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Number of items: 8.

Qin, Jian, Liu, Ying, Grosvenor, Roger, Lacan, Franck and Jiang, Zhigang 2019. Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation. Journal of Cleaner Production , p. 118702. 10.1016/j.jclepro.2019.118702
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Chen, Chong, Liu, Ying, Kumar, Maneesh, Qin, Jian and Ren, Yunxia 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
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Qin, Jian 2019. Advanced data analytics for additive manufacturing energy consumption modelling, prediction, and management under Industry 4.0. PhD Thesis, Cardiff University.
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Qin, Jian, Liu, Ying and Grosvenor, Roger 2018. Multi-source data analytics for AM energy consumption prediction. Advanced Engineering Informatics 38 , pp. 840-850. 10.1016/j.aei.2018.10.008
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Chen, Chong, Liu, Ying, Kumar, Maneesh and Qin, Jian 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
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Qin, Jian, Liu, Ying and Grosvenor, Roger 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
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Qin, Jian, Liu, Ying and Grosvenor, Roger 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.
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Qin, Jian, Liu, Ying and Grosvenor, Roger 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
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This list was generated on Wed Oct 21 04:06:06 2020 BST.