Dr Yixin Li

2024

• Li, Y. 2024. Leveraging deep learning for energy consumption prediction in selective laser sintering. PhD Thesis, Cardiff University.
• Li, Y., Liu, Y., Robson, S. and Ryan, M. 2024. Towards an energy consumption optimisation framework in selective laser sintering system: Leveraging deep learning and FPGA technologies. Presented at: 4th ICPR AEM Poznań 2024, Poznan, Poland, 28 June- 3 July 2024.

2023

• 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)

2022

• 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)

2021

• 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)