Multivariate quality prediction of thin-walled parts machining using multi-task parallel deep transfer learning-Reference-Cited by-同舟云学术

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Multivariate quality prediction of thin-walled parts machining using multi-task parallel deep transfer learning

Published:2024-09-09 Issue: Volume: Page:1-32
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Wang Pei¹²,Huang Pengde¹²,Tao Haizhen¹²,Liu Yongkui¹²^ORCID,Wang Tao¹²,Qu Hai¹²

Affiliation:

1. School of Mechano-Electronic Engineering, Xidian University, Xi’an, People’s Republic of China

2. State Key Laboratory of Electromechanical Integrated Manufacturing of High-performance Electronic Equipments, Xidian University, Xi’an, People’s Republic of China

Funder

National Natural Science Foundation of China

Natural Science Basic Research Plan in Shannxi Province of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2394099

Reference49 articles.

1. Welded joints integrity analysis and optimization for fiber laser welding of dissimilar materials

2. Arjovsky, M., Soumith Chintala, and Léon Bottou. 2017. “Wasserstein Generative Adversarial Networks.” In Proceedings of the 34th International Conference on Machine Learning, edited by Doina Precup and Yee Whye Teh, Vol. 70, 214–223. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v70/arjovsky17a.html.

3. XGBoost

4. Multi-task learning for data-efficient spatiotemporal modeling of tool surface progression in ultrasonic metal welding

5. Task’s Choice: Pruning-Based Feature Sharing (PBFS) for Multi-Task Learning

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