Natural Frequency Prediction Method for 6R Machining Industrial Robot

Abstract

The industrial robot machining performance is highly dependent on dynamic behavior of the robot, especially the natural frequency. This paper aims at introducing a method to predict the natural frequency of a 6R industrial robot at random configuration, for improving dynamic performance during robot machining. A prediction model of natural frequency which expresses the mathematical relation between natural frequency and configuration is constructed for a 6R robot. Joint angles are used as input variables to represent the configurations in the model. The quantity and range of variables are limited for efficiency and practicability. Then sample configurations are selected by central composite design method due to its capacity of disposing nonlinear effects, and natural frequency data is acquired through experimental modal test. The model, which is in form of regression equation, is fitted and optimized with sample data through partial least square (PLS) method. The proposed model is verified with random configurations and compared with the original model and a model fitted by least square method. Prediction results indicate that the model fitted and optimized by PLS method has the best prediction ability. The universality of the proposed method is validated through implementation onto a similar 6R robot.

Special Issue on “Machining Dynamics and Parameters Process Optimization”

“…This task demands accurate dynamic models and sophisticated approaches to characterize new machine tools. In the industrial scenario, the machining performance with robots is highly dependent on their dynamics; Sun et al [3] introduced a method to predict natural frequencies of industrial robots that brings new knowledge for improving a 6R Robot's dynamics.…”

Natural Frequencies Identification by FEM Applied to a 2-DOF Planar Robot and Its Validation Using MUSIC Algorithm

“…During the last decade, robots, in particular those with servomotors, have gained momentum in diverse areas such as rehabilitation systems [ 1 , 2 , 3 ], human interaction systems [ 4 ], bio-inspired systems [ 5 , 6 , 7 , 8 ], and in surgery [ 9 ]. In general, their functionality depends on their performance trajectory; in this regard, an essential aspect to be considered in each programmed task is the value of the natural frequencies (NFs) of the robot, which can magnify the robot vibrations, affecting its performance [ 10 , 11 , 12 , 13 , 14 ]. Hence, it is of paramount importance to implement a method to accurately identify the NFs in order to obtain the best performance of the robot.…”