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Number of items: 10.

Hu, Fu 2023. Task-driven data fusion for additive manufacturing. PhD Thesis, Cardiff University.
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Hu, Fu, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Li, Yixin, Ma, Shuai, Qin, Jian, Song, Jun, Feng, Qixiang, Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766 and Tang, Qian 2023. Task-driven data fusion for additive manufacturing: framework, approaches, and case studies. Journal of Industrial Information Integration 34 , 100484. 10.1016/j.jii.2023.100484
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Li, Yixin, Hu, Fu, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Ryan, Michael ORCID: https://orcid.org/0000-0002-8104-0121 and Wang, Ray 2023. A hybrid model compression approach via knowledge distillation for predicting energy consumption in additive manufacturing. International Journal of Production Research 61 (13) , pp. 4525-4547. 10.1080/00207543.2022.2160501
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Li, Yixin, Hu, Fu, Ryan, Michael ORCID: https://orcid.org/0000-0002-8104-0121, Wang, Ray and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 2022. Knowledge distillation for energy consumption prediction in additive manufacturing. Presented at: 14th IFAC Workshop on Intelligent Manufacturing Systems (IMS 2022), Tel-Aviv, Israel, 28-30 March 2022. IFAC-PapersOnLine. IFAC-PapersOnLine. , vol.55(2) Elsevier, pp. 390-395. 10.1016/j.ifacol.2022.04.225
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Qin, Jian, Hu, Fu, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Witherell, Paul, Wang, Charlie C.L., Rosen, David W., Simpson, Timothy, Lu, Yan and Tang, Qian 2022. Research and application of machine learning for additive manufacturing. Additive Manufacturing 52 , 102691. 10.1016/j.addma.2022.102691
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You, Yingchao, Chen, Chong, Hu, Fu, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2022. Advances of digital twins for predictive maintenance. Presented at: 3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2021), Linz, Austria, 17-19 November 2021. , vol.200 Procedia Computer Science, Vol 200: pp. 1471-1480. 10.1016/j.procs.2022.01.348
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Wan, Yuwei, Chen, Zheyuan, Hu, Fu, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Wang, Rui 2022. Exploiting knowledge graph for multi-faceted conceptual modelling using GCN. Presented at: 3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2021), Linz, Austria, 17-19 November 2021. , vol.200 Procedia Computer Science, Vol 200, pp. 1174-1183. 10.1016/j.procs.2022.01.317
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Hu, Fu, Qin, Jian, Li, Yixin, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766 2021. Deep fusion for energy consumption prediction in additive manufacturing. Presented at: 54th CIRP Conference on Manufacturing Systems (CMS 2021), Virtual, 22-24 September 2021. 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0. Procedia CIRP , vol.104 Elsevier, pp. 1878-1883. 10.1016/j.procir.2021.11.317
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Li, Yixin, Hu, Fu, Qin, Jian, Ryan, Michael ORCID: https://orcid.org/0000-0002-8104-0121, Wang, Ray and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 2021. A hybrid machine learning approach for energy consumption prediction in additive manufacturing. Presented at: 25th International Conference on Pattern Recognition (ICPR 2020), Virtual, 15 January 2021. Pattern Recognition. ICPR International Workshops and Challenges Virtual Event, January 10–15, 2021, Proceedings, Part IV. Lecture Notes in Computer Science/Image Processing, Computer Vision, Pattern Recognition, and Graphics (12664) Springer, pp. 622-636. 10.1007/978-3-030-68799-1_45
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Hu, Fu, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Qin, Jian, Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766 and Witherell, Paul 2020. Feature-level data fusion for energy consumption analytics in additive manufacturing. Presented at: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Virtual, 20-24 August 2020. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, pp. 612-617. 10.1109/CASE48305.2020.9216947
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This list was generated on Tue Jun 25 10:54:12 2024 BST.