A multitask context-aware approach for design lesson-learned knowledge recommendation in collaborative product design
Author
Abstract
Suggested Citation
DOI: 10.1007/s10845-021-01889-7
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Peter Chhim & Ratna Babu Chinnam & Noureddin Sadawi, 2019. "Product design and manufacturing process based ontology for manufacturing knowledge reuse," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 905-916, February.
- Zuoxu Wang & Chun-Hsien Chen & Pai Zheng & Xinyu Li & Li Pheng Khoo, 2021. "A graph-based context-aware requirement elicitation approach in smart product-service systems," International Journal of Production Research, Taylor & Francis Journals, vol. 59(2), pages 635-651, January.
- Zhenyong Wu & Lina He & Yuan Wang & Mark Goh & Xinguo Ming, 2020. "Knowledge recommendation for product development using integrated rough set-information entropy correction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1559-1578, August.
- Jason Xinghang Dai & Nada Matta & Guillaume Ducellier, 2014. "Knowledge discovery in collaborative design projects," Post-Print hal-02920349, HAL.
- Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
- Xinyu Li & Zuhua Jiang & Lijun Liu & Bo Song, 2018. "A novel approach for analysing evolutional motivation of empirical engineering knowledge," International Journal of Production Research, Taylor & Francis Journals, vol. 56(8), pages 2897-2923, April.
- Yongwen Huang & Zuhua Jiang & Chengneng He & Jianfeng Liu & Bo Song & Lijun Liu, 2015. "A semantic-based visualised wiki system (SVWkS) for lesson-learned knowledge reuse situated in product design," International Journal of Production Research, Taylor & Francis Journals, vol. 53(8), pages 2524-2541, April.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Sihan Huang & Guoxin Wang & Shiqi Nie & Bin Wang & Yan Yan, 2023. "Part family formation method for delayed reconfigurable manufacturing system based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2849-2863, August.
- Han Cheng & Xianguang Kong & Qibin Wang & Hongbo Ma & Shengkang Yang & Gaige Chen, 2023. "Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 587-613, February.
- Rui Wang & Xiangyu Guo & Shisheng Zhong & Gaolei Peng & Lin Wang, 2022. "Decision rule mining for machining method chains based on rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 799-807, March.
- Chen-Fu Chien & Hsin-Jung Wu, 2024. "Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 275-287, January.
- Pulin Li & Kai Cheng & Pingyu Jiang & Kanet Katchasuwanmanee, 2022. "Investigation on industrial dataspace for advanced machining workshops: enabling machining operations control with domain knowledge and application case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 103-119, January.
- Gautam Dutta & Ravinder Kumar & Rahul Sindhwani & Rajesh Kr. Singh, 2021. "Digitalization priorities of quality control processes for SMEs: a conceptual study in perspective of Industry 4.0 adoption," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1679-1698, August.
- Zhenyong Wu & Lina He & Yuan Wang & Mark Goh & Xinguo Ming, 2020. "Knowledge recommendation for product development using integrated rough set-information entropy correction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1559-1578, August.
- Xiaochen Zheng & Xiaodu Hu & Rebeca Arista & Jinzhi Lu & Jyri Sorvari & Joachim Lentes & Fernando Ubis & Dimitris Kiritsis, 2024. "A semantic-driven tradespace framework to accelerate aircraft manufacturing system design," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 175-198, January.
- Wai Sze Yip & Suet To & Hongting Zhou, 2022. "Current status, challenges and opportunities of sustainable ultra-precision manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2193-2205, December.
More about this item
Keywords
Multitask; Context-aware; Collaborative product design; Knowledge recommendation; Design lesson-learned knowledge;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01889-7. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.