CN-115186531-B - Robot processing stability prediction method and device based on pose characteristics

CN115186531BCN 115186531 BCN115186531 BCN 115186531BCN-115186531-B

Abstract

The invention discloses a robot processing stability prediction method based on pose characteristics. The method comprises the steps of establishing a dynamic model of a robot milling system, carrying out dynamic performance analysis on the dynamic model of the robot milling system, obtaining tool tip frequency response functions of different robots under all reachable processing positions, solving joint angles, a robot body mass matrix and a robot body stiffness matrix of the robots under all reachable redundancy angles, obtaining modal mass, modal damping and modal stiffness under all reachable redundancy angles based on the tool tip frequency response functions under all reachable processing positions, and obtaining a stability prediction graph of the redundancy angles and the limit cutting depth according to the modal mass, the modal damping and the modal stiffness under all reachable redundancy angles. The stable prediction graph can provide a machining process parameter selection guide for the robot during milling operation under different poses, so that the generation of milling chatter of the robot is effectively avoided, and a support is provided for improving the milling quality and efficiency of the robot.

Inventors

LIANG ZHIQIANG
XIE LIJING
ZHAO BIN
YAN PEI
DU YUCHAO
SHI GUIHONG
CHEN SICHEN
QIU TIANYANG
LIU ZHIBING
JIAO LI
ZHOU TIANFENG
WANG XIBIN

Assignees

北京理工大学

Dates

Publication Date: 20260512
Application Date: 20220615

Claims (10)

1. The robot processing stability prediction method based on pose characteristics is characterized by comprising the following steps of: Establishing a dynamic model of a robot milling system; Carrying out dynamic performance analysis on a dynamic model of the robot milling system to obtain a tool nose frequency response function of each robot under the processing pose; Solving joint angles, a robot body mass matrix and a robot body stiffness matrix of the robot under each reachable redundancy angle based on inverse kinematics, wherein different reachable redundancy angles correspond to gestures under different reachable processing positions; Acquiring modal quality, modal damping and modal stiffness under each reachable redundant angle according to the joint angle, the robot body mass matrix and the robot body stiffness matrix of the robot under each reachable redundant angle based on the tool tip frequency response function under each reachable processing pose; Based on a regenerative chatter prediction model, obtaining a limit cutting depth corresponding to each reachable redundancy angle according to the lower modal quality, 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; The regeneration flutter prediction model is a two-degree-of-freedom flutter prediction model considering the regeneration effect, and can be solved by using a full-discrete method.
2. The method of claim 1, wherein the modeling of dynamics of a robotic milling system comprises: establishing a robot body kinematic model by adopting a modified D-H method; Establishing a dynamic model of a main shaft system and a rigidity model of a main shaft-knife handle-tool joint surface; And integrating the robot body power model, the spindle system dynamics model and the spindle-tool handle-tool joint surface stiffness model to establish a robot milling system dynamics model.
3. The method of claim 2, wherein the spindle-shank-tool interface stiffness model accounts for interface normal stiffness, interface tangential stiffness, and interface torsional contact stiffness.
4. The method of claim 1, wherein the performing a dynamic performance analysis on the dynamic 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. The utility model provides a robot processing stability prediction unit based on pose characteristic which characterized in that includes: The dynamic model building unit is used for building 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 to acquire tool nose frequency response functions of different robots under various reachable processing positions; the inverse kinematics solving unit is used for solving the joint angle, the robot body mass matrix and the robot body stiffness matrix of the robot under each reachable redundancy angle based on inverse kinematics, wherein different reachable redundancy angles correspond to the gestures under different reachable processing positions; The tool nose modal parameter acquisition unit is used for acquiring modal quality, modal damping and modal rigidity under each reachable redundant angle according to the joint angle, the robot body mass matrix and the robot body rigidity matrix of the robot under each reachable redundant angle based on the tool nose frequency response function under each reachable processing pose; the stability prediction unit is used for obtaining the limit cutting depth corresponding to each reachable redundancy angle according to the modal quality, modal damping and modal rigidity under each reachable redundancy angle based on the regeneration flutter prediction model, and obtaining a stability prediction graph of the redundancy angle and the limit cutting depth; The regeneration flutter prediction model is a two-degree-of-freedom flutter prediction model considering the regeneration effect, and can be solved by using a full-discrete method.
6. The apparatus of claim 5, wherein the dynamics model building unit is further configured to: establishing a robot body kinematic model by adopting a modified D-H method; Establishing a dynamic model of a main shaft system and a rigidity model of a main shaft-knife handle-tool joint surface; And integrating the robot body power model, the spindle system dynamics model and the spindle-tool handle-tool joint surface stiffness model to establish a robot milling system dynamics model.
7. The apparatus of claim 6 wherein the spindle-shank-tool interface stiffness model accounts for interface normal stiffness, interface tangential stiffness, and interface 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, characterized in that, the electronic device includes: Processor, and A memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1-4.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores one or more programs, the one or more programs, when executed by a processor, implement the method of any of claims 1-4.

Description

Robot processing stability prediction method and device based on pose characteristics Technical Field The invention relates to the field of robot processing, in particular to a robot processing stability prediction method and device based on pose characteristics, electronic equipment and a computer readable storage medium. Background The large-scale complex structural member, such as a spacecraft cabin, a large-scale aircraft skin, a wind driven generator blade, a ship propeller and the like, has wide application in the industries of aerospace, aviation, energy, national defense and the like. The robot has more and more outstanding advantages as a representative of intelligent manufacturing in the modern manufacturing field, and the robot is more and more widely applied to the processing of large 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 characteristics of the open chain type serial structure of the robot causes the problem that the robot is very easy to generate chatter in milling, seriously influences the milling surface quality, aggravates the cutter abrasion, and is one of the main problems which restrict the application of the robot to the field of high-precision milling operation of large structural parts. In order to solve the chatter problem in milling, reasonable configuration of process parameters through stability prediction is required. However, the pose-dependent nature of robot dynamics results in a significant difference in stability at different processing poses. In the method for predicting the machining stability of the robot in the prior art, a mode experiment method is generally required to acquire dynamic parameters of the tool nose of the robot in different poses, so that the stability of a designated pose is predicted, the efficiency is low, and milling chatter is difficult to effectively control when the robot changes and processes the pose operation. How to construct a stability prediction method related to the robot processing pose, so that a stability prediction result can be applied to any reachable processing pose becomes a problem to be solved urgently. Disclosure of Invention The present invention has been made in view of the above problems, and has as its object to provide a robot processing stability prediction method, apparatus, electronic device, computer-readable storage medium based on pose characteristics, which overcomes or at least partially solves 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 a robot milling system; Carrying out dynamic performance analysis on a dynamic model of the robot milling system to obtain a tool nose frequency response function of each robot under the processing pose; Solving joint angles, a robot body mass matrix and a robot body stiffness matrix of the robot under each reachable redundancy angle based on inverse kinematics, wherein different reachable redundancy angles correspond to gestures under different reachable processing positions; Acquiring modal quality, modal damping and modal stiffness under each reachable redundant angle according to the joint angle, the robot body mass matrix and the robot body stiffness matrix of the robot under each reachable redundant angle based on the tool tip frequency response function under each reachable processing pose; And based on the regeneration flutter prediction model, obtaining the limit cutting depth corresponding to each reachable redundancy angle according to the modal quality, modal damping and modal rigidity under each reachable redundancy angle, and obtaining a stability prediction graph of the redundancy angle and the limit cutting depth. Optionally, the establishing a dynamics model of the robotic milling system includes: establishing a robot body kinematic model by adopting a modified D-H method; Establishing a dynamic model of a main shaft system and a rigidity model of a main shaft-knife handle-tool joint surface; And integrating the robot body power model, the spindle system dynamics model and the spindle-tool handle-tool joint surface stiffness model to establish a robot milling system dynamics model. Optionally, the spindle-shank-tool interface stiffness model considers interface normal stiffness, interface tangential stiffness, and interface torsional contact stiffness. Optionally, the dynamic performance analysis of the dynamic model of the robotic 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 robot processing stability prediction apparatus based on pose characteristics, including: The dynamic mo