Elasto-dynamic performance evaluation of a 6-DOF hybrid polishing robot based on kinematic modeling and CAE technology

被引：0
|
作者：
Ma, Yue [1 ,2 ]
Liu, Haitao [3 ]
Zhang, Mian [1 ,2 ]
Li, Bin [1 ,2 ]
Liu, Qi [1 ,2 ]
Dong, Chenglin [4 ]
机构：
[1] Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin,300384, China
[2] National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), Tianjin,300384, China
[3] Key Laboratory of Mechanism Theory and Equipment Design of The State Education Ministry, Tianjin University, Tianjin,300072, China
[4] Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu,610213, China
来源：
Mechanism and Machine Theory | 2022年 / 176卷
基金：
中国国家自然科学基金;
关键词：
Finite element method - Fixed platforms - Industrial robots - Inverse kinematics - Inverse problems - Machine design - Polishing;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
By taking a 6-DOF hybrid polishing robot as an example, this paper presents a generalized methodology for elasto-dynamic analysis of hybrid robots in an easy, accurate and rapid manner. The approach can be implemented by three steps: (1) creating an inverse kinematic model for calculating the configuration parameters of the body-fixed frame of each substructure and the coordinates of the joint frames with respect to the reference frame of the fixed platform; (2) building a parameterized Finite Element (FE) model at the home pose by using the modal reduction techniques and Parametric Design Languages (PDL) embedded in the CAE system; (3) driving the FE model to specified poses using the calculated configuration parameters of substructures and coordinates of joint frames in the batch mode, and evaluating the elasto-dynamic performance of the robot over its entire workspace. The merit of this approach lies in that parallel or hybrid robots having different topologies can be modeled without complex mathematical derivation and operation, allowing the lower-order dynamic performance to be rapidly predicted with sufficient accuracy. © 2022 Elsevier Ltd
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