Eka Marliana
Design Optimization of a 3-PUU Parallel Machine for Friction Stir Welding Robots
Marliana, Eka; Nurahmi, Latifah; Wahjudi, Arif; Batan, I Made Londen; Wei, Guowu
Authors
Latifah Nurahmi
Arif Wahjudi
I Made Londen Batan
Dr Guowu Wei G.Wei@salford.ac.uk
Reader of Robotics
Abstract
Friction Stir Welding (FSW) has recently emerged as an effective solid-state joining technique for welding high-strength aluminum alloys and light metals, particularly in the railway industry. However, a limitation of using FSW in this industry is the restricted workspace available. To address this issue, a 3-PUU (Prismatic and Universal Joints) parallel machine with 3-DoF (Degrees of Freedom) translational movement is utilized to support the FSW robot’s tool head, allowing the robot to operate over a larger horizontal workspace. This paper focuses on the design and optimization of a 3-PUU parallel machine to achieve an optimal configuration based on performance indices, specifically the Local Conditioning Index (LCI) and the dynamic isotropy index ( d1 ). Initially, a design space atlas was created, mapping both LCI and d1 were identified. A Genetic Algorithm (GA) was then used to determine a single optimal design within this region. A cuboid workspace was defined for this optimal design, and two test trajectories were generated for the robot to follow. A prototype of the optimal design was built, and a series of experiments were conducted to verify and validate the kinematic and dynamic models within the defined cuboid workspace, which provides foundations for further industrial applications of the proposed machine in FSW.
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 23, 2025 |
Publication Date | Jan 29, 2025 |
Deposit Date | Mar 19, 2025 |
Publicly Available Date | Mar 19, 2025 |
Journal | IEEE Access |
Electronic ISSN | 2169-3536 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 13 |
Pages | 23888 - 23901 |
DOI | https://doi.org/10.1109/access.2025.3536482 |
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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