Variable stiffness identification and configuration optimization of industrial robots for machining tasks

Abstract

Industrial robots are increasingly used in machining tasks because of their high flexibility and intelligence. However, the low structural stiffness of the robot seriously affects the positional accuracy and machining quality of robot operation equipment. Studying robot stiffness characteristics and optimization methods is an effective way to improve robot stiffness performance. Accordingly, aiming at the poor accuracy of stiffness modeling caused by approximating stiffness of each joint as constant, a variable stiffness identification method is proposed based on space gridding. Then, a task-oriented axial stiffness evaluation index is proposed to realize quantitative assessment of the stiffness performance in the machining direction. Besides, by analyzing the redundant kinematic characteristics of the robot machining system, a configuration optimization method is further come up with to maximize the index. For a large number of points or trajectory processing tasks, a configuration smoothing strategy is proposed to achieve fast acquisition of optimized configurations. Finally, experiments on a KR500 robot are conducted to verify the feasibility and validity of proposed stiffness identification and configuration optimization methods.

Modeling, Analysis, and Optimization of Robotic Light Machining Tasks for Empowering Digital Twin: Generalized Impulse Model Approach

“…With the increasing applications of automation, various research activities have been carried out to design [11] and control [12] the drilling and milling robotic mechanism for aviation [13,14] and surgical applications [15,16]. Also, there were many previous works related to the modeling of machining tasks [17][18][19][20][21][22][23][24]. Energy-based analytical technique relying on the chip velocity and flow direction was introduced by Matsumura et al [17,18] to predict the thrust force and torque in drilling and milling operations.…”

“…The low stiffness of the robot can seriously affect the positional accuracy and machining quality in different machining tasks. Several research activities have discussed this important research issue [23][24][25][26]. In those methods, authors focused on robot accuracy during drilling task by analyzing the elasto-static behavior of robot manipulators.…”