Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31528
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dc.contributor.authorMa, G-
dc.contributor.authorWang, Z-
dc.contributor.authorYang, Z-
dc.contributor.authorChen, R-
dc.contributor.authorLiu, W-
dc.contributor.authorZhang, Y-
dc.contributor.authorYan, S-
dc.date.accessioned2025-07-10T12:53:12Z-
dc.date.available2025-07-10T12:53:12Z-
dc.date.issued2025-04-08-
dc.identifierORCiD: Guijun Ma https://orcid.org/0000-0003-0300-646X-
dc.identifierORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401-
dc.identifierORCiD: Zeyuan Yang https://orcid.org/0000-0002-0870-0162-
dc.identifierORCiD: Weibo Liu https://orcid.org/0000-0002-8169-3261-
dc.identifierORCiD: Yong Zhang https://orcid.org/0000-0002-1537-4588-
dc.identifierORCiD: Sijie Yan https://orcid.org/0000-0002-0492-6121-
dc.identifier.citationMa, 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.issn1551-3203-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/31528-
dc.description.abstractAccurate 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.sponsorship10.13039/501100001809-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.extent5150 - 5159-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 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.urihttps://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/-
dc.subjectmaterial removal depthen_US
dc.subjectaveraged surface roughnessen_US
dc.subjectdomain adaptationen_US
dc.subjectdual-task learningen_US
dc.subjectrobotic belt grindingen_US
dc.titleA Novel Pairwise Domain-Adaptation-Assisted Dual-Task Learning Approach to Coprediction of Robotic Machining Efficiency and Quality in New Parameter Spacesen_US
dc.typeArticleen_US
dc.date.dateAccepted2025-03-07-
dc.identifier.doihttps://doi.org/10.1109/TII.2025.3552652-
dc.relation.isPartOfIEEE Transactions on Industrial Informatics-
pubs.issue7-
pubs.publication-statusPublished-
pubs.volume21-
dc.identifier.eissn1941-0050-
dcterms.dateAccepted2025-03-07-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Computer Science Research Papers

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