Binary tree robot milling system frequency response predication method based on RCSA (Risk and Control Self-Assessment)

Abstract

The invention relates to the field of milling machining of robots and discloses a binary tree robot milling system frequency response predication method based on RCSA (Risk and Control Self-Assessment). The binary tree robot milling system frequency response predication method comprises the following steps: (a) dividing a milling system into a substructure B, a substructure A and a flexible combining part; (b) acquiring a frequency response function of the substructure B and carrying out modal parameter standardization on the frequency response function; solving modal parameters and a couplingfunction of the substructure B under a to-be-solved posture; (c) establishing a finite element model of the substructure A and optimizing tool material parameters of the finite element model; acquiring a frequency response function and a response matrix of the finite element model; (d) carrying out an experiment to measure the frequency response function of a whole structure under the to-be-solved posture, and carrying out inverse computation through an IRCSA method to obtain a response matrix of the flexible combining part; (e) calculating a milling system tool end frequency response function under the to-be-solved posture according to the RCSA method. By adopting the predication method provided by the invention, the tool end frequency response function under different postures of the robot milling system is accurately acquired, and accurate predication of the milling stability under the different postures of the robot is realized.

Description

technical field [0001] The invention relates to the field of dynamic characteristic testing of milling equipment, and more specifically, to an RCSA-based binary tree robot milling system frequency response prediction method. Background technique [0002] With the rapid development of industrial automation and intelligence, industrial robots are being used more and more in marine propellers, aircraft skins, aeroengine blades and Grinding, milling, polishing, drilling, and boring of large complex curved surface parts such as rocket wall tubes. Compared with general-purpose CNC equipment, the robot processing system has the characteristics of low stiffness and significant stiffness-pose dependence, which seriously limits the performance and improvement of the robot in the field of machining. [0003] Affected by the low rigidity of the robot, the robot milling system is prone to flutter during milling, which will not only reduce the processing efficiency and affect the quality...

AI Technical Summary

Problems solved by technology

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a binary tree robot milling system frequency response prediction method based on RCSA, by performing hammering experiments on the robot spindle-knife handle substructure under 32 different attitudes of the robot, Using the RCSA-based binary tree frequency response prediction method to predict the robot spindle-tool handle substructure response matrix at any given attitude, combined with the tool substructure finite element analysis model, can accurately identify the tool end of the robot milling system under different attitudes Frequency response function, thus solving the technical problems of complex operation and low prediction accuracy of tool end frequency response prediction

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