RLTube: Reinforcement learning based deposition path planner for thin-walled bent tubes with optionally varying diameter manufactured by wire-arc additive manufacturing
Open access
Date
2024-07Type
- Journal Article
ETH Bibliography
yes
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Abstract
This study presents RLTube, an algorithm that uses reinforcement learning (RL) to compute the deposition path for thin-walled bent tubes produced by wire-arc additive manufacturing. Rigid mathematical rules are used by state-of-the-art methods and the developed Brute Force Approach (BFA) to achieve this goal. In contrast, RLTube offers greater flexibility, adaptability and efficiency. This RL-based architecture uses 2D images of bent tubes as input, eliminating the need for additional feature extraction steps. As a result, RLTube deposition paths outperform BFA in terms of the developed evaluation criteria reflecting their quality. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000662505Publication status
publishedExternal links
Journal / series
Manufacturing LettersVolume
Pages / Article No.
Publisher
ElsevierSubject
Wire-arc additive manufacturing; Machine learning; Reinforcement learning; Path planning; Bent tubeMore
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ETH Bibliography
yes
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