Articles

• Qin, J.et al. 2019. Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation. Journal of Cleaner Production, pp. 118702. (10.1016/j.jclepro.2019.118702)
• Chen, C.et al. 2019. Energy consumption modelling using deep learning embedded semi-supervised learning. Computers and Industrial Engineering 135, pp. 757-765. (10.1016/j.cie.2019.06.052)
• Qin, J., Liu, Y. and Grosvenor, R. 2018. Multi-source data analytics for AM energy consumption prediction. Advanced Engineering Informatics 38, pp. 840-850. (10.1016/j.aei.2018.10.008)
• Chen, C.et al. 2018. Energy consumption modelling using deep learning technique — a case study of EAF. Procedia CIRP 72, pp. 1063-1068. (10.1016/j.procir.2018.03.095)
• Qin, J., Liu, Y. and Grosvenor, R. 2017. A framework of energy consumption modelling for additive manufacturing using Internet of Things. Procedia CIRP Conference on Manufacturing System 63, pp. 307-312. (10.1016/j.procir.2017.02.036)
• Qin, J., Liu, Y. and Grosvenor, R. 2016. A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP 52, pp. 173-178. (10.1016/j.procir.2016.08.005)

Conferences

• Qin, J., Liu, Y. and Grosvenor, R. 2017. Data analytics for energy consumption of digital manufacturing systems using Internet of Things method. Presented at: IEEE International Conference on Automation Science and Engineering, Xi'an, China, 20-23 August 2017.