Articles | Volume 13, issue 1
https://doi.org/10.5194/ms-13-123-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/ms-13-123-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Feb 2022
Research article |
| 25 Feb 2022
| 25 Feb 2022
Multi-objective optimization of a redundantly actuated parallel robot mechanism for special machining
College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components, Shanghai University of Engineering Science, Shanghai 201620, China
Jianglong Tang
College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Qing Gao
College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Guohua Cui
Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components, Shanghai University of Engineering Science, Shanghai 201620, China
College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Yan'an Yao
College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
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Short summary
Compared with the two traditional intelligent optimization algorithms, the results show that the particle swarm optimization configuration algorithm, based on an improved orthogonal experiment design, has a better optimization effect, and the results are improved to a certain extent. The evaluation indexes of a multi-objective optimization design are often contradictory. The designer needs to compromise and select an appropriate parameter design value.
Compared with the two traditional intelligent optimization algorithms, the results show that the...
Special issue