Machining quality prediction of multi-feature parts using integrated multi-source domain dynamic adaptive transfer learning Article Swipe
Pei Wang
,
Jingshuai Qi
,
Xun Xu
,
Sheng Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.rcim.2024.102815
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.rcim.2024.102815
Machining quality prediction of multi-feature parts has been a challenging problem because of small dataset and inconsistent quality data distribution with respect to each machining feature. Transfer learning that leverages knowledge of one task and can be repurposed on another task seems a good solution for this purpose. However, traditional transfer learning typically has a single source domain and a target domain, which limits its applications in multi-source scenarios (e.g., multi-feature). To solve this issue, this paper proposes a novel integrated multi-source domain dynamic adaptive transfer learning (IMD-DATL) framework for machining quality prediction of multi-feature part machining systems. Specifically, a domain-sample similarity double matching multi-source domain integration method is designed to construct the integration knowledge transfer from multiple source domains to the target domain. A residual feature extraction network based on sample entropy-dynamic channel double-layer attention structure and a fine-grained transferable feature attention module are designed. These three attentions are used to improve the feature learning ability and the adaptation level to the predicted object in the three dimensions of sample, channel and data feature. Finally, multiple sets of comparative experiments in thin-walled part machining systems confirm the effectiveness and superiority of the proposed method for cross-domain quality prediction. Compared with other traditional transfer learning methods, the MAE, RMSE and Score on average of this method are increased by 5.47 %, 4.59 % and 4.84 %, respectively, compared with other multi-source domain adaptation methods, the MAE, RMSE and Score on average of this method are increased by 7.13 %, 7.37 % and 6.52 %, respectively.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.rcim.2024.102815
- OA Status
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- Cited By
- 8
- References
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- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4400079841Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.rcim.2024.102815Digital Object Identifier
- Title
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Machining quality prediction of multi-feature parts using integrated multi-source domain dynamic adaptive transfer learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-27Full publication date if available
- Authors
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Pei Wang, Jingshuai Qi, Xun Xu, Sheng YangList of authors in order
- Landing page
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https://doi.org/10.1016/j.rcim.2024.102815Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.rcim.2024.102815Direct OA link when available
- Concepts
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Transfer of learning, Computer science, Feature (linguistics), Machining, Artificial intelligence, Feature extraction, Pattern recognition (psychology), Data mining, Domain (mathematical analysis), Machine learning, Engineering, Mathematics, Mechanical engineering, Mathematical analysis, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
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2025: 6, 2024: 2Per-year citation counts (last 5 years)
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57Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Robotics and Computer-Integrated Manufacturing |
| primary_location.landing_page_url | https://doi.org/10.1016/j.rcim.2024.102815 |
| publication_date | 2024-06-27 |
| publication_year | 2024 |
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| corresponding_author_ids | https://openalex.org/A5042769332, https://openalex.org/A5056875120 |
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