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Title: | A Novel Pairwise Domain-Adaptation-Assisted Dual-Task Learning Approach to Coprediction of Robotic Machining Efficiency and Quality in New Parameter Spaces |
Authors: | Ma, G Wang, Z Yang, Z Chen, R Liu, W Zhang, Y Yan, S |
Keywords: | material removal depth;averaged surface roughness;domain adaptation;dual-task learning;robotic belt grinding |
Issue Date: | 8-Apr-2025 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Ma, G. et al (2025) 'A Novel Pairwise Domain-Adaptation-Assisted Dual-Task Learning Approach to Coprediction of Robotic Machining Efficiency and Quality in New Parameter Spaces', IEEE Transactions on Industrial Informatics, 21 (7), pp. 5150 - 5159. doi: 10.1109/TII.2025.3552652. |
Abstract: | Accurate prediction of material removal depth and averaged surface roughness is crucial for evaluating the performance of robotic belt grinding (RBG). Nevertheless, the machining parameters of RBG across different spaces exhibit various data distributions, which often results in prediction shifts on unseen machining parameters when using conventional approaches. In this article, we introduce a pairwise domain adaptation-assisted dual-task learning (PW-DA-DTL) method for copredicting material removal depth and averaged surface roughness with regard to new RBG machining parameter spaces. The multigate mixture-of-experts method is employed as the foundational framework for dual-task learning, effectively capturing and modeling the relationships between material removal depth and average surface roughness by leveraging their inherent task interdependencies. The pairwise domain adaptation strategy is put forward to simultaneously enhance sample diversity and mitigate cross-domain data distribution discrepancy between the existing and new RBG machining parameter spaces. Comparative experiments are presented to demonstrate the effectiveness and superiority of the proposed PW-DA-DTL method. |
URI: | https://bura.brunel.ac.uk/handle/2438/31528 |
DOI: | https://doi.org/10.1109/TII.2025.3552652 |
ISSN: | 1551-3203 |
Other Identifiers: | ORCiD: Guijun Ma https://orcid.org/0000-0003-0300-646X ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 ORCiD: Zeyuan Yang https://orcid.org/0000-0002-0870-0162 ORCiD: Weibo Liu https://orcid.org/0000-0002-8169-3261 ORCiD: Yong Zhang https://orcid.org/0000-0002-1537-4588 ORCiD: Sijie Yan https://orcid.org/0000-0002-0492-6121 |
Appears in Collections: | Dept of Computer Science Research Papers |
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