CN115186531A - Robot processing stability prediction method and device based on pose characteristics - Google Patents
Robot processing stability prediction method and device based on pose characteristics Download PDFInfo
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Abstract
The invention discloses a robot machining stability prediction method based on pose characteristics. The method comprises the following steps: establishing a dynamic model of the robot milling system; dynamic performance analysis is carried out on a dynamic model of the robot milling system, and tool nose frequency response functions of different robots in each reachable processing pose are obtained; solving joint angles, a robot body mass matrix and a robot body rigidity matrix of the robot under each reachable redundant angle; obtaining modal mass, modal damping and modal stiffness under each reachable redundant angle based on the tool nose frequency response function under each reachable processing pose; and obtaining a stability prediction graph of the redundancy angle and the limit cutting depth according to the modal mass, modal damping and modal stiffness under each redundancy angle. The stability prediction graph can provide a processing process parameter selection guide for the milling operation of the robot at different poses, so that the milling flutter of the robot is effectively avoided, and a support is provided for the improvement of the milling quality and efficiency of the robot.
Description
Technical Field
The invention relates to the field of robot machining, in particular to a pose characteristic-based robot machining stability prediction method and device, electronic equipment and a computer readable storage medium.
Background
Large-scale complex structural components, such as spacecraft cabins, large-scale aircraft skins, wind driven generator blades, ship propellers and the like, are widely applied to the industries of aerospace, aviation, energy, national defense and the like. The manufacturing capability of the large-scale complex structural part reflects the whole core strength of the manufacturing industry of China, and is related to the major strategic requirements of China and the development of national economy, and the national defense safety of China is directly influenced. In recent years, with the proposition of "industrial 4.0" and "chinese manufacturing 2025" strategies, the robot has more and more prominent advantages in the modern manufacturing field as a representative of intelligent manufacturing, and the robot is more and more widely applied to the processing of large and complex structural members due to the unique advantages of large operation space, high flexibility, low cost, high efficiency and the like.
However, the weak rigidity characteristic determined by the open chain type serial structure characteristics of the robot causes the problem that the robot is very easy to generate flutter in milling, the milling surface quality is seriously influenced, cutter abrasion is aggravated, and the open chain type serial structure characteristics are one of main problems which restrict the application of the open chain type serial structure characteristics to the field of high-precision milling operation of large structural members.
In order to solve the problem of chatter in the milling process, technological parameters need to be reasonably configured through stability prediction. However, the pose dependence of the robot dynamics leads to obvious differences in stability under different processing poses. The robot machining stability prediction method in the prior art generally needs a modal experiment method to obtain the dynamic parameters of the tool nose of the robot under different poses, so that the stability of the specified pose is predicted, the efficiency is low, and the milling chatter vibration is difficult to effectively control when the robot changes the machining pose.
How to construct a stability prediction method related to the processing pose of the robot and enable the prediction result of the stability to be applied to any reachable processing pose becomes an urgent problem to be solved.
Disclosure of Invention
In view of the above, the present invention has been made to provide a robot processing stability prediction method based on pose characteristics, an apparatus, an electronic device, a computer readable storage medium that overcome or at least partially solve the above problems.
One embodiment of the invention provides a robot processing stability prediction method based on pose characteristics, which comprises the following steps:
establishing a dynamic model of the robot milling system;
dynamic performance analysis is carried out on the dynamic model of the robot milling system, and tool nose frequency response functions of different robots in each reachable processing pose are obtained;
solving a joint angle, a robot body quality matrix and a robot body rigidity matrix of the robot under each reachable redundancy angle based on inverse kinematics, wherein different reachable redundancy angles correspond to postures under different reachable processing positions;
based on the tool nose frequency response function under each reachable processing pose, obtaining modal mass, modal damping and modal stiffness under each reachable redundant angle according to the joint angle of the robot, the robot body mass matrix and the robot body stiffness matrix under each reachable redundant angle;
and based on a regenerative flutter prediction model, obtaining the ultimate cutting depth corresponding to each reachable redundancy angle according to the modal mass, modal damping and modal stiffness under each reachable redundancy angle, and obtaining a stability prediction graph of the redundancy angle and the ultimate cutting depth.
Optionally, the establishing a dynamic model of the robotic milling system includes:
establishing a robot body kinematics model by adopting a modified D-H method;
establishing a main shaft system dynamic model and a main shaft-tool handle-tool joint surface rigidity model;
and integrating the robot body power model, the main shaft system dynamic model and the main shaft-tool shank-tool joint surface rigidity model to establish a dynamic model of the robot milling system.
Optionally, the stiffness model of the joint surface of the spindle, the tool holder and the tool considers the normal stiffness of the joint surface, the tangential stiffness of the joint surface and the torsional contact stiffness of the joint surface.
Optionally, the performing dynamic performance analysis on the dynamic model of the robot milling system includes:
and carrying out dynamic performance analysis on the dynamic model of the robot milling system based on a finite element analysis method.
Another embodiment of the present invention provides a pose characteristic-based robot processing stability prediction apparatus, including:
the dynamic model establishing unit is used for establishing a dynamic model of the robot milling system;
the tool nose frequency response function acquisition unit is used for carrying out dynamic performance analysis on a dynamic model of the robot milling system and acquiring tool nose frequency response functions of different robots in each reachable processing pose;
the inverse kinematics solving unit is used for solving joint angles, a robot body mass matrix and a robot body rigidity matrix of the robot under each reachable redundancy angle based on inverse kinematics, wherein different reachable redundancy angles correspond to postures under different reachable processing positions;
the tool nose modal parameter acquisition unit is used for acquiring modal mass, modal damping and modal stiffness under each reachable redundant angle according to the joint angle of the robot, the robot body mass matrix and the robot body stiffness matrix under each reachable redundant angle based on the tool nose frequency response function under each reachable processing pose;
and the stability prediction unit is used for obtaining the ultimate cutting depth corresponding to each reachable redundancy angle according to the modal mass, the modal damping and the modal stiffness under each reachable redundancy angle based on the regenerative chatter prediction model, and obtaining a stability prediction graph of the redundancy angle and the ultimate cutting depth.
Optionally, the dynamic model building unit is further configured to:
establishing a robot body kinematics model by adopting a modified D-H method;
establishing a main shaft system dynamic model and a main shaft-tool handle-tool joint surface rigidity model;
and integrating the robot body power model, the main shaft system dynamic model and the main shaft-tool shank-tool joint surface rigidity model to establish a dynamic model of the robot milling system.
Optionally, the rigidity model of the joint surface of the spindle, the tool shank and the tool considers the normal rigidity of the joint surface, the tangential rigidity of the joint surface and the torsional contact rigidity of the joint surface.
Optionally, the nose frequency response function obtaining unit is further configured to:
and carrying out dynamic performance analysis on the dynamic model of the robot milling system based on a finite element analysis method.
Another embodiment of the present invention provides an electronic apparatus, wherein the electronic apparatus includes:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method described above.
Another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs which, when executed by a processor, implement the above-described method.
The method has the advantages that the stable prediction graph obtained by the method can provide a processing parameter selection guide for the milling operation of the robot at different poses, so that the milling flutter of the robot is effectively avoided, and powerful support is provided for the improvement of the milling quality and efficiency of the robot.
Drawings
Fig. 1 is a flow chart illustrating a stability prediction method related to a robot processing pose according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a stability prediction method associated with a robotic processing pose according to one embodiment of the present invention;
FIG. 3 is a graph of stability prediction of redundant angle versus extreme depth of cut for one embodiment of the present invention;
fig. 4 is a schematic structural diagram of a stability prediction apparatus related to a robot processing pose according to an embodiment of the present invention;
FIG. 5 shows a schematic structural diagram of an electronic device according to an embodiment of the invention;
fig. 6 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a stability prediction method related to a processing pose of a robot according to an embodiment of the present invention. As shown in fig. 1, the method includes:
s11: establishing a dynamic model of the robot milling system;
it can be understood that the dynamic model of the robot milling system of the embodiment of the invention comprises a robot body kinematics model, a main shaft system dynamics model and a main shaft-tool shank-tool joint surface rigidity model.
S12: dynamic performance analysis is carried out on the dynamic model of the robot milling system, and tool nose frequency response functions of different robots in each reachable processing pose are obtained;
it is understood that the reachable processing pose of the robot refers to the position and corresponding posture that the robot can reach during the milling process.
It should be noted that the dynamic performance analysis of the embodiment of the present invention includes a modal analysis and a harmonic response analysis. The modes are the natural vibration characteristics of the structure or the system as the result of the decoupling of the dynamic characteristics in the physical space, and each order of modes has different natural frequencies and mode vibration types; harmonic response analysis is a technique used to analyze the periodic response of a sustained periodic load in a structural system and to determine the stable response of a linear structure when subjected to a load that varies sinusoidally with time, the purpose of the analysis being to calculate the response of the structure at the excitation frequency, i.e. the response displacement and the response stress, and to obtain a curve of the dynamic response of the system with the system vibration frequency, called the amplitude-frequency curve.
In practical application, modal analysis of the established dynamic model of the robot milling system can be realized through linear perturbation frequency analysis steps in Abaqus, and the modal analysis is solved through a subspace method to obtain inherent frequencies and mode shape graphs of each order of the model.
S13: solving joint angles, a robot body mass matrix and a robot body rigidity matrix of the robot under each reachable redundancy angle based on inverse kinematics, wherein different reachable redundancy angles correspond to postures under different reachable processing positions;
it is understood that inverse kinematics is the solving of joint position variables from the position and attitude of the tip.
It should be noted that, by rotating the robot by an arbitrary angle in the tool axis direction around the tool axis coordinate system, machining in different postures can be realized without changing the machining position and the tool axis direction, and this rotation angle is defined as a redundant angle.
The machining attitude has a non-negligible effect on the tool tip dynamics, so the machining attitude is preferred as one of the methods of avoiding regenerative chatter.
It is understood that regenerative chatter refers to a change in chip thickness and cutting force caused by a phase difference between a corrugated surface left by a previous cutter tooth and a corrugated surface generated by a current cutter tooth during machining. The robot processing system has the advantage of motion redundancy, different robot postures can be realized for the same processing position and the same cutter shaft direction, and the processing posture can be optimized through the optimization of a redundant angle so as to avoid regeneration flutter.
S14: based on the tool nose frequency response function under each reachable processing pose, obtaining modal mass, modal damping and modal stiffness under each reachable redundant angle according to the joint angle of the robot, the mass matrix of the robot body and the stiffness matrix of the robot body under each reachable redundant angle;
s15: and obtaining the limit cutting depth corresponding to each reachable redundancy angle according to the modal mass, the modal damping and the modal stiffness under each reachable redundancy angle based on a regenerative chatter prediction model, and obtaining a stability prediction graph of the redundancy angle and the limit cutting depth.
It can be understood that the embodiment of the invention determines the limit cutting depth corresponding to each reachable redundant angle under the condition of determining the spindle rotating speed, the feeding speed and the radial cutting depth based on the regenerative chatter prediction model, and generates the stability prediction graph according to the limit cutting depth corresponding to each reachable redundant angle.
The stability prediction graph obtained by the stability prediction method related to the machining pose of the robot in the embodiment of the invention can provide guidance for selecting the machining process parameters when the robot performs milling operation under different poses, so that the generation of milling chatter of the robot is effectively avoided, and powerful support is provided for the improvement of the milling quality and efficiency of the robot.
In an optional implementation manner of the embodiment of the present invention, the establishing a dynamic model of a robot milling system includes:
establishing a robot body kinematics model by adopting a Modified Denavit-Hartenberg method;
establishing a main shaft system dynamic model and a main shaft-tool handle-tool joint surface rigidity model;
and integrating the robot body power model, the main shaft system dynamic model and the main shaft-tool shank-tool joint surface rigidity model to establish a dynamic model of the robot milling system.
The robot body kinematics model established by the modified D-H method is an MDH model.
In practical application, a robot body dynamic model, a main shaft system dynamic model and a main shaft-tool handle-tool joint surface rigidity model can be integrated based on finite element analysis software to establish a dynamic model of a robot milling system.
Specifically, the rigidity model of the joint surface of the spindle, the tool shank and the tool considers the normal rigidity of the joint surface, the tangential rigidity of the joint surface and the torsional contact rigidity of the joint surface.
It can be understood that the connection performance of a plurality of joint surfaces such as a main shaft-tool handle, a tool handle-spring clamp, a spring clamp-tool and the like in the main shaft-tool system is a key factor influencing the dynamic characteristic. For a combined surface of the main shaft and the tool holder, when the combined surface is considered to be rigid, a certain difference is generated between the natural frequency and the frequency response function of a dynamic model of the main shaft, the tool holder and the tool system and the reality. Therefore, to establish an accurate dynamic model of the spindle-shank-tool system, the accuracy of the joint surface stiffness model is of great importance.
In order to improve the accuracy of the rigidity model of the joint surface of the spindle, the tool handle and the tool, the rigidity model of the joint surface of the spindle, the tool handle and the tool simultaneously considers three factors of the normal rigidity of the joint surface, the tangential rigidity of the joint surface and the torsional contact rigidity of the joint surface.
Specifically, the performing dynamic performance analysis on the dynamic model of the robot milling system includes:
and carrying out dynamic performance analysis on the dynamic model of the robot milling system based on a finite element analysis method.
It can be understood that, in order to improve the prediction efficiency, the embodiment of the invention can perform dynamic performance analysis based on a finite element analysis method through finite element analysis software.
In practical application, the regeneration flutter prediction model is a two-degree-of-freedom flutter prediction model considering regeneration effect, and can be solved by using a full-discrete method.
Specifically, the two-degree-of-freedom flutter prediction model considering the regenerative effect is shown as formula (1):
wherein m, c and k are respectively modal mass, modal damping and modal stiffness, a p Cutting axially; h is xx 、h xy 、h yx 、h yy As shown in equation (2):
wherein, K tc And K rc Tangential and radial cutting force coefficients, respectively;
the angular position of the jth cutter tooth of the milling cutter can be expressed as formula (3):
wherein, omega is the rotating speed, and N is the number of cutter teeth;
window function g (phi) j (t)) is used to determine whether the jth tooth of the milling cutter is in a cutting state, and can be expressed as formula (4):
wherein the content of the first and second substances,
and
respectively, the cutting-in angle and the cutting-out angle of the jth cutter tooth:
wherein, a e For radial cutting depth, D is the diameter of the cutter.
Definition of
Converting equation (1) to a state equation:
wherein A is 0 The matrix is a constant matrix and represents the time-invariant characteristic of the system, and B (t) is a periodic coefficient matrix and respectively represented as follows; u (T) is a state term, and U (T-T) is a time lag term.
Fig. 2 is a schematic diagram of a stability prediction method related to a robot processing pose according to an embodiment of the present invention. As shown in fig. 2, the method for predicting the stability of the robot in relation to the processing pose according to the embodiment of the present invention includes:
(1) Inputting an MDH model, machining parameters, cutter parameters, cutting force coefficients and the like of the robot;
the processing parameters comprise main shaft rotating speed, radial cutting depth and feeding speed, and the cutter parameters comprise cutter diameter, cutter tooth number and helix angle.
(2) Determining the machining position and the corresponding tool spindle coordinate system, and determining the redundant angle theta x The range of (1).
(3) Scanning redundancy angle theta x (i)。
(4) Calculating the pose of a coordinate system of the cutter;
the embodiment of the invention adopts the tool coordinate system pose to represent the processing pose of the robot.
(5) Solving a joint angle and a robot body M and K matrix under each reachable redundant angle;
wherein the M matrix is a robot body quality matrix; the K matrix is a robot body rigidity matrix.
(6) Acquiring tool tip frequency response functions and tool tip modal parameters of the robot under each reachable processing pose;
the tool nose modal parameters comprise modal mass, modal damping and modal stiffness.
(7) Calculating the limit cutting depth a corresponding to different tool modal parameters by using a regenerative chatter prediction model plim (i)；
(8) Plotting theta x –a plim And (5) polar coordinate graph.
In a practical embodiment, the spindle speed n is 3000r/min, the feed speed f is 0.05m/s, and the radial cutting depth a is selected e And the value is 1mm, a stability prediction graph about redundant angles and limit cutting depths is obtained by using the method, and is shown in a figure 3, wherein a sector is an accessible pose area of the robot at a selected processing position, a gray area is a stable processing area, and a white area is a regenerative chatter area.
Fig. 4 is a schematic structural diagram of a stability prediction apparatus related to a processing pose of a robot according to an embodiment of the present invention. As shown in fig. 4, the apparatus includes:
a dynamic model establishing unit 41, configured to establish a dynamic model of the robot milling system;
the tool nose frequency response function acquisition unit 42 is used for carrying out dynamic performance analysis on a dynamic model of the robot milling system and acquiring tool nose frequency response functions of different robots in each reachable processing pose;
an inverse kinematics solving unit 43, configured to solve, based on inverse kinematics, a joint angle, a robot body mass matrix, and a robot body stiffness matrix of the robot at each reachable redundancy angle, where different reachable redundancy angles correspond to poses at different reachable processing positions;
a tool tip modal parameter obtaining unit 44, configured to obtain, based on the tool tip frequency response function in each reachable processing pose, a modal mass, a modal damping, and a modal stiffness in each reachable redundant angle according to the joint angle of the robot in each reachable redundant angle, the robot body mass matrix, and the robot body stiffness matrix;
and the stability prediction unit 45 is used for obtaining the limit cutting depth corresponding to each reachable redundancy angle according to the modal mass, the modal damping and the modal stiffness under each reachable redundancy angle based on the regenerative chatter prediction model, and obtaining a stability prediction graph of the redundancy angle and the limit cutting depth.
In an optional implementation manner of the embodiment of the present invention, the dynamic model building unit 41 is further configured to:
establishing a robot body kinematics model by adopting a modified D-H method;
establishing a main shaft system dynamic model and a main shaft-tool handle-tool joint surface rigidity model;
and integrating the robot body power model, the main shaft system dynamic model and the main shaft-tool shank-tool joint surface rigidity model to establish a dynamic model of the robot milling system.
Specifically, the rigidity model of the joint surface of the spindle, the tool shank and the tool considers the normal rigidity of the joint surface, the tangential rigidity of the joint surface and the torsional contact rigidity of the joint surface.
The nose frequency response function obtaining unit 42 is further configured to:
and carrying out dynamic performance analysis on the dynamic model of the robot milling system based on a finite element analysis method.
It should be noted that the stability prediction apparatuses related to the robot processing poses in the above embodiments can be respectively used for executing the methods in the foregoing embodiments, and therefore, the detailed description thereof is omitted.
In conclusion, the stability prediction graph obtained by the stability prediction method related to the machining pose of the robot in the embodiment of the invention can provide guidance for selecting the machining process parameters when the robot performs the milling operation in different poses, so that the generation of milling vibration of the robot is effectively avoided, and powerful support is provided for the improvement of the milling quality and efficiency of the robot.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may 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, and the like) having computer-usable program code embodied therein.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing an arrangement of this type will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, 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 in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore, may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the apparatus for detecting a wearing state of an electronic device according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website, or provided on a carrier signal, or provided in any other form.
For example, fig. 5 shows a schematic structural diagram of an electronic device according to an embodiment of the invention. The electronic device conventionally comprises 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 a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory 52 has a storage space 53 storing program code 54 for performing the steps of the method shown in fig. 1 and in any of the embodiments. For example, the storage space 53 for storing the program code may comprise respective program codes 54 for implementing the various steps in the above method, respectively. The program code can be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. Such a computer program product is typically a computer readable storage medium such as described in fig. 6. The computer readable storage medium may have memory segments, memory spaces, etc. arranged similarly to the memory 52 in the electronic device of fig. 5. The program code may be compressed, for example, in a suitable form. In general, the memory space stores program code 61 for performing the steps of the method according to the invention, i.e. there may be program code, such as read by the processor 51, which, when run by the electronic device, causes the electronic device to perform the steps of the method described above.
While the foregoing is directed to embodiments of the present invention, other modifications and variations of the present invention may be devised by those skilled in the art in light of the above teachings. It should be understood by those skilled in the art that the foregoing detailed description is for the purpose of better explaining the present invention, and the scope of the present invention should be determined by the scope of the appended claims.
Claims (10)
1. A robot processing stability prediction method based on pose characteristics is characterized by comprising the following steps:
establishing a dynamic model of the robot milling system;
dynamic performance analysis is carried out on the dynamic model of the robot milling system, and tool nose frequency response functions of different robots in each reachable processing pose are obtained;
solving joint angles, a robot body mass matrix and a robot body rigidity matrix of the robot under each reachable redundancy angle based on inverse kinematics, wherein different reachable redundancy angles correspond to postures under different reachable processing positions;
based on the tool nose frequency response function under each reachable processing pose, obtaining modal mass, modal damping and modal stiffness under each reachable redundant angle according to the joint angle of the robot, the mass matrix of the robot body and the stiffness matrix of the robot body under each reachable redundant angle;
and based on a regenerative chatter prediction model, obtaining the limit cutting depth corresponding to each reachable redundancy angle according to the lower modal mass, modal damping and modal stiffness of each reachable redundancy angle, and obtaining a stability prediction graph of the redundancy angle and the limit cutting depth.
2. The method of claim 1, wherein the modeling dynamics of the robotic milling system comprises:
establishing a robot body kinematics model by adopting a modified D-H method;
establishing a main shaft system dynamic model and a main shaft-tool handle-tool joint surface rigidity model;
and integrating the robot body power model, the main shaft system dynamic model and the main shaft-tool shank-tool joint surface rigidity model to establish a dynamic model of the robot milling system.
3. The method of claim 2, wherein the spindle-shank-tool interface stiffness model takes into account interface normal stiffness, interface tangential stiffness, and interface torsional contact stiffness.
4. The method of claim 1, wherein the dynamic performance analysis of the kinetic model of the robotic milling system comprises:
and carrying out dynamic performance analysis on the dynamic model of the robot milling system based on a finite element analysis method.
5. A robot processing stability prediction apparatus based on pose characteristics, comprising:
the dynamic model establishing unit is used for establishing a dynamic model of the robot milling system;
the tool nose frequency response function acquisition unit is used for carrying out dynamic performance analysis on a dynamic model of the robot milling system and acquiring tool nose frequency response functions of different robots in each reachable processing pose;
the inverse kinematics solving unit is used for solving the joint angle, the robot body quality matrix and the robot body rigidity matrix of the robot under each reachable redundant angle based on inverse kinematics, wherein different reachable redundant angles correspond to postures under different reachable processing positions;
the tool nose modal parameter acquisition unit is used for acquiring modal mass, modal damping and modal stiffness under each reachable redundant angle according to the joint angle of the robot, the robot body mass matrix and the robot body stiffness matrix under each reachable redundant angle based on the tool nose frequency response function under each reachable processing pose;
and the stability prediction unit is used for obtaining the ultimate cutting depth corresponding to each reachable redundancy angle according to the modal mass, modal damping and modal stiffness under each reachable redundancy angle based on the regenerative chatter prediction model, and obtaining a stability prediction graph of the redundancy angle and the ultimate cutting depth.
6. The apparatus of claim 5, wherein the kinetic model building unit is further configured to:
establishing a robot body kinematics model by adopting a modified D-H method;
establishing a main shaft system dynamic model and a main shaft-tool handle-tool joint surface rigidity model;
and integrating the robot body power model, the main shaft system dynamic model and the main shaft-tool shank-tool joint surface rigidity model to establish a dynamic model of the robot milling system.
7. The apparatus of claim 6, wherein the spindle-shank-tool junction surface stiffness model takes into account junction surface normal stiffness, junction surface tangential stiffness, and junction surface torsional contact stiffness.
8. The apparatus of claim 5, wherein the nose frequency response function obtaining unit is further configured to:
and carrying out dynamic performance analysis on the dynamic model of the robot milling system based on a finite element analysis method.
9. An electronic device, comprising:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method of 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 which, when executed by a processor, implement the method of any of claims 1-4.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116611175A (en) * | 2023-07-18 | 2023-08-18 | 北京航空航天大学 | Prediction method for free degree flutter of large aspect ratio aircraft body |
| CN117103280A (en) * | 2023-10-19 | 2023-11-24 | 中国长江电力股份有限公司 | Material reduction processing method and system for large-sized water turbine top cover on-site robot |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116611175A (en) * | 2023-07-18 | 2023-08-18 | 北京航空航天大学 | Prediction method for free degree flutter of large aspect ratio aircraft body |
| CN116611175B (en) * | 2023-07-18 | 2023-09-12 | 北京航空航天大学 | Prediction method for free degree flutter of large aspect ratio aircraft body |
| CN117103280A (en) * | 2023-10-19 | 2023-11-24 | 中国长江电力股份有限公司 | Material reduction processing method and system for large-sized water turbine top cover on-site robot |
| CN117103280B (en) * | 2023-10-19 | 2023-12-22 | 中国长江电力股份有限公司 | Material reduction processing method and system for large-sized water turbine top cover on-site robot |
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