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DC Field | Value | Language |
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dc.contributor.author | Ma, G | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Yang, Z | - |
dc.contributor.author | Chen, R | - |
dc.contributor.author | Liu, W | - |
dc.contributor.author | Zhang, Y | - |
dc.contributor.author | Yan, S | - |
dc.date.accessioned | 2025-07-10T12:53:12Z | - |
dc.date.available | 2025-07-10T12:53:12Z | - |
dc.date.issued | 2025-04-08 | - |
dc.identifier | ORCiD: Guijun Ma https://orcid.org/0000-0003-0300-646X | - |
dc.identifier | ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 | - |
dc.identifier | ORCiD: Zeyuan Yang https://orcid.org/0000-0002-0870-0162 | - |
dc.identifier | ORCiD: Weibo Liu https://orcid.org/0000-0002-8169-3261 | - |
dc.identifier | ORCiD: Yong Zhang https://orcid.org/0000-0002-1537-4588 | - |
dc.identifier | ORCiD: Sijie Yan https://orcid.org/0000-0002-0492-6121 | - |
dc.identifier.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. | en_US |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/31528 | - |
dc.description.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. | en_US |
dc.description.sponsorship | 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 52188102); 10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2024M750991); Postdoctor Project of Hubei Province of China (Grant Number: 2024HBBHCXA010); Open Project Fund of Key Laboratory of Image Processing and Intelligent Control; 10.13039/501100002338-Ministry of Education of the People's Republic of China. | en_US |
dc.format.extent | 5150 - 5159 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2025 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works ( https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ ). | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.subject | material removal depth | en_US |
dc.subject | averaged surface roughness | en_US |
dc.subject | domain adaptation | en_US |
dc.subject | dual-task learning | en_US |
dc.subject | robotic belt grinding | en_US |
dc.title | A Novel Pairwise Domain-Adaptation-Assisted Dual-Task Learning Approach to Coprediction of Robotic Machining Efficiency and Quality in New Parameter Spaces | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2025-03-07 | - |
dc.identifier.doi | https://doi.org/10.1109/TII.2025.3552652 | - |
dc.relation.isPartOf | IEEE Transactions on Industrial Informatics | - |
pubs.issue | 7 | - |
pubs.publication-status | Published | - |
pubs.volume | 21 | - |
dc.identifier.eissn | 1941-0050 | - |
dcterms.dateAccepted | 2025-03-07 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Copyright © 2025 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works ( https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ ). | 1.33 MB | Adobe PDF | View/Open |
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