Design and Multi-Objective Optimization of a Dexterous Mobile Parallel Mechanism for Fusion Reactor Vacuum Vessel Assembly
The present paper presents a newly designed dexterous mobile parallel mechanism for fusion reactor vacuum vessel assembly, the robot system has advantages in terms of compact design, the capability to carry out heavy-duty machining tasks, evacuation, and has less space occupation compared to other r...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ed117a08d67647029b42965230ff0768 |
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| Sumario: | The present paper presents a newly designed dexterous mobile parallel mechanism for fusion reactor vacuum vessel assembly, the robot system has advantages in terms of compact design, the capability to carry out heavy-duty machining tasks, evacuation, and has less space occupation compared to other robot systems in existence. Despite different robot systems are studied in the fusion reactor, there is still a lack of research on mechanism development for vacuum vessel assembly, which is attractive to future fusion reactors. In the fusion reactor, the robot systems will carry out different tasks, such as welding and machining. The assembly tasks of the vacuum vessel will be performed from inside of the vacuum vessel on-site. Then the paper introduces the single-objective and multi-objective optimization design of the proposed mechanism, the optimized objective is considered to be a combination of parallel mechanism dynamic machining force, dexterity, stiffness, and workspace volume. The design variables are derived from the geometry of the fixed and movable platforms, which include mass, inertia, the sizes of the platforms, and distances between universal joints located on the platforms. In the multi-objective optimization, non-dominated sorting genetic algorithm II is adopted and different trajectories are designed to simulate the machining process, which further turns the local optimization problem into a global optimization problem. Finally, the optimized results are extracted and analyzed. Simulation results indicate the effectiveness of the proposed multi-objective optimization approaches and multi-objective optimization is found to be more reliable than single-objective optimization. |
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