;

[0100] The modal parameters of the blade tip include modal mass, modal damping, and modal stiffness.

[0101] (7) Calculate the limiting depth of cut corresponding to different tool modal parameters using the regenerative chatter prediction model. ;

[0102] (8) Drawing Polar coordinate graph.

[0103] In a specific implementation of the practical application, the spindle speed n is selected as 3000 r / min, the feed rate f is 0.05 m / s, and the radial depth of cut is... The value is 1mm. Using the above method, the stability prediction diagram of the redundancy angle and the limit cutting depth is shown in Figure 3. In the figure, the sector represents the achievable pose area of ​​the robot at the selected machining position. The gray area is the stable machining area, and the white area is the regenerative chatter area.

[0104] Figure 4 This is a schematic diagram of a stability prediction device related to robot machining pose according to an embodiment of the present invention. Figure 4 As shown, the device includes:

[0105] Dynamics model building unit 41 is used to build the dynamics model of the robot milling system;

[0106] The tool tip frequency response function acquisition unit 42 is used to perform dynamic performance analysis on the dynamic model of the robot milling system and obtain the tool tip frequency response function under different robot reachable machining poses.

[0107] The inverse kinematics solving unit 43 is used to solve the joint angles, robot body mass matrix and robot body stiffness matrix of the robot under each reachable redundancy angle based on inverse kinematics. Different reachable redundancy angles correspond to different postures under different reachable processing positions.

[0108] The tool tip modal parameter acquisition unit 44 is used to obtain the modal mass, modal damping and modal stiffness under each reachable redundancy angle based on the tool tip frequency response function under each reachable machining pose, and according to the robot's joint angle, robot body mass matrix and robot body stiffness matrix under each reachable redundancy angle.

[0109] The stability prediction unit 45 is used to obtain the limit cutting depth corresponding to each reachable redundancy angle based on the modal mass, modal damping and modal stiffness under each reachable redundancy angle, based on the regenerative flutter prediction model, and to obtain the stability prediction diagram of the redundancy angle and the limit cutting depth.

[0110] In an optional embodiment of the present invention, the dynamic model establishment unit 41 is further configured to:

[0111] A modified DH method is used to establish the kinematic model of the robot body;

[0112] Establish a dynamic model of the spindle system and a stiffness model of the spindle-tool holder-tool interface;

[0113] The dynamic model of the robot body, the dynamic model of the spindle system, and the stiffness model of the spindle-tool holder-tool interface are integrated to establish the dynamic model of the robot milling system.

[0114] Specifically, the spindle-tool holder-tool mating surface stiffness model considers the normal stiffness, tangential stiffness, and torsional contact stiffness of the mating surface.

[0115] The blade tip frequency response function acquisition unit 42 is further used for:

[0116] Dynamic performance analysis of the dynamic model of the robot milling system is performed based on the finite element method.

[0117] It should be noted that the stability prediction devices related to robot processing pose in the above embodiments can be used to execute the methods in the foregoing embodiments, and therefore will not be described in detail one by one.

[0118] In summary, the stability prediction map obtained by the stability prediction method related to robot machining pose in the embodiments of the present invention can provide guidance for selecting machining process parameters when the robot is milling in different poses, thereby effectively avoiding the generation of chatter in robot milling and providing strong support for improving the quality and efficiency of robot milling.

[0119] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] It should be noted that:

[0121] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used in conjunction with the teachings herein. The required structure for constructing such devices is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0122] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0123] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0124] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0125] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0126] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the device for detecting the wearing status of an electronic device according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can take the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0127] For example, Figure 5 A schematic diagram of an electronic device according to an embodiment of the present invention is shown. The electronic device conventionally includes a processor 51 and a memory 52 arranged to store computer-executable instructions (program code). The memory 52 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Figure 1Storage space 53 for program code 54 of any method steps shown and in any of the embodiments. For example, storage space 53 for storing program code may include various program codes 54 for implementing the various steps in the methods above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. Such computer program products are typically, for example, Figure 6 The aforementioned computer-readable storage medium. This computer-readable storage medium may have the same characteristics as... Figure 5 The memory 52 in the electronic device is similarly arranged as a storage segment, storage space, etc. The program code can be compressed, for example, in a suitable form. Typically, the storage space stores program code 61 for performing the steps of the method according to the invention, that is, program code that can be read by a processor 51, which, when run by the electronic device, causes the electronic device to perform the various steps of the method described above.

[0128] The above description is merely a specific embodiment of the present invention. Under the teachings of the present invention, those skilled in the art can make other improvements or modifications based on the above embodiments. Those skilled in the art should understand that the above specific description is only to better explain the purpose of the present invention, and the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting robot machining stability based on pose characteristics, characterized in that, include: Establish a dynamic model of the robot milling system; Dynamic performance analysis was performed on the dynamic model of the robot milling system to obtain the tool tip frequency response function under different robot reachable machining poses; Based on inverse kinematics, the joint angles, robot mass matrix, and robot stiffness matrix of the robot under each reachable redundancy angle are solved. Different reachable redundancy angles correspond to different postures under different reachable machining positions. Based on the tool tip frequency response function under each achievable machining pose, the modal mass, modal damping, and modal stiffness under each achievable redundancy angle are obtained according to the robot's joint angle, robot body mass matrix, and robot body stiffness matrix under each achievable redundancy angle. Based on the regenerative flutter prediction model, the limiting cutting depth corresponding to each achievable redundancy angle is obtained according to the lower modal mass, modal damping and modal stiffness of each achievable redundancy angle, and a stability prediction diagram of redundancy angle and limiting cutting depth is obtained. The regenerative flutter prediction model is a two-degree-of-freedom flutter prediction model that considers the regenerative effect, and it can be solved using the fully discrete method.

2. The method according to claim 1, characterized in that, The establishment of the dynamic model of the robot milling system includes: A modified DH method is used to establish the kinematic model of the robot body; Establish a dynamic model of the spindle system and a stiffness model of the spindle-tool holder-tool interface; The dynamic model of the robot body, the dynamic model of the spindle system, and the stiffness model of the spindle-tool holder-tool interface are integrated to establish the dynamic model of the robot milling system.

3. The method according to claim 2, characterized in that, The spindle-tool holder-tool mating surface stiffness model considers the normal stiffness, tangential stiffness, and torsional contact stiffness of the mating surface.

4. The method according to claim 1, characterized in that, The dynamic performance analysis of the dynamic model of the robot milling system includes: Dynamic performance analysis of the dynamic model of the robot milling system is performed based on the finite element method.

5. A robot machining stability prediction device based on pose characteristics, characterized in that, include: The dynamic model building unit is used to build the dynamic model of the robot milling system. The tool tip frequency response function acquisition unit is used to perform dynamic performance analysis on the dynamic model of the robot milling system and obtain the tool tip frequency response function under different robot reachable machining poses. The inverse kinematics solving unit is used to solve the robot's joint angles, robot body mass matrix, and robot body stiffness matrix based on inverse kinematics for each reachable redundancy angle. Different reachable redundancy angles correspond to different postures at different reachable machining positions. The tool tip modal parameter acquisition unit is used to obtain the modal mass, modal damping, and modal stiffness under each achievable machining pose based on the tool tip frequency response function under each achievable machining pose, and according to the robot's joint angle, robot body mass matrix, and robot body stiffness matrix under each achievable redundancy angle. The stability prediction unit is used to obtain the limit cutting depth corresponding to each reachable redundancy angle based on the modal mass, modal damping and modal stiffness under each reachable redundancy angle, based on the regenerative flutter prediction model, and to obtain the stability prediction diagram of the redundancy angle and the limit cutting depth. The regenerative flutter prediction model is a two-degree-of-freedom flutter prediction model that considers the regenerative effect, and it can be solved using the fully discrete method.

6. The apparatus according to claim 5, characterized in that, The dynamic model building unit is further used for: A modified DH method is used to establish the kinematic model of the robot body; Establish a dynamic model of the spindle system and a stiffness model of the spindle-tool holder-tool interface; The dynamic model of the robot body, the dynamic model of the spindle system, and the stiffness model of the spindle-tool holder-tool interface are integrated to establish the dynamic model of the robot milling system.

7. The apparatus according to claim 6, characterized in that, The spindle-tool holder-tool mating surface stiffness model considers the normal stiffness, tangential stiffness, and torsional contact stiffness of the mating surface.

8. The apparatus according to claim 5, characterized in that, The blade tip frequency response function acquisition unit is further used for: Dynamic performance analysis of the dynamic model of the robot milling system is performed based on the finite element method.

9. An electronic device, characterized in that, The electronic device includes: Processor; and, A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1-4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs that, when executed by a processor, implement the method of any one of claims 1-4.

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