CN-121979109-A - Machining cutting control monitoring system and method

CN121979109ACN 121979109 ACN121979109 ACN 121979109ACN-121979109-A

Abstract

The application provides a machining cutting control monitoring system and method, wherein a target machining gesture section is identified through a preset machining path of a cutting robot, a machining point position corresponding to a minimum static stiffness value is selected from the target machining gesture section to serve as a standard point, a main shaft in an end effector of the cutting robot runs at the standard point at a plurality of preset idle rotation speeds and vibration acceleration signals are collected, dominant modal frequencies are extracted from the vibration acceleration signals, machining gesture association analysis is conducted on the standard point based on the dominant modal frequencies and the static stiffness value at the standard point to obtain a stable lobe diagram associated with the machining gesture of the standard point, an optimal cutting parameter combination is matched for the target machining gesture section associated with the standard point according to the stable lobe diagram, and when the cutting robot moves to the target machining gesture section, the optimal cutting parameter combination is called for cutting. According to the technical scheme provided by the application, the precise cutting of the cutting machining surface by the cutting robot can be realized under the condition of cutting chatter.

Inventors

OU YI
HE JINPING
Zhai Binyuan
OU LI
YUAN JING

Assignees

广元中核职业技术学院(四川核工业技师学院)

Dates

Publication Date: 20260505
Application Date: 20260106

Claims (10)

1. A machining cutting control method, characterized by comprising the steps of: carrying out gesture sensitive segmentation on a preset processing path of the cutting robot, and further identifying a target processing gesture segment of which the static stiffness value of an end effector of the cutting robot in the processing path is lower than a preset stiffness threshold; Selecting a processing point position corresponding to the minimum static stiffness value from a target processing attitude section as a standard point, controlling the cutting robot to move to the standard point, enabling an end effector of the cutting robot to drive a main shaft to operate at a plurality of preset idle rotation speeds, and collecting vibration acceleration signals of the main shaft at each idle rotation speed; extracting a vibration frequency component with the maximum amplitude from the vibration acceleration signal as a dominant modal frequency, and carrying out processing gesture association analysis on the calibration point based on the dominant modal frequency and a static stiffness value at the calibration point to further obtain a stability lobe diagram associated with the processing gesture of the calibration point; And matching an optimal cutting parameter combination for resisting cutting chatter for the target processing attitude section associated with the standard point according to the stability lobe diagram, and calling the optimal cutting parameter combination to cut when the cutting robot moves to the target processing attitude section.
2. The method of claim 1, wherein performing gesture-sensitive segmentation on a preset machining path of the cutting robot, and further identifying a target machining gesture segment in the machining path for which a static stiffness value of an end effector of the cutting robot is lower than a preset stiffness threshold value specifically comprises: Acquiring a discrete attitude point set of a preset processing path of the cutting robot; Calculating a static stiffness value of the end effector of the cutting robot corresponding to each discrete attitude point based on the discrete attitude point set; Carrying out gesture sensitive segmentation on the processing path according to the static stiffness value of each discrete gesture point to obtain a plurality of processing gesture segments; And judging whether static stiffness values of discrete attitude points in each processing attitude section are lower than a preset stiffness threshold value, and if so, determining the corresponding processing attitude section as a target processing attitude section.
3. The method of claim 1, wherein controlling the movement of the cutting robot to the setpoint to cause the cutting robot end effector to operate the spindle at a predetermined plurality of idle speeds comprises: Extracting pose information of the standard points, and generating a motion control instruction of the cutting robot based on the pose information; The motion control instruction is sent to the cutting robot, and an end effector of the cutting robot is controlled to drive a main shaft to move to the standard point and complete gesture positioning; Acquiring a plurality of preset idle rotation speed parameters, and sequentially generating a main shaft rotation speed control instruction based on the idle rotation speed parameters; and controlling the main shaft to sequentially run at a plurality of preset idle speeds based on the main shaft rotating speed control instruction.
4. The method according to claim 1, wherein extracting the vibration frequency component with the largest amplitude from the vibration acceleration signal as the dominant modal frequency comprises: Noise reduction processing is carried out on the vibration acceleration signal to obtain a noise-reduced vibration acceleration signal; Performing Fourier transform on the vibration acceleration signal after noise reduction to obtain a frequency-amplitude corresponding relation corresponding to the vibration acceleration signal; Extracting an amplitude maximum value and a corresponding vibration frequency component from the frequency-amplitude correspondence; And determining the vibration frequency component corresponding to the maximum amplitude value as a dominant modal frequency.
5. The method of claim 1, wherein matching an optimal cutting parameter combination for cutting chatter resistance for the target process gesture segment associated with the calibration point based on the stability lobe map comprises: acquiring a stability lobe diagram associated with a target point machining gesture and cutting parameter constraint conditions corresponding to a target machining gesture section; Screening a candidate cutting parameter set conforming to the cutting parameter constraint condition from a stable region of the stable vane map; And matching an optimal cutting parameter combination resisting cutting chatter from the candidate cutting parameter set with the aim of maximizing the processing efficiency.
6. The method of claim 1, wherein invoking the optimal cutting parameter combination for cutting when the cutting robot moves to a target machining pose segment specifically comprises: Collecting current attitude information of an end effector of the cutting robot in real time; Comparing the current gesture information with the target processing gesture section information, and judging whether the cutting robot moves to the target processing gesture section; if the cutting robot is judged to move to the target processing gesture section, an optimal cutting parameter combination matched with the target processing gesture section is called, and the optimal cutting parameter combination is converted into a cutting control instruction of an executing mechanism of the cutting robot; And controlling the cutting robot to finish cutting machining according to the optimal cutting parameter combination based on the cutting control instruction.
7. The method of claim 1, wherein the vibratory acceleration signal of the spindle at each idle speed is acquired by a piezoelectric acceleration sensor.
8. A machining cut control monitoring system comprising a cut control unit, the cut control unit comprising: The identification module is used for carrying out gesture sensitive segmentation on a preset processing path of the cutting robot, so as to identify a target processing gesture section of which the static stiffness value of an end effector of the cutting robot in the processing path is lower than a preset stiffness threshold; The processing module is used for selecting a processing point position corresponding to the minimum static stiffness value from the target processing attitude section as a standard point, controlling the cutting robot to move to the standard point, enabling an end effector of the cutting robot to drive a main shaft to operate at a plurality of preset idle rotation speeds, and collecting vibration acceleration signals of the main shaft at each idle rotation speed; The processing module is further used for extracting a vibration frequency component with the maximum amplitude corresponding to the vibration acceleration signal as a dominant modal frequency, carrying out processing gesture association analysis on the calibration point based on the dominant modal frequency and the static stiffness value at the calibration point, and further obtaining a stability lobe diagram associated with the processing gesture of the calibration point; And the execution module is used for matching an optimal cutting parameter combination for resisting cutting chatter according to the target processing gesture section associated with the standard point by the stability lobe diagram, and calling the optimal cutting parameter combination for cutting when the cutting robot moves to the target processing gesture section.
9. A computer device comprising a memory storing code and a processor configured to obtain the code and to execute the machining cut control method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the machining cutting control method according to any one of claims 1 to 7.

Description

Machining cutting control monitoring system and method Technical Field The application relates to the technical field of cutting control, in particular to a machining cutting control monitoring system and a machining cutting control monitoring method. Background The cutting control aims at realizing accurate regulation and control of a material removal process, along with the development of the manufacturing industry to a high-precision and high-efficiency direction, the traditional control mode which depends on manual experience and a rigid program is difficult to meet higher requirements on machining quality, efficiency and manufacturing of complex parts, the modern cutting control is based on a numerical control system, a sensor network and a real-time algorithm, and the rotating speed, the feeding speed and the cutting path of a main shaft are dynamically optimized to realize the stability, the precision improvement and the service life extension of a cutter in the machining process, and the cutting control is a key basis of an intelligent manufacturing and flexible production line and directly relates to the quality of a final product. In the existing cutting control, the core of the cutting control is that physical signals of a machining process are acquired in real time through sensors such as force, vibration and acoustic emission arranged on a machine tool, an algorithm (such as a PID controller and an adaptive control algorithm) in a control system analyzes and processes the signals and is compared with a preset optimization target, an adjustment command is immediately generated once a deviation system is detected so as to stabilize the machining process within an optimal range, however, in the machining cutting control, the static rigidity of the tail end of the cutting robot in different postures is remarkably different, when the tail end of the cutting robot moves to a specific posture (such as a joint approaches to a singular position or is far away from an optimal rigidity area), the comprehensive rigidity of the tail end of the cutting robot is greatly reduced, a low-rigidity posture window is formed, so that the cutting parameters which are originally stabilized in the high-rigidity posture of the cutting robot are extremely easy to cause severe posture-dependent cutting chatter in the low-rigidity posture section, and further cause cutting damage to the cutting machined surface, and therefore, how to realize accurate cutting of the cutting machined surface of the cutting robot under the cutting chatter becomes a difficult problem in the industry. Disclosure of Invention The application provides a machining cutting control monitoring system and a machining cutting control monitoring method, which can realize accurate cutting of a cutting robot on a cutting machining surface under cutting chatter. In a first aspect, the present application provides a machining cutting control method comprising the steps of: carrying out gesture sensitive segmentation on a preset processing path of the cutting robot, and further identifying a target processing gesture segment of which the static stiffness value of an end effector of the cutting robot in the processing path is lower than a preset stiffness threshold; Selecting a processing point position corresponding to the minimum static stiffness value from a target processing attitude section as a standard point, controlling the cutting robot to move to the standard point, enabling an end effector of the cutting robot to drive a main shaft to operate at a plurality of preset idle rotation speeds, and collecting vibration acceleration signals of the main shaft at each idle rotation speed; extracting a vibration frequency component with the maximum amplitude from the vibration acceleration signal as a dominant modal frequency, and carrying out processing gesture association analysis on the calibration point based on the dominant modal frequency and a static stiffness value at the calibration point to further obtain a stability lobe diagram associated with the processing gesture of the calibration point; And matching an optimal cutting parameter combination for resisting cutting chatter for the target processing attitude section associated with the standard point according to the stability lobe diagram, and calling the optimal cutting parameter combination to cut when the cutting robot moves to the target processing attitude section. In some embodiments, performing gesture-sensitive segmentation on a preset machining path of the cutting robot, and further identifying a target machining gesture segment in the machining path, where the static stiffness value of the end effector of the cutting robot is lower than a preset stiffness threshold, specifically includes: Acquiring a discrete attitude point set of a preset processing path of the cutting robot; Calculating a static stiffness value of the end effector of the cutting robot corresponding to each discrete a