Fu Hu

Articles

• Wan, Y., Liu, Y., Chen, Z., Chen, C., Li, X., Hu, F. and Packianather, M. 2024. Making knowledge graphs work for smart manufacturing: Research topics, applications and prospects. Journal of Manufacturing Systems 76, pp. 103-132. (10.1016/j.jmsy.2024.07.009)
• Hu, F. et al. 2023. Task-driven data fusion for additive manufacturing: framework, approaches, and case studies. Journal of Industrial Information Integration 34, article number: 100484. (10.1016/j.jii.2023.100484)
• Li, Y., Hu, F., Liu, Y., Ryan, M. and Wang, R. 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)
• Qin, J. et al. 2022. Research and application of machine learning for additive manufacturing. Additive Manufacturing 52, article number: 102691. (10.1016/j.addma.2022.102691)

Thesis

• Hu, F. 2023. Task-driven data fusion for additive manufacturing. PhD Thesis, Cardiff University.

Conferences

• Li, Y., Hu, F., Ryan, M., Wang, R. and Liu, Y. 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 2022IFAC-PapersOnLine, Vol. 55(2). Elsevier pp. 390-395., (10.1016/j.ifacol.2022.04.225)
• You, Y., Chen, C., Hu, F., Liu, Y. and Ji, Z. 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)
• Wan, Y., Chen, Z., Hu, F., Liu, Y., Packianather, M. and Wang, R. 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)
• Hu, F., Qin, J., Li, Y., Liu, Y. and Sun, X. 2021. Deep fusion for energy consumption prediction in additive manufacturing. Presented at: 54th CIRP Conference on Manufacturing Systems (CMS 2021), Virtual, 22-24 September 202154th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0, Vol. 104. Procedia CIRP Elsevier pp. 1878-1883., (10.1016/j.procir.2021.11.317)
• Li, Y., Hu, F., Qin, J., Ryan, M., Wang, R. and Liu, Y. 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 2021Pattern 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 Vol. 12664. Springer pp. 622-636., (10.1007/978-3-030-68799-1_45)
• Hu, F., Liu, Y., Qin, J., Sun, X. and Witherell, P. 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 20202020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE pp. 612-617., (10.1109/CASE48305.2020.9216947)